Future Hydrological Drought Research Articles

Attribution and Risk Projections of Hydrological Drought Over Water‐Scarce Central Asia

AbstractCentral Asia (CA), a typical arid and semiarid region, has experienced worsening droughts, adversely impacting agricultural production and socioeconomic development. However, the evolution of hydrological droughts in CA remains unclear. Here, we used instrumental streamflow and reanalysis to demonstrate a decline in surface runoff in CA since the 1990s, with 44.6% and 33.2% of the area dominated by reductions in snowmelt and precipitation, respectively. We found that global warming contributes to the long‐term decrease in surface runoff, while short‐term fluctuations in surface runoff are caused by the El Niño‐Southern Oscillation, such as southern CA drying induced by decreasing precipitation during La Niña. We project the future hydrological drought characteristics based on state‐of‐the‐art global hydrological simulations and found increasing duration and severity of hydrological droughts in CA, especially in the Amu Darya basin, and the Caspian Sea East Coast basin. These increasing droughts are exacerbated by higher anthropogenic emissions, posing high‐level risks to 39.01% of land area and 35.9% of human population under an extremely high emissions scenario. These findings highlight the need for improved water conservation technologies and concerted development strategies should be considered by national policy makers in this water‐scarce and climatically sensitive region.

Simulating future hydrological droughts and sediment yield by integrating different climate scenarios for a semiarid basin in Brazil

Simulating future hydrological droughts and sediment yield by integrating different climate scenarios for a semiarid basin in Brazil

Characterize and analysis of meteorological and hydrological drought trends under future climate change conditions in South Wollo, North Wollo, and Oromia Zones, in Ethiopia

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

Hydrological Drought and Flood Projection in the Upper Heihe River Basin Based on a Multi-GCM Ensemble and the Optimal GCM

To ensure water use and water resource security along “the Belt and Road”, the runoff and hydrological droughts and floods under future climate change conditions in the upper Heihe River Basin were projected in this study, based on the observed meteorological and runoff data from 1987 to 2014, and data from 10 GCMs from 1987 to 2014 and from 2026 to 2100, using the SWAT model, the Standardized Runoff Index, run length theory, and the entropy-weighted TOPSIS method. Both the multi-GCM ensemble (MME) and the optimal model were used to assess future hydrological drought and flood responses to climate change. The results showed that (1) the future multi-year average runoff from the MME was projected to be close to the historical period under the SSP245 scenario and to increase by 2.3% under the SSP585 scenario, and those from the optimal model CMCC-ESM2 were projected to decrease under both scenarios; (2) both the MME and the optimal model showed that drought duration and flood intensity in the future were projected to decrease, while drought intensity, drought peak, flood duration, and flood peak were projected to increase under both scenarios in their multi-year average levels; (3) drought duration was projected to decrease most after 2080, and drought intensity, flood duration, and flood peak were projected to increase most after 2080, according to the MME. The MME and the optimal model reached a consensus on the sign of hydrological extreme characteristic responses to climate change, but showed differences in the magnitude of trends.

Evaluating the future total water storage change and hydrological drought under climate change over lake basins, East Africa

Climate change led an increase in the frequency of severe droughts, the effects of which are exacerbated by a lack of water storage. Despite increasing research on global-scale total water storage (TWS) change and drought prediction, basin-scale long-term hydrological drought, TWS change, and El Nino–Southern Oscillation (ENSO) influence in East Africa (EA) remain unexplored. In this paper, we used multiple approaches, of ensemble machine learning (ML) and weighted averaging, to conduct a basin-scale exploratory analysis of future hydrological drought and TWS change for the period 2025–2099. Climate change will affect future hydrological conditions in the basins, with maximum severity values of −90.3, −726.6, and −2567.7 Gt in the Tana, Turkana, and Victoria basins, respectively, under RCP6.0. Similarly, we found maximum severities of −187.5 and −277.7 Gt in the Abaya-Chamo and Tanganyika basins, respectively, under RCP 2.6. The most severe El Nino drought and exceptional La Nina conditions in the basins were found under both RCP scenarios. To ensure future water security in EA, sustainable water resource management strategies that can mitigate the impacts of extreme climate events in the future are urgently needed.

Future changes in hydrological drought across the Yangtze River Basin: identification, spatial–temporal characteristics, and concurrent probability

Hydrological droughts have caused greater damage due to climate change and rapid socio-economic development. Exploring future hydrological drought variations is imperative for drought defense and infrastructure construction. This study aims to investigate future changes in hydrological droughts across the Yangtze River Basin during 2025 ∼ 2100. The Distributed Time-Variant Gain Model coupled with the Standard Operation Policy module (DTVGM-SOP) was employed to project future runoff driven by the bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) data. Subsequently, future spatial–temporal changes in the characteristics of hydrological droughts were analyzed based on the Standardized Runoff Index (SRI). Lastly, future changes in the probability of concurrent hydrological droughts were evaluated using the bivariate copula functions. Results show that: (1) The DTVGM-SOP model can satisfactorily simulate monthly streamflow with a high Nash-Sutcliff Efficiency (NSE) of 0.75 ∼ 0.92. Future annual maximum discharge and annual average discharge in the mainstream and the northern tributaries are expected to increase by 3.34%∼58.19% and 3.11%∼32.66% respectively, while annual maximum discharge in the southern tributaries decreases by 0.54%∼20.09%. (2) The future annual and seasonal hydrological drought frequency is expected to decrease in most regions, while the frequency in spring increases in the Qinghai Tibet Plateau. The duration and severity of future hydrological droughts are expected to decrease by 9.52%∼78.69% and 5.13%∼81.36% in the whole basin except the source area and the Yalong River Basin. (3) Future concurrent hydrological droughts will be mitigated with decreasing frequency. The probability of concurrent hydrological droughts is expected to decrease by 46.15%∼96.47%, while future changes in the conditional probability of the mainstream are diverse in different concurrent scenarios with a range of −93.41%∼40.09%. The findings are helpful to improve the understanding of future hydrological drought variations in the Yangtze River Basin and provide essential information for damage reduction.

Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia

The objective of this study is to investigate and perform long–term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.

Future hydrological drought changes over the upper Yellow River basin: The role of climate change, land cover change and reservoir operation

The upper Yellow River Basin (UYRB) provides more than 50 % of freshwater for the Yellow River. Recent research reveals continuous climatic wetting and vegetation greening over the UYRB in a warming future, but future hydrological drought changes remain unclear due to complex interactions between climate change, land cover change and reservoir operations. Here we project hydrological drought changes at different global warming levels and quantify contributions from each driving factor. The Conjunctive Surface-Subsurface Process eco-hydrological model is coupled with a reservoir module, and the new CSSPv2 + Reservoir model is used to perform future projections driven by bias-corrected meteorological forcings from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The CSSPv2 + Reservoir model simulates monthly streamflow over the UYRB reasonably well with a Kling-Gupta efficiency of 0.75. The increasing precipitation during dry seasons reduces hydrological drought duration by 9 %/12 %/19 % at the 1.5℃/2.0℃/4.0℃ warming level, while its impact on drought severity is limited. The dramatic increase of leaf area index at the 4.0℃ warming level robustly exacerbates hydrological drought severity to 28 % by intensifying evapotranspiration. The reservoir operation, designed to reduce seasonality in monthly releases to ensure stable hydropower generation, further decreases drought duration, but increases drought frequency and severity to 63 %/48 %/108 % and 9 %/11 %/32 % at the 1.5℃/2.0℃/4.0℃ warming levels respectively. A mitigated operation rule which stores less water during wet seasons, however, can reduce hydrological drought severity and duration significantly. Thus, future reservoir operations should pay more attention to the trade-off between hydropower generation and hydrological drought mitigation.

Comparing surface water supply index and streamflow drought index for hydrological drought analysis in Ethiopia

Recently, floods and drought have become common natural hydroclimatic hazards in several countries. Consequently, the identification of an appropriate drought index is now a challenging task for researchers. It is obvious that there is not a single best drought index; rather a comparison of indices will give a relative option. The objective of this study was to compare two hydrological drought indices; the modified surface water supply index (M1SWSI) and streamflow drought index (SDI) over eight river basins, in Ethiopia. The M1SWSI and SDI value was computed from 1973 to 2014 using 34 streamflow stations, 42 rainfall gauge stations, and 3 lake-level data. The two indices results showed that the 1980s were the most severe drought years for all river basins. But for the case of Genale Dawa and Wabishebele basins, the drought severity increased from 2000 to 2014. Hydrological drought analysis using SDI has more drought occurrence frequency than M1SWSI. In all river basins from 1973 to 2014, there were a total of 18 severe drought events when using M1SWSI, but there were a total of 39 severe and 12 extreme drought events when using SDI. This implied that M1SWSI reduced the occurrence probability of severe drought by 53.85% and extreme drought by 100%. It is known that Ethiopia is stricken by extreme droughts in the last few decades. But M1SWSI doesn't detect those invidious drought events. In this study, SDI is found to be a better hydrological drought index. Therefore, policy and strategic planners, master plan developers, and decision-makers can use SDI to analyze historical and future hydrological drought trends to develop effective drought mitigation measures.

Hydrological Drought Forecasting Using a Deep Transformer Model

Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were collected from these two stations. The two deep learning models were used to predict stage data for five different time steps: 30, 60, 90, 120, and 180 days. A drought series was created from the forecasted values using a monthly fixed threshold of the 75th percentile (75Q). The transformer model outperformed the LSTM model for all of the timescales at both locations when considering the following averages: MSE=0.11, MAE=0.21, RSME=0.31, and R2=0.92 for the Chattahoochee station, and MSE=0.06, MAE=0.19, RSME=0.23, and R2=0.93 for the Blountstown station. The transformer model exhibited greater accuracy in generating the same drought series as the observed data after applying the 75Q threshold, with few exceptions. Considering the evaluation criteria, the transformer deep learning model accurately forecasts hydrological drought in the Apalachicola River, which could be helpful for drought planning and mitigation in this area of contested water resources, and likely has broad applicability elsewhere.

Investigating the effects of climate change on future hydrological drought in mountainous basins using SWAT model based on CMIP5 model

Investigating the effects of climate change on future hydrological drought in mountainous basins using SWAT model based on CMIP5 model

Projections of the characteristics and probability of spatially concurrent hydrological drought in a cascade reservoirs area under CMIP6

Improving the understanding of the future hydrological drought variability in cascade reservoirs area is indispensable for water resource management, especially in the context of changing climate. This study aims to investigate the hydrological drought variations in the upper Yellow River Basin (UYRB) during 2021–2100 employing the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. The study area is divided into three sub-regions based on the location of the two large reservoirs, i.e., Region 1 (R1) and Region 2 (R2) have reservoirs while Region 3 (R3) has none. The meteorological data from 10 CMIP6 models are downscaled by utilizing the bias-corrected spatial disaggregation (BCSD) method and then used to force the improved abcd hydrological model for future monthly runoff simulations. The changes in severity, duration, and frequency of drought events in three sub-regions under two future climate scenarios (SSP2-4.5 and SSP5-8.5) relative to the historical period are identified. Then, the copula function is applied to explore the changes in probabilities of spatially concurrent drought for different cases in the future. The most likely combination of drought intensity for R1 and R2 corresponding to 10-years conditional return periods is estimated to demonstrate changes in drought risks across the reservoir areas. Results show that the drought frequency of R2 and R3 is expected to increase in spring and summer, and the drought events are projected to have a shorter duration and lower severity overall under two scenarios. Additionally, the spring probabilities of concurrent drought for different cases are expected to increase in the near future (increase by 1.9%–19.3%), but only increase in R2&R3 case in the far future. It is noted that although the drought probabilities will decrease in winter, R1 and R2 are expected to experience more severe drought when drought occurs in R3. The findings are helpful to improve the understanding of future hydrological drought risk in the UYRB and provide guidelines for making adaptation strategies to climate change.

Spatiotemporal characteristics of meteorological to hydrological drought propagation under natural conditions in China

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.

Correction to: Future Hydrological Drought Analysis Considering Agricultural Water Withdrawal Under SSP Scenarios

Correction to: Future Hydrological Drought Analysis Considering Agricultural Water Withdrawal Under SSP Scenarios

Glacial runoff buffers droughts through the 21st century

Abstract. Global climate model projections suggest that 21st century climate change will bring significant drying in the terrestrial midlatitudes. Recent glacier modeling suggests that runoff from glaciers will continue to provide substantial freshwater in many drainage basins, though the supply will generally diminish throughout the century. In the absence of dynamic glacier ice within global climate models (GCMs), a comprehensive picture of future hydrological drought conditions in glaciated regions has been elusive. Here, we leverage the results of existing GCM simulations and a global glacier model to evaluate glacial buffering of droughts in the Standardized Precipitation-Evapotranspiration Index (SPEI). We compute one baseline version of the SPEI and one version modified to account for glacial runoff changing over time, and we compare the two for each of 56 large-scale glaciated basins worldwide. We find that accounting for glacial runoff tends to increase the multi-model ensemble mean SPEI and reduce drought frequency and severity, even in basins with relatively little (<2 %) glacier cover. When glacial runoff is included in the SPEI, the number of droughts is reduced in 40 of 56 basins and the average drought severity is reduced in 53 of 56 basins, with similar counts in each time period we study. Glacial drought buffering persists even as glacial runoff is projected to decline through the 21st century. Results are similar under representative concentration pathway (RCP) 4.5 and 8.5 emissions scenarios, though results for the higher-emissions RCP 8.5 scenario show wider multi-model spread (uncertainty) and greater incidence of decreasing buffering later in the century. A k-means clustering analysis shows that glacial drought buffering is strongest in moderately glaciated, arid basins.

Future Hydrological Drought Analysis Considering Agricultural Water Withdrawal Under SSP Scenarios

Hydrological drought is assessed through river flow, which depends on river runoff and water withdrawal. This study proposed a framework to project future hydrological droughts considering agricultural water withdrawal (AWW) for shared socioeconomic pathway (SSP) scenarios. The relationship between AWW and potential evapotranspiration (PET) was determined using a deep belief network (DBN) model and then applied to estimate future AWW using projections of the twelve global climate models (GCMs). 12 GCMs were bias-corrected using the quantile mapping method, climate variables were generated, and river flow was estimated using the soil and water assessment tool (SWAT) model. The standardized runoff index (SRI) was used to project the changes in hydrological drought characteristics. The results revealed a higher occurrence of severe droughts in the future. Droughts would be more frequent in the near future (2021–2060) than in the far future (2061–2100) and more severe when AWW is considered. Droughts would also be more severe for SSP5-8.5 than for SSP2-4.5. The study revealed that the increased PET due to rising temperatures is the primary cause of the increased drought frequency and severity. The AWW will accelerate the drought severities in the future in the Yeongsan River basin.

Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management

Trustworthy projections of hydrological droughts are pivotal for identifying the key hydroclimatic factors that affect future groundwater level (GWL) fluctuations in drought-prone karstic aquifers that provide water for human consumption and sustainable ecosystems. Herein, we introduce an explainable artificial intelligence (XAI) framework integrated with scenario-based downscaled climate projections from global circulation models. We use the integrated framework to investigate nonlinear hydroclimatic dependencies and interactions behind future hydrological droughts in the Edwards Aquifer Region, an ecologically fragile groundwater-dependent semi-arid region in southern United States. We project GWLs under different future climate scenarios to evaluate the likelihood of severe hydrological droughts under a warm-wet future in terms of mandated groundwater pumping reductions in droughts as part of the habitat conservation plan in effect to protect threatened and endangered endemic aquatic species. The XAI model accounts for the expected non-linear dynamics between GWLs and climatic variables in the complex human-natural system, which is not captured in simple linear models. The XAI-based analysis reveals the critical temperature inflection point beyond which groundwater depletion is triggered despite increased average precipitation. Compound effects of increased evapotranspiration, lower soil moisture, and reduced diffuse recharge due to warmer temperatures could amplify severe hydrological droughts that lower GWLs, potentially exacerbating the groundwater sustainability challenges in the drought-prone karstic aquifer despite an increasing precipitation trend.

Increased Vegetation in Mountainous Headwaters Amplifies Water Stress During Dry Periods

AbstractThe dynamics of blue and green water partitioning under vegetation and climate change, as well as their different interactions during wet and dry periods, are poorly understood in the literature. We analyzed the impact of vegetation changes on blue water generation in a central Spanish Pyrenees basin undergoing intense afforestation. We found that vegetation change is a key driver of large decreases in blue water availability. The effect of vegetation increase is amplified during dry years, and mainly during the dry season, with streamflow reductions of more than 50%. This pattern can be attributed primarily to increased plant water consumption. Our findings highlight the importance of vegetation changes in reinforcing the decrease in water resource availability. With aridity expected to rise in southern Europe over the next few decades, interactions between climate and land management practices appear to be amplifying future hydrological drought risk in the region.

Combined effects of predicted climate and land use changes on future hydrological droughts in the Luanhe River basin, China

Both climate and land-use changes can influence drought in different ways. Thus, to predict future drought conditions, hydrological simulations, as an ideal means, can be used to account for both projected climate change and projected land-use change. In this study, projected climate and land-use changes were integrated with the Soil and Water Assessment Tool (SWAT) model to estimate the combined impact of climate and land-use projections on hydrological droughts in the Lutheran River basin. We showed that the measured runoff and the remote sensing inversion of soil water content were simultaneously used to validate the model to ensure the reliability of the model parameters. Following calibration and validation, the SWAT model was forced with downscaled precipitation and temperature outputs from a suite of nine global climate models (GCMs) based on CMIP5, corresponding to three different representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5) for three distinct time periods: 2011–2040, 2041–2070 and 2071–2100, referred to as early century, mid-century and late-century, respectively, and the land use predicted by the CA–Markov model in the same future periods. Hydrological droughts were quantified using the standardized runoff index (SRI). Compared to the baseline scenario (1961–1990), mild drought occurred more frequently during the next three periods (except for the 2080s under the RCP2.6 emissions scenario). Under the RCP8.5 emissions scenario, the probability of severe drought or above occurring in the 2080s increased, the duration was prolonged, and the severity increased. Under the RCP2.6 scenario, the upper central region of the Luanhe River in the 2020s and upper reaches of the Luanhe River in the 2080s were more likely to experience extreme drought events. Under the RCP8.5 scenario, the middle and lower Luanhe River in the 2080s was more likely to experience these conditions.Graphic abstract

Quantitative study on characteristics of hydrological drought in arid area of Northwest China under changing environment

Under climate change, drought events have a great impact on different regions of the world. Quantitative evaluation of regional drought characteristics is of great significance for regional drought risk aversion. This study simulates the changes in the historical and future hydrological runoff based on historical observation data, the Community Land Model-Distributed Time Variant Gain Model (CLM-DTVGM), and CMIP5 data. Subsequently, it reveals the historical and future hydrological drought characteristics in the Aksu River Basin under a changing environment. The conclusions are as follows: (1) although both the asynchronous mutated precipitation and runoff in the upper reaches of the Aksu River Basin increased, with a maximum increase of 3.3 mm and 1.2 m3/s, the synchronous mutation of precipitation and runoff in the downstream indicated that human activities only affected the runoff in Alaer station; (2) CLM-DTGVM could be well applied in historical and future runoff simulation of the Aksu River basin with complex transformation of different land use under the changing environment; and (3) the seasons of drought development, maximum intensity, and drought termination mainly occurred in winter, followed by spring, summer, and autumn, while human activities had an obvious impact on the drought characteristics in the Aksu River Basin. These findings are of great significance for improving water-saving awareness and warning against drought risk.