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![Percentage change of spatial mean of severity based on SPEI with respect to historical for all the GCMs over different regions under RCP4.5 scenario [Colour figure can be viewed at wileyonlinelibrary.com]](publication/351047628/figure/fig5/AS:11431281245530507@1716177275511/Percentage-change-of-spatial-mean-of-severity-based-on-SPEI-with-respect-to-historical.png)
Percentage change of spatial mean of severity based on SPEI with respect to historical for all the GCMs over different regions under RCP4.5 scenario [Colour figure can be viewed at wileyonlinelibrary.com]
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The present study aims to answer the following two research questions using the future outputs from 19 GCMs of the novel NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) under two Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. (a) What will be the possible variability of future meteorological drought properties...
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In the current scenario of climate change, there has been a substantial increase in the frequency and severity of drought events. Therefore, it is necessary to investigate spatio-temporal characteristics of different drought events to plan for water resource utilization. The present study aims to assess and quantify the impact of meteorological, hy...
Cotton is one of the major crops cultivated in Parbhani district and it is a major cash crop to the Marathwada region. Cotton cultivation in this region is facing severe challenges due to an increase in the frequency of droughts, monsoon variability and dry spells during critical growth stages of crop. Use of seasonal forecast products extended ran...
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... The ERA5 reanalysis data spanning 1979-2020 (Hersbach et al. 2019) was used for historic analysis and the bias corrected CMIP6 data from 1985 to 2100 for the SSP5-8.5 scenario from the NEX-GDDP-CMIP6 (Thrasher et al. 2022) was used for climate change analyses. The CMIP6 models in the NEX-GDDP-CMIP6 dataset are bias corrected using the Bias-Correction Spatial Disaggregation (BCSD) technique and have been used in numerous studies conducted in different regions of the world (Das et al. 2021;De Mendonça et al. 2024;Singh et al. 2019;F. Wu et al. 2023;Xu et al. 2019).The SSP 5-8.5 reflects high emission scenario with increasing radiative forcing by 8.5 W/m 2 by the end of the century and is used in this study to evaluate the worst-case scenario. ...
Comprehensive studies on aridity, reference evapotranspiration (ETo) and their driving variables are lacking in the context of the Indian region. This study comprehensively analyzes the spatio-temporal trends in aridity, ETo and the driving variables across different zones over the Indian subcontinent under historic and future climate change. ETo is estimated based on the Penman-Monteith (PM) and a modified PM method incorporating CO2 levels (PM-CO2) that takes into consideration the influence of CO2 on surface resistance. Additionally, the partial least squares regression (PLSR) is employed to assess the relative contributions of the different climate variables to ETo trends and investigate the existence of the “evapotranspiration paradox” over the Indian region. The findings reveal that a substantial portion of the subcontinent has experienced a decline in annual ETo (70%) and aridity over the historical period. Under climate change projections ETo diverge considerably based on PM and PM-CO2 approach with PM projecting higher ETo across the region. Contribution analysis outcomes show temperature and net radiation are the most influential factors affecting ETo variability under both the historic and future periods. Furthermore, results also show the existence of the “evapotranspiration paradox” over majority of the region under historic conditions which is found to be diminishing under future climate change. The paradox in the historic period is explained by reductions in net radiation and wind speed dominating the increases in temperatures across the region. Outcomes from the study have implications towards future irrigation water usage and drought projections over the Indian subcontinent.
... (Kokilavani et al., 2021) used SPI to assess spatial and temporal variation in drought and its strong potential in the identification of drought. Das et al., 2021 studied the future drought conditions like its frequency, duration, severity, and peak using SPI and SPEI with the help of GCMs of the novel NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). Long-term drought trends and variations in drought parameters (duration, frequency, intensity) were identified by using Climate Research Unit (CRU) based SPEI (Choudhury et al., 2021). ...
Severe weather events, such as heat waves, floods, pollution, and health threats, are becoming more common in metropolitan places across the world. Overcrowding, poor infrastructure, and fast, unsustainable urbanization are some of the problems that India faces, and the country is also susceptible to natural disasters. This research analyzes climatic variables affecting urban hazards in Bangalore (also known as Bengaluru) via a thorough review. Heat waves, urban floods, heat islands, and drought were identified in 156 qualifying publications using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. Contributing variables were also considered. City development and urbanization were key to changing climate and increasing urban dangers. While long-term climatic variable distribution is uneven, warming is evident. The report promotes strong urban planning techniques, comprehensive policies, more green areas, and sustainable development beyond short-term heat response programs to boost urban climate resilience. This study shows how climate, land use, and urban dangers are interconnected. Future studies may benefit by categorizing urban risk studies and identifying climatic factors.
... (Bisht et al., 2019;Das et al., 2021;Gaitán et al., 2020;Haile et al., 2020;Khan et al., 2020;Swain & Hayhoe, 2015) (Jain et al., 2019;Sood & Smakhtin, 2015) . (Wang et al., 2018) . ...
Recognizing the effects of climate change in different sectors, as well as the integration of GCM models and the development of Ensemble Climate Models (ECM) are vital. In this study, the efficacy of the climate models from the CMIP5 in simulating atmospheric variables impacting the potential for water harvesting was assessed. These variables encompass mean air temperature, wind speed, relative humidity, and the feasible quantity of water harvested from air moisture. Also, assessing the efficiency of the optimization algorithm (Genetic Algorithm) in the development of an ensemble climate model was another goal of this research. It is noteworthy that the present investigation employed data from 16 synoptic stations situated in the northern and northwestern regions of Iran during the statistical period of 1991-2005. Results indicated that the performance of individual climate models in simulating variations in wind speed and relative air humidity is deemed poorly. Conversely, GA has yielded a reduction in both error magnitude and biases in climatic outputs in estimating wind speed and relative air humidity. Furthermore, the evaluation of the efficacy of climate models in estimating the water harvesting potential from air humidity indicates the acceptable performance of ECM in simulating changes in the amount of extractable water from air humidity. In general, the results showed that Manjil and Bandar-Anzali stations are the most suitable areas for the implementation of water harvesting projects from air humidity. Conversely, Arak and Hamedan stations exhibit the least potential for water harvesting. Based on the results, the average water that can be extracted from air humidity in the summer season for Manjil and Bandar-Anzali stations is estimated to be 1.56 and 1.78 (l/day.m2). Also, the seasonal changes of water harvesting potential from air humidity showed that the potential of extracting water in summer is more than the other seasons. This accentuates the urgency of water resource management and agricultural planning, prompting the implementation of substantial measures to use this water source. The potential applications of using this source encompass agricultural sectors, green space irrigation, and potentially catering to a portion of drinking water demands, contingent upon quantity and quality parameters. Cite this article: Ramezani Etedali, H., Koohi, S., & Partovi, Z. (2024) Evaluation of Ensemble Climate Model development methods based on CMIP5 to investigate the potential of water harvesting from air humidity,
... While decreased precipitation resulted in a significant (decreasing) trend in SPI12 and negative and positive insignificant trends in SPI3, the combination of decreased precipitation and increased temperature resulting in increased evaporation and a significant decreasing trend in SPEI3 and SPEI12. The present findings are in line with those obtained by Spinoni et al. (2020), Das et al. (2021) and Meseguer-Ruiz et al. (2024), using various sets of global climate model datasets. ...
The frequency and severity of extreme climatic phenomena, including droughts, have increased across the globe. The aim of this study is to detect changes in the severity and trends of short- and medium- term droughts (3 and 12 months) across Iran (1990–2019) and project the near (2059–2030) and far (2095–2066) future changes. SPI and SPEI indices were used for this purpose. The non-parametric Mann–Kendall test was used for trend analysis, and the G * (Geties and Ord) index was exerted for hot spot analysis. The results revealed that in 1990–2020, except for 3 month SPI (SPI3) index, the SPI and SPEI trends tended to be negative and statistically significant at the 0.05 significance level. During the same period, specified and significant hot spots of the indices were formed in the northern and northwestern Iran, and significant cold spots were concentrated only in Zabol and Birjand stations in eastern Iran. Near and far future minimum and maximum monthly precipitation projections for four selected stations show increases in monthly maximum and minimum temperatures and decreases in precipitation at all stations, though the decrease in precipitation for Esfahan station only with RCP8.5. The Projected trends of 3 and 12 months SPI and SPEI (SPI3, SPI12, SPEI3 and SPEI12) in near and far future show the dominance of significant negative trends. Projected trends of SPI and SPEI for the near future was negative for all stations, though it was positive and significant for far future.
... The NEX-GDDP dataset provides valuable climate information at a finer spatial and temporal resolution, enabling researchers and users to delve into localised climate trends and impacts (Thrasher et al. 2022). These downscaled projections have been used widely Musie et al. 2020;Das et al. 2021) and serve as an essential resource for investigating the potential consequences of climate change on various sectors and ecosystems, contributing to informed decision-making and adaptation strategies. ...
The hydrologic analysis relies on the availability of accurate and reliable data to effectively analyse the complex behaviour of hydrologic processes. However, the constraints and scarcity of data present significant challenges in achieving the desired accuracy and reliability. Researchers have leveraged various data sources to overcome data constraints, including ground-based observations, global and reanalysis datasets, and remote sensing information. Hydrologic analysis can be significantly enhanced by integrating these diverse data sources, enabling a deeper understanding of the complex phenomenon of water exchange between the soil-plan-atmosphere continuum. Furthermore, accurate hydrologic analyses facilitate improved water allocation, flood forecasting, drought management, and assessment of climate change impacts on water resources. The availability of reanalysis data, climate projections, and satellite-derived estimates from various sources provides valuable insights for researchers and practitioners involved in water resource management and climate change impact assessment. By capitalising on these rich data sources, researchers can develop robust models that support informed decision-making processes in water management and adaptation strategies. This chapter aims to explore the extensive range of data sources that can be readily availed to carry out hydrologic studies, focusing on addressing the challenges posed by limited data availability.
... RCP6.0, and RCP8.5 scenarios for the period of 2011-2100 (Shivam et al. 2017). Similar study was carried out by Das et al. (2021) for projection of drought hotspot using SPI index. Statistical downscaling is supposed to be highly good at forecasting future data from global to local for any meteorological parameter in climate change related studies (Mahmood and Babel 2014). ...
The complex topography of the Himalayan region makes it difcult to analyze its climatic variables over the region. The
study has been carried out to identify the trends in climate variables and drought analysis over the Beas River basin in the
western Himalayas. To understand the impact of changing climate on the Beas River basin, fve downscaled global circulation
models (GCMs) were used, namely BNU-ESM, Can-ESM2, CNRM CM5, MPI-ESM MR, and MPI-ESM LR. These GCMs
were obtained for two representative concentration pathway (RCP) scenarios: 4.5, which represents the normal scenario,
and 8.5, which represents the most extreme scenario for anticipated concentrations of carbon and greenhouse gases. The
multi-model ensemble (MME) of these 5 GCMs were used to project rainfall and temperature. Further Innovative Trends
Analysis (ITA) and modifed Mann–Kendell (mMK) trend tests have been used for trend analysis at a 5% signifcance level.
The drought pattern in the future timescale of the ensembled model is calculated using the Standardised Precipitation Index
(SPI) for both RCPs. The ITA, Mann–Kendell, and Sen’s slope trends showed decreased precipitation under RCP 4.5 in the
Manali region and showed an increasing trend for the remaining locations under both scenarios. Furthermore, SPI values
showed frequent droughts under both RCPs. The study outcomes will serve as a scientifc foundation for the sustainability
of water resources and agricultural output in arid inland regions vulnerable to changing climate.
... Zone F constitutes a humid region with temperate monsoonal climate characteristics. [28,29] (https://cds.nccs.nasa.gov/nex-gddp/ (accessed on 25 February 2021)). ...
The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-change distributions. The climate–land use–drought modeling nexus enables the generation of refined spatio-temporal projections of meteorological and hydrological drought risks in the Yellow River Basin (YRB) in the future period of 2025–2060. The results highlight the increased vulnerability of the upper YRB to sub-seasonal meteorological droughts, as well as the heightened sub-seasonal hydrological drought risks in the Loess Plateau. Furthermore, downstream areas experience escalated seasonal hydrological drought exposure due to urbanization. By providing actionable insights into localized future drought patterns, this integrated assessment approach advances preparedness and climate adaptation strategies. The findings of the study enhance our understanding of potential changes in this integral system under the combined pressures of global climate change and land use shifts.
... Projections of future climate under varying emissions scenarios are valuable for assessing meteorological and hydrological drought hazards. The National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset provides statistically downscaled climate projections at high temporal (daily) and spatial (0.25° x 0.25°) resolutions [28][29]. Compared to traditional general circulation models (GCMs), the NEX-GDDP increases spatial resolution and enhances simulation of extreme climate values in the historical period, especially for topographically complex regions. ...
The assessment and prediction of drought risk under future climate change and land use land cover (LULC) scenarios is critically important for drought prevention and mitigation, as it enables a clearer understanding of potential shifts in drought patterns. The primary aim of this study is to evaluate sub-seasonal and seasonal meteorological and hydrological drought hazards across the Yellow River Basin (YRB) under projected future climate conditions and LULC patterns. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent future climate for 2025-2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future LULC distributions. Meteorological and hydrological drought risks across different YRB zones are evaluated through a copula-based risk assessment approach, based on the joint probability distribution of drought duration and severity. The results indicate that sub-seasonal meteorological and hydrological droughts will likely be the primary concern moving forward. Specifically, the upper YRB (zones A, B, C) exhibits greater vulnerability to sub-seasonal meteorological drought, while the Loess Plateau (zones C, E) shows higher susceptibility to sub-seasonal hydrological drought. Moreover, zone F in the downstream region may experience increased seasonal hydrological drought risk due to projected urban expansion in the middle and lower portions of the YRB.
... The urban drought has a direct correlation with at least five sustainable development goals, i.e., Goal Six (clean water and sanitation), Eleven (Sustainable cities and communities), Twelve (Responsible production and consumption), Thirteen (Climate Action), and Fifteen (Life on Land) respectively. It impacts the urban population's well-being, economic conditions, and the region's sustainability (S. Das et al., 2021a;Dilling et al., 2019;Zhang et al., 2019). It is worth mentioning that various other factors will induce the urban drought risk in the region, like Urban local bodies' water supply regulation practices, the number of water supply sources, water quality, migration due to climate change, etc. ...
In 2015 the beginning of the Indian Smart Cities’ mission was one of the significant steps taken by the Indian government to make the urban environment resilient to climate change impact and extreme weather events like drought, floods, heatwaves, etc. This study computes the urban drought risk for Indian smart cities before the beginning of the Indian smart cities mission. This study considers three decadal variability (1982–2013) in meteorological, hydrological, vegetation, and soil moisture parameters for inducing water scarcity and drought conditions in urban regions. Hazards associated with urban drought-inducing parameters variability, vulnerability, and exposure of Indian smart cities were used to compute the Urban drought risk. The research investigations revealed the maximum urban drought risk for Bangalore, Chennai, and Surat cities. Northwest, West Central, and South Peninsular urban regions have higher risk among all the urban regions of India. Indian smart cities mission can be used to make Indian cities resilient to urban drought risk and increase their sustainability. The present research aligned with several national and international agreements by providing an urban drought risk rank for Indian cities, making them less vulnerable to extreme weather events and improving their resilience to climate change.
... This inherent variability necessitates an intricate and region-based understanding of droughts to deal with their impacts. While the increase in the frequency and severity of extreme weather phenomena can be linked to global warming [4][5][6][7][8], shorter-term events like meteorological and agricultural droughts often exhibit more complex behavior. Thus, it is vital to accurately forecast droughts and establish drought early warning systems for effective planning and resilience [9]. ...
The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Türkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Türkiye.
