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Meteorological and Hydrological Droughts in the Lancang-Mekong River Basin: Spatiotemporal Patterns and Propagation
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Understanding the time required for meteorological drought to propagate to hydrological drought is crucial for producing early warnings of future hydrological droughts. However, most previous studies of this topic have used observed runoff (or streamflow), which usually has been disturbed by human activities, and accordingly, the calculated drought propagation time (DPT) cannot accurately characterize the real propagation characteristics under natural conditions. In this study, we quantified the meteorological to hydrological DPT during the period 1962–2018 based on natural runoff and streamflow datasets and then analyzed the primary meteorological factors in influencing the spatial distribution of DPT. The results show the following: (1) The overall average DPT in China is about 6 months, decreasing from the northwest (9–12 months) to the southeast (1–2 months), and the DPT in spring and winter is generally longer than in summer and autumn. (2) The most sensitive areas for drought propagation during the period 1991–2018 increased in extent by 1.73% when compared with the extent during the period 1962–1990, and river-flow routing processes led to longer DPTs in southeast China and shorter DPTs in northwest China. (3) Precipitation and maximum temperature are the dominant meteorological factors influencing the spatial variability of DPT across China, while river-flow routing changes one of these dominant factors from maximum temperature to mean temperature.
The current situation in the Lancang–Mekong River basin is emblematic of the issues faced by many transboundary basins around the world: riparian countries prioritize national water–energy policies and provide limited information on how major infrastructures are operated. In turn, such infrastructures and their management become a source of controversy. Here, we turn our attention to the Upper Mekong River, or Lancang, where a system of 11 mainstream dams controls about 55 % of the annual flow to Northern Thailand and Laos. Yet, assessing their actual impact is a challenging task because of the chronic lack of data on reservoir storage and dam release decisions. To overcome this challenge, we focus on the 10 largest reservoirs and leverage satellite observations to infer 13-year time series of monthly storage variations. Specifically, we use area–storage curves (derived from a digital elevation model) and time series of water surface area, which we estimate from Landsat images through a novel algorithm that removes the effects of clouds and other disturbances. We also use satellite radar altimetry water level data (Jason and Sentinel-3) to validate the results obtained from satellite imagery. Our results describe the evolution of the hydropower system and highlight the pivotal role played by Xiaowan and Nuozhadu reservoirs, which make up to ∼ 85 % of the total system's storage in the Lancang River basin. We show that these two reservoirs were filled in about 2 years and that their operations were marginally affected by the drought that occurred in the region in 2019–2020. Deciphering these operating strategies will help enrich existing monitoring tools and hydrological models, thereby supporting riparian countries in the design of more cooperative water–energy policies.
According to the widely accepted definition of drought, meteorological and hydrological droughts originally develop from rainfall and runoff deficits, respectively. Runoff deficit is mainly derived from rainfall deficit, and the propagation from meteorological drought to hydrological drought is critical for agricultural water management. Nevertheless, the characteristics and dynamics of drought propagation in the spatiotemporal scale remain unresolved. To this end, the characteristics and dynamics of drought propagation in different seasons and their linkages with key forcing factors are evaluated. In this study, meteorological and hydrological droughts are characterized by the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI), respectively. Propagation time is identified by the corresponding timescale of the maximum correlation coefficient between the SPI and the SRI. Then, a 20-year sliding window is adopted to explore the propagation dynamic in various seasons. Furthermore, the multiple linear regression model is established to quantitatively explore the influence of meteorological factors, underlying surface features and teleconnection factors on the propagation time variations. The Wei River Basin, a typical Loess Plateau watershed in China, is selected as a case study. Results indicate the following: (1) the propagation time from meteorological to hydrological drought is shorter in summer (2 months) and autumn (3 months), whereas it is longer in spring (8 months) and winter (13 months). Moreover, the propagation rates exhibit a decreasing trend in warm seasons, which, however, show an increasing trend in cold seasons; (2) a significant slowing propagation in autumn is mainly caused by the decreasing soil moisture and precipitation, whereas the non-significant tendency in summer is generally induced by the offset between insignificant increasing precipitation and significant decreasing soil moisture; (3) the replenishment from streamflow to groundwater in advance prompts the faster propagation from meteorological to hydrological drought in spring and winter and (4) teleconnection factors have strong influences on the propagation in autumn, in which Arctic Oscillation, El Niño-Southern Oscillation and Pacific Decadal Oscillation mainly affect participation, arid index and soil moisture, thereby impacting drought propagation. HIGHLIGHTS
The propagation dynamics at a seasonal timescale were explored.;
The impacts of diverse factors on the propagation dynamics were investigated. Autumn exhibits a significant increasing propagation time mainly induced by decreasing soil moisture and precipitation.;
Winter shows a significant decreasing propagation time mainly caused by earlier groundwater supply.;
Teleconnection factors exert strong influences on the propagation process in autumn.;
Information on the relationship between meteorological drought (MD) and hydrological drought (HD) can serve as the basis for early warning and mitigation of HD. In this study, the standardized precipitation index and standardized streamflow index were applied to characterize MD and HD, respectively, and the evolution characteristics of MD and HD were assessed in the upstream regions of the Lancang–Mekong River (ULMR) from 1961 to 2015. Furthermore, the relationship between MD and HD was investigated using the Pearson correlation and wavelet analysis. The results revealed that (1) there was no significant change in the annual precipitation and streamflow; however, the ULMR experienced successive alternations of wet and dry episodes; (2) the average duration and magnitude of MD and HD increased with an increase in the time scale, while the duration and magnitude of MD lengthened and amplified in HD; (3) MD more likely propagated to HD as the time scale increased, and the propagation time exhibited marked seasonality, which was shorter in the wet season and longer in the dry season; and (4) there was a positive correlation between MD and HD; these two types of drought exhibited similar resonance frequency and phase-shift characteristics, and HD lagged behind MD. HIGHLIGHTS
Duration and magnitude of meteorological drought lengthened and amplified in hydrological drought.;
Longer time scales of meteorological drought more likely propagated to hydrological drought.;
Propagation time from meteorological to hydrological drought exhibited marked seasonality.;
Hydrological and meteorological drought presented similar patterns in terms of resonance frequency and phase shift.;
Of particular importance in a world of increasing water scarcity is the temporal and spatial relationship between a shortage in rainfall—meteorological drought—and a shortage in available water, or hydrological drought. Propagation time from meteorological to hydrological drought should be calculated at a higher (sub-monthly) temporal resolution with considerations for spatial expansion. A new framework for propagation time calculation from one index to another is established that uses the run theory, high-resolution remote sensing data on a daily time step. The framework is demonstrated using standardized drought indices representing precipitation, runoff, evapotranspiration, and soil moisture in the Central Asian subcontinent to measure the temporal shift (propagation time) in affected drought area, bringing a new perspective to contemporary drought analysis. Several variables from the water cycle are chosen to provide hydrological context for different propagation times. Correlation analyses for propagation time are shown to be ambiguous in interpretation and precision when compared to the temporal shift, which is clearly defined and calculated on a daily time step. Moreover, the results of temporal shift analyses indicate that deficits in evapotranspiration and runoff can precede deficits in precipitation, while soil moisture deficit almost always follows, highlighting the effects of additional influencing factors aside from precipitation on types of hydrological drought. While it is limited by current understanding of drought definition techniques, availability and quality of remote sensing products, and selection of characteristics for observation, use of the temporal shift over the correlation analysis provides a sub-monthly estimate of drought propagation time that may prove useful for detailed analyses, particularly in rapidly developing flash drought events.
Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management.
The full ERA5-Land hourly and monthly averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.
Meteorological drought is the fuse of hydrological drought, and understanding the propagation mechanisms from meteorological to hydrological drought is of great significance to the monitoring and prevention of hydrological drought. Besides the linear dependence, this study thoroughly investigated the propagation from meteorological to hydrological drought by introducing the nonlinear dependence with directed information transfer index (DITI) for the first time. In this study, the standardized precipitation index (SPI) and the standardized runoff index (SRI) were used to represent meteorological drought and hydrological drought, respectively. A new drought response time (DRT) evaluation system was constructed based on the maximum Pearson correlation coefficient (PCC) and DITI, simultaneously considering the linear and nonlinear relationships between meteorological drought and hydrological drought. Moreover, the relationships of drought characteristics (duration and severity) between these two types of drought were established by using run theory and mathematical function, and the the trigger thresholds from meteorological to hydrological drought were then determined. The results indicate that: (1) the effective drought propagation rate was mainly affected by the characteristics of meteorological drought events and the sensitivity of hydrological drought to meteorological drought; (2) the DRT in the Pearl River Basin (PRB) was mainly concentrated in 2-5 months, and the drought translation rate in the PRB was relatively large; and (3) the duration of hydrological drought events was longer in the sub-regions with smaller meteorological drought trigger thresholds.
Study Region: Mekong River Basin and surrounding areas.
Study Focus: This study investigated the impacts of climate change on future meteorological and hydrological droughts in the Mekong River Basin and its surrounding areas. Our work is based on the output of five global climate models (GCMs) and simulations using the geomorphology-based hydrological model (GBHM) for the historical (1975-2004), near future (2010-2039), middle future (2040-2069), and far future (2070-2099) periods. The meteorological droughts in the study area were measured using SPI and SPEI, while the hydrological droughts were measured using SSI.
New Hydrological Insights for the Region: The results suggest that droughts will generally reduce in the future over most of the study area, but will be more unevenly distributed with an eastward migration as compared to the historical period. Both meteorological and hydrological droughts will intensify in the near future, but will then reduce in intensity. Meteorological droughts will increase in the northeastern areas in the near future, followed by migration towards the south. Hydrological droughts showed similar aggravation followed by reduction, with upstream areas showing greater variability. In the general context of drought alleviation, southwestern China and the Mekong River estuary may suffer from a continuously increasing drought intensity in the future. This finding is based on 100-year extreme drought events.
Spatially-invariant land use and cover changes (LUCC) are not suitable for managing non-stationary drought conditions. Therefore, developing a spatially varying framework for managing land resources is necessary. In this study, the Dongjiang River Basin in South China is used to exemplify the significance of spatial heterogeneity in land planning optimization for mitigating drought risks. Using ERA5 that is the 5th major atmospheric reanalysis from the European Centre for Medium-Range Weather Forecast, we computed the Standardized Runoff Index (SRI) to quantify the hydrologic drought during 1992 to 2018. Also, based on Climate Change Initiative land use product, The Geographically Weighted Principal Component Analysis was used to identify the most dominant land types in the same period. Then, we used the Emerging Hot Spots Analysis to characterize the spatiotemporal evolution of historical LUCC and SRI. The spatially varying coefficients of Geographically and Temporally Weighted Regression models were used to reveal the empirical relationships between land types and the SRI. Results indicated that rainfed cropland with herbaceous cover, mosaic tress and shrub, shrubland, and grassland were four land types having statistical correlations with drought conditions over 27 years. Moreover, since 2003, the DRB was becoming drier, and the northern areas generally experienced severer hydrologic drought than the south. More importantly, we proposed region-specific land-use strategies for drought risk reductions. At a basin scale, we recommended to 1) increase rainfed herbaceous cropland and 2) reduce mosaic tree and shrub. At a sub-basin scale, the extents of shrub and grassland were suggested to increase in the northern DRB but to reduce in the south. Region-specific land use planning, including suitable locations, scales, and strategies, will contribute to handling current ‘one-size-fits-all’ LUCC. Planners are suggested to integrate spatial characteristics into future LUCC for regional hydrologic management.
Many European countries rely on groundwater for public and industrial water supply. Due to a scarcity of near-real-time water table depth (wtd) observations, establishing a spatially consistent groundwater monitoring system at the continental scale is a challenge. Hence, it is necessary to develop alternative methods for estimating wtd anomalies (wtda) using other hydrometeorological observations routinely available near real time. In this work, we explore the potential of Long Short-Term Memory (LSTM) networks for producing monthly wtda using monthly precipitation anomalies (pra) as input. LSTM networks are a special category of artificial neural networks that are useful for detecting a long-term dependency within sequences, in our case time series, which is expected in the relationship between pra and wtda. In the proposed methodology, spatiotemporally continuous data were obtained from daily terrestrial simulations of the Terrestrial Systems Modeling Platform (TSMP) over Europe (hereafter termed the TSMP-G2A data set), with a spatial resolution of 0.11∘, ranging from the years 1996 to 2016. The data were separated into a training set (1996–2012), a validation set (2013–2014), and a test set (2015–2016) to establish local networks at selected pixels across Europe. The modeled wtda maps from LSTM networks agreed well with TSMP-G2A wtda maps on spatially distributed dry and wet events, with 2003 and 2015 constituting drought years over Europe. Moreover, we categorized the test performances of the networks based on intervals of yearly averaged wtd, evapotranspiration (ET), soil moisture (θ), snow water equivalent (Sw), soil type (St), and dominant plant functional type (PFT). Superior test performance was found at the pixels with wtd < 3 m, ET > 200 mm, θ>0.15 m3 m-3, and Sw<10 mm, revealing a significant impact of the local factors on the ability of the networks to process information. Furthermore, results of the cross-wavelet transform (XWT) showed a change in the temporal pattern between TSMP-G2A pra and wtda at some selected pixels, which can be a reason for undesired network behavior. Our results demonstrate that LSTM networks are useful for producing high-quality wtda based on other hydrometeorological data measured and predicted at large scales, such as pra. This contribution may facilitate the establishment of an effective groundwater monitoring system over Europe that is relevant to water management.
Accurate quantification of global land evapotranspiration is necessary for understanding variability in the global water cycle, which is expected to intensify under climate change1–3. Current global evapotranspiration products are derived from a variety of sources, including models4,5, remote sensing6,7 and in situ observations8–10. However, existing approaches contain extensive uncertainties; for example, relating to model structure or the upscaling of observations to a global level11. As a result, variability and trends in global evapotranspiration remain unclear12. Here we show that global land evapotranspiration increased by 10 ± 2 per cent between 2003 and 2019, and that land precipitation is increasingly partitioned into evapotranspiration rather than runoff. Our results are based on an independent water-balance ensemble time series of global land evapotranspiration and the corresponding uncertainty distribution, using data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) satellites13. Variability in global land evapotranspiration is positively correlated with El Niño–Southern Oscillation. The main driver of the trend, however, is increasing land temperature. Our findings provide an observational constraint on global land evapotranspiration, and are consistent with the hypothesis that global evapotranspiration should increase in a warming climate. Using a global mass-balance approach to calculate evapotranspiration, it is shown that global land evapotranspiration increased by 10% between 2003 and 2019, driven mainly by warming land temperatures.
The Lower Mekong River basin (LMB) has experienced droughts in recent decades, causing detrimental economic losses and food security conundrums. This study quantified the impact of climate change on drought, and rainfed rice production in the LMB. The Soil and Water Assessment Tool (SWAT) and AquaCrop models were used to evaluate long-term drought indices and rainfed rice yields under historical and future climate conditions (1954–2099) with four climate models and two emission scenarios (RCP 4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We found that rice yield might increase (24–43%) due to the elevated levels of atmospheric CO2 concentration (+ 34.3 to + 121.9%) and increases in precipitation. Contrastingly, considerable decreases in rice yield up to 1.5 ton/ha in the Vietnam Central High Plain (VCHP) region could be expected resulting from reduced precipitation by about 34% during drought years. To avert any major food crisis, an expansion of irrigation areas could be required to compensate for the expected reduction in rice yields. We conclude that a framework combining hydrology and crop models to assess climate change impacts on food production is key to develop adaptation strategies in the future.
Monitoring drought and mastering the laws of drought propagation are the basis for regional drought prevention and resistance. Multivariate drought indicators considering meteorological, agricultural and hydrological information may fully describe drought conditions. However, series of hydrological variables in cold and arid regions that are too short or missing make it difficult to monitor drought. This paper proposed a method combining Soil and Water Assessment Tool (SWAT) and empirical Kendall distribution function (KC′) for drought monitoring. The SWAT model, based on the principle of runoff formation, was used to simulate the hydrological variables of the drought evolution process. Three univariate drought indexes, namely meteorological drought (standardized precipitation evapotranspiration index; SPEI), agricultural drought (standardized soil moisture index; SSI) and hydrological drought (standardized streamflow drought index; SDI), were constructed using a parametric or non-parametric method to analyze the propagation time from meteorological drought to agricultural drought and hydrological drought. The KC′ was used to build a multivariable comprehensive meteorology–agriculture–hydrology drought index (MAHDI) that integrated meteorological, agricultural and hydrological drought to analyze the characteristics of a comprehensive drought evolution. The Jinta River in the inland basin of northwestern China was used as the study area. The results showed that agricultural and hydrological drought had a seasonal lag time from meteorological drought. The degree of drought in this basin was high in the northern and low in the southern regions. MAHDI proved to be acceptable in that it was consistent with historical drought records, could catch drought conditions characterized by univariate drought indexes, and capture the occurrence and end of droughts. Nevertheless, its ability to characterize mild and moderate droughts was stronger than severe droughts. In addition, the comprehensive drought conditions showed insignificant aggravating trends in spring and summer and showed insignificant alleviating trends in autumn and winter and at annual scales. The results provided theoretical support for the drought monitoring in the Jinta River basin. This method provided the possibility for drought monitoring in other watersheds lacking measured data.
Current gridded precipitation datasets are hard to meet the requirements of hydrological and meteorological applications in complex-terrain areas due to their coarse spatial resolution and large uncertainties. High-resolution atmospheric simulations are capable of describing the influence of topography on precipitation but are difficult to be used to obtain long-term precipitation datasets because they are computationally expensive, while reanalysis data has a long-term coverage and can provide reasonable large-scale spatial and temporal variability of precipitation. This study presents an approach to obtain long-term high-resolution precipitation datasets over complex-terrain areas by combining the ERA5 reanalysis with short-term high-resolution atmospheric simulation. The approach consists of two main steps: first, the ERA5 precipitation is corrected by the high-resolution simulation at the coarse spatial resolution; second, the corrected data is downscaled using a convolution neural network (CNN) based model at daily scale. The proposed approach is applied to the Tibetan Plateau (TP). The downscaled results from ERA5 have a finer spatial structure than ERA5 and can reproduce the spatial patterns of precipitation revealed by the high-resolution simulation. An evaluation based on rain gauge data shows that the downscaled ERA5 has remarkably lower biases than the original ERA5 which overestimates precipitation a lot, and even higher accuracy than the high-resolution simulation data over the TP. The downscaled ERA5 preserves the temporal characteristics of ERA5 which are more consistent with the rain gauge data than that of high-resolution simulation. Since this approach is much less computing resources consuming than the high-resolution simulation, it is an effective method to obtain long-term high-resolution precipitation datasets in complex-terrain areas and is expected to have extensive applications.
Drought events occur more frequently under recent climate change. Generally, meteorological drought is the fuse of hydrological drought; thus, it is important to understand the characteristics of meteorological drought and its propagation to hydrological drought for early warning. Taking the Pearl River Basin (PRB) in China as study area, this study adopted K‐means cluster analysis method to divide the PRB into subregions with similar precipitation characteristics. Then, standardized precipitation index and standardized runoff index were used to analyze the characteristics of meteorological drought and hydrological drought, respectively, and the maximum Pearson correlation coefficient was used to determine the drought propagation time (DPT) between these two types of drought. Moreover, the link between meteorological drought and hydrological drought was explored based on continuous wavelet transform and cross wavelet transform. The results revealed that: the PRB has experienced severe meteorological and hydrological droughts since early 2000s, and hydrological drought was more serious than meteorological drought in each of the five subregions in the PRB. The DPTs from meteorological drought to hydrological drought were mainly 2–6 months, and the periodic characteristics of meteorological drought were mainly responsible for those of hydrological drought. Precipitation and runoff could greatly affect the DPT, while the impacts of evapotranspiration and shallow soil moisture on the DPT were not significant. Furthermore, El‐Niño Southern Oscillation and Pacific Decadal Oscillation are important factors that affect the DPT from meteorological to hydrological drought in the PRB.
The Lancang‐Mekong River Basin (LMRB) in Mainland Southeast Asia is home to ~70 million people, mostly living in poverty and typically working in primary freshwater‐related sectors, particularly agriculture and fishery. Understanding the mechanisms of the historical variability in precipitation (as the crucial water source) plays a key role in regional sustainable development throughout the LMRB. Herein, the spatiotemporal variability in interannual and intra‐annual precipitation over the LMRB was analyzed using the Global Precipitation Climatology Centre (GPCC) data for the period 1952–2015. The empirical orthogonal function (EOF) and wavelet transform coherence methods were utilized to investigate the relationships of such historical variations in annual (water year: November–October), dry season (November–May), and wet season (June–October) precipitation with 13 different climate teleconnections (eight large‐scale oceanic‐atmospheric circulation patterns and five summer monsoons). On the basin scale, only a significant (p < 0.05) wetting trend in the dry season precipitation (DSP) was uncovered. Spatially, significant wetting (drying) trends in annual precipitation detected over the northeastern (most western) parts of the Mekong River Basin during the water years 1952–2015, largely contributed by the substantial increases (decreases) in historical wet season precipitation. The most important precipitation pattern (EOF1) was identified as a strong (relatively weak) positive center in the eastern (southwestern) Mekong River Basin accompanying by a significantly high (relatively low) positive value for the first EOF mode of the dry season precipitation (wet season precipitation). Precipitation variability in the LMRB was significantly associated with the South Asian Summer Monsoon Index, Southern Oscillation Index, and Indian Summer Monsoon Index.
Drought impacts on the society and environment are influenced by how droughts propagate through the hydrologic cycle; however, the physical processes involved in drought propagation are not well understood. In this study, a physically based hydrologic model is used to understand the drought propagation mechanisms and their controlling factors in multiple watersheds selected from different regions of the contiguous United States (CONUS). The characteristics of hydrologic droughts are found to be significantly different in three regions of the CONUS: (1) western U.S. watersheds have long and intense streamflow droughts, (2) Great Plains and southwest U.S. watersheds have low intensity and duration of streamflow droughts, and (3) eastern U.S. watersheds have short but intense streamflow droughts. This spatial pattern of hydrologic drought characteristics is found to coincide with the pattern of climate properties. Highly seasonal recharge driven by winter precipitation or snowmelt leads to longer streamflow droughts in the western United States; whereas humid climate with low seasonality in the eastern United States leads to shorter streamflow droughts. Furthermore, the storage‐discharge relationship is identified as a key watershed property which controls the intensity of hydrologic droughts. Higher sensitivity of baseflow to watershed storage in the western and the eastern U.S. watersheds leads to more intense streamflow droughts in these regions. In the Great Plains and southwest United States, watersheds have a lower sensitivity of baseflow to catchment storage which, combined with the highly arid climate, leads to low variability in baseflow, and in turn results in low intensity of streamflow droughts in the region.
The regional and national scales variation and propagation characteristics of different types of droughts are critical for improving drought resilience, while information is limited in China. The objective of this research was to investigate the evolution and propagation characteristics of three types of droughts using standardized indices at multi-timescales in different sub-regions of China. The indices included Standardized Precipitation/Soil Moisture/Runoff Index (SPI/SSI/SRI) using the optimal probability density function, representing meteorological, agricultural, and hydrological droughts based on precipitation, soil water storage, and baseflow-groundwater runoff, respectively. Wavelet analysis was used to reveal their periodical characteristics. Modified Mann-Kendall trend test was used to compare the trend among drought indices. Correlation coefficients between SPI and SSI/SRI were calculated to identify the time-lags of SPI with SSI and SRI. In general, droughts indicated by SPI agreed well with the historical drought events at different sub-regions. The main periods of SSI were closer to SPI than SRI, indicating stronger connections of agricultural drought with meteorological drought. A weaker connection between meteorological and agricultural/hydrological droughts at shorter timescales was observed in northwestern arid and semi-arid regions. The propagation from meteorological to agricultural or hydrological droughts were well denoted by the lagged time (months) from SPI to SSI or SRI at a timescale ranged from 0 (mostly located in south China) to 5 months (mostly located in northeastern China) for 1-, 3-, 6-, 12-, or 24-month timescale; this was a new finding for China. The methods of wavelet combining trend test and Pearson coefficient showed meaningful power for revealing the drought propagation characteristics and the obtained results can be a good reference for other regions of the world since this study compared different climate zones from arid to humid conditions. The study provides crucial information and guidance to develop drought management strategies at regional to national scale and their critical time of action.
Drought is among the costliest natural disasters that affect the economy, food and water security, and socioeconomic well‐being of about 1.4 billion people in India. Despite the profound implications of droughts, the propagation of meteorological to hydrological droughts in India is not examined. Here, we use observations and simulations from a well‐calibrated and evaluated Variable Infiltration Capacity (VIC) model to estimate drought propagation in India. Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSMI), and Standardized Streamflow Index (SSI) were estimated for 223 catchments in India to represent meteorological, agricultural, and hydrological droughts, respectively. We estimated drought propagation time for these catchments located in 18 major Indian subcontinental river basins. Internal propagation of hydrological drought was estimated using optimal hydrological Instantaneous Development Speed (IDS) and Instantaneous Recovery Speed (IRS) from onset to the termination. Indus, Sabarmati, and Godavari river basins have higher propagation time of meteorological to hydrological droughts. The high (low) development rate of hydrological drought is followed by the high (low) recovery rate for most of the locations. We find significant influence of Seasonality Index (SI) and Base Flow Index (BFI) on propagation time of meteorological to hydrological droughts in the Indian subcontinental river basins. Overall, understanding of drought propagation, development/recovery speed, and their deriving factors can assist in the management and planning of water resources in India.
Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, which is known as a reanalysis. While the current generation of NWP models output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/, last access: 28 June 2020) by coupling surface and sub-surface runoff from the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1∘ at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 d behind real time). The reanalysis was evaluated against a global network of 1801 daily river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling–Gupta efficiency skill score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for applications ranging from monitoring global flood and drought conditions to the identification of hydroclimatic variability and change and as raw input for post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview (last access: 28 June 2020) with the following DOI: 10.24381/cds.a4fdd6b9 (C3S, 2019).
Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.
How human activities have altered hydrological droughts (streamflow deficits) in China during the past five decades (1961–2016) is investigated using the latest version (v2.0) of PCR-GLOBWB model at high spatial resolution (~10 km). Although both human activities and climate variability have significant effects on river flows over China, there are large regional north-south contrasts. Over northern China, human activities generally intensify hydrological droughts. We find that human activities exacerbated drought deficit by about 70–200% from 2004 to 2015. In contrast, droughts over southern China are generally alleviated by human activities. For instance, irrigation and water management (such as reservoir operation and water abstraction) increase drought StDef (standardized drought deficit volume) by about 80% in the Yellow River (north) but reduce it by about 20% in the Yangtze River (south). Human activities slightly reduce drought deficit in the Yangtze River due to the combination of large reservoir storage and low ratio of agriculture consumption to abstracted irrigation water. In contrast, hydrological drought is aggravated in the semiarid Yellow River basin because of high water consumption from agricultural sectors. This study suggests that human activities have contrasting influences on hydrological drought characteristics in the northern (intensification) and southern (mitigation) parts of China. Therefore, it is critical to consider the variable roles of human activities on hydrological drought in China when developing mitigation and adaptation strategies.
In the context of climate change, hydrological processes between surface water and groundwater become increasingly complex, which may aggravate the risk of hydrological drought. As the complexity of factors influencing drought increases, traditional univariate drought indices are likely to be insufficient for accurate drought analysis. Therefore, it is important to develop an appropriate index combining surface water and groundwater for comprehensive monitoring of hydrological drought, and to understand the process of drought propagation to reduce the impact of droughts. This study develops the standardized runoff groundwater drought index (SRGI), which is a multi-variable model based on a combination of the standardized groundwater level drought index (SGI) and the standardized runoff drought index (SRI). The cross-wavelet transforms and correlation coefficient methods are used to reveal the linkages and propagation characteristics between meteorological and hydrological drought. The results indicate that (1) the generalized extreme value (GEV) distribution is generally suitable for the SGI calculation in the midstream of the Heihe River Basin (HHRB); (2) the SRGI, combining the advantages of the SRI and SGI, can detect droughts more accurately and timely than univariate drought indices, and it demonstrates a drought warning capability to some extent; (3) the lag time of the SRI/SRGI responding to the SPI notably varied with seasons, the longer in spring and winter and the shorter in summer and autumn. Additionally, the propagation time from the SRI to the SPI is shorter than that from the SRGI to the SPI in spring and autumn, the opposite is true in summer. (4) Positive correlations exist between the SRI/SRGI and the SPI during the period 1986–2010, which are relatively stable over longer periods, but ambiguous over shorter periods.
Precipitation estimation at a global scale is essential for global water cycle simulation and water resources management. The precipitation estimation from gauge‐based, satellite retrieval, and reanalysis data sets has heterogeneous uncertainties for different areas at global land. Here, the 13 monthly precipitation data sets and the 11 daily precipitation data sets are analyzed to examine the relative uncertainty of individual data based on the developed generalized three‐cornered hat (TCH) method. The generalized TCH method can be used to evaluate the uncertainty of multiple (>3) precipitation products in an iterative optimization process. A weighting scheme is designed to merge the individual precipitation data sets to generate a new weighted precipitation using the inverse error variance‐covariance matrix of TCH estimated uncertainty. The weighted precipitation is then validated using gauged data with the minimal uncertainty among all the individual products. The merged results indicate the superiority of the weighted precipitation with substantially reduced random errors over individual data sets and a state‐of‐the‐art multisatellite merged product, namely, the Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement at validated areas. The weighted data set can largely reproduce the interannual and seasonal variations of regional precipitation. The TCH‐based merging results outperform two other mean‐based merging methods at both monthly and daily scales. Overall, the merging scheme based on the generalized TCH method is effective to produce a new precipitation data set integrating information from multiple products for hydrometeorological applications.
As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen’s slope, Mann–Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.
Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), Gene Expression Programming (GEP), and M5 model trees (MT). The results indicate that SPI delivered higher accuracy. Moreover, MT model performed better in predicting SSI by a CC of 0.8195 and a RMSE of 0.8186.
Abbreviations: ANFIS: adaptive neuro-fuzzy inference system; ANN: artificial neural network; ANN: artificial neural network; BS-SVR: boosted-support Vector Regression; CC: correlation coefficient; ELM: extreme learning machine; GEP: gene Expression Programming; GP: genetic Programming; GPR: Gaussian process regression; KNN: k-nearest neighbor; LSSVM: least squares Support Vector Machine; LSSVR: least support vector regression; MAE: mean absolute error; MARS: multivariate adaptive regression splines; MLP: multilayer perceptron; MLR: multiple linear regression; MT: M5 model tree; P: precipitation; PDSI: palmer drought severity index; PET: potential evapotranspiration; RAE: relative absolute error; RMSE: root mean square error; RVM: relevance vector machine; SAR: sodium absorption index; SDR: standard deviation reduction; SPEI: standardized precipitation evapotranspiration index; SPI: standardized precipitation index; SSI: standardized streamflow index; SVM: support vector machine; SVR: support vector regression; WAANN: Wavelet-ARIMA-ANN; WANFIS: Wavelet-Adaptive Neuro-Fuzzy Inference System; WN: wavelet network
The evolution of hydrological drought events is a result of complex (nonlinear) interactions between climate and catchment processes. To investigate such nonlinear relationship, we integrated a machine learning modeling framework based on the random forest (RF) algorithms with an interpretation framework to quantify the role of climate and catchment controls on hydrological drought. More particularly, our framework interprets a built RF machine‐learning model to identify dominant variables and visualize their functional dependence and interaction effects on hydrological drought characteristics utilizing concepts of minimal depth, interactive depth, and partial dependence. We test our proposed modeling framework based on a set of 652 continental United States catchments with minimal human interference for a period of 1979–2010. Application of this framework indicated presence of three distinct drought regimes, which includes, Regime 1: droughts with longer duration, less frequent and lesser intensity; Regime 2: droughts with moderate duration, moderate frequency, and moderate intensity; and Regime 3: droughts with shorter duration, more frequent, and more intense. RF algorithm was able to accurately model the drought characteristics (intensity, duration, and number of events) for all the three drought regimes as a function of selected variables. It was observed that the type of dominant variables as well as their nonlinear functional relationship with hydrological droughts characteristics can vary between three selected regimes. Our interpretation framework indicated that catchment characteristics have a significant role in controlling the hydrologic drought for catchments (regime 1), whereas both climate and catchment characteristics control hydrological drought in regimes 2 and 3.
Drought is a major natural disaster that creates a negative impact on socioeconomic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001-2018 period, along with the Mann-Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.
Drought is a costly natural hazard with far-reaching impacts on agriculture, ecosystem, water supply, and socio-economy. While propagating through the water cycle, drought evolves into different types and affects the natural system and human society. Despite much progress made in recent decades, a synthesis of the characteristics, approaches, processes, and controlling factors of drought propagation is still lacking. We bridge this gap by reviewing the recent progress of drought propagation and discussing challenges and future directions. We first introduce drought propagation characteristics (e.g., response time scale, lag time), followed by different approaches, including statistical analysis and hydrological modeling. The recent progress in the propagation from meteorological drought to different types of drought (agricultural drought, hydrological drought, and ecological drought) is then synthesized, including the basic process, commonly used indicators, data sources, and main findings of drought propagation characteristics. Different controlling factors of drought propagations, including climatic (e.g., aridity, seasonality, and anomalies of meteorological variables), catchment properties (e.g., slope, elevation, land cover, aquifer, baseflow), and human activities (e.g., reservoir operation and water diversion, irrigation, and groundwater abstraction), are then summarized. Challenges in drought propagation include the discrepancy in drought indicators (and approaches) and difficulty in characterizing the full propagation process and isolating influencing factors. Future analysis of drought propagation should shift from single indicators to multiple indicators, from individual drivers to combined drivers, from uni-direction analysis to feedbacks, from hazards to impacts, and from stationary to nonstationary assumption. This review is expected to be useful for drought prediction and management across different regions under global warming.
Recent drought events in the Mekong River Basin (MRB) have resulted in devastating environmental and economic losses, and climate change and human-induced alterations have exacerbated drought conditions. Using hydrologic models and multiple climate change scenarios, this study quantified the future climate change impacts on conventional and flash drought conditions in the MRB. The Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models were applied to estimate long-term drought indices for conventional and flash drought conditions over historical and future periods (1966–2099), using two emission scenarios (RCP 4.5 and RCP8.5), and four climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For the conventional drought assessment, monthly scale drought indices were estimated, and pentad-scale (5 days) drought indices were computed for the flash drought evaluations. There were overall increases in droughts from the SWAT model for the conventional drought conditions and overall decreases from the VIC model. For the flash drought conditions, the SWAT-driven drought indices showed overall increases in drought occurrences (up to 165%). On the contrary, the VIC-driven drought indices presented decreases in drought occurrences (up to −44%). The conventional and flash drought evaluations differ between these models as they partition the water budget, specifically soil moisture differently. We conclude that the proposed framework, which includes hydrologic models, various emission scenarios, and projections, allows us to assess the various perspectives on drought conditions. Basin countries have differential impacts, so targeted future adaptation strategy is required.
As the most widespread natural hazard, drought has significant impacts on the livelihood and ecosystems in the Lancang-Mekong River Basin (LMRB). However, few studies focus on the seasonal characteristics of drought in the LMRB, especially under future climate projections by the Sixth Phase of Coupled Model Inter-comparison Project (CMIP6). This study filled the knowledge gap using SPI and SPEI, based on eight GCMs of CMIP6 under three scenarios (i.e., SSP1-2.6, SSP2-4.5, SSP5-8.5). Our results show that the LMRB tends to experience wetter wet season and drier dry season with the rising temperature considered (based on SPEI), while the temporal trend of dry-season SPI is not significant. The future trends of SPEI are -0.006 per year and -0.011 per year under SSP2-4.5 and SSP5-8.5, respectively. The trend magnitudes demonstrate spatial heterogeneities. Our evaluation based on SPEI show that the most notable increases of dry-season drought (in terms of duration and intensity) are distributed in the middle reaches of LMRB. The upper Lancang and middle Mekong basin will likely experience more wet-season droughts. The dry-season drought accounts for 60% of total drought events in the near future (i.e., 2021-2025) and more than 80% in the far future (i.e., 2061-2095) under SSP2-4.5. Effective strategies are needed to enhance flood and drinking water security in the LMRB, especially for the dry seasons under a changing climate.
Understanding the relationship among different types of drought is crucial for drought mitigation and early warnings. Much attention has been recently focused on the propagation from meteorological drought (MD) to hydrological drought (HD); however, the influences of human activities on drought propagation have rarely been explored. The novelty of the study was to propose an effective framework to quantify the impacts of human activities on MD-HD propagation. We adopted the framework to comprehensively evaluate the anthropic impacts on hydrological drought variations and time, thresholds, and probabilities of MD-HD propagation in the Weihe River Basin (WRB) during different periods. The results showed that human activities did significantly disturb HD variations and MD-HD propagation characteristics. Specifically, human activities increased the frequency and extremes of HD and weakened its correlation with MD. The MD-HD propagation characteristics showed spatiotemporal differences across three subbasins because of the different levels of human activities. The thresholds of MD triggering different levels of HD generally became larger with change rates from 1% to 143% and 3% to more than 189% during two periods, respectively. Meanwhile, we also found that the thresholds became distinctly smaller, which could only be observed in spring and winter. Moreover, the relationship between natural and human-induced probabilities of HD occurrence showed three patterns with the increase of MD severity. The quantitative results of this study can provide guide information on adaptation strategies to promote drought preparedness in the WRB. The proposed framework can be also applied in other regions to improve the understanding of hydrological drought mechanisms.
The construction of Three Gorges Dam (TGD) has exerted substantial effects on local hydrological regime and eco-system since its operation in 2003. Due to this significant anthropogenic activity, drought propagation processes from Meteorological drought to Hydrological (M-H) and Soil moisture (M-S) drought are inevitably modified. However, current studies are still insufficient to answer whether the TGD operation mitigated or exacerbated the drought propagation process due to its complex impacts. To explore this key issue, this study proposed an integrated framework to quantify the impact of TGD construction on drought propagation process in the Yangtze River Basin (YRB). The drought characteristics propagation models and propagation time were calculated to identify the modification of drought propagation process between different TGD operation scenarios. Trend and attribution analyses were also utilized to explore potential influence factors. Results demonstrated that TGD construction did significantly aggravate the drought characteristics propagation. Specifically, the slopes of hydrological drought duration and severity in linear propagation models increased 23.98% and 29.23% in average. While for M-S propagation, the slopes of duration and severity models increased 28.72% and 21.91%, respectively. This research also found that, after TGD operation, the M-H propagation speed had been slowed down but the M-S lag time became shorter. Additionally, the variations and trend patterns of six potential influence factors at four separate periods were full analyzed to provide implications for the drought propagation modifications. And TGD operation was confirmed to become a major factor inducing the variations of annual runoff, which accounted for 56.75% for the entire YRB. The above quantitative findings are expected to reveal the potential changes of drought propagation process caused by the construction of TGD in YRB. The proposed integrated framework is also beneficial to the drought early warning system and understanding of drought development not only in YRB, but also in other basins at the global scale.
Regulation of streamflow by large reservoirs alters the characteristics of hydrological drought. However, few studies have focused on the impacts of regulation on the water required (WR) for hydrological drought recovery, nor on the response relationship between WR and drought characteristics (i.e., duration and severity). Therefore, we proposed a comparative approach that integrates reconstructing unregulated streamflow and a drought identification method using variable threshold levels (VTLs) to evaluate the impacts. Long-term (> 40 years) observational datasets for streamflow at a hydrometric station and inflow and outflow at multiple large reservoirs in the Dongjiang River Basin were used. Using VTLs that accounted for seasonal differences in hydrological processes, our method performed well in identifying historical hydrological droughts and their characteristics in the study area. The WR mainly depended on drought severity rather than drought duration and there was a clear nonlinear response relationship (i.e., power function) between WR and drought severity. The performance indices (i.e., R² = 0.92, NSE = 0.97, PBIAS = 13.57%) indicated that the optimal nonlinear function model could accurately simulate the WR of hydrological drought events. Large reservoirs reduce the frequency of hydrological droughts, shorten drought duration, and reduce drought severity by storing water in the flood season and releasing it in the dry season. The presence of reservoirs shifted the relationship between WR and drought severity from linear to nonlinear. These findings showed that the proposed methodology can help us to optimize the management of water resources for drought prevention and disaster reduction under changing environments.
The propagation of meteorological drought in the hydrological cycle not only leads to the deficits of surface soil moisture, which results in agricultural drought, but also probably affects groundwater to trigger groundwater drought. Improving the understanding about the correlation and propagation among meteorological, agricultural and groundwater droughts is necessary to lessen the risks resulted from them. Moreover, the distinction of the correlation and propagation over the basins with different climate conditions is not yet sufficiently understood. As a case study of the humid and arid/semi-arid basins in China, the standardized precipitation index, the standardized soil moisture index and groundwater drought index based on GRACE (Gravity Recovery and Climate Experiment) are used to characterize meteorological, agricultural and groundwater drought respectively over the Yangtze River Basin with a humid climate condition and Yellow River Basin with an arid/semi-arid climate condition. The Spearman rank correlation coefficient is applied to investigate the correlation and propagation among the above three types of drought in the two basins. The results indicated that: (1) The meteorological, agricultural and groundwater droughts in the Yangtze River Basin and the meteorological and agricultural droughts in the Yellow River Basin are decreasing from April 2002 to March 2020, while the groundwater drought in the Yellow River Basin is aggravating in recent years. (2) There is a strong link between meteorological and agricultural droughts, and the propagation time from meteorological to agricultural drought in summer and autumn is shorter than that in winter and spring in the two basins. When comparing the propagation time in the two selected basins, it is longer in the Yangtze River Basin. (3) Groundwater extraction may be the main factor in the aggravation of groundwater drought in the two basins, which is different from the contributors to agricultural drought, which is mainly the propagation of meteorological drought.
Drought incidents and the pressure on water resources have increased in recent years, which has threatened sustainable development. Recently, research has been conducted on drought propagation. However, few studies have investigated the characteristics and mechanisms of drought propagation in plateau mountainous regions with complex topography, which limits the efforts to mitigate drought. We used the Longchuan River Basin (LRB) in Southwest China as a case study to analyze the spatiotemporal variations of meteorological, hydrological, and agricultural droughts and the process of drought propagation in plateau mountainous regions. Our results demonstrated that: (1) the variation in the intensity, frequency, and coverage of droughts indicated that meteorological droughts and hydrological droughts were increasingly serious, while agricultural droughts were eased from 2000 to 2015; (2) the propagation time between different types of droughts was approximately 2 months; and (3) the propagation sequences of droughts varied by altitude; in particular, agricultural droughts propagated to hydrological droughts at higher altitudes, and the opposite occurred at lower altitudes. We concluded that elevation plays a critical role in the time-space differentiation of drought propagation in plateau mountains. More attention should be paid to the spatial differentiation of drought propagation based on land use under different topographic conditions. The results of this study can provide a new perspective for future drought propagation studies.
An understanding of the propagation process from meteorological to hydrological drought contributes to accurate prediction hydrological drought. However, the comprehensive influence of direct human activities involved in drought propagation is not well understood. In this study, an identification framework for drought propagation time was constructed to quantify the effects of direct human activities (i.e., reservoir storage, irrigation, industrial, domestic and agricultural water consumption) on drought propagation. Subsequently, the effects of meteorological and underlying surface factors on the drought propagation process were clarified based on random forest method, and the driving effect of teleconnection factors was investigated from top to bottom. The Wei River Basin (WRB), the largest tributary of the Yellow River Basin, was selected as the case study. Results disclosed that the propagation time from meteorological to hydrological drought was short in summer (approximately 2 months) and autumn (approximately 3 months), while long in spring (approximately 3–5 months) and winter (approximately 3–8 months), exhibiting noticeable spatial variability. In a changing environment, the propagation time generally showed a decreasing trend in spring and winter, while increasing propagation time was observed in summer and autumn. The dynamic drought propagation time of each season was all jointly controlled by the different extent variation of meteorological and underlying surface conditions, and the basic flow is all relatively significant throughout the period. Direct human activities had an effect on the seasonal dynamics of drought propagation, especially during the winter of the non-flood season, which alleviated the severity of winter hydrological drought to some extent, thus delaying the transmission of meteorological signals to hydrological systems. Sunspots, the dominant direct teleconnection driving force in the WRB, could indirectly affect the local precipitation and base flow in spring, autumn, and winter and interferes with the drought propagation process. This study sheds new insights into the attribution of drought propagation dynamics in a changing environment.
Precipitation phase (e.g., rainfall and snowfall) and snow (e.g., snowpack and snowmelt runoff) in high-mountain regions may largely affect runoff generation, which is critical to water supply, hydropower generation, agricultural irrigation, and ecosystems downstream. Accurately modeling precipitation phase and snow is therefore fundamental to developing a better understanding of hydrological processes for high-mountain regions and their lower reaches. The Lancang River (LR, or the Upper Mekong River) in China, among the most important transboundary rivers originating from the Tibetan Plateau, features active dam construction and complex water resources allocation of various stakeholders in Southeast Asian countries under climate change. This study aims to improve precipitation phase and snow modeling for the LR basin with a hydrological model and multisource remotely sensed data. Results show that joint use of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product with high spatial resolution (1 km×1 km) and an air temperature product can more precisely distinguish precipitation phase than air and wet-bulb temperature products in the LR basin. Snowfall and snowmelt were found to be controlled primarily by rainfall and snowfall temperature thresholds in snow modeling. The rainfall and snowfall temperature thresholds derived from the hydrological model through calibration with remotely sensed snowpack at basin scales were considerably lower than those derived from in situ observations. Rainfall and snowfall temperature thresholds derived from in situ observations could lead to the overestimation of snowmelt runoff due mostly to the lack of representation of point-based measurements at basin scales. This study serves as a basis for better modeling and predicting snow for the LR basin and potentially other similar basins globally.
Hydrological extremes both dry extremes and wet extremes can be exacerbated by climate change and threat water security in Lancang-Mekong River Basin (LMRB). Reservoirs can be managed effectively mitigate the risks of these extreme events. However, current knowledge about changes in hydrological extreme events under climate change and the effectiveness of reservoir regulation in LMRB remains limited. This study fills the knowledge gap by evaluating the effectiveness of reservoir regulation for changing hydrological extremes in the 21st century. The VIC-Reservoir hydrological model forced by the bias-corrected CMIP6 climate forcing data were used to project the future streamflow changes in LMRB, and the copula-based joint Standardized Streamflow Index (SSI) was adopted to identify basin-wide dry and wet hydrological extremes. Our results indicate that the streamflow in LMRB will first decrease until 2038 and then increase under the SSP5-RCP8.5 scenario (Similarly, 2020 in the SSP1-RCP2.6 scenario and 2042 in the SSP3-RCP7.0 scenario), which will lead to a substantial increase in basin-wide dry hydrological extremes (up to 33% in the 2040s) and wet hydrological extremes (up to 363% by the end of the 21st century). Reservoir regulation can mitigate the basin-wide dry extreme events by 100% and the wet extreme by 32%. While the future dry hydrological extreme can be mitigated by reservoir regulation, the lack of the reservoir storage capacity to deal with wet hydrological extreme poses a challenge to transboundary water management in the basin.
Drought can lead to considerable agricultural, ecological, and societal damage. Improving our understanding of the propagation relationship between meteorological and hydrological drought is necessary to lessen drought impacts. The different drought responses and underlying mechanisms among different climate types are not yet sufficiently understood. By applying the standardized precipitation index and standardized runoff index, we investigated the propagation relationship between meteorological and hydrological drought. Because of short-term response between meteorological and hydrological droughts, the propagation time was considered among time scales of 1-12 months. Wavelet analysis was employed to examine the two types of drought from 1902 to 2014. Our results showed that arid environments had a weaker propagation relationship than moist environments. There was a stronger relationship between the two types of drought in summer and autumn than in spring and winter. The climate was not the only factor impacting drought propagation; land (cover and topographic feature) may also impact propagation time and intensity from meteorological to hydrological drought. This study analyzed and highlighted that the most susceptible regions in China and global scale, respectively. The most susceptible regions were tropical and subtropical Chinese southern zones in China and equatorial and warm temperate climate zones in global; however, arid climate zones showed little interaction between the two kinds of drought. Other factors that impact drought propagation, such as land cover, landforms, and human activity, should be considered in future research.
Hydrological drought usually lags behind meteorological drought. Obtaining the propagation threshold (PT) from meteorological drought to hydrological drought is important for providing early warnings of hydrological drought. Previous studies have only used single timescales to characterize PT; however, a single timescale cannot accurately describe the propagation attributes from meteorological to hydrological drought because drought has multi-timescale features. In addition, several methods can be used to obtain PT, such as run theory, correlation analysis, and non-linear response methods. However, these methods might produce different estimates of PT. Here, multi-timescale drought indices, namely the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI), were used to represent meteorological drought and hydrological drought. PT estimates at multiple timescales (e.g., 1-month, 3-month, and 12-month) obtained from run theory, correlation analysis, and non-linear response methods were compared, and the possible reasons for differences in the PT estimates are discussed. We conducted a case study of three sub-basins (Xinfengjiang River, Qiuxiangjiang River, and Andunshui River) with low levels of human activity in the Dongjiang River Basin, which is located in a humid region in southern China. We found that estimates of PT differed at different timescales of drought indices and with different methods at the same timescales. Longer timescales of hydrological drought corresponded to larger PT and vice versa. The major cause of this pattern was the fact that different timescales of drought indices showed different response sensitivities to drought events. The PT obtained from run theory was the shortest; thus, run theory can provide conservative warnings to aid drought prevention and mitigation. Our findings can help drought managers select effective tools to manage the early stages of hydrological drought based on meteorological forecasts and thus minimize the negative impacts of hazards posed by drought.
Hydrologic models are commonly used to assess climate change impact on water resources. Several studies have reported that hydrologic models often experience severe performance degradation under climatic conditions different from calibration periods. With the advancement of artificial intelligence technology, the long short-term memory (LSTM) network has recently shown great potentials in rainfall-runoff modeling. However, little is known about the robustness of the LSTM network when used in changing climatic conditions. In this study, we compare the robustness of the LSTM network and two conceptual hydrologic models in runoff prediction in changing climatic conditions in 278 Model Parameter Estimation Experiment (MOPEX) basins. For calibration periods, the two hydrologic models have better performance in wet periods than in dry periods, while the LSTM network shows little performance difference under different climatic conditions. For validation periods, the three models suffer the largest performance loss when calibrated in a wet period and validated in a dry period. The performance losses of the LSTM network are primarily affected by the climate contrast between calibration and validation periods, while the performance losses of the two hydrologic models are mainly dependent on the climatic condition of validation periods. We also find that the length of the calibration period is an important factor affecting the relative performance of the models. Increasing the length of the calibration period has little effect on the validation performance of the two hydrologic models but enhances the LSTM network's performance. If sufficient calibration data is available, the LSTM network is a preferred tool for runoff simulation. On the other hand, the hydrologic models could have more advantages over the LSTM network in case of limited calibration data available.
This paper assesses the recently intensified saline water intrusion (SI) and drought in the Vietnamese Mekong Delta (VMD). While the existing literature predominantly points the cause of drought to the hydropower dams in the upstream of the Mekong Basin, we contribute new physical evidence of the intensification of saline water intrusion (through backwater effect) in the VMD caused by three anthropogenic drivers: riverbed incision (due to both riverbed mining and dam construction), sea level rise and land subsidence. Thereupon, we highlight that it is critical to not underestimate the impacts from the localized factors, especially the riverbed-mining which can incise the channel by up to 15 cm/year and amplify the salinity intrusion. Our analysis is based on the extensive sets of hourly-to-daily hydrological time series from 11 gauge stations across the VMD. First, several signs of significantly increased tidal amplification (up to 66 %) were revealed through the spectral analysis of the hourly water level data. This trend was further validated through the changes in slopes of the rating curves at the tidal zones, implying the relationships between the shift of the backwater effects on the rivers in VMD and the lowered water levels caused by the riverbed incision. Finally, we introduce a novel approach using the annual incision rates of the riverbed to compare four SI driving factors in terms of their relative contributions to the balance between fresh and saline water in the VMD.
In this paper, future drought characteristics (frequency, duration and intensity) over China are analysed by using
four climate models from CMIP6 under the seven SSP-RCP (shared socioeconomic pathway-representative
concentration pathway) scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, SSP370, and SSP585) for three
defned periods of 2021–2040 (near-term), 2041–2060 (mid-term) and 2081–2100 (long-term). The corresponding four climate models output from CMIP5 are also used to conduct a comparison analysis between CMIP5
and CMIP6 to address the improvements added to CMIP6 in terms of drought identifcation. The drought
characteristics are identifed by applying the standardized precipitation-evapotranspiration index (SPEI) at a 12-
month timescale and run theory. The results show that CMIP6 has a robust capability to capture historical
(1986–2005) drought characteristics. For the future period of 2021–2040, the decrease in precipitation and
increase in potential evapotranspiration will lead to continuous dry conditions in the upper and middle Yangtze
River basin and eastern Pearl River basin. Relative to the reference period, drought events will be more frequent
and severe with longer durations in the Northwest River basins and middle Yangtze River basin. Furthermore,
higher emissions signify a greater increase in drought frequency and intensity in the long-term period. Except for
the SSP585 scenario, the lower emission scenario corresponds to the higher drought duration soon and in the
mid-21st century (2021–2060). This fnding is regarded as a “strange phenomenon”, which cannot be detected by
the previous CMIP5-based emission scenarios (RCP2.6, RCP4.5 and the unlikely pathway RCP8.5). Therefore,
additional “possible future”-based scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, and SSP370) should be
included in extreme climate studies, especially for the near future and mid-21st century. Notably, compared with
CMIP5, the reduced biases in drought characteristics are more likely associated with improvements in the representation of physical processes in climate models from CMIP6. The results of this study could provide a basis
for the development of drought adaptation measures over China
The Lancang-Mekong River Basin (LMRB) is one of the most important transboundary river basins in Asia. While climate change perturbs the streamflow and affects flood events, reservoir operation may mitigate or aggravate this impact. Therefore, quantitative assessment of the climate change impact and reservoir effect on the LMRB is a vital prerequisite for future hydropower development and environmental protection. This study aimed to estimate the variation of the streamflow and flood characteristics affected by climate change and reservoir operation within the LMRB. A reservoir module was incorporated into the Variable Infiltration Capacity (VIC) model to simulate the streamflow susceptible to the reservoirs. It was found that the reservoirs had a substantial influence on the streamflow during 2008–2016, when many reservoirs were constructed in the LMRB. The reservoirs across the Lancang River (the upper Mekong River located in China) reduced the annual average streamflow by 5% at Chiang Sean station (northern Thailand) in 2008–2016, whereas their influence became undetectable downstream of Vientiane station (northern Laos). The streamflow changes downstream of Mukdahan station at southern Laos (including the stations in Cambodia and southern Vietnam) were mainly attributed to the local reservoirs and climate change. Compared with the baseline period of 1985–2007, the upstream reservoir operation dramatically affected streamflow at the midstream stations with higher dry season streamflow (+15% to +37%), but lower wet season streamflow was less affected (−2% to −24%) in 2008–2016. Climate change increased the magnitude and frequency of the flood by up to 14% and 45%, respectively, whereas the reservoir operation reduced them by 16% and 36%, respectively. Our findings provide insights into the interaction between climate change and reservoir operation and their integrated effects on the streamflow, informing and supporting water management and hydropower development in the LMRB.
What the extent of meteorological drought could trigger the corresponding hydrological drought with different levels? This question is an important topic in the field of drought propagation, which however has not been resolved. Therefore, a novel model based on a Bayesian network was proposed to address this issue in this study. In this model, the drought pooling and excluding methods were applied to eliminate minor drought events. A drought matching approach based on drought propagation time was proposed to achieve the one by one matching between different types of drought. Moreover, based on the matched drought events and the copula-based conditional probability model, the drought propagation thresholds of meteorological drought for triggering hydrological drought at various levels were determined. In addition, the interval conditional probability was calculated to further explore the sensitivity of hydrological drought response to different meteorological drought conditions. Furthermore, the propagation ratio was proposed to characterize the differences of drought propagation threshold among various regions. The Wei River Basin was selected as a case study. Results indicated that the results of drought propagation threshold were reliable and accurate. The increase of interval conditional probability showed a typical S-curve, which can intuitively obtain the probability of hydrological drought occurrence at different levels under specific meteorological drought condition, so as to effectively guide drought preparedness and mitigation. The propagation ratio can describe the overall resistance of the basin to meteorological drought, and it mainly depended on the meteorological and underlying surface conditions as well as groundwater supply.
Identification of thresholds associated with key climate, catchment and morphological variables for hydrological droughts can further improve our understanding of evolution and propagation of droughts in a complex water resource system. These thresholds are associated with complex interaction between climate and catchment variables and they are often connected through hierarchical as well as non-linear relationships. The advantage of selecting a multi-factor predictor domain can detect multiple thresholds that may not be observed by analyses limited to single predictors. In the present study, we developed a conceptual modeling framework by integrating a hydrological model developed based on the Soil and Water Assessment Tool (SWAT) and statistical models to quantify the potential influence of climate, catchment, and morphological variables and their thresholds on hydrological drought duration and severity for the watersheds located in Savannah River Basin (SRB). The concept of standardized runoff index (SRI) was used to derive the multiscale hydrological drought time series (i.e., SRI 1, SRI 6, and SRI 12) to investigate short term, medium term, and long term drought events based on their duration and severity. It was observed that the linear models developed based on the climate variables may not be capable for predicting the duration of multiscale hydrological droughts, whereas, the performance of statistical models can be significantly improved by the addition of catchment and morphological variables. In addition, among the morphological variables stream order seems to have a significant control over short, medium and long term drought duration across the study area. In the second phase of our analysis, we employed classification and regression tree (CART) algorithm for quantifying the thresholds associated with climate, catchment, and morphological variables that have potential influence on the hydrological drought. The result indicates that the variables and its associated threshold vary for short, medium, and long term drought. The proposed modeling framework can be extended for ungauged basins to improve the drought management.
It is important to understand the propagation of an agricultural drought, which is crucial for early warning. Recent studies have partly revealed this hidden process and regarded it as another critical feature of drought, but the relevant studies are still limited. Here, we propose a quantitative method to explore the full propagation process of agricultural drought by using cross-wavelets combined with multiple drought indices and spatial autocorrelation methods. The Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Soil Moisture Index (SSI) and Vegetation Health Index (VHI) were adopted to characterize meteorological, hydrological, soil moisture and vegetation droughts, respectively. The propagation time of agricultural drought was investigated by the cross wavelet analysis. The spatial relationship of those droughts was examined by spatial autocorrelation method. Results demonstrated that the propagation time was within one month from meteorological to hydrological drought, and within two months from hydrological to soil moisture drought, and between two to three months from hydrological to vegetation drought in most areas of Yangtze River Basin, respectively. It was also found the meteorological and hydrological droughts, hydrological and soil moisture droughts, hydrological and vegetation droughts were all characterized by statistical linkages on both long and short time scales. The global Moran's Index of SPI, SRI and SSI were higher than 0.7 and the local Moran's Index were mainly High-High and Low-Low clustering, indicating those subtype droughts were closely associated with the neighboring regions. This study clearly revealed the full propagation of agricultural drought in Yangtze River Basin both from spatial and temporal perspective for the first time, which provides valuable knowledge for understanding and predicting agricultural drought.