No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Global trends and patterns of drought from space
Abstract
This paper analyzes changes in areas under droughts over the past three
decades and alters our understanding of how amplitude and frequency of
droughts differ in the Southern Hemisphere (SH) and Northern Hemisphere
(NH). Unlike most previous global-scale studies that have been based on
climate models, this study is based on satellite gauge-adjusted
precipitation observations. Here, we show that droughts in terms of both
amplitude and frequency are more variable over land in the SH than in
the NH. The results reveal no significant trend in the areas under
drought over land in the past three decades. However, after
investigating land in the NH and the SH separately, the results exhibit
a significant positive trend in the area under drought over land in the
SH, while no significant trend is observed over land in the NH. We
investigate the spatial patterns of the wetness and dryness over the
past three decades, and we show that several regions, such as the
southwestern United States, Texas, parts of the Amazon, the Horn of
Africa, northern India, and parts of the Mediterranean region, exhibit a
significant drying trend. The global trend maps indicate that central
Africa, parts of southwest Asia (e.g., Thailand, Taiwan), Central
America, northern Australia, and parts of eastern Europe show a wetting
trend during the same time span. The results of this satellite-based
study disagree with several model-based studies which indicate that
droughts have been increasing over land. On the other hand, our findings
concur with some of the observation-based studies.
... The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms [17]. ...
... Drought risk management in Ethiopia is a critical focus for both the government and international agencies, especially in regions reliant on pastoral and agro-pastoral activities [1,17] With recurring droughts severely impacting food security and livelihoods, particularly in the Oromia and Somali regions, Ethiopia has implemented strategies like the Productive Safety Net Program (PSNP), which offers food aid, cash transfers, and livelihood support to vulnerable communities during droughts [37,53]. These initiatives aim to reduce the reliance on emergency relief while enhancing community resilience against recurrent climate stress [24]. ...
... The influence of climate change and human activities, such as irrigation, complicates drought forecasting. Hydrological drought, for instance, is closely tied to human interventions, necessitating models that incorporate these factors [17,62]. Current efforts primarily focus on natural processes, with limited but growing research integrating human dimensions. ...
Ethiopia faces significant vulnerability to climate change due to its limited adaptive capacity and heavy reliance on rain-fed agriculture for livelihoods. Drought, a critical aspect of climate change, is a persistent and silent disaster that gradually affects extensive areas across the country. Unlike sudden natural disasters such as floods or tornadoes, the impacts of drought develop slowly and are not immediately apparent. This paper examines the critical drought impacts and its risk management in Ethiopia, a country that faces recurrent droughts fanned by climate change, significantly impacting millions of people, particularly in rural areas. The aim of the paper is to investigate the socio-environmental challenges raised by water scarcity, which affects agricultural productivity, food security, and public health. The study emphasizes the importance of integrated drought management strategies that combine government initiatives, community engagement, and international support to enhance resilience among vulnerable populations. The implementation of the Productive Safety Net Program (PSNP), community-driven adaptation measures, and the role of social capital in fostering cooperation and resource sharing during crises is a key strategy of short term drought adaptation. Advanced monitoring and predictive technologies to improve preparedness and response to drought events is crucial. By addressing both the technical and social dimensions of drought risk management, this research contributes to the development of sustainable solutions that aim to mitigate the impacts of drought and promote long-term resilience in Ethiopia.
... Studies have reported an increase in the frequency and intensity of droughts in Eastern Africa over the past three decades particularly in Somalia, Ethiopia and Kenya, (e.g. Viste et al. 2013;Shongwe et al. 2011;Damberg and Aghakouchak 2014). Some of these studies have projected that this increase may continue into the future due to global warming (IPCC 2014;Dai 2011;Shongwe et al. 2011;Anyah and Qiu 2010). ...
... Omumbo et al. (2011) found a statistically significant upward trend in minimum, maximum and mean temperatures over some parts of Eastern Africa for the past 30 years. These temperature trends will generally accelerate drought condition in the region (Damberg and Aghakouchak 2014). Patricola and Cook (2011) simulated large precipitation reductions at the end of twenty-first century over parts of Eastern Africa. ...
This study examines the impacts of 1.5 °C and 2.0 °C global warming levels on the characteristics of four major drought modes over Eastern Africa in the future under two climate forcing scenarios (RCP4.5 and RCP8.5). The droughts were quantified using two drought indices: the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI) at 12-month scale. Four major drought modes were identified with the principal component analysis (PCA). Multi-model simulation datasets from the Coordinated Regional Climate Downscaling Experiment (CORDEX) were analysed for the study. The skill of the models to reproduce the spatial distribution and frequency of past drought modes over Eastern Africa was examined by comparing the simulated results with the Climate Research Unit (CRU) observation. The models give realistic simulations of the historical drought modes over the region. The correlation between the simulated and observed spatial pattern of the drought modes is high ( r ≥ 0.7). Over the hotspot of the drought modes, the observed drought frequency is within the simulated values, and the simulations agree with the observation that the frequency of SPI-12 droughts is less than that of SPEI-12 droughts. For both RCP4.5 and RCP8.5 scenarios, the simulation ensemble projects no changes in the spatial structure of the drought modes but suggests an increase in SPEI-12 drought intensity and frequency over the hotspots of the drought modes. The magnitude of the increase, which varies over the drought mode hotspots, is generally higher at 2 °C than at 1.5 °C global warming levels. More than 75% of the simulations agree on these projections. The projections also show that the increase in drought intensity and frequency is more from increased potential evapotranspiration than from reduced precipitation. Hence, the study suggests that to reduce impacts of global warming on future drought, the adaptation activities should focus on reducing evaporative loss surface water.
... Long-term data records with adequate spatial and temporal coverage are required for drought characterization and drought index calculation using ground gauges [13]. However, drought monitoring in Central Asia's arid zones, like many other arid and semi-arid countries around the world, is hampered by a lack of reliable precipitation observations. ...
... The climate stations are sparse and unevenly distributed in many droughtprone areas, resulting in large spatial gaps. Overall, the long latency of data acquisition, limited spatial representation, and data gaps in ground observations limit meteorological drought monitoring [13,14]. For example, many studies have lamented the scarcity of observations in Central Asia's arid zones [15,16]. ...
Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (>27%) and a high missing ratio (>33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD > 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered.
... As sessile organisms, plants are exposed to various ecological strains that contribute to reduced quality traits of plants, such as height, stem girth, and leaf size, leading to decreased water content, lowered leaf water potential, turgor loss, cell enlargement, and photosynthetic pigments (Damberg and AghaKouchak, 2014). Stresstolerant plant varieties have evolved defensive mechanisms against stressors, controlling growth and performance. ...
Introduction
Temporary and extended drought stress accelerates phytohormones and reactive oxygen species (ROS) in plants, however, the fate of the plants under stress is mostly determined by the metabolic and molecular reprogramming, which can be modulated by the application of habitat-adapted fungi that triggers resistance to stress upon symbiotic association.
Methods
The present research exhibited the exploitation of the newly isolated, drought habitat-adapted fungal endophytic consortium of SAB (Aspergillus oryzae) and CBW (Aspergillus fumigatus), on maize under drought stress. SAB and CBW primarily hosted the root tissues of Conyza bonariensis L., which have not been reported earlier, and sufficiently produced growth-promoting metabolites and antioxidants.
Results
SAB and CBW adeptly inhabited the maize roots. They promoted biomass, primary metabolites, osmolytes (protein, sugar, lipids, proline, phenolics, flavonoids), and IAA production while reducing tannins, ABA, and H2O2 contents and increasing antioxidant enzyme activities. In addition, the enhanced adventitious root development at the root/stem interface, and elongated main root development optimum stomatal activity of SAB- and CBW-inoculated maize plants were observed under drought stress. SAB and CBW modulated the expression of the ZmBSK1, ZmAPX, and ZmCAT1 genes in the maize shoot and root tissues under drought stress vs. control, signifying an essential regulatory function for SAB/CBW-induced drought stress tolerance via phytohormonal signaling pathway leading to the antioxidant upregulation.
Discussion
These findings imply that the exogenous administration of the SAB/CBW consortium might be a rather efficient strategy that contributes to optimizing the physio-hormonal attributes and antioxidant potential to alleviate the drought stress in maize.
... Driven by the imbalance between rainfall and evapotranspiration, drought is a natural hazard associated with a lack of water resources [1], threatening the ecological environment, economic development, and even human existence [2]. Under the threat of global warming, the frequency and severity of drought have increased since the second half of the 20th century [3][4][5]. With the widespread effects of drought, agricultural systems are facing significant and intense shocks in both developed and developing countries [6][7][8][9]. ...
As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m³/m³) and the highest coefficient of determination (R², above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices.
... Being sessile organisms, plants are susceptible to several environmental stressors, including low temperatures, salinity, as well as drought [1]. Stress-tolerant plant varieties adapt physiologically, morphologically, biochemically, and molecularly to control growth and performance during drought, affecting 21% of the world's land area [2,3]. Drought impacts agricultural output and plant growth, necessitating stress-reduction strategies like hyperactivating Reactive Oxygen Species (ROS) scavenging machinery, increasing antioxidant enzyme activities, and activating stress-tolerant genotypes to prevent cell damage [4]. ...
Drought stress negatively impacts agricultural crop yields. By using mineral fertilizers and chemical regulators to encourage plant development and growth, its impact can be mitigated. The current study revealed that exogenous silicon (Si) (potassium silicate; K2Si2O5 at 1000 ppm) and molybdenum (Mo) (ammonium molybdate; (NH4)6Mo7O24•4H2O at 100 ppm) improved drought tolerance in quinoa (Chenopodium quinoa Willd). The research was conducted in a randomized complete block design with three biological replicates. The treatments comprised T0 (control, water spray), T4 (drought stress), and T1, T2, T3, T5, T6, and T7, i.e., foliar applications of silicon and molybdenum solutions individually and in combination. Results revealed that drought stress predominantly affected the quinoa yield by decreasing the growth, physiological, biochemical, metabolic, hormonal, antioxidant, and ionic attributes. On the contrary, the supplementation of Si and Mo enhanced the growth attributes (shoot, panicle, and root length, No. of leaves per plant, shoot and panicle fresh/dry weight, root fresh/dry weight, No. of seeds and seeds fresh weight per plant), physiological traits (relative water content, chlorophyll, and carotenoids content), biochemical characteristics (total soluble sugars, protein and lipid content), metabolic attributes (total phenolic, flavonoids, tannins, lycopene, carotene), hormonal contents (indoleacetic acid (IAA), gibberellic acid (GA), salicylic acid (SA)), enzymatic and non-enzymatic antioxidants (catalase, peroxidase and ascorbic acid), and ionic content (potassium (K), (calcium) Ca, (magnesium) Mg, Si and Mo). Under drought stress, Si and Mo reduced electrolyte leakage, abscisic acid (ABA) content, H2O2 production, and sodium uptake. In addition, combined Si and Mo supplementation elevated the expression of the sucrose non-fermenting 1 (SNF1)-associated protein kinase 2 (SnRK2) (CqSNRK2.10) gene in quinoa under drought stress vs. control, signifying an essential regulatory function for Si and Mo-induced drought stress tolerance. These results imply that the exogenous administration of Si and Mo in combination might be an efficient method to alleviate drought stress on quinoa.
... Among these indices, the SPI is widely employed in various studies. Despite criticism for its assumption of the dominance of precipitation in drought impact, SPI remains widely used due to its flexibility in time scale, straightforward calculation, and comparability across different climatic regions [16,17]. On the other hand, the Palmer Drought Severity Index (PDSI) is a drought index based on the balance between water supply and demand. ...
Understanding drought evolution and its driving factors is crucial for effective water resource management and forecasting. This study enhances the analysis of drought probability by constructing bivariate distributions, providing a more realistic perspective than single-characteristic approaches. Additionally, a meteorological drought migration model is established to explore spatiotemporal paths and related characteristics of major drought events in the Choushui River alluvial fan. The results reveal a significant increase in the probability of southward-moving drought events after 1981. Before 1981, drought paths were diverse, while after 1981, these paths became remarkably similar, following a trajectory from north to south. This is primarily attributed to the higher rainfall in the northern region of the Choushui River alluvial fan from February to April, leading to a consistent southward movement of drought centroids. This study proposes that climate change is a primary factor influencing changes in the spatiotemporal paths of drought. It implies that changes in rainfall patterns and climate conditions can be discerned through the meteorological drought migration model. As a result, it provides the potential for simplifying drought-monitoring methods. These research findings provide further insight into the dynamic process of drought in the Choushui River alluvial fan and serve as valuable references for future water resource management.
... For example, such studies compare the PERSIANN products to other SPPs, ground observations, and model simulations (Li et al., 2003;Mehran & AghaKouchak, 2014;Miao et al., 2015;Nguyen and Thorstensen et al., 2017b;S Sorooshian et al., 2002;Yilmaz et al., 2005). Various studies have been used these products for runoff prediction (AghaKouchak et al., 2010;Ashouri et al., 2016;Behrangi et al., 2011;Hsu et al., 2013;Liu et al., 2017), monitoring drought (AghaKouchak & Nakhjiri, 2012;Katiraie-Boroujerdy et al., 2017a), frequency analysis (Gado et al., 2017), precipitation forecasting (Zahraei et al., 2013), assimilation into climate models (Yi, 2002), trend analysis (Damberg & AghaKouchak, 2014;Nguyen and Sorooshian et al., 2017a), modeling soil moisture (Juglea et al., 2010), and tracking typhoons (Nguyen et al., 2014). According to a recent study conducted by Salehi et al. (2022), the PER-SIANN-CCS, PERSIANN-CDR, and PDIR are capable of hydrological simulations as well as producing accurate precipitation estimates. ...
Due to the scarcity of established rain gauge stations, obtaining continuous time series daily rainfall observation data is a major challenge. The availability of these data is very essential to conduct hydrological studies and predicting flood events. Nowadays, several satellites could be used to provide such data. This study aims to investigate the appropriate remote sensing (RS) rainfall product and use its data to develop frequency analysis and intensity–duration–frequency curve (IDF curve) at 5-, 10-, 25-, 50-, 100-, and 200-year return periods in the study area. The evaluation of the PERSIANN family products (PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now) with NASA-POWER datasets compared to the available annual maximum of daily observation rainfall in Suez Rain Gauge (SRG) station as a reference was conducted. The results show that the PERSIANN-CDR product is the appropriate satellite-based rainfall dataset product based on the outcomes of the comparison criteria (RMSE, Bias, CC, and R ² error measures) of rainfall characteristics analysis and Chi-squared test of distributions. The frequency analysis results confirmed that the values of about 30.3–38.6 and 40–53.3 mm/day corresponding to the biggest flood reported in 1965 and 2020 in SRG station and PERSIANN-CDR product have a return period of 50–100 years, classifying it as an extreme event in Wadi Ghoweiba. These findings would considerably benefit decision-makers in estimating flood risks and planning the appropriate structure protections in the Wadi Ghoweiba Watershed.
... Moreover, the maximum daily precipitation has increased annually in nearly two thirds of global land areas since 1970 [2]. This trend has created wetter weather patterns in numerous regions, including central Africa, some parts of southwest Asia (e.g., Thailand and Taiwan), Central America, northern Australia, and eastern Europe between 1979 and 2010 [3]. These increases in precipitation are primarily seen in humid regions of the globe, potentially increasing flood intensity in more than 75% of those regions [4]. ...
Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.
... This method has been widely used (Yue et al. 2002, Birsan et al. 2005, Shi et al. 2015, Yilmaz and Perera 2015, Longobardi et al. 2016 to assess the presence of significant trends in meteorological and hydrological time series. The Mann-Kendall test uses the order of all values to evaluate if there are more increasing or decreasing values in the data (Damberg and AghaKouchak 2014). The null hypothesis (H0) of the Mann-Kendall test is "there is no trend". ...
Knowing the spatiotemporal patterns of precipitation is essential to quantify water supply, flood control, soil erosion , and the possibility of drought in a particular region. In other words, temporal and spatial analysis of precipitation is very important for water resources management. In addition, rainfall is the most important climatological variable in the tropical region. Although an increase in problems related to extreme rainfall events has occurred over the last several decades in Brazil, few studies have been conducted investigating rainfall trends in this country. Therefore, the objective of this work was to evaluate the spatial and temporal variability of rainfall in Mato Grosso do Sul State (Brazil), during the dry season (May to September) and rainy season (October to April) over four decades. For that, initially, a linear regression analysis was applied to identify the direction of the monotonic tendency of the precipitation. Next, the trends were investigated using the nonparametric Mann-Kendall trend method. The Pettitt test was also employed to identify any changes in the time series of precipitation. For the spatial distribution of rainfall data, the Ordinary Kriging was used. The results demonstrated that the Western region of the State (Pantanal South-Mato-Grossense) showed a significant negative trend of precipitation in the rainy season. KEYWORDS Spatiotemporal analysis; precipitation pattern; temporal variability; tropical region; trend analysis.
RESUMO Conhecer os padrões espaço-temporais de precipitação é essencial para quantificar o abastecimento de água, o con-trole de enchentes, a erosão do solo e a possibilidade de seca em uma determinada região. Em outras palavras, a análise temporal e espacial da precipitação é muito importante para a gestão dos recursos hídricos. Além disso, a precipitação é a variável climatológica mais importante na região tropical. Embora um aumento nos problemas re-lacionados a eventos extremos de chuva tenha ocorrido nas últimas décadas no Brasil, poucos estudos foram condu-zidos investigando as tendências de chuvas neste país. Portanto, o objetivo deste trabalho foi avaliar a variabilidade espacial e temporal das chuvas no estado de Mato Grosso do Sul (Brasil), durante a estação seca (maio a setembro) e chuvosa (outubro a abril) ao longo de quatro décadas. Para isso, inicialmente, foi aplicada uma análise de regressão linear para identificar a direção da tendência monotônica da precipitação. Em seguida, as tendências foram investi-gadas usando o método de tendência não paramétrico de Mann-Kendall. O teste de Pettitt também foi empregado para identificar eventuais mudanças nas séries temporais de precipitação. Para a distribuição espacial dos dados pluviométricos, foi utilizada a Krigagem Ordinária. Os resultados demonstraram que a região oeste do estado (Pantanal sul-mato-grossense) apresentou tendência negativa significativa de precipitação no período chuvoso. PALAVRAS-CHAVE Análise espaço-temporal; padrão de precipitação; variabilidade temporal; região tropical; análise de tendências.
RESUMEN Conocer los patrones espacio-temporales de las precipitaciones es fundamental para cuantificar el suministro de agua, el control de inundaciones, la erosión del suelo y la posibilidad de sequía en una región determinada. En otras palabras, el análisis temporal y espacial de la precipitación es muy importante para la gestión de los recursos hídricos. Además, la precipitación es la variable climatológica más importante en la región tropical. Aunque en las últimas décadas se ha producido un aumento de los problemas relacionados con eventos extremos de lluvia en Brasil, se han realizado pocos estudios que investiguen las tendencias de las precipitaciones en este país. Por lo tanto, el objetivo de este trabajo fue evaluar la variabilidad espacial y temporal de las precipitaciones en el estado de Mato Grosso do Sul (Brasil), durante la estación seca (mayo a septiembre) y la estación lluviosa (octubre a abril) a lo largo de cuatro déca-das. Para lograr esto, inicialmente se aplicó un análisis de regresión lineal para identificar la dirección de la tendencia de la precipitación monótona. Luego, las tendencias se investigaron utilizando el método de tendencias no paramétri-co de Mann-Kendall. También se utilizó la prueba de Pettitt para identificar posibles cambios en la serie temporal de precipitación. Para la distribución espacial de los datos de lluvia se utilizó el Kriging Ordinario. Los resultados dem-ostraron que la región occidental del estado (Pantanal Sul-Mato Grosso) mostró una tendencia negativa significativa en las precipitaciones durante la temporada de lluvias. PALABRAS CLAVE Análisis espacio-temporal; patrón de precipitación; variabilidad temporal; región tropical; análisis de tendência.
... Реалније калкулације, засноване на фундаменталним физичким принципима који узимају у обзир и промјене доступне енергије, влажности и брзине вјетра, указују на то да се режим суша мало промијенио током протеклих шездесет година (Sheffield et al. 2012). Студија Damberg and AghaKouchak (2014), заснована на сателитским осматрањима падавина, показује да у областима у којима се јавља суша не постоји значајан тренд у протекле три деценије. Међутим, ако се хемисфере посматрају одвојено, на јужној хемисфери присутан је значајан позитиван тренд, који није примијећен на сјеверној хемисфери. ...
Climate change is one of the greatest challenges of modern society. Combustion of fossil fuels, ie. anthropogenic emissions of carbon dioxide and other gases that cause the negative effect of the "greenhouse" are a key cause of global climate change. The global increase in air temperature was the trigger for changes in other climatic elements, and above all: evaporation, precipitation regime, the appearance of snow, stormy winds, long-lasting heat waves, etc. The last decade (2010‒2020) was the warmest in the instrumental period. The average annual temperature increased by 1 oC compared to the pre-industrial period. In addition to the increase in the average annual air temperature, there was also an increase in extreme temperatures, and changes in climate indices that are conditioned by the air temperature (frost, summer and tropical days). Such changes in air temperature and climate indices have led to increased evaporation and greater variability of the atmosphere. Climate change has caused a higher frequency and intensity of climate extremes, which have a very unfavorable impact on the environment and natural resources throughout the world. The area of Southeast Europe, ie. the Western Balkans, is one of the most endangered in the world in terms of floods, droughts, long-lasting heat waves and stormy winds. Climate change in the Republik of Srpska and Bosnia and Herzegovina has been particularly pronounced in the last three decades, when they have put increasing pressure on many natural resources, especially water, agricultural land, biodiversity and forest ecosystems. Climate models point to even more intense changes in the near future, which will require a planned approach in planning the resilience of the most vulnerable sectors to climate change on a scientific basis.
... However, even a moderate drought over farmland can lead to severe consequences (Berihun et al. 2019). While numerous previous studies have looked at how drought patterns changed over time in HOA (Damberg and AghaKouchak 2014;Qu and Hao 2018;Han et al. 2022), none of them have tried to determine the changes in drought occurrence over croplands or the changes in affected croplands by different severities and durations of droughts. Nevertheless, this knowledge is essential to shed light on changing hazards faced by farmers and guide policymakers in devising effective strategies to mitigate droughts and improve sustainable development initiatives. ...
The Horn of Africa (HOA) is frequently plagued by severe droughts, significantly affecting food security, particularly in less developed and rapidly population-growing regions. This study aims to provide crucial insights into the long-term spatiotemporal patterns of drought and their impact on croplands in the HOA. The findings will contribute to developing effective early warning systems to mitigate drought-related risks, ensure sustainable water and food production and alleviate the consequences of droughts in the region. To achieve this, the study estimates the standardised precipitation evapotranspiration index (SPEI) for different time scales (3, 6 and 12 months). Over the previous 120 years (1901-2020), it examines the geographical and temporal trends of drought frequency, intensity, length, affected areas, and the extent of agricultural land impacted. The results reveal that moderate droughts of varying duration occur approximately once every 5 years throughout most of the HOA, while severe droughts occur once every 10 to 15 years. On the contrary, extreme droughts are predominantly observed in the eastern part of the HOA. The study also highlights a gradual decline in atmospheric water availability in the region. There has been increased rainfall variability in recent decades compared to the early twentieth century, leading to a rise in droughts. The analysis indicates a significant increase in 3-month droughts, particularly in the eastern part of the HOA, which historically has been more susceptible to droughts. Before 1940, HOA did not experience severe 3-month droughts, but they have become more common throughout the region since 1981. Similar trends have been observed for droughts of 6 and 12 months. Furthermore, the frequency of croplands affected by droughts of different durations has nearly doubled between 1981 and 2020 compared to 1901-1940. These findings shed light on the increasing risk of drought in the HOA and emphasise the need for proactive measures to address the challenges posed by droughts.
... In a decertified zone, which encompasses a total area of between 6 and 12 million square kilometres worldwide, between 1 and 6% of the population resides (Xu et al., 2019). According to research by Damberg and AghaKouchak (2014), parts of South Asia have undergone drying during the past thirty years (UNEP-GEF, 2008). Rural populations in developing nations, particularly in South Asia, continue to be the most vulnerable to the whims of extreme climate events (IPCC, 2014;Hasnat et al., 2018). ...
The purpose of this study is to evaluate the desertification vulnerability and the future trends of desertification expansion in the Bahawalpur division of Punjab, Pakistan. This aim is achieved by analyzing Landsat data between the years 1990 and 2019. The biophysical index and socio-economic index were used to identify the Desertification Vulnerability Index (DVI) and its changes which have taken place over the study period between the years 1990 and 2019. The findings indicated that there was a decrease in the rate of desertification vulnerability from 1990 to 2019. In addition, the central and southern part of the Bahawalpur division is classified as a highly vulnerable zone in comparison to the other part of the region. The overall results show that the barren land and the desert area have been showing a decreasing trend, accompanied by substantial growth in vegetation from 1990 to 2019. The findings of the DVI analysis indicate that the Highly Vulnerable Area has decreased spatially from 61.12 in 1990 to 55.3% in 2019, while the Moderately Vulnerable Area and the Least Vulnerable Area have grown from 25.59% and 17.2% in 1990 to 28.56 and 19.53% in 2019, respectively. The decreasing trend demonstrates the effectiveness of efforts to combat desertification and the government could develop the best strategies for rehabilitation works and control the land degradation process in the most vulnerable areas in the Bahawalpur division of Punjab, Pakistan.
... Research suggested that these regions' precipitation reduction might be related to the variation of radiative forcing 55 and ENSO. 56 In addition, although widespread greening was found over much of Australia because of wetter conditions, 57 there were still large areas with decreasing vegetation affected by droughts. 58 Current research emphasizes precipitation's dominant role in the dryland's dramatic vegetation change. ...
Temperature and precipitation changes are among the vital climatic driving forces of global vegetation change. However, the strategy to separate the relative contributions of these two critical climatic factors is still lacking. Here, we propose an index CRTP (contribution ratio of temperature and precipitation) to quantify their impacts on vegetation and then construct the CRTP classification prediction models based on climatic, geographic, and environmental factors using the Random Forest classifier. We find that precipitation predominates more than 70% of the significant vegetation change, mainly located in the low and middle latitudes during 2000–2021. Precipitation will remain the dominant climatic factor affecting global vegetation change in the coming six decades, whereas areas with temperature-dominated vegetation change will expand under higher radiative forcings. Hopefully, the promising index CRTP will be applied in the research about climatic attribution for regional vegetation degradation, monitoring drought-type conversion, and alarming the potential ecological risk.
... The southern hemisphere is dominated by drying trends whereas we found a more balanced distribution of drying, wetting, and no temporal trends in the northern hemisphere. This is in line with other studies on historical trends in precipitation and drought indices reporting drying trends (although for different periods) for South and East Africa (Dai, 2013;Huang et al., 2016), as well as a significant positive trend in the land area under drought, higher drought frequency, and amplitude in the southern hemisphere making droughts more variable (Damberg & AghaKouchak, 2014). Studies also found large parts of South America, northern Australia, and Central Asia were characterized by wetting trends over six decades spanning ~1948-2010 (Dai, 2013;Huang et al., 2016). ...
Increasing aridity is one major consequence of ongoing global climate change and is expected to cause widespread changes in key ecosystem attributes, functions, and dynamics. This is especially the case in naturally vulnerable ecosystems, such as drylands. While we have an overall understanding of past aridity trends, the linkage between temporal dynamics in aridity and dryland ecosystem responses remain largely unknown. Here, we examined recent trends in aridity over the past two decades within global drylands as a basis for exploring the response of ecosystem state variables associated with land and atmosphere processes (e.g., vegetation cover, vegetation functioning, soil water availability, land cover, burned area, vapor-pressure deficit) to these trends. We identified five clusters, characterizing spatio-temporal patterns in aridity between 2000 and 2020. Overall, we observe that 44.5% of all areas are getting dryer, 31.6% getting wetter and 23.8% have no trends in aridity. Our results show strongest correlations between trends in ecosystem state variables and aridity in clusters with increasing aridity, which matches expectations of systemic acclimatization of the ecosystem to a reduction in water availability/ water stress. Trends in vegetation (expressed by Leaf Area Index (LAI)) are affected differently by potential driving factors (e.g., environmental, and climatic factors, soil properties, population density) in areas experiencing water-related stress as compared to areas not exposed to water-related stress. Canopy height for example, has a positive impact on trends in LAI when the system is stressed but does not impact the trends in non-stressed systems. Conversely, opposite relationships were found for soil parameters such as root-zone water storage capacity and organic carbon density. How potential driving factors impact dryland vegetation differently depending on stress/ non-stress conditions is important, for example within management strategies to maintain and restore dryland vegetation.
... SPI is considered for its applicability and practical implications over time periods (3, 6, or 12 months). Several studies compared the effectiveness of SPI-based drought monitoring to that of other indices in various climatic locations [48][49][50][51][52][53][54]. They resulted in increased efficiency of SPI over other indices in detecting drought incidents. ...
The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. For effective drought monitoring/assessment, satellite-based precipitation products offer more reliable rainfall estimates with higher accuracy and spatial coverage than conventional rain gauge data. The present study on satellite-based drought monitoring and reliability evaluation was conducted using four high-resolution precipitation products, i.e., IMERGH, TRMM, CHIRPS, and PERSIANN, during the northeast monsoon season of 2015, 2016, and 2017 in the state of Tamil Nadu, India. These four precipitation products were evaluated for accuracy and confidence level by assessing the meteorological drought using standard precipitation index (SPI) and by comparing the results with automatic weather station (AWS) and rain gauge network data-derived SPI. Furthermore, considering the limited number of precipitation products available, the study also indirectly addressed the demanding need for high-resolution precipitation products with consistent temporal resolution. Among different products, IMERGH and TRMM rainfall estimates were found equipollent with the minimum range predictions, i.e., 149.8, 32.07, 80.05 mm and 144.31, 34.40, 75.01 mm, respectively, during NEM of 2015, 2016, and 2017. The rainfall data from CHIRPS were commensurable in the maximum range of 1564, 421, and 723 mm in these three consequent years (2015 to 2017) compared to AWS data. CHIRPS data recorded a higher per cent of agreement (>85%) compared to AWS data than other precipitation products in all the agro-climatic zones of Tamil Nadu. The SPI values were positive > 1.0 during 2015 and negative < −0.99 for 2016 and 2017, indicating normal/wet and dry conditions in the study area, respectively. This study highlighted discrepancies in the capability of the precipitation products IMERGH and TRMM estimates for low rainfall conditions and CHIRPS estimates in high rainfall regimes.
... For SSA region, the change-point distribution of the duff moisture code, fine fuel moisture code, and fire weather index is consistent with that of the drought code. Moreover, the increasing drought trend in this region was also reported by Damberg et al. (Damberg and AghaKouchak 2014). Ultimately, drier weather was the dominant factor affecting fuel and fire weather changes. ...
Most of studies on change-point at a regional or global scale have only examined a single hydrometeorological variable and have been unable to identify any underlying explanations. In this study, we identified change-points and long-term trends of six wildfire-related variables and attempted to explain the cause of change-point from atmospheric–oceanic indices. As a result, we discovered that the main change-point dates for the precipitation, temperature, and drought codes, as well as the duff moisture code, fine fuel moisture code, and fire weather index, were 1995–2000 and 2000–2005, respectively. Furthermore, the relationship between the change-point of six variables and atmospheric–oceanic indices was discussed through the correlation coefficient. For example, the Atlantic Multidecadal Oscillation was found to dominate the precipitation in West Africa. In addition, we divided the globe into eight homogenous wildfire weather zones based on the change-point dates and long-term trends of the six variables.
... The rank-based Mann-Kendall (M-K) trend test, a nonparametric statistical test, is used frequently to evaluate significance in a monotonic increasing or decreasing trend in hydro-meteorological time series (Sicard et al. 2010;Kumar et al. 2009) including a series of drought indices (Damberg and AghaKouchak 2014). The M-K test had high computational efficiency and was not sensitive to measurement error, missing values, and outlier data. ...
The incidence of droughts and their intensity in recent times are affected by climatic variability and change, consequently affecting the agro-based economy of red and laterite zone (RLZ) India. In the present study, changing characteristics of meteorological droughts have been investigated over the sub-humid RLZ of West Bengal, India, using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). SPI and SPEI were computed over 1-, 3-, and 12-month time scales from monthly meteorological data from 1930 to 2019 to explore variations in drought frequency, intensity, duration, and spatial extent in the RLZ. It was observed that since the 1990s, the RLZ has been frequently affected by short-term extreme to severe drought, even in the wet-monsoon months. The frequency and intensity of droughts were observed to have increased in the recent period with a decrease in the duration. The Mann–Kendall test on the drought trend analysis of the region indicated a rising trend in monsoon months. The SPEI-monsoon was found to be more significantly correlated (r² = 0.65) with the rainfed Kharif (monsoon)-rice production anomaly than the SPI. SPEI appeared to have a more pronounced impact on drought incidences in the region over the recent decades. Field surveys were conducted to validate the two recent drought occurrences and associated crop failure. A total of 95% of the farmers in the survey reported crop failure during the short-term meteorological droughts in monsoon months. It is therefore suggested to monitor changing patterns and extent of droughts, particularly in water-scarce RLZs, to design appropriate drought preparedness planning.
... SPI can be used to study different drought classes by selecting different timescales (e.g., 1, 3, or 6 months). In addition, satellite precipitation has also been used in different types of modelling-based drought assessment studies (Anderson et al. 2007;Damberg and AghaKouchak 2014;Naumann et al. 2012;Sheffield et al. 2012;Yaduvanshi et al. 2015). For example, Aghakouchak and Nakhjiri (2012) developed a long-term climate dataset of droughts by combining the high spatial resolution satellite-based precipitation with low-resolution gauge-adjusted data. ...
Drought is a frequently occurring hydrometeorological event, which is defined as a reduction in water availability in different hydrologic elements. Over the last century, the hydrologists around the world have put substantial efforts to improve the monitoring and prediction of droughts through the development of new drought indices and prediction models. However, the scarcity of site-based observations has constrained these efforts to date. Remote sensing has emerged as an alternative to supplement these observations and has enabled the progress in drought studies in data-scarce parts of the world. This chapter describes the applicability of remote sensing in evaluation and assessment of drought (i.e., meteorological, agricultural, and hydrological). We also discuss the limitations associated with remote sensing applications (resolution, continuity, and uncertainty) and future perspectives. Further, a case study on remote sensing application in assessment of drought impact on Net Primary Production (NPP) in India is also presented, which highlights the importance of remote sensing in providing information of ecohydrological variables that are difficult to monitor on ground.
... These results coincide to a great extent with those presented by Damberg & AghaKouchak (2014), except for certain regions in the Sahel, Central Africa and Central America, for which their method indicated significant wetting trends. This difference is explained by the fact that they used the SPI scale 6 for drought exploration, which only takes into account an anomaly accumulation of 6 mo, which is less suitable for capturing long droughts (Limones et al. 2022) like those occurring recently in the mentioned regions. ...
ABSTRACT: We present a global spatiotemporal characterization of meteorological droughts
using historical precipitation data through the Drought Exceedance Probability Index (DEPI). The
re lationship between meteorological drought characteristics and monthly precipitation is explored
at a global level. This study contributes to our understanding of the drought features observed in
different areas of the planet, which can help predict the behavior of future droughts. The DEPI
was applied to the Climate Research Unit global gridded high-resolution rainfall data set covering
the period 1901−2019. Monthly drought index series were examined to extract the number of
droughts experienced in each pixel (0.50° × 0.50°) of the globe, as well as their durations, intensities
and severities. Results show agreement with other global drought characterization efforts,
revealing areas with a greater drought occurrence. This paper demonstrates that regions with less
seasonality and less intra- and inter-annual rainfall variability report fewer drought episodes.
Duration and severity of droughts are also related to these rainfall features. The last part of the
study describes the temporal distribution of droughts throughout the world. We conclude that
regions with many events show stable, even distributions over time, but many pixels in the
intertropical regions, the Middle East and smaller patches in Mongolia, China, Siberia and Canada
currently show higher-intensity and longer-duration drought events than at the beginning of
the twentieth century, while the opposite occurs in parts of Scandinavia, Russia, Argentina and
Tanzania. The analysis demonstrates that DEPI is easy to use, is applicable to different climates
and is effective in detecting the onset, end and intensity of droughts.
... One of the challenges is to develop a model that is efficient at a large scale to evaluate droughts for drought-prone regions (e.g. Iran) (Rezaeian-Zadeh and , Damberg and Aghakouchak 2014, Golian et al. 2015). The developed model should be able to be implemented over the entire region to ensure the spatiotemporal validity and reliability of the data (Zamani et al. 2015). ...
Shannon’s entropy theory measures the average uncertainty in the outcomes of an event. Since drought monitoring is an important issue, it is imperative to develop more user-friendly, modern methods for it. The present study investigates the uncertainty of the difference between monthly mean precipitation and potential evapotranspiration utilizing Shannon’s entropy over short-, mid-, and long-term dynamic time scales in the Karkheh Basin of Iran. An entropy-based precipitation-evapotranspiration index (EPEI) is defined for drought classification. EPEI is compared with (SPEI) in terms of the spatiotemporal patterns. The results indicate a higher similarity between EPEI and SPEI under existing conditions with low average precipitation. Over short scales, both indices yield similar results in duration, frequency, and intensity. Over long scales, the consistency of the drought duration, frequency, and intensity results decline. The main difference between both is the EPEI index’s capability of determining the early onset of the drought event.
... These studies were based on climate models; and few studies have also assessed global drought changes using satellite data. Damberg and AghaKouchak (2014) analysed SPI 6 using rainfall data from Global Precipitation Climatology Project (GPCP) and found significant variation in drought trends between Northern Hemisphere. While, the study found a significant positive trend in the Southern Hemisphere, there was no trend in the Northern Hemisphere. ...
Droughts are the most spatially complex natural hazards that exert global impacts and are further aggravated by climate change. The investigation of drought events is challenging as it involves numerous factors ranging from detection and assessment to modelling, management and mitigation. The analysis of these factors and their quantitative assessments have significantly evolved in recent times. In this paper, we review recent methods used to examine and model droughts from a spatial viewpoint. Our analysis was conducted at three spatial scales (point-wise, regional and global) and we evaluated how recent spatial methods have advanced our understanding of drought through case study examples. Further, we also examine and provide a broad overview of relevant case studies related to future drought occurrences under climate change. This study is a comprehensive synthesis of the various quantitative techniques used to assess the spatial characteristics of droughts at different spatial scales, and not an exhaustive review of all drought aspects. However, this serves as a basis for understanding the key milestones and advances accomplished through new spatial concepts relative to the traditional approaches to study drought. This work also aims to address the gaps in knowledge that are in need of further attention and provides recommendations to improve our understanding of droughts.
... Temperature affected vegetation dynamics by altering phenological (Garonna et al., 2016;Yuan et al., 2020). In spring and autumn, warming extended the vegetation growing season by accelerating the biochemical activity of the vegetation, thus increasing the vegetation cover (Damberg and AghaKouchak, 2014;Liu et al., 2016). An increase in winter temperatures positively affected the growth of overwintering vegetation, especially for winter wheat. ...
As an important ecological corridor, the Yellow River basin (YRB) is crucial for the eco-environmental security and sustainable socio-economic development of China. Systematic studies on the spatiotemporal evolution of vegetation cover and the response of vegetation dynamics to climate change in the YRB at different timescales are lacking. Utilizing a long-term remotely sensed Normalized Difference Vegetation Index (NDVI) and gridded climate dataset, we examined the spatiotemporal variability of vegetation cover and its response to climate variables in the Yellow River Basin (YRB) at multiple timescales by using the Mann-Kendall test, rescaled range analysis, and partial correlation analysis. Results indicated that the annual NDVI in the YRB decreased spatially from southeast to northwest, and peaked in August. From 1982 to 2015, the YRB experienced greening during the annual, growing season and spring, with statistically significant NDVI increases (p < 0.05) recorded in over 55% of the vegetated areas. NDVI trends should be expected to persist in the future, as evidenced by the Hurst index exceeding 0.5 in over 85% areas of the YRB. Temperature and precipitation determined the spatiotemporal pattern of vegetation cover in the YRB, and vegetation dynamics response to climatic variations varied among seasons and climatic zones. In contrast to other seasons, spring NDVI was significantly correlated with temperature, whereas winter vegetation was more vulnerable to suppression by increased precipitation. Vegetation growth was more susceptible to precipitation than to temperature in the arid and semiarid zones, while temperature dominated vegetation dynamics in the semi-humid zone, and the sunshine duration was essential for vegetation growth in high-altitude regions. The study contributes to a deeper understanding of the interrelationship between vegetation dynamics and climate change in the YRB and provides useful suggestions for the regional ecological conservation in the context of global warming.
... These technologies have also brought a sustained global-scale source of information that promotes high-resolution cloud detection and precipitation estimation more efficiently and accurately (Sun and Tang 2020;Houze 2014). Satellite precipitation retrieval schemes offer a diverse range of applications from near-real-time high-resolution estimates for flood warning systems and short-term weather predictions, to long-term climate data for the monitoring of global trends (Sorooshian et al. 2000;Damberg and AghaKouchak 2014;Nguyen et al. 2016). ...
Recent developments in “headline-making” Deep Neural Networks (DNNs), specifically Convolutional Neural Networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep neural network high SpatioTEmporal resolution Precipitation estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4-km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are: (1) it learns and estimates complex precipitation systems directly from raw measurements in near-real-time, (2) it uses the automatic spatial neighborhood feature extraction approach, and (3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.
... Reduction in precipitation amount is a controlling factor in forming persistent drought events (Damberg & AghaKouchak 2014;Van Loon and Laaha 2015;Yan et al., 2018). Hence accurate long-term precipitation datasets are essential for drought monitoring and analysis, as rainfall is characterized by a major temporal and spatial variability (Zeng et al., 2012;Zhu et al., 2019). ...
Remote Sensing Capabilities for Observational Drought Assessment:
Remote sensing plays a crucial role in observational drought management by providing valuable data and insights. Here are some key aspects of remote sensing capabilities in this context:
Satellites equipped with various sensors can capture images of the Earth's surface at different wavelengths. These images are used to monitor changes in vegetation, land surface temperature, and soil moisture. For drought management, this data helps track drought impacts on agricultural and natural ecosystems.
Remote sensing can assess vegetation health by measuring indices such as the Normalized Difference Vegetation Index (NDVI). A decrease in NDVI values over time can indicate declining vegetation health, which may be a sign of drought stress.
Microwave and infrared sensors on satellites can measure soil moisture content. By monitoring changes in soil moisture levels, remote sensing can help identify regions experiencing drought conditions.
Remote sensing data can be used to create drought severity maps, which provide a visual representation of drought-affected areas. These maps can help decision-makers allocate resources and plan mitigation strategies effectively.
Remote sensing, combined with climate data and weather forecasting, can contribute to the development of early warning systems for droughts. Timely alerts based on observed conditions can enable preparedness and response measures.
Remote sensing can track changes in reservoir levels, river flow, and groundwater levels. This information is vital for managing water resources during droughts, ensuring a sustainable supply for agriculture, industry, and communities.
Remote sensing archives provide historical data that can be used to analyze long-term drought trends and assess the impact of climate change on drought patterns.
Remote sensing data can be tailored to specific regional or local needs for drought monitoring. This customization allows for more accurate and detailed assessments.
Remote sensing data is often integrated with GIS to create interactive maps and decision support tools for better visualization and analysis of drought-related information.
This chapter book discusses how remote sensing technologies provide a powerful means to observe, monitor, and manage drought conditions by offering timely and accurate data on various environmental factors. This information is crucial for mitigating the impact of droughts on agriculture, water resources, and ecosystems.
... Current climatic models predict that the air temperature will increase about 2℉ across the United States by the end of the century with a prolonged period of drought. This significant increase in temperature is also observed in the historical temperature data recorded across the United States (Damberg et al. 2014). These changes in air temperature directly and/or indirectly affect the climate pattern which subsequently leads to extreme climate events such as heavy rainfall, drought, and flood , Moftakhari et al. 2017. ...
In this study, a new climate-adaptive design method is developed to investigate the impact of extreme climate events on the safety and serviceability performances of building footing through incorporating the site-specific hydrological loads such as precipitation, evapotranspiration, and water table depth to soil strength and stiffness parameters. The site-specific extreme hydrological cycle was determined based on historical climate records. The Richards equation was used to compute the temporal and spatial variations of the degree of saturation and matric suction considering the hydrological loads as the top and bottom boundary conditions. The proposed method was applied to a semiarid climate site in Austin, TX, as a sample application. The results show that the critical ultimate bearing capacity and settlement obtained from the proposed method are 28% higher and 35% lower, respectively, than those calculated using the conventional deterministic approaches assuming soil is fully saturated.
The current drought situation seriously threatens global food and water security. To assess the risks associated with drought, we conducted an investigation using the Standardized Precipitation Index and Soil Moisture Anomaly Index to analyze the intramonthly variability of meteorological and agricultural drought in Northern South America from 1982 to 2022. The monthly precipitation and soil moisture changes were analyzed using satellite data from the Climate Hazards Group Infra-Red Precipitation with Stations and the global dataset for the fifth generation of European ReAnalysis land component. This study shows that the regions in our study domain have, on average, encountered 80 seasonal drought episodes. A noteworthy percentage of these droughts, ranging from 20 to 36%, were severe, while 5–15% were categorized as extreme. Remarkably, the coastal zones of Colombia and Venezuela are more prone to droughts from January to March. As for agricultural droughts, we estimate that there were approximately 94 events, with extreme ones accounting for 13–34%. Notably, February and March saw the highest severity and extent of agricultural droughts. Over the past four decades, significant trends in drought have been observed within the study region. These trends are characterized by a marked reduction in precipitation during the months of February and September, with mean decreases of 0.43 mm/month and 1.43 mm/month, respectively. Furthermore, the Orinoco region has experienced more pronounced drought-related impacts during the latter months, particularly in February, which raises concerns regarding agricultural sustainability and productivity. This study found that Colombia and Venezuela’s coastal zones experienced moderate to severe meteorological events from January to March. Additionally, the Magdalena basin region in Colombia had frequent agricultural droughts in February.
With global environmental change, an in-depth understanding of the changing patterns in the frequency, duration and severity of drought events is of great significance to regional water resources management and agricultural production. The standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) are two frequently used meteorological drought indices in characterizing the drought characteristics. The elucidation of the differences between the two indices is an important work to reduce the uncertainty in drought hazard analysis and give suggestions of the appropriate index for the regional drought detection. Shandong Province is considered to be one of the major agricultural production bases in northern China. However, the comparative analysis of the suitability of SPI and SPEI for this area and the spatial variability of drought hazard have not been systematically studied, which forms the basis for this research. In this study, we identified drought events according to run theory with consideration of merging neighboring medium-term drought events. The results showed that both SPI and SPEI can efficiently describe drought in the study area and drought events identified by these two indices were quite similar. The drought duration and severity of the most severe drought event identified by SPI were 11 months and 12.8, respectively, with a joint return period of 90 years, and the drought duration and severity of the most severe drought event identified by SPEI were 12 months and 12.98, with a joint return period of 65 years. The duration and severity of drought given by SPEI, however, were generally longer and larger than those given by SPI at the same meteorological station. A comparison of the return period using the bivariate copula function indicated that minor differences existed for the same drought event in the same meteorological station of SPI and SPEI. The spatial distributions of mean drought duration and severity as well as the return periods suggested that the northwest part of the study area and Weifang city were more likely to experience longer and more severe drought events, which also indicated a relatively higher potential for drought hazard in these areas.
Drought appears to be one of the main natural factors contributing to the degradation of agricultural landscapes and economic frameworks. The occurrence of drought episodes becomes noticeable following a prolonged absence of precipitation; however, it is difficult to determine their onset, extent, and resolution. Therefore, the precise assessment of drought characteristics based on drought intensity, extent, duration, and geographic coverage presents significant complexities. In this scientific article, an effort is made to evaluate the meteorological situation of drought in the Ain Oussera Plain, which is located in Algeria, using two widely recognized drought indices, namely the precipitation index (SPI) and the precipitation evapotranspiration index (SPEI). The calculation of potential evapotranspiration (PET) required for SPEI evaluation was performed using the Thornthwaite methodology. Water deficiency was detected during the specified period, characterized by a decrease in precipitation levels associated with an increase in potential evapotranspiration rates. The calculation of the SPI and SPEI over durations of 3, 6, 9, and 12 months was carried out to examine the temporal fluctuations of different drought levels. The results of the analysis revealed that the years and 2021 were drought periods according to both indices on almost all temporal scales with a notable predominance of normal and moderate drought classifications. The study mainly reveals that the SPI and SPEI show a significant correlation at the same time scales used in this research. These findings highlight the consistency in detecting periods of severe drought using the SPI and SPEI indices.
Climate change is having unprecedented impacts on human health, including increasing infectious disease risk. Despite this, health systems across the world are currently not prepared for novel disease scenarios anticipated with climate change. While the need for health systems to develop climate change adaptation strategies has been stressed in the past, there is no clear consensus on how this can be achieved, especially in rural areas in low- and middle-income countries that experience high disease burdens and climate change impacts simultaneously. Here, we highlight the need to put health systems in the context of climate change and demonstrate how this can be achieved by taking into account all aspects of infectious disease risk (i.e., pathogen hazards, and exposure and vulnerability to these pathogen hazards). The framework focuses on rural communities in East Africa since communities in this region experience climate change impacts, present specific vulnerabilities and exposure to climate-related hazards, and have regular exposure to a high burden of infectious diseases. Implementing the outlined approach can help make health systems climate adapted and avoid slowing momentum towards achieving global health grand challenge targets.
Understanding aridity and its consequences for ecosystems and societies is critical in today’s changing climate. Aridity—the relative, long-term lack of available, life-sustaining moisture in terrestrial climates—significantly affects land degradation, desertification and the overall resilience of ecosystems and human communities. Aridity-related land degradation and water scarcity have been linked to food and water insecurity, poor soil fertility, losses in crop and plant productivity, biodiversity declines, ecosystem degradation, intense sand and dust storms, wildfires, poor health and large-scale human migration. Human-caused climate change,
meanwhile, is a main culprit for changing aridity around the world.
Assessing aridity trends and future projections can help to develop resilient adaptation and mitigation strategies in the face of climate change. This report addresses the challenges in assessing aridity and provides a novel and thorough assessment of aridity’s current and future trends—including aridity’s multifaceted and often cascading impacts—using new analyses and an up-to-date literature review. The report underscores the importance of adopting a widely accepted climatic approach based on the aridity index (AI)—a measure of aridity that uses
the ratio of precipitation to potential evapotranspiration over the medium to long term—and highlights the importance of distinguishing the long-term, climatic condition of aridity from the short-term, anomalous periods of water shortage known as droughts.
https://authors.elsevier.com/a/1jeUd52cufA8U
50 days' free access to the article
As warmer temperatures enhance atmospheric moisture, hydrological droughts tend to intensify in most regions of the globe. Consequently, younger generations are expected to face a more severe risk of hydrological drought during their lifetimes, emphasizing the critical issue of intergenerational inequity due to climate change. To quantify exposure to hydrological drought across generations, we constructed a cascade model chain for drought simulation using hybrid terrestrial models, based on 5 GCM outputs under SSP5-85, five hydrological models and a deep learning model. We then projected future univariate and bivariate hydrological drought evolution in 4091 river basins, and quantified lifetime exposure to drought for the age groups born in 2020 and 1960. Drought severity and duration are projected to increase substantially in the Eastern America, Southern Brazil and Western Europe, over 79% of basins. Extreme droughts far beyond historical records are expected to become more frequent and impact Western Europe in particular. Of note, the exposure of the different age groups to hydrological drought shows a notable disequilibrium. Exposure of people born in 2020 to hydrological drought hazards is projected to increase by 12% over the late 21st century compared to those born in 1960, indicating that the acceleration of climate change is expected to increase the lifetime risk of future generations. The exposure factor of the newborns is 1.4 times higher than that of 80 years of age under warming condition. Our findings underscore that future drought conditions under extreme warming pose a significant threat to the living conditions of younger generations.
Most terrestrial models synchronously calculate net primary productivity (NPP) using the input climate variable, without the consideration of time-lag effects, which may increase the uncertainty of NPP simulation. Based on Normalized Difference Vegetation Index (NDVI) and climate data, we used the time lag cross-correlation method to investigate the time-lag effects of temperature, precipitation, and solar radiation in different seasons on NDVI values. Then, we selected the Carnegie–Ames–Stanford approach (CASA) model to estimate the NPP of China from 2002 to 2017. The results showed that the response of vegetation growth to climate factors had an obvious lag effect, with the longest time lag in solar radiation and the shortest time lag in temperature. The time lag of vegetation to the climate variable showed great tempo-spatial heterogeneities among vegetation types, climate types, and vegetation growth periods. Based on the validation using eddy covariance data, the results showed that the simulation accuracy of the CASA model considering the time-lag effects was effectively improved. By considering the time-lag effects, the average total amount of NPP modeled by CASA during 2001–2017 in China was 3.977 PgC a−1, which is 11.37% higher than that of the original model. This study highlights the importance of considering the time lag for the simulation of vegetation growth, and provides a useful tool for the improvement of the vegetation productivity model.
Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM Multi-satellite Precipitation Analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four Artificial Intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through Severity-Duration-Frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information.
Semiarid climate has made Iraq one of the most vulnerable regions to droughts. Semiarid climate has made Iraq one of the most vulnerable regions to droughts. Rising temperatures and declining precipitation, as projected by climate models, would aggravate droughts in the country. This study examined the spatiotemporal variation of drought in Iraq using CMIP6 models for the shared socioeconomic pathways (SSPs). The historical simulations of 21 GCMs were evaluated to choose a GCM subset. A support vector machine (SVM) was used to downscaled the simulations of the selected GCM to 0.5° resolution. Downscaled simulations were used to estimate drought employing standardized precipitation evapotranspiration index (SPEI) for the near (2020–2059) and far future (2060–2099) in comparison to the reference period (1975–2014). EC-Earth3-Veg, BCC-CSM2-MR and ACCESS-CM2 performed the best in simulating Iraq's climate. Historical drought simulation revealed northern Iraq is most prone to droughts of all severities and time scales. The projections of drought revealed a decline in drought frequency in the near but a large rise in the late period. A greater decline in the near future and a rise in the far future were projected for SSP2-4.5 and SSP5-8.5. In both scenarios, drought frequency for all severities was projected to decrease between 0 and 40% in the near future, while moderate and severe droughts were to increase by up to 45% in the far future. Most scenarios showed a decrease in extreme droughts up to 30% in the drought-prone northern region, signifying a shift in the extreme drought-prone zone in Iraq. Drought management in Iraq can be benefited from the maps created in this study. The drought projections for SSPs can be used to update the strategies adopted based on RCPs.
Droughts are very common and threatening disasters in regions where groundwater storage is difficult. High-resolution satellite estimations such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) provide a new solution for drought monitoring in areas with this special terrain. In this study, the pre-evaluation of products is conducted before drought monitoring, then based on the Standardized Precipitation Index (SPI), the application in drought presentation is evaluated over Guangxi, a Chinese province where the karst areas are being eroded by rocky desertification. The main results are as follows: (1) The monthly estimating by IMERG products is reliable, and the best performance for daily scale appears in regions with rich rainfall and severe rocky desertification, while high errors occur in the same place. (2) IMERG products can be an alternative to gauge-based data in drought monitoring over karst rocky desertification regions like Guangxi, where multiple droughts happened from 2000 to 2017. The SPI of IMERG_F has the best spatial consistency, and better results are presented over humid rocky desertification regions.
We present a global spatiotemporal characterization of meteorological droughts using historical precipitation data through the Drought Exceedance Probability Index (DEPI). The relationship between meteorological drought characteristics and monthly precipitation is explored at a global level. This study contributes to our understanding of the drought features observed in different areas of the planet, which can help predict the behavior of future droughts. The DEPI was applied to the Climate Research Unit global gridded high-resolution rainfall data set covering the period 1901-2019. Monthly drought index series were examined to extract the number of droughts experienced in each pixel (0.50° × 0.50°) of the globe, as well as their durations, intensities and severities. Results show agreement with other global drought characterization efforts, revealing areas with a greater drought occurrence. This paper demonstrates that regions with less seasonality and less intra- and inter-annual rainfall variability report fewer drought episodes. Duration and severity of droughts are also related to these rainfall features. The last part of the study describes the temporal distribution of droughts throughout the world. We conclude that regions with many events show stable, even distributions over time, but many pixels in the intertropical regions, the Middle East and smaller patches in Mongolia, China, Siberia and Canada currently show higher-intensity and longer-duration drought events than at the beginning of the twentieth century, while the opposite occurs in parts of Scandinavia, Russia, Argentina and Tanzania. The analysis demonstrates that DEPI is easy to use, is applicable to different climates and is effective in detecting the onset, end and intensity of droughts.
Extreme events like droughts and floods are important parts of human history, dating back to the Holocene. These events contributed to the collapse of ancient civilizations and continue to affect modern society. For instance, approximately 55 million people globally are affected by droughts every year. Unfortunately, the magnitude of these events is expected to increase, further subjecting human societies to perilous conditions, including financial and environmental problems. In this day and age, living with droughts is now a way of life. However, it is also a concept that must be fully understood. This understanding will underpin a blue print to enhance adaptation plans and drought resilience looking forward. This chapter explores various approaches, including data and novel techniques to measuring and assessing drought characteristics (e.g., intensity, duration, extents, etc.) to improve understanding of their impacts. Major drought hotspots are identified and used as a test-bed to illustrate the potential of advanced statistical methods and machine learning to quantify the contributions of global climate as a first step to identifying drought risk.
"Debido a sus características interdisciplinarias y transdiciplinarias, el campo de la historia ambiental involucra una amplia gama de herramientas teórico-conceptuales y metodológicas, que contribuyen a responder preguntas o plantear problematizaciones sobre los cambios ecológicos y geográficos en el pasado. Por tanto, el panorama de análisis y aplicación es abundante y complejo, pero no por ello deja de ser fascinante y digno de compartirse. Con el libro Historia ambiental de América Latina. Enfoques, procedimientos y cotidianidades, especialistas de diversas procedencias, pretenden mostrar las distintas formas de aproximación a este campo emergente. La compilación contenida muestra miradas variopintas y abordajes que, si bien son interdisciplinarios y transdisciplinarios, aportan desde campos específicos muy diversos, como la geografía, la historia, la arqueología, la ecología, la economía, la agronomía o el arte. También revela que se puede escudriñar el pasado desde un archivo histórico, desde los anillos de crecimiento de un árbol, desde la carta de un inmigrante, desde una película, practicando senderismo o pedaleando en bicicleta. En la diversidad de formaciones de las autorías está también la posible riqueza en la propuesta del libro."
Declining condition of water resources cannot be shown by single-variable drought indices because of changes in precipitation and temperature due to climate change. Accordingly, multivariate drought indices are considered such as the Standardized Precipitation-Evapotranspiration Index (SPEI). In order to study the trend of changes in the severity of meteorological drought in the geographical zone of Iran, the SPEI global network data was used over a 30-year statistical period, and trend detection test and Sen's slope estimator were performed on their seasonal series. Then, their results were mapped in the GIS and the results showed that the highest drought trend is in the winter between seasons, and more than 94 percent of the country's area shows a declining slope in the drought index time series. The trend of this slope in the west and north-east of the country is statistically significant. Also, the eastern half of the country shows a drought trend in the summer, but a better situation prevailing in Iran in spring and autumn, and a small percentage of the country's area indicates a significant drought trend. So that in the spring and autumn, the slope of the trend line in 85 and 91 percent of the country is less than and equal to 2 percent, respectively. Generally speaking, the result of the Standardized Precipitation-Evapotranspiration Index showed that climatic changes are occurred in Iran and the country is going towards more dry periods in future years.
este trabajo describe un procedimiento de recopilación de datos históricos con expresión espacial y la respectiva representación cartográfica que permiten explicar la conformación histórica del actual estado de Zacatecas, México, desde los primeros patrones de poblamiento prehispánico detectados hasta la consumación de la independencia de los Estados Unidos Mexicanos, a principios del siglo XIX.
“It is increasingly alarming that being in dire need of food assistance in the GHA is becoming a permanent feature of the region. Almost every year, including 2014, 2015, 2016 and 2017 famine headlines appear in the news as drought related crisis”—[39].
“The failure of drought studies in the region to analyze and/or incorporate the impacts of topography and/or gauge density on the analysis results could lead to confusion and/or reduced confidence on drought analysis results due to inconsistencies between various indicators. These inconsistencies could arise from propagation of varying impacts of topography and gauge density on different products during drought characterization.”—[6].
Greater Horn of Africa (GHA, Fig. 1.1a), one of the most food insecure regions in the world comprises of 11 countries; Burundi, Djibouti, Ethiopia, Eritrea, Kenya, Rwanda, Somalia, Sudan, Tanzania, Uganda and South Sudan, majority of which are classified as least developed where most of the societies survive on less than one dollar per day [2, 16]. Abshir [1] puts the area of Horn of Africa (which excludes Tanzania, Rwanda and Burundi) to 5.2 million square kilometers with a population of 230 millions.
Standard Precipitation Index (SPI) computed at multiple time scales is considered as a key indicator for short-term agricultural to long-term hydrological drought monitoring. SPI computed at multiple timescales namely 1, 3–6 and 12 months represent meteorological, agricultural and hydrological droughts, respectively. Traditionally, precipitation based SPI drought index is being computed using raingauge station data, which is often limited by sparse and uneven distribution of raingauge stations. However, uncertainty in aerial estimation of rainfall and data scarcity regions can be overcome by envisaging various quasi-global satellite derived precipitation products such as TRMM multi-satellite Precipitation Analysis (TMPA), Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS), Climate Research Unit (CRU) data for drought monitoring. Hence in this study, characterization of spatially varying drought occurrences and its severity at different time scales was carried out using various precipitation gridded products (TRMM, CRU and CHIRPS) for part of Indo-Gangetic Plain, India. Further, drought severities assessed by these gridded products were relatively evaluated with reference to rain gauge based IMD gridded product. The spatial pattern of TRMM and IMD based SPI for all the time scales were observed to be similar for both wet and dry years. The spatial pattern of low and high number of drought events is mostly similar for CHIRPS, TRMM and IMD. Overall, it was observed that spatial pattern of drought frequency identified through CRU based SPI was completely distinct compared to other datasets. In general, CHIRPS appears to have overestimated the drought area and frequency compared to IMD, while TRMM data exhibited a similar pattern as that of IMD product which could be due to the fact that CHIRPS underestimates the rainfall.
Numerous definitions of drought are reviewed to determine those characteristics scientists consider most essential for a description and an understanding of the phenomenon. Discusses the far-reaching impacts of drought on society, and suggests that definitions of drought are typically simplistic, and, in that way, often lead to a rather poor understanding of the dimensions of the concept.-from Authors
The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000-09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.
Drought is by far the most costly natural disaster that can lead to widespread impacts, including water and food crises. Here we present data sets available from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS), which provides drought information based on multiple drought indicators. The system provides meteorological and agricultural drought information based on multiple satellite-, and model-based precipitation and soil moisture data sets. GIDMaPS includes a near real-time monitoring component and a seasonal probabilistic prediction module. The data sets include historical drought severity data from the monitoring component, and probabilistic seasonal forecasts from the prediction module. The probabilistic forecasts provide essential information for early warning, taking preventive measures, and planning mitigation strategies. GIDMaPS data sets are a significant extension to current capabilities and data sets for global drought assessment and early warning. The presented data sets would be instrumental in reducing drought impacts especially in developing countries. Our results indicate that GIDMaPS data sets reliably captured several major droughts from across the globe.
This study contributes to characterization of satellite precipitation
error which is fundamental to develop uncertainty models and bias
reduction algorithms. Systematic and random error components of several
satellite precipitation products are investigated over different
seasons, thresholds and temporal accumulations. The analyses show that
the spatial distribution of systematic error has similar patterns for
all precipitation products. However, the systematic (random) error of
daily accumulations is significantly less (more) than that of high
resolution 3-hr data. One should note that the systematic biases of
satellite precipitation are distinctively different in the summer and
winter. The systematic (random) error is remarkably higher (lower)
during the winter. Furthermore, the systematic error seems to be
proportional to the rain rate magnitude. The findings of this study
highlight that bias removal methods should take into account the
spatiotemporal characteristics of error as well as the proportionality
of error to the magnitude of rain rate.
Overview Of recOmmendatiOns (i) Uncertainty of merged products and multisensor observations warrants a great deal of research. Quantification of uncertainties and their propa-gation into combined products is vital for future development. (ii) Future improvements in satellite-based precipi-tation retrieval algorithms will rely on more in-depth research on error properties in different climate regions, storm regimes, surface condi-tions, seasons, and altitudes. Given such infor-mation, precipitation algorithms for retrieval, downscaling, and data fusion can be optimized for different situations. (iii) Based on the currently available data, global multichannel precipitation estimates with spatial and temporal resolutions of 4 km and 30 min can be considered as the target dataset that can be achieved in the near future. At high resolutions, however, achieving desirable accuracy is the main challenge. Extensive development and validation efforts are required to make such a dataset available to the community for research and applications. (iv) Development of metrics for validation and uncer-tainty analysis are of great importance. Various metrics with emphasis on different aspects of performance are required so that users can decide which product fits their purposes/applications best. Furthermore, developing diagnostic statis-tics (shifting and rotation) will help to capture the systematic deficiency inherent in precipitation retrieval algorithms.
While numerous studies have addressed changes in climate extremes, analyses of concurrence of climate extremes are scarce, and climate change effects on joint extremes are rarely considered. This study assesses the occurrence of joint (concurrent) monthly continental precipitation and temperature extremes in Climate Research Unit (CRU) and University of Delaware (UD) observations, and in 13 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate simulations. The joint occurrences of precipitation and temperature extremes simulated by CMIP5 climate models are compared with those derived from the CRU and UD observations for warm/wet, warm/dry, cold/wet, and cold/dry combinations of joint extremes. The number of occurrences of these four combinations during the second half of the 20th century (1951–2004) is assessed on a common global grid. CRU and UD observations show substantial increases in the occurrence of joint warm/dry and warm/wet combinations for the period 1978–2004 relative to 1951–1977. The results show that with respect to the sign of change in the concurrent extremes, the CMIP5 climate model simulations are in reasonable overall agreement with observations. However, the results reveal notable discrepancies between regional patterns and the magnitude of change in individual climate model simulations relative to the observations of precipitation and temperature.
Reliable drought monitoring requires long-term and continuous precipitation data. High resolution satellite measurements provide valuable precipitation information on a quasi-global scale. However, their short lengths of records limit their applications in drought monitoring. In addition to this limitation, long-term low resolution satellite-based gauge-adjusted data sets such as the Global Precipitation Climatology Project (GPCP) one are not available in near real-time form for timely drought monitoring. This study bridges the gap between low resolution long-term satellite gauge-adjusted data and the emerging high resolution satellite precipitation data sets to create a long-term climate data record of droughts. To accomplish this, a Bayesian correction algorithm is used to combine GPCP data with real-time satellite precipitation data sets for drought monitoring and analysis. The results showed that the combined data sets after the Bayesian correction were a significant improvement compared to the uncorrected data. Furthermore, several recent major droughts such as the 2011 Texas, 2010 Amazon and 2010 Horn of Africa droughts were detected in the combined real-time and long-term satellite observations. This highlights the potential application of satellite precipitation data for regional to global drought monitoring. The final product is a real-time data-driven satellite-based standardized precipitation index that can be used for drought monitoring especially over remote and/or ungauged regions.
The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N-S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate-dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the surface observation-based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other finescale estimators. Examples are provided of a flood event and diurnal cycle determination.
Satellite-based precipitation estimates have great potential for a wide range of critical applications, but their error characteristics need to be examined and understood. In this study, six (6) high-resolution, satellite-based precipitation data sets are evaluated over the contiguous United States against a gauge-based product. An error decomposition scheme is devised to separate the errors into three independent components, hit bias, missed precipitation, and false precipitation, to better track the error sources associated with the satellite retrieval processes. Our analysis reveals the following. (1) The three components for each product are all substantial, with large spatial and temporal variations. (2) The amplitude of individual components sometimes is larger than that of the total errors. In such cases, the smaller total errors are resulting from the three components canceling one another. (3) All the products detected strong precipitation (>40 mm/d) well, but with various biases. They tend to overestimate in summer and underestimate in winter, by as much as 50% in either season, and they all miss a significant amount of light precipitation (
PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution of 0.25° × 0.25° every half-hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain-rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° × 1° daily. The authors' current GOES-IR-TRMM TMI based product, named PERSIANN-GT, was evaluated over the region 30°S-30°N, 90°E-30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain-rate estimates agree well with the National Climatic Data Center radar-gauge composite data over Florida and Texas (correlation coefficient r > 0.7). The product also compares well (r ~ 0.77-0.90) with the monthly World Meteorological Organization gauge measurements for 5° × 5° grid locations having high gauge densities. The PERSIANN-GT product was evaluated further by comparing it with current TRMM products (3A11, 3B31, 3B42, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1° × 1° monthly product, but the PERSIANN-GT products indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index-based TRMM 3B42 product.
Droughts are difficult to detect and monitor. Drought indices, most commonly the Palmer Drought Severity Index (PDSI), have been used with limited success as operational drought monitoring tools and triggers for policy responses. Recently, a new index, the Standardized Precipitation Index (SPI), was developed to improve drought detection and monitoring capabilities. The SPI has several characteristics that are an improvement over previous indices, including its simplicity and temporal flexibility, that allow its application for water resources on all timescales. In this article, the 1996 drought in the southern plains and southwestern United States is examined using the SPI. A series of maps are used to illustrate how the SPI would have assisted in being able to detect the onset of the drought and monitor its progression. A case study investigating the drought in greater detail for Texas is also given. The SPI demonstrated that it is a tool that should be used operationally as part of a state, regional, or national drought watch system in the United States. During the 1996 drought, the SPI detected the onset of the drought at least 1 month in advance of the PDSI. This timeliness will be invaluable for improving mitigation and response actions of state and federal government to drought-affected regions in the future.
This paper shows an application of copulas to the probabilistic analysis of drought characteristics. Drought occurrences are analyzed by the Standardized Precipitation Index (SPI) computed on the mean areal precipitation, aggregated at 6months, observed in Sicily between 1921 and 2003. Assuming a drought period as a consecutive number of intervals where SPI values are less than −1, several characteristics are determined, namely: drought length, mean and minimum SPI values, and drought mean areal extent.Results of a preliminary analysis based on Kendall’s correlation and upper tail dependence coefficient, computed on observed and resampled data, show significant dependence properties between almost all the considered pairs. The four-dimensional joint distribution required to correctly model the stochastic structure of variables is determined by resorting to copula approach. This allows flexibility in choosing suitable marginals and dependence structure, and in simplifying the inference procedure as well. Drought return periods are then computed as mean interarrival time, taking into account two drought characteristics at a time by means of the corresponding bivariate marginals of the fitted four-dimensional distribution.Application of the proposed methodology to Sicilian precipitation series shows a good correspondence between empirical and theoretical joint return periods, thus indicating that copulas are adequate to jointly model drought characteristics and to compute exceedance probabilities of drought events.
This paper investigates the impact of climate change on drought by addressing two questions: (1) How reliable is the assessment of climate change impact on drought based on state-of-the-art climate change projections and downscaling techniques? and (2) Will the impact be at the same level from meteorological, agricultural, and hydrologic perspectives? Regional climate change projections based on dynamical downscaling through regional climate models (RCMs) are used to assess drought frequency, intensity, and duration, and the impact propagation from meteorological to agricultural to hydrological systems. The impact on a meteorological drought index (standardized precipitation index, SPI) is first assessed on the basis of daily climate inputs from RCMs driven by three general circulation models (GCMs). Two periods and two emission scenarios, i.e., 1991-2000 and 2091-2100 under B1 and A1Fi for Parallel Climate Model (PCM), 1990-1999 and 2090-2099 under A1B and A1Fi for Community Climate System Model, version 3.0 (CCSM3), 1980-1989 and 2090-2099 under B2 and A2 for Hadley Centre CGCM (HadCM3), are undertaken and dynamically downscaled through the RCMs. The climate projections are fed to a calibrated hydro-agronomic model at the watershed scale in Central Illinois, and agricultural drought indexed by the standardized soil water index (SSWI) and hydrological drought by the standardized runoff index (SRI) and crop yield impacts are assessed. SSWI, in particular with extreme droughts, is more sensitive to climate change than either SPI or SRI. The climate change impact on drought in terms of intensity, frequency, and duration grows from meteorological to agricultural to hydrological drought, especially for CCSM3-RCM. Significant changes of SSWI and SRI are found because of the temperature increase and precipitation decrease during the crop season, as well as the nonlinear hydrological response to precipitation and temperature change.
The occurrence of widespread, severe drought in Africa, India, North America, China, the USSR, Australia, and western Europe has once again underscored the vulnerability of developed and developing societies to drought The occurrence of severe drought during 1982-83 is shown in Fig. 1. These recent droughts have emphasized the need for more research on the causes as well as the impacts of drought and the need for additional planning to help mitigate the possible worst effects of future droughts. Drought has been the subject of a great deal of systematic study, particularly reconstructions of drought history, computations of drought frequency, and, to a lesser extent, investigations of first-, second-, and even third-order impacts of drought on society.
A new global dataset of derived indicators has been compiled to clarify whether frequency and/or severity of climatic extremes changed during the second half of the 20th century, This period provides the best spatial coverage of homogenous daily series, which can be used for calculating the proportion of global land area exhibiting a significant change in extreme or severe weather. The authors chose 10 indicators of extreme climatic events, defined from a larger selection, that could be applied to a large variety of climates. It was assumed that data producers were more inclined to release derived data in the form of annual indicator time series than releasing their original daily observations. The indicators are based on daily maximum and minimum temperature series, as well as daily totals of precipitation, and represent changes in all seasons of the year. Only time series which had 40 yr or more of almost complete records were used, A total of about 3000 indicator time series were extracted from national climate archives and collated into the unique dataset described here. Global maps showing significant changes from one multi-decadal period to another during the interval from 1946 to 1999 were produced. Coherent spatial patterns of statistically significant changes emerge, particularly an increase in warm summer nights, a decrease in the number of frost days and a decrease in intra-annual extreme temperature range. All but one of the temperature-based indicators show a significant change. Indicators based on daily precipitation data show more mixed patterns of change but significant increases have been seen in the extreme amount derived from wet spells and number of heavy rainfall events. We can conclude that a significant proportion of the global land area was increasingly affected by a significant change in climatic extremes during the second half of the 20th century. These clear signs of change are very robust; however, large areas are still not represented, especially Africa and South America.
The high-resolution precipitation products (HRPPs) is a combination of a multitude of spaceborne remotely estimated and ground-based datasets in producing a precipitation product, a finer spatial than any of the individual input datasets. The basic building blocks of an HRPP are provided by the sensors on board low Earth orbiting (LEO) and geostationary environmental satellite systems. These HRPPs are relevant to various applications relating to Earth's hydrological cycle. Meanwhile, the first workshop of the Program for the Evaluation of High-Resolution Precipitation Products (PEHRPP) was held at the World Meteorological Organizations (WMO) in order to assess the status and requirements for HRPP error analysis. The workshop is consisted of 2 days of formal presentations and a third day focused to working groups separated into validation, error metrics, and applications.
The Second Global Soil Wetness Project (GSWP-2) is a land-surface modeling activity of the Global Land-Atmosphere System Study (GLASS) and the International Satellite Land-Surface Climatology Project (ISLSCP), both contributing projects of the Global Energy and Water Cycle Experiment (GEWEX). The first phase of GSWP-2, a global 10-year multi-model simulation and comparison using the ISLSCP Initiative II data set (1986-1995), began in February 2003 and will continue through this year. In addition to providing a large-scale test-bed for comparison of land surface schemes (LSSs), several sub-projects will be conducted. Estimates of continental and global-scale surface energy and water budgets will be calculated, and inter-model uncertainties will be established. The ability of multiple LSSs to simulate large-scale interannual variations will be investigated. GSWP-2 will serve as a global platform for the application of remote sensing to LSS calibration, validation and assimilation. Sensitivity of simulated fluxes and state variables to uncertainties in atmospheric forcings, soil, and vegetation parameters will be examined. The ability of simple and intermediate models to replicate the behavior of complex LSSs will be explored, as a tool for better understanding of surface processes. In situ validation of LSSs with data from numerous field campaigns conducted during the 10-year period will also be possible. GSWP-2 will also explore promising new data management technologies, including the capability to perform model integration and analysis with distributed data sets, reducing the data management burden on participants. Participation is open, and final data sets will be made available to the public.
The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 8 latitude 3 2.58 longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge obser- vations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the prem- icrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.
The year 2010 featured a widespread drought in the Amazon rain forest, which was more severe than the “once-in-a-century” drought of 2005. Water levels of major Amazon tributaries fell drastically to unprecedented low values, and isolated the floodplain population whose transportation depends upon on local streams which completely dried up. The drought of 2010 in Amazonia started in early austral summer during El Niño and then was intensified as a consequence of the warming of the tropical North Atlantic. An observed tendency for an increase in dry and very dry events, particularly in southern Amazonia during the dry season, is concomitant with an increase in the length of the dry season. Our results suggest that it is by means of a longer dry season that warming in the tropical North Atlantic affects the hydrology of the Amazon Rivers at the end of the recession period (austral spring). This process is, sometimes, further aggravated by deficient rainfall in the previous wet season.
1] North American Land Data Assimilation System (NLDAS) land surface models have been run for a retrospective period forced by atmospheric observations from the Eta analysis and actual precipitation and downward solar radiation to calculate land hydrology. We evaluated these simulations using in situ observations over the southern Great Plains for the periods of May–September of 1998 and 1999 by comparing the model outputs with surface latent, sensible, and ground heat fluxes at 24 Atmospheric Radiation Measurement/Cloud and Radiation Testbed stations and with soil temperature and soil moisture observations at 72 Oklahoma Mesonet stations. The standard NLDAS models do a fairly good job but with differences in the surface energy partition and in soil moisture between models and observations and among models during the summer, while they agree quite well on the soil temperature simulations. To investigate why, we performed a series of experiments accounting for differences between model-specified soil types and vegetation and those observed at the stations, and differences in model treatment of different soil types, vegetation properties, canopy resistance, soil column depth, rooting depth, root density, snow-free albedo, infiltration, aerodynamic resistance, and soil thermal diffusivity. The diagnosis and model enhancements demonstrate how the models can be improved so that they can be used in actual data assimilation mode.
Recently the Southwest has experienced a spate of dryness, which presents a challenge to the sustainability of current water use by human and natural systems in the region. In the Colorado River Basin, the early 21st century drought has been the most extreme in over a century of Colorado River flows, and might occur in any given century with probability of only 60%. However, hydrological model runs from downscaled Intergovernmental Panel on Climate Change Fourth Assessment climate change simulations suggest that the region is likely to become drier and experience more severe droughts than this. In the latter half of the 21st century the models produced considerably greater drought activity, particularly in the Colorado River Basin, as judged from soil moisture anomalies and other hydrological measures. As in the historical record, most of the simulated extreme droughts build up and persist over many years. Durations of depleted soil moisture over the historical record ranged from 4 to 10 years, but in the 21st century simulations, some of the dry events persisted for 12 years or more. Summers during the observed early 21st century drought were remarkably warm, a feature also evident in many simulated droughts of the 21st century. These severe future droughts are aggravated by enhanced, globally warmed temperatures that reduce spring snowpack and late spring and summer soil moisture. As the climate continues to warm and soil moisture deficits accumulate beyond historical levels, the model simulations suggest that sustaining water supplies in parts of the Southwest will be a challenge.
In 2005, large sections of southwestern Amazonia experienced one of the most intense droughts of the last hundred years. The drought severely affected human population along the main channel of the Amazon River and its western and southwestern tributaries, the Solimões (also known as the Amazon River in the other Amazon countries) and the Madeira Rivers, respectively. The river levels fell to historic low levels and navigation along these rivers had to be suspended. The drought did not affect central or eastern Amazonia, a pattern different from the El Niño–related droughts in 1926, 1983, and 1998. The choice of rainfall data used influenced the detection of the drought. While most datasets (station or gridded data) showed negative departures from mean rainfall, one dataset exhibited above-normal rainfall in western Amazonia.
The causes of the drought were not related to El Niño but to (i) the anomalously warm tropical North Atlantic, (ii) the reduced intensity in northeast trade wind moisture transport into southern Amazonia during the peak summertime season, and (iii) the weakened upward motion over this section of Amazonia, resulting in reduced convective development and rainfall. The drought conditions were intensified during the dry season into September 2005 when humidity was lower than normal and air temperatures were 3°–5°C warmer than normal. Because of the extended dry season in the region, forest fires affected part of southwestern Amazonia. Rains returned in October 2005 and generated flooding after February 2006.
A suite of climate change indices derived from daily temperature and precipitation data, with a primary focus on extreme events, were computed and analyzed. By setting an exact formula for each index and using specially designed software, analyses done in different countries have been combined seamlessly. This has enabled the presentation of the most up-to-date and comprehensive global picture of trends in extreme temperature and precipitation indices using results from a number of workshops held in data-sparse regions and high-quality station data supplied by numerous scientists world wide. Seasonal and annual indices for the period 1951-2003 were gridded. Trends in the gridded fields were computed and tested for statistical significance. Results showed widespread significant changes in temperature extremes associated with warming, especially for those indices derived from daily minimum temperature. Over 70% of the global land area sampled showed a significant decrease in the annual occurrence of cold nights and a significant increase in the annual occurrence of warm nights. Some regions experienced a more than doubling of these indices. This implies a positive shift in the distribution of daily minimum temperature throughout the globe. Daily maximum temperature indices showed similar changes but with smaller magnitudes. Precipitation changes showed a widespread and significant increase, but the changes are much less spatially coherent compared with temperature change. Probability distributions of indices derived from approximately 200 temperature and 600 precipitation stations, with near-complete data for 1901-2003 and covering a very large region of the Northern Hemisphere midlatitudes (and parts of Australia for precipitation) were analyzed for the periods 1901-1950, 1951-1978 and 1979-2003. Results indicate a significant warming throughout the 20th century. Differences in temperature indices distributions are particularly pronounced between the most recent two periods and for those indices related to minimum temperature. An analysis of those indices for which seasonal time series are available shows that these changes occur for all seasons although they are generally least pronounced for September to November. Precipitation indices show a tendency toward wetter conditions throughout the 20th century.
The purpose of this study is to define the occurrence and variability of drought in the United States in order to furnish climatologists and drought mitigation planners with information on how to put current drought into historical perspective. The opposite of drought is a period of anomalously wet conditions. Analyses of both drought and wet periods on national and regional scales are provided. Analysis of drought and wet periods in terms of areal coverage, intensity, duration, and variability at these different space and time scales provides valuable insight not only into the historical perspective of anomalously dry and wet conditions, but also into the long-term variation of climate in the United States.
Historical records of precipitation, streamflow and drought indices all
show increased aridity since 1950 over many land areas. Analyses of
model-simulated soil moisture, drought indices and
precipitation-minus-evaporation suggest increased risk of drought in the
twenty-first century. There are, however, large differences in the
observed and model-simulated drying patterns. Reconciling these
differences is necessary before the model predictions can be trusted.
Previous studies show that changes in sea surface temperatures have
large influences on land precipitation and the inability of the coupled
models to reproduce many observed regional precipitation changes is
linked to the lack of the observed, largely natural change patterns in
sea surface temperatures in coupled model simulations. Here I show that
the models reproduce not only the influence of El Niño-Southern
Oscillation on drought over land, but also the observed global mean
aridity trend from 1923 to 2010. Regional differences in observed and
model-simulated aridity changes result mainly from natural variations in
tropical sea surface temperatures that are often not captured by the
coupled models. The unforced natural variations vary among model runs
owing to different initial conditions and thus are irreproducible. I
conclude that the observed global aridity changes up to 2010 are
consistent with model predictions, which suggest severe and widespread
droughts in the next 30-90 years over many land areas resulting from
either decreased precipitation and/or increased evaporation.
Defining droughts based on a single variable/index (e.g., precipitation,
soil moisture, or runoff) may not be sufficient for reliable risk
assessment and decision-making. In this paper, a multivariate,
multi-index drought-modeling approach is proposed using the concept of
copulas. The proposed model, named Multivariate Standardized Drought
Index (MSDI), probabilistically combines the Standardized Precipitation
Index (SPI) and the Standardized Soil Moisture Index (SSI) for drought
characterization. In other words, MSDI incorporates the meteorological
and agricultural drought conditions for overall characterization of
drought. In this study, the proposed MSDI is utilized to characterize
the drought conditions over several Climate Divisions in California and
North Carolina. The MSDI-based drought analyses are then compared with
SPI and SSI. The results reveal that MSDI indicates the drought onset
and termination based on the combination of SPI and SSI, with onset
being dominated by SPI and drought persistence being more similar to SSI
behavior. Overall, the proposed MSDI is shown to be a reasonable model
for combining multiple indices probabilistically.
A simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate Forecast System (CFS). Before predicting SPI, the precipitation (P) forecasts from the coarse-resolution CFS global model were bias corrected and downscaled to a regional grid of 50 km. The downscaled CFS P forecasts, out to 9 months, were appended to the P analyses to form an extended P dataset. The SPIs were calculated from this new time series. Five downscaling methods were tested: 1) bilinear interpolation; 2) a bias correction and spatial downscaling (BCSD) method based on the probability distribution functions; 3) a conditional probability estimation approach using the mean P ensemble forecasts developed by J. Schaake, 4) a Bayesian approach that bias corrects and downscales P using all ensemble forecast members, as developed by the Princeton University group; and 5) multimethod ensemble as the equally weighted mean of the BCSD, Schaake, and Bayesian forecasts. For initial conditions from April to May, statistical downscaling methods were compared with dynamic downscaling based on the NCEP regional spectral model and forecasts from a high-resolution CFS T382 model. The skill is regionally and seasonally dependent. Overall, the 6-month SPI is skillful out to 3-4 months. For the first 3-month lead times, forecast skill comes from the P analyses prior to the forecast time. After 3 months, the multimethod ensemble has small advantages, but forecast skill may be too low to be useful in practice.
The twin Mars Exploration Rovers (MER) delivered an unprecedented array of image sensors to the Mars surface. These cameras were essential for operations, science, and public engagement. The Multimission Image Processing Laboratory (MIPL) at the Jet Propulsion Laboratory was responsible for the first-order processing of all of the images returned by these cameras. This processing included reconstruction of the original images, systematic and ad hoc generation of a wide variety of products derived from those images, and delivery of the data to a variety of customers, within tight time constraints. A combination of automated and manual processes was developed to meet these requirements, with significant inheritance from prior missions. This paper describes the image products generated by MIPL for MER and the processes used to produce and deliver them.
Quantification of sources and sinks of carbon at global and regional scales requires not only a good description of the land sources and sinks of carbon, but also of the synoptic and mesoscale meteorology. An experiment was performed in Les Landes, southwest France, during May–June 2005, to determine the variability in concentration gradients and fluxes of CO2 The CarboEurope Regional Experiment Strategy (CERES; see also http://carboregional.mediasfrance.org/index) aimed to produce aggregated estimates of the carbon balance of a region that can be meaningfully compared to those obtained from the smallest downscaled information of atmospheric measurements and continental-scale inversions. We deployed several aircraft to sample the CO2 concentration and fluxes over the whole area, while fixed stations observed the fluxes and concentrations at high accuracy. Several (mesoscale) meteorological modeling tools were used to plan the experiment and flight patterns.
Results show that at regional scale the relation between profiles and fluxes is not obvious, and is strongly influenced by airmass history and mesoscale flow patterns. In particular, we show from an analysis of data for a single day that taking either the concentration at several locations as representative of local fluxes or taking the flux measurements at those sites as representative of larger regions would lead to incorrect conclusions about the distribution of sources and sinks of carbon. Joint consideration of the synoptic and regional flow, fluxes, and land surface is required for a correct interpretation. This calls for an experimental and modeling strategy that takes into account the large spatial gradients in concentrations and the variability in sources and sinks that arise from different land use types. We briefly describe how such an analysis can be performed and evaluate the usefulness of the data for planning of future networks or longer campaigns with reduced experimental efforts.
This paper focuses on estimating the error uncertainty of the monthly 2.5° × 2.5° rainfall products of the Global Precipitation Climatology Project (GPCP) using rain gauge observations. Two kinds of GPCP products are evaluated: the satellite-only (MS) product, and the satellite gauge (SG) merged product. The error variance separation (EVS) method has been proposed previously as a means of estimating the error uncertainty of the GPCP products. In this paper, the accuracy of the EVS results is examined for a variety of gauge densities. Three validation sites---two in North Dakota and one in Thailand---all with a large number of rain gauges, were selected. The very high density of the selected sites justifies the assumption that the errors are negligible if all gauges are used. Monte Carlo simulation studies were performed to evaluate sampling uncertainty for selected rain gauge network densities. Results are presented in terms of EVS error uncertainty normalized by the true error uncertainty. These results show that the accuracy of the EVS error uncertainty estimates for the SG product differs from that of the MS product. The key factors that affect the errors of the EVS results, such as the gauge density, the gauge network, and the sample size, have been identified and their influence has been quantified. One major finding of this study is that 8 10 gauges, at the 2.5° scale, are required as a minimum to get good error uncertainty estimates for the SG products from the EVS method. For eight or more gauges, the normalized error uncertainty is about 0.86 ± 0.10 (North Dakota: Box 1) and 0.95 ± 0.10 (North Dakota: Box 2). Results show that, despite its error, the EVS method performs better than the root-mean-square error (rmse) approach that ignores the rain gauge sampling error. For the MS products, both the EVS method and the rmse approach give negligible bias. As expected, results show that the SG products give better rainfall estimates than the MS products, according to most of the criteria used.
A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. The model was initially calibrated over the Japanese Islands using remotely sensed infrared data collected by the Geostationary Meteorological Satellite (GMS) and ground-based data collected by the Automated Meteorological Data Acquisition System (AMeDAS). The model was then validated for both the Japanese Islands (using GMS and AMeDAS data) and the Florida peninsula (using GOES-8 and NEXRAD data). An adaptive procedure is used to recursively update the network parameters when ground-based data are available. This feature dramatically improves the estimation performance in response to the diverse precipitation characteristics of different geographical regions and time of year. The model can also be successfully updated using only spatially and/or temporally limited observation data such as ground-based rainfall measurements. Another important feature is a procedure that provides insights into the functional relationships between the input variables and output rainfall rate.
Droughts have severe economic, environmental and social impacts. Timely determination of the current level of drought may aid the decision making process in reducing the impacts from drought. In this study, high-resolution, land surface hydrology simulations using the Variable Infiltration Capacity (VIC) model are used to derive a hydrologically based drought index. Soil moisture data from a retrospective simulation from 1950 to 1999 over the continental United States are used to develop probability distributions of monthly average soil moisture, and the relative position of soil moisture fields within the historic distribution provides a measure of drought in relation to the long-term behavior. The index is able to identify the major drought events during the latter part of the twentieth century and shows good agreement with the time series of U.S. drought from two Palmer Drought Severity Index (PDSI) data sets. On average, 30% of the United States experienced dry conditions (
Many current metrics of drought are derived solely from analyses of climate variables such as precipitation and temperature. Drought is clearly a consequence of climate anomalies, as well as of human water use practices, but many impacts to society are more directly related to hydrologic conditions resulting from these two factors. Modern hydrology models can provide a valuable counterpart to existing climate-based drought indices by simulating hydrologic variables such as land surface runoff. We contrast the behavior of a standardized runoff index (SRI) with that of the well-known standardized precipitation index (SPI) during drought events in a snowmelt region. Although the SRI and SPI are similar when based on long accumulation periods, the SRI incorporates hydrologic processes that determine seasonal lags in the influence of climate on streamflow. As a result, on monthly to seasonal time scales, the SRI is a useful complement to the SPI for depicting hydrologic aspects of drought.
Drought indices derived from the North American Land Data Assimilation System (NLDAS) Variable Infiltration Capacity (VIC) and Noah models from 1950 to 2000 are intercompared and evaluated for their ability to classify drought across the United States. For meteorological drought, the standardized precipitation index (SPI) is used to measure precipitation deficits. The standardized runoff index (SRI), which is similar to the SPI, is used to classify hydrological drought. Agricultural drought is measured by monthly-mean soil moisture (SM) anomaly percentiles based on probability distributions (PDs). The PDs for total SM are regionally dependent and influenced by the seasonal cycle, but the PDs for SM monthly-mean anomalies are unimodal and Gaussian.
Across the eastern United States (east of 95°W), the indices derived from VIC and Noah are similar, and they are able to detect the same drought events. Indices are also well correlated. For river forecast centers (RFCs) across the eastern United States, different drought indices are likely to detect the same drought events.
The monthly-mean soil moisture (SM) percentiles and runoff indices between VIC and Noah have large differences across the western interior of the United States. For small areas with a horizontal resolution of 0.5° on the time scales of one to three months, the differences of SM percentiles and SRI between VIC and Noah are larger than the thresholds used to classify drought. For the western RFCs, drought events selected according to SM percentiles or SRI derived from different NLDAS systems do not always overlap.
Droughts can be characterized by their severity, frequency and duration, and areal extent. Depth-area-duration analysis, widely used to characterize precipitation extremes, provides a basis for the evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. Gridded precipitation and temperature data were used to force a physically based macroscale hydrologic model at 1/ 2° spatial resolution over the continental United States, and construct a drought history from 1920 to 2003 based on the model-simulated soil moisture and runoff. A clustering algorithm was used to identify individual drought events and their spatial extent from monthly summaries of the simulated data. A series of severity-area-duration (SAD) curves were constructed to relate the area of each drought to its severity. An envelope of the most severe drought events in terms of their SAD characteristics was then constructed. The results show that (a) the droughts of the 1930s and 1950s were the most severe of the twentieth century for large areas; (b) the early 2000s drought in the western United States is among the most severe in the period of record, especially for small areas and short durations; (c) the most severe agricultural droughts were also among the most severe hydrologic droughts, however, the early 2000s western U.S. drought occupies a larger portion of the hydrologic drought envelope curve than does its agricultural companion; and (d) runoff tends to recover in response to precipitation more quickly than soil moisture, so the severity of hydrologic drought during the 1930s and 1950s was dampened by short wet spells, while the severity of the early 2000s drought remained high because of the relative absence of these short-term phenomena.
Drought is expected to increase in frequency and severity in the future as a result of climate change, mainly as a consequence of decreases in regional precipitation but also because of increasing evaporation driven by global warming. Previous assessments of historic changes in drought over the late twentieth and early twenty-first centuries indicate that this may already be happening globally. In particular, calculations of the Palmer Drought Severity Index (PDSI) show a decrease in moisture globally since the 1970s with a commensurate increase in the area in drought that is attributed, in part, to global warming. The simplicity of the PDSI, which is calculated from a simple water-balance model forced by monthly precipitation and temperature data, makes it an attractive tool in large-scale drought assessments, but may give biased results in the context of climate change. Here we show that the previously reported increase in global drought is overestimated because the PDSI uses a simplified model of potential evaporation that responds only to changes in temperature and thus responds incorrectly to global warming in recent decades. More realistic calculations, based on the underlying physical principles that take into account changes in available energy, humidity and wind speed, suggest that there has been little change in drought over the past 60 years. The results have implications for how we interpret the impact of global warming on the hydrological cycle and its extremes, and may help to explain why palaeoclimate drought reconstructions based on tree-ring data diverge from the PDSI-based drought record in recent years.
There is likely to be an increase in the area of the globe affected by drought under enhanced greenhouse gas conditions. Therefore water management and drought policy may need to be modified accordingly. Rainfall and potential evapotranspiration (PET) are the key factors defining meteorological drought, and the development of drought projections is facilitated by global climate model (GCM) simulations. This paper assesses how well a set of GCMs can reproduce observed characteristics of historical rainfall and PET on a regional basis and explores the implications for regional drought projections if the poorer performing GCMs are omitted. Fourteen of the GCMs used in the IPCC's 4th Assesment Report are considered and their results compared with 1951–2006 observed rainfall and PET over Australia. The results indicate that some GCMs can reproduce the observed spatial patterns of both the means and variability (represented as the coefficient of variation), but most GCMs fail to reproduce the linear long-term trends. There is less clear difference between the better and poorer GCMs at a national level, but there is a clearer distinction at the regional level. The omission of the poorer GCMs leads to a clearer sign of the likely change (either increase or decrease) in future drought intensity in some regions. It also results in a decreased range of model-to-model uncertainty in some regions. It is hoped such uncertainty reduction can be useful to end users, particularly for those dealing with water management. Copyright © 2010 Royal Meteorological Society
1] The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high-resolution satellite-based precipitation estimates and gauge-corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5° Â 5° latitude-longitude grids shows about 12–16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall-runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall-generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval.
This article reviews recent literature on drought of the last millennium, followed by an update on global aridity changes from 1950 to 2008. Projected future aridity is presented based on recent studies and our analysis of model simulations. Dry periods lasting for years to decades have occurred many times during the last millennium over, for example, North America, West Africa, and East Asia. These droughts were likely triggered by anomalous tropical sea surface temperatures (SSTs), with La Niña-like SST anomalies leading to drought in North America, and El-Niño-like SSTs causing drought in East China. Over Africa, the southward shift of the warmest SSTs in the Atlantic and warming in the Indian Ocean are responsible for the recent Sahel droughts. Local feedbacks may enhance and prolong drought. Global aridity has increased substantially since the 1970s due to recent drying over Africa, southern Europe, East and South Asia, and eastern Australia. Although El Niño-Southern Oscillation (ENSO), tropical Atlantic SSTs, and Asian monsoons have played a large role in the recent drying, recent warming has increased atmospheric moisture demand and likely altered atmospheric circulation patterns, both contributing to the drying. Climate models project increased aridity in the 21st century over most of Africa, southern Europe and the Middle East, most of the Americas, Australia, and Southeast Asia. Regions like the United States have avoided prolonged droughts during the last 50 years due to natural climate variations, but might see persistent droughts in the next 20–50 years. Future efforts to predict drought will depend on models' ability to predict tropical SSTs. WIREs Clim Change 2011 2 45–65 DOI: 10.1002/wcc.81
For further resources related to this article, please visit the WIREs website
A physically based conceptual framework is put forward that explains why an increase in heavy precipitation events should be a primary manifestation of the climate change that accompanies increases in greenhouse gases in the atmosphere. Increased concentrations of greenhouse gases in the atmosphere increase downwelling infrared radiation, and this global heating at the surface not only acts to increase temperatures but also increases evaporation which enhances the atmospheric moisture content. Consequently all weather systems, ranging from individual clouds and thunderstorms to extratropical cyclones, which feed on the available moisture through storm-scale moisture convergence, are likely to produce correspondingly enhanced precipitation rates. Increases in heavy rainfall at the expense of more moderate rainfall are the consequence along with increased runoff and risk of flooding. However, because of constraints in the surface energy budget, there are also implications for the frequency and/or efficiency of precipitation. It follows that increased attention should be given to trends in atmospheric moisture content, and datasets on hourly precipitation rates and frequency need to be developed and analyzed as well as total accumulation.
Recent and potential future increases in global temperatures are likely to be associated with impacts on the hydrologic cycle,
including changes to precipitation and increases in extreme events such as droughts. We analyze changes in drought occurrence
using soil moisture data for the SRES B1, A1B and A2 future climate scenarios relative to the PICNTRL pre-industrial control
and 20C3M twentieth century simulations from eight AOGCMs that participated in the IPCC AR4. Comparison with observation forced
land surface model estimates indicates that the models do reasonably well at replicating our best estimates of twentieth century,
large scale drought occurrence, although the frequency of long-term (more than 12-month duration) droughts are over-estimated.
Under the future projections, the models show decreases in soil moisture globally for all scenarios with a corresponding doubling
of the spatial extent of severe soil moisture deficits and frequency of short-term (4–6-month duration) droughts from the
mid-twentieth century to the end of the twenty-first. Long-term droughts become three times more common. Regionally, the Mediterranean,
west African, central Asian and central American regions show large increases most notably for long-term frequencies as do
mid-latitude North American regions but with larger variation between scenarios. In general, changes under the higher emission
scenarios, A1B and A2 are the greatest, and despite following a reduced emissions pathway relative to the present day, the
B1 scenario shows smaller but still substantial increases in drought, globally and for most regions. Increases in drought
are driven primarily by reductions in precipitation with increased evaporation from higher temperatures modulating the changes.
In some regions, increases in precipitation are offset by increased evaporation. Although the predicted future changes in
drought occurrence are essentially monotonic increasing globally and in many regions, they are generally not statistically
different from contemporary climate (as estimated from the 1961–1990 period of the 20C3M simulations) or natural variability
(as estimated from the PICNTRL simulations) for multiple decades, in contrast to primary climate variables, such as global
mean surface air temperature and precipitation. On the other hand, changes in annual and seasonal means of terrestrial hydrologic
variables, such as evaporation and soil moisture, are essentially undetectable within the twenty-first century. Changes in
the extremes of climate and their hydrological impacts may therefore be more detectable than changes in their means.
This study aims to model the joint drought duration and severity distribution using two-dimensional copulas. The method of inference function for margins (IFM method) is employed to construct copulas. Two separate maximum likelihood estimations of univariate marginal distributions are performed first, then followed by a maximization of the bivariate likelihood as a function of the dependence parameters. The drought duration and severity are assumed to be exponential and gamma distributions, respectively. Several copulas are tested to determine the best data fitted copula. Droughts, defined using the Standardized Precipitation Index (SPI), of Wushantou (Taiwan) are employed as an example to illustrate the proposed methodology. The copula fitting results for drought duration and severity are quite satisfactory. The bivariate drought analyses, including the joint probabilities and bivariate return periods, based on the derived copula-based joint distribution are also investigated to demonstrate the advantages of bivariate modeling of droughts.
In many hydrological studies, two non-parametric rank-based statistical tests, namely the Mann–Kendall test and Spearman's rho test are used for detecting monotonic trends in time series data. However, the power of these tests has not been well documented. This study investigates the power of the tests by Monte Carlo simulation. Simulation results indicate that their power depends on the pre-assigned significance level, magnitude of trend, sample size, and the amount of variation within a time series. That is, the bigger the absolute magnitude of trend, the more powerful are the tests; as the sample size increases, the tests become more powerful; and as the amount of variation increases within a time series, the power of the tests decrease. When a trend is present, the power is also dependent on the distribution type and skewness of the time series. The simulation results also demonstrate that these two tests have similar power in detecting a trend, to the point of being indistinguishable in practice.
El Niño brings widespread drought (i.e. precipitation deficit) to the tropics. Stronger or more frequent El Niño events in the future and/or their intersection with local changes in the mean climate towards a future with reduced precipitation would exacerbate drought risk in highly vulnerable tropical areas. Projected changes in El Niño characteristics and associated teleconnections are investigated between the 20th and 21st centuries. For climate change models that reproduce realistic oceanic variability of the El Niño-Southern Oscillation (ENSO) phenomenon, results suggest no robust changes in the strength or frequency of El Niño events. These models exhibit realistic patterns, magnitude, and spatial extent of El Niño-induced drought patterns in the 20th century, and the teleconnections are not projected to change in the 21st century; although a possible slight reduction in the spatial extent of droughts is indicated over the tropics as a whole. All model groups investigated show similar changes in mean precipitation for the end of the 21st century, with increased precipitation projected between 10oS and 10oN, independent of the ability of the models to replicate ENSO variability. These results suggest separability between climate change and ENSO-like climate variability in the tropics. As El Niño induced precipitation drought patterns are not projected to change, the observed 20th century variability is used in combination with model projected changes in mean precipitation for assessing year-to-year drought risk in the 21st century. Results suggest more locally consistent changes in regional drought risk among models with good fidelity in reproducing ENSO variability. Pages: 6456-6476