Drought Risk Research Articles

A novel framework for assessing the joint and relative impacts of precipitation and groundwater on ecological drought processes in Northwest China

AbstractEcological drought has emerged as a critical research topic within eco‐hydrology, driven by the increased drought risk associated with global warming. This study aims to unravel the hydrological processes driving ecological drought in Northwest China by (1) constructing standardized indices for precipitation, groundwater storage anomaly, and ecological water deficit to detect variation across different hydrological components; (2) developing a framework to assess the joint and relative impacts of precipitation and groundwater variations on ecological drought; and (3) identifying the primary hydrological drivers of ecological drought across different regions. The results indicate that the joint impact of precipitation and groundwater on ecological drought variation dominates approximately 60% of the area, primarily in arid and semi‐arid regions. The average contribution of this joint impact to the alleviation of ecological drought ranges between 0.26 and 0.43 across all seasons. Notably, groundwater scarcity, rather than precipitation variation, is the primary driver of ecological drought in regions such as southern Shaanxi, southeastern Gansu, and southern Qinghai, accounting for 12.7% to 21.8% of the total area. These insights into the complex hydrological processes underlying ecological drought have significant implications for water resource management and ecosystem conservation in drought‐prone regions. This research provides valuable information for mitigating drought impacts and protecting vulnerable ecosystems in Northwest China and similar regions worldwide.

Livelihood Vulnerability from Drought among Smallholder Livestock Farmers in South Africa

The impacts of drought and climate change on agriculture have become increasingly apparent, and affect smallholder livestock farmers. Farmers’ livelihoods rely on agriculture; thus, they are vulnerable to the primary and secondary impacts of climate change. In South Africa, policies for increasing the resilience of smallholder livestock farmers who have suffered from agricultural drought have not paid sufficient attention to the level of livelihood vulnerability. This study assessed the level of livelihood vulnerability of smallholder livestock farmers in the drought-stricken Frances Baard District Municipality in the Northern Cape Province of South Africa. The livelihood vulnerability of 217 randomly selected farmers from the municipality were determined using the Livelihood Vulnerability Index (LVI) and Livelihood Vulnerability Index of the Intergovernmental Panel on Climate Change (LVI-IPCC), which includes seven components and 34 subcomponents addressing livelihood. A high level of livelihood vulnerability, with an LVI score of 0.436, was determined and attributed to high-risk livelihood strategies, food, social networks, health, water, sociodemographics, natural disasters, and climate change. The LVI-IPCC of 0.04 also showed moderate vulnerability due to high exposure, high sensitivity, and low adaptive capacity, especially for the Phokwane, Dikgatlong, and Magareng districts in the Frances Baard municipality. Given continued drought recurrences, it is crucial for the government and other stakeholders to implement strategic and targeted sustainable interventions. The resilience of smallholder livestock farmers should be enhanced by increasing their adaptive capacity through diversified livelihood options while decreasing exposure and sensitivity to agricultural drought risks.

An integrated approach for managing drought risks in the eastern himalayan region of India

Climate Risk Assessment (CRA), when applied through an integrated approach, enables the evaluation of hazard, exposure, and vulnerability within a unified framework, recognizing the interconnections between climate risks and both natural and social systems. This study uses Sikkim, a small state in the Indian Eastern Himalayan Region, as a case study to conduct a climate risk assessment of agricultural drought at the sub-national level. The objective was to identify the district most at risk from agricultural drought and determine the primary contributors to drought risk to support the development of effective adaptation strategies. The findings reveal that while all districts in Sikkim are moderately drought-prone, the main factor contributing to drought risk is the high sensitivity of the agricultural sector to drought and the limited adaptive capacity of farmers. Many marginal and small farmers rely on rainfed irrigation and have poor adaptive capacity, making them highly vulnerable to even moderate drought conditions. The study highlights that while mitigating drought hazards is important, it is a complex and long-term endeavor. Therefore, strengthening farmers' resilience by reducing their sensitivity and enhancing their adaptive capacity is essential for effectively reducing drought risks in Sikkim.

Drought risk management in agriculture: A copula perspective on crop diversification

AbstractDrought events are a major cause of large crop yield losses with implications for food security and farmers’ incomes. Growing multiple crops simultaneously during a cropping season is a well‐known on‐farm risk management strategy to cope with these drought risks. However, the effectiveness of this crop diversification under different severity levels of drought and how this effectiveness is influenced by the crop composition is unclear. This article provides new methodological and empirical insights to assess the effectiveness of such diversification, in particular to cope with extreme drought. We apply and evaluate nested Archimedean copulas and elliptical copulas to assess simultaneous farm‐level yield losses of different cash crops in German agriculture (winter wheat, winter barley, winter rapeseed, sugar beet, and grain maize) under different drought severity levels (N = 249,756; regionally pooled farm‐level crop‐yield pairs, 1995–2019). We show that on‐farm crop diversification contributes to cope with drought risks, but its effectiveness varies considerably across regions, crop pairs, and drought severity. Our results underline that cropping system diversification alone is often not sufficient to cope with drought risks, but that the right crop combinations are needed. For example, during a severe drought (one in 20 years event), 26.4% of farmers in eastern Germany suffered simultaneous yield losses of at least 20% in winter wheat and winter barley, while 19.1% of farmers in eastern Germany suffered simultaneous yield losses of at least 20% in winter wheat and sugar beet. Farmers should therefore be encouraged to grow crops with more diverse phenological requirements throughout the year.

Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach

This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.

Sustainable Land Management for Mitigating Soil Erosion at the Catchment Scale

This study addresses this challenge by focusing on the Revised Universal Soil Loss Equation (RUSLE) to combat excessive soil erosion rates within the Kayacık Dam Basin, located in the semi-arid region of the Tigris-Euphrates Basin in Türkiye. The primary objective is to maintain an average annual soil loss of no more than 10 tons per hectare. To achieve this, the RUSLE model is employed to evaluate sustainable land management (SLM) strategies. These strategies encompass agronomic, vegetative, and structural measures, all of which are intricately linked to RUSLE's C and P factors. Spatial analysis reveals that severe erosion (10-20 t ha-1 y-1) is predominantly concentrated in agricultural areas, spanning 2536.37 hectares. In contrast, very severe erosion (>20 t ha-1 y-1) affects 834.13 hectares of grassland. Statistical analyses underscore the significant contributions of various model factors to predicted soil losses (A). LS, C, K, and R factors account for 80.24%, 44.68%, 0.97%, and 0.27% of the effect, respectively. Remarkably, topography exerts the most substantial influence on agriculture, pasture, and forest/other land uses, contributing 84.46%, 57.29%, and 39.27% to the variation, respectively. These findings highlight the pivotal role of effective agronomic and vegetative practices in ecosystems management, especially within steep-slope landscapes encompassing agriculture, grassland, and forest. Furthermore, the semi-arid regions under investigation contend with the intricate interplay of heightened drought risk and the ramifications of intensive irrigated agriculture on soil erosion. This complex dynamic presents distinct challenges for implementing SLM strategies in the face of evolving climatic conditions and underscores the need for climate-resilient solutions.

Drought prediction in Jilin Province based on deep learning and spatio-temporal sequence modeling

Jilin Province, a key agricultural hub in Northeast China, has long been impacted by climate change, with drought disasters significantly affecting its agricultural output and ecological environment. Accurate drought prediction is essential for the effective utilization of water resources and agricultural production. This study proposes a novel drought prediction model that utilizes the SSA-VMD (Sparrow Search Algorithm Optimized Variational Mode Decomposition) technique to decompose meteorological data, followed by the reconstruction of the decomposed components using four entropy algorithms, including approximate entropy. The model integrates ARIMA (AutoRegressive Integrated Moving Average) and BiLSTM (Bidirectional Long Short-Term Memory network) for forecasting the reconstructed components, subsequently combining their outputs. In terms of spatio-temporal data, the study employs prediction models including the spatio-temporal cube, spatio-temporal hotspot analysis integrated with empirical kriging, and local outlier analysis to examine spatial distribution. The model’s predictive performance is validated from three perspectives: statistical characteristics of the indicators, comparison between predicted and observed values through prediction curve plots, and box plots. The results demonstrate that the combined SSA-VMD-ARIMA-BiLSTM model significantly enhances prediction accuracy compared to single models, as exemplified by its application in Changchun City. The model achieved an R2 of 0.938 and a root mean square error (RMSE) of 0.047 in drought prediction, outperforming the single ARIMA model (R2: 0.636, RMSE: 0.709) and the BiLSTM model (R2: 0.514, RMSE: 0.901). Additionally, across the entire province, the model’s R2, MAE, and RMSE are 0.82, 0.15, and 0.083, respectively, suggesting that the model exhibits not only high prediction accuracy but also a degree of generalizability. Furthermore, the results from the spatio-temporal cube, spatio-temporal hotspot analysis, and local outlier analysis demonstrate the method’s high accuracy and stability in predicting both short-term and long-term droughts. Particularly in short-term drought prediction, the model effectively captures the spatio-temporal distribution characteristics of short-term meteorological droughts. This study offers new methodological support for enhancing the early warning capabilities of drought risk in Jilin Province, providing a robust foundation for addressing the challenges posed by climate change. The findings not only address certain shortcomings in current drought prediction research but also introduce new methodologies and perspectives for future studies.

The effects of climate change and regional water supply capacity on integrated drought risk

Due to climate change, the frequency and duration of meteorological drought have increased. In addition, local water supply capacity has not met water demand in many regions, which will eventually lead to serious water shortages. To mitigate the effects of drought on sustainable water use, it is necessary to understand how climate change affects regional water supply capacity and drought risk. To this end, this study evaluated the drought response capacity of regional water supply systems and assessed the comprehensive drought risk in terms of drought hazard, vulnerability, and response capacity. To avoid subjectivity in risk analysis, structural equation modeling was used to select primary indicators and probability and statistical methods were used to assign weights to the indicators. The changes in drought risk in different climate change scenarios were assessed using sensitivity analyses. The overall results indicate that the future drought risks in Gyeonggi, Gyeongsang, Chungcheong, Jeolla, and Gangwon are 18, 12, 13, 9, and 10% higher, respectively, than the current risk level. The sensitivity analyses showed that Jinju in Gyeongsang province, which has a high drought response capacity, had the largest decreasing rate in drought risk. The quantified changes in drought risk under future climate change scenarios will be useful for identifying areas with a high drought risk and making decisions about drought mitigation under climate change.

Projected impact of solar radiation modification geoengineering on water deficit risk over major Central African river basins

Abstract The economy of Central African countries strongly depends on rain-fed agriculture and hydropower generation. However, most countries in this subregion do not yet have the irrigation technologies that are already applied in many more advanced nations, which further exposes them to the serious risk of severe drought caused by global warming. This study investigates the potential impact of solar radiation modification (SRM) geoengineering on the water availability over the four major river basins that cross most of Central African countries (i.e. Niger Basin, Lake Chad Basin, Cameroon Atlantic Basin (CAB) and Congo Basin). For this purpose a potential water availability index was computed based on an ensemble-mean simulations carried out in the framework of Phase 6 of the Geoengineering Model Intercomparison Project considering two SRM simulation experiments: the stratospheric sulphate aerosol injection (G6sulfur) and the global solar dimming (G6solar). The climate change simulation results in a robust decreases by up to 60% in water availability, most pronounced over the CAB under the high radiative forcing scenario. Therefore, in a business-as-usual world, the reduction in water availability combined with the rapid population growth expected by 2050 in the studied subregion, could result in a significant water deficit over Central African countries towards the end of the 21st century. This water deficit could affect all activities that depend on water resources, such as water supply, agriculture and hydropower generation. Furthermore, the results also show that SRM methods have the potential to significantly reduce this deficit by increasing water availability (as compared to climate change) by up to 50% over the affected river basins, with a more accentuated increase found in the CAB when the global solar dimming is applied. These results suggest good possibilities of adaptation for populations living in the geographical areas of these river basins.

Incorporation crisis lifecycle theory into full-stage flash drought spatio-temporal pattern identification and risk analysis

Compared to conventional slow-developing droughts, the characteristics and drivers of flash droughts are often more complex. This complexity challenges global drought early warning systems, leading to severe losses in crop yields during flash drought events. To address this problem, our study introduces the Full Life Cycle Flash Drought Identification Framework (FLCFDIF), which incorporates crisis lifecycle theories to link the lifecycle progression of flash droughts with corresponding risk management measures. The results indicate significant differences in the risk characteristics of flash drought events are observed between monsoon and non-monsoon regions of China, with flash droughts in monsoon regions erupting more rapidly, lasting longer, and recovering more slowly than flash droughts in non-monsoon areas. Flash droughts typically feature negative precipitation anomalies and positive anomalies in maximum temperature and evapotranspiration during the progression of the droughts. However, the high-intensity precipitation events that drive flash drought recovery primarily occur during the termination phase rather than the previously assumed recovery phase. Therefore, employing a 40% soil moisture percentile as a threshold for determining the end of flash droughts can reduce disturbances from low-intensity precipitation events and establish a link between flash droughts and seasonal droughts. In Southeast China, the probability of flash droughts transitioning to seasonal droughts is approximately 10%. In future flash drought risk management, it is crucial to integrate the four stages of flash drought progression (development, duration, recovery, and termination) and implement timely emergency response actions, including warning, assessment, mitigation, and monitoring, to address the environmental impacts of flash drought events.

Identification of critical drought thresholds affecting vegetation on the Mongolian Plateau

The increasing frequency of drought events due to global climate change has significant impacts on ecosystems, often resulting in reduced vegetation productivity. However, vegetation responses to drought often exhibit non-linear threshold phenomena, where vegetation growth sharply decreases once water stress reaches a certain level. Our study, conducted on the Mongolian Plateau, focuses on revealing the drought response thresholds and distribution patterns of different vegetation types. These thresholds mark critical turning points from high drought resistance to high vulnerability in vegetation response and serve as early warning indicators, crucial for accurately assessing how terrestrial ecosystems adapt to climate change. We utilized remote sensing observation datasets and inversion datasets spanning from 2001 to 2020, including six representative vegetation indices. The results indicate that: (1) Over 70 % of the Mongolian Plateau experiences low vegetation growth (<10th) predominantly influenced by drought, with low vegetation growth often occurring when water availability falls below the 30th percentile; (2) Areas with higher vegetation coverage and relatively moist climates have lower proportions of drought thresholds, implying that more complex ecosystems can better mitigate water stress, while background climatic conditions also regulate vegetation response to water availability; (3) According to multi-model climate predictions, the overall probability of drought risk on the Mongolian Plateau is projected to significantly increase during 2080–2100, especially in drought-prone regions. These findings provide important insights into better understanding the impacts of drought on ecosystems. Additionally, they emphasize the urgency of taking early measures to adapt to future drought risks.

Climate change loss and damage from droughts: key insights from Fiji’s sugar industry

ABSTRACT Climate change loss and damage (L&D) due to both sudden and slow-onset events are a growing concern for vulnerable countries. Over the last decade, agricultural communities in Fiji have suffered a range of serious L&D from severe drought events. Through a grounded theoretical lens, qualitative research was used to gain in-depth insights into L&D from droughts in the Fijian sugar industry. In-depth semi-structured interviews (n = 68) were conducted in two Indo-Fijian sugarcane communities, Barotu and Toko settlements in Western Viti Levu, Fiji, and with key stakeholders from government ministries, academia, and climate change experts at the national level. Purposive sampling was initially used to identify smallholder sugarcane farmers and key stakeholders. Theoretical sampling further identified key stakeholders for the interview. Despite implementing various adaptation measures, the Fijian sugar industry has suffered severe L&D, including non-economic L&D (NELD) and associated cascading effects. L&D included reduced yields, loss of crops, and reduced income. NELD included deterioration of mental, physical, and emotional health, loss of hope, and uncertainty. Cascading effects included increased food insecurity risks and impacts on children’s education. Key policy interventions are recommended, such as removing adaptation constraints, developing drought risk profiles and early warning systems, investing in livelihood diversification, and engaging in new markets to facilitate social and ecological transformations that will promote livelihood resilience. Key Policy Highlights: Climatic and non-climatic stressors are inherently interconnected and interact in a complex and complicated manner to enhance social-ecological systems (SES) vulnerability. Inadequate drought adaptation measures further marginalise farmers and ultimately increase their vulnerability to drought risks and loss and damage (L&D). L&D presents itself in various ways, including non-economic L&D and cascading effects that are impacting upon future generations. With projected climate change and the likelihood of impacts worsening, there is an urgency to address L&D more coherently through crucial policy interventions.

Soil moisture plays an increasingly important role in constraining vegetation productivity in China over the past two decades

Decreasing soil moisture (SM) and increasing vapor pressure deficit (VPD) are the main drought affecting factors of terrestrial vegetation productivity. Nevertheless, the impact of continued warming on the changing trend of SM and VPD constraints affecting vegetation productivity remains uncertain. Understanding the complex interactive effects of SM and VPD on vegetation is crucial for assessing drought risk and its ecological implications. This study aims to comprehensively quantify the trends in the respective contributions of SM and VPD to vegetation ecosystem productivity in China from 2000 to 2022. The results showed that both low SM and high VPD had significant inhibitory effects on gross primary productivity (GPP) anomaly. Furthermore, the magnitude of water stress on vegetation productivity increases, and is accompanied by a gradual expansion of areas experiencing significant water deficit during the study period. By decoupling the interactions between SM and VPD, we found that SM and VPD were the primary influencing factor of water stress on vegetation productivity, accounting for 60 % and 40 % of the total vegetated area, respectively. Notably, the effects of SM and VPD on GPP exhibited significant variations across different vegetation and climate gradients. The amount of land dominated by the SM constraint expanded, and SM gradually played an increasingly important role in the water stress of GPP over the past two decades. The results of the study are important to accurately assess the interrelationship between vegetation and climate in the context of climate change.

Projected change in the rainfall behaviour over Odisha

ABSTRACT Climate change has impacted the rainfall characteristics and the extremes are on the rise across the world. These changes threaten agriculture, water resources, and disaster management. Odisha, an eastern Indian state with an agrarian economy, heavily relies on monsoon rainfall. The study analyses the projected change in the rainfall characteristics across Odisha at different specific warming targets of the Paris Climate Agreement using high-resolution regional climate models. The study adopts a bi-fold approach; first, it employs a robust method to select the best model experiments; afterwards, the model ensemble is used to examine the projected rainfall characteristics. The results indicate a 4–16% increase in projected rainfall over Odisha, with an extended rainy season. The projected number of consecutive wet days, moderate and extreme rainfall, is expected to rise under the global warming scenario. The prolonged rainy season with heavy rainfall can result in disasters like post-monsoon floods, while higher rainfall variability will increase the risk of floods and droughts across Odisha, threatening agriculture. The results will help pinpoint regions most vulnerable to climate change. The study also suggested measures to assist governments and planners in developing short-term and long-term strategies for adaptation and mitigation to lessen climate change impacts.

An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods

Predetermining the risk of possible future droughts enables proactive measures to be taken in key areas such as agriculture, water management, and food security. Through these predictions, governments, non-governmental organizations, and farmers can develop water-saving strategies, encourage more efficient use of water, and minimize economic losses that may occur due to drought. Thus, future drought forecasts stand out as a strategic planning tool for the protection of natural resources. To achieve this aim, forecasted drought conditions for the next decade (2024–2034) at nine meteorological stations in the Sakarya basin, located northwest of Türkiye, are examined, using historical monthly precipitation data from 1991 to 2023. This study uses the Standardized Precipitation Index (SPI) and Long Short-Term Memory (LSTM) deep learning methods to investigate future meteorological droughts. The research confirms the compatibility and reliability of the LSTM method for forecasting meteorological droughts by comparing historical and forecasted SPI values’ correlograms and trends. In addition, drought maps are created to visually represent the spatial distribution of the most severe droughts expected in the coming years, and areas at risk of drought in the Sakarya Basin are determined. The study contributes to the limited literature on forward-looking drought forecasts in the Sakarya Basin and provides valuable information for long-term water resource planning and drought management in the region.

Utilizing the Google Earth Engine for Agricultural Drought Conditions and Hazard Assessment Using Drought Indices in the Najd Region, Sultanate of Oman

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p &lt; 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities.

A multi-sensor drought index for improved agricultural drought monitoring and risk assessment in the heterogeneous landscapes of the China–Pakistan Economic Corridor (CPEC)

Droughts cause significant economic damage worldwide. Evaluating their impacts on crop yield and water resources can help mitigate these losses. Using single variables such as precipitation, temperature, the soil moisture condition index (SMCI) and the vegetation condition index (VCI) to estimate drought impacts does not provide sufficient information on these complex conditions. Therefore, this study uses station-based and remote-sensing-based data to develop new composite drought indexes (CDIs), including the principal component analysis drought index (PSDI) and the gradient boosting method drought index (GBMDI). The first dataset includes historical observations of the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and the self-calibrated Palmer drought severity index (SC-PDSI) at the 1-, 3-, 6-, and 12-month timescales. The second dataset consists of remote-sensing-based data including the VCI, SMCI, temperature condition index (TCI), and precipitation condition index (PCI). We validated the results of PSDI and GBMDI by comparing them with historical drought events, in-situ drought indices, and annual winter wheat crop yield data from 2003 to 2022 using a regression model. Our temporal analysis revealed extreme to severe drought events during1990s and 2010s. GBMDI typically aligned with actual drought events and exhibited stronger correlations with in-situ drought indices than PSDI. We observed that drought intensity in winter were more severe than in summer. GBMDI was the most effective method, followed by PSDI, for assessing drought impacts on winter wheat yields. Thus, the proposed integrated monitoring framework and indexes offered a valuable and innovative approach to addressing the complexities of agricultural drought, particularly in evaluating its effects.

Analysis of Meteorological Drought for Selected Districts of Saurashtra Region of Gujarat

Drought, in particular, is a complex and recurring phenomenon triggered by insufficient precipitation or changes in its distribution, often resulting in decreased water availability. It evolves slowly, affecting large populations' access to food and water, disrupting the entire hydrological cycle, and causing long-term economic losses. The present study attempts a comprehensive analysis of meteorological drought characteristics for selected districts (Junagadh, Amreli, Jamnagar, and Rajkot) in the semi-arid Saurashtra region of Gujarat. The analysis of meteorological drought was performed using rainfall (RF), maximum temperature (Tmax), and minimum temperature (Tmin) data from four stations (each district has one station) for the 43 years duration (1981–2023). The Standardized Precipitation Evapotranspiration Index (SPEI) is utilized across multiple timescales to assess the number of drought affected years, occurrence of drought and drought parameters (total drought duration, magnitude, and intensity). The meteorological drought incidence varied significantly across Junagadh, Amreli, Jamnagar and Rajkot, with Junagadh experiencing droughts in 29% of years, Amreli in 32%, and higher rates in Jamnagar (37%) and Rajkot (36%). The most severe drought years in the four districts were 1987 and 2002, with additional major droughts occurred in 1985, 1986, 1987, 1993, 2002 and 2012. These districts also faced consecutive droughts from 1985 to 1987 and again from 1999 to 2002.Analysis of drought parameters in Junagadh, Amreli, Jamnagar and Rajkot districts showed that drought duration (DD) ranged from 29.46% (Junagadh) to 35.85% (Jamnagar) of months, drought magnitude (DM) varied between -37.58% (Jamnagar) and -34.10% (Junagadh), and drought intensity (DI) spanned from -1.17 (Rajkot) to -1.04 (Junagadh). The analysis of drought occurrence in all districts shows that mild and moderate droughts have been most frequent while severe and extreme droughts have been less common. The study's findings enhance water managers' understanding of how climate change influences drought patterns in the region, thereby assisting policymakers in conducting effective drought risk assessments.

Assessment of Long-Term Streamflow Response to Flash Drought in the São Francisco River Basin over the Last Three Decades (1991–2020)

Flash droughts, characterized by a rapid onset and severe intensity, pose a serious threat to water resource management. Extensive research has indicated that drought has lagged impacts on streamflow. Nevertheless, the hydrometeorological conditions by which streamflow dynamics respond to drought within the São Francisco River Basin (SFRB) remain ambiguous. To bridge this gap, we conducted a study on long-term streamflow responses to flash drought in the SFRB from 1991 to 2020, combining the Standardized Antecedent Precipitation Evapotranspiration Index (SAPEI) and quantile streamflow with a trend analysis. This study employed the SAPEI, a daily drought index, to identify flash droughts and assess the response of streamflow to the identified events across the SFRB. Our findings reveal insights into the direct response of streamflow to flash drought conditions, stimulated by the application of the SAPEI for analysis of flash drought events. The interannual flash droughts fluctuated over the years, with the middle part of the SFRB experiencing frequent, longer flash droughts, while the south part encountered shorter but less frequent events. About 55% of the study area is trended towards drying conditions. A comparative analysis of the SAPEI and streamflow identified a synchronized response to the onset of flash drought events, but the recovery timescale for the SAPEI and streamflow varied among the events. This study enhances understanding of the flash-drought–streamflow relationship in the SFRB and provides theoretical support for addressing drought risks under climate change.

Pywr-DRB: An open-source Python model for water availability and drought risk assessment in the Delaware River Basin

The Delaware River Basin (DRB) in the Mid-Atlantic region of the United States is an institutionally complex water resources system that provides drinking water for 13.5 million people, plus water for energy, industry, recreation, and ecosystems. This paper introduces Pywr-DRB, an open-source Python model exploring the impacts of reservoir operations, transbasin diversions, and minimum flow targets on water availability and drought risk in the DRB. Pywr-DRB draws on streamflow estimates from emerging data resources, bridging advances in large-scale hydrologic modeling with an improved representation of the basin's evolving water infrastructure and management institutions. Our detailed model diagnostic assessment demonstrates that Pywr-DRB provides substantial improvements over sole use of hydrologic models in capturing the DRB's dynamics. We also explore how water management alters model-derived risk estimates for low flows and water demand shortfalls. Our approach to diagnostic benchmarking and water systems modeling is broadly applicable to other major basins.