Drought Preparedness Research Articles

Dynamic evolution of meteorological and hydrological droughts under climatic and anthropogenic pressures in water‐scarce regions

AbstractClimate change and anthropogenic influences amplify drought complexity, entangle non‐stationarity (NS) and further challenge drought comprehension. This study aims to understand the dynamic evolution of drought propagation patterns due to climatic and anthropogenic pressures by assessing the non‐stationary linkages between hydrological variables and drought characteristics. It employs four standardized drought indicators to comprehensively examine the spatio‐temporal evolution of meteorological (MD) and hydrological (HD) drought characteristics. Data from 29 semi‐arid catchments from six river basins in Peninsular India, are analyzed to uncover distinct drought propagation patterns. This study utilizes a novel Non‐overlapping Block‐stratified Random Sampling (NBRS) approach to detect NS in drought characteristics and hydrological variables, shedding light on the underlying drivers of this dynamic behavior. The results indicate similarities in drought behavior for the Sabarmati, Mahi and Tapi (SMT) basins compared with the Godavari, Krishna and Pennar (GKP) basins, with shorter (longer) propagation times noted for SMT (GKP) basins. While HD severity decreases over time in SMT basins, it intensifies in GKP basins, which are linked to intensive anthropogenic interventions such as river regulation and reservoir operations, thus resulting in prolonged and intensified droughts. Rainfall primarily exhibits time‐invariance, while significant NS is observed in potential evapotranspiration (particularly in the Krishna and Pennar basins), streamflow and baseflow across all basins. The study also identified three distinct drought propagation patterns in these basins, highlighting cases where MD did not transition to HD, instances of HD occurring without preceding MD and synchronous propagation of MD to HD. The study outcomes provide profound insights into the evolution of drought dynamics under climatic and anthropogenic pressures, which will aid policymakers and stakeholders in formulating strategies for drought preparedness and response.

Analysis of Meteorological Drought in Cases of North Wello Zone, Ethiopia

Drought is a commonly used term, but the most complex and least realized of all the natural hazards affecting more people than any other hazards. The main objective of the study was to investigate the spatial-temporal occurrence of Meteorological drought indices analysis in the study areas. The study was undertaken to investigate the magnitude, frequency, and trends of drought incidence in North Wello Zone Ethiopia using monthly rainfall records for the period 1990 to 2020 of North Wello zone stations and CHIRPS satellite data. Drought Index Calculator used to analyse standard precipitation index (SPI). The correlation between station data and CHIRPS data in this study was 0.89 which shows highly correlation between them. the result of meteorological drought indices map analysis on the years 1999, 2000, and 2008 severely dry to extremely dry conditions in the Belg season, similarly in 19990, 199308, 201507, 201508, and 201509 have got meteorological drought indices with extremely dry Kiremt season on the North Wello Zone. The drought magnitude of different time scales varied from slight to extremely severe in the studied stations. The identification of the temporal and spatial characteristics of droughts in the North Wello Zone will be useful for the development of a drought preparedness plan in the Zone. Generally increasing tendencies of drought were observed during the main rainy season and decreasing tendencies of drought during the short rainy season and annual scale were observed in the study area.

Drought modelling: A comprehensive review

Droughts are a recurring natural hazard with significant social, economic, and environmental consequences. Accurate drought modelling is crucial for effective drought preparedness, mitigation, and response strategies. This review paper comprehensively overviews drought modelling approaches, including traditional statistical, physically-based and emerging machine learning techniques. We discuss the strengths and weaknesses of each approach, highlighting recent advancements and future research directions. Additionally, we examine the various drought indices employed for drought characterization and explore the integration of climate change scenarios into drought modelling frameworks. Finally, the review addresses the challenges and limitations of drought modelling and emphasizes the importance of data availability, model validation, and user-centric applications.

Construction of the water balance model for the Bontanga irrigation dam in Northern Ghana

ABSTRACT Freshwater resources are essential for the sustenance of all living things, for crop production and socio-economic activities. The objective of this study was to construct a water balance model aimed at optimizing reservoir management by analyzing variations in water availability in the reservoir. This model will help dam operators to adopt water allocation strategies, timing of water releases and storage decisions. An echo sounder was used to measure the depth of water. Crop water requirements were computed using FAO CROPWAT 8 software, and the InVEST-SDR model was used to estimate sediments exported downstream of the watershed. Model construction utilized multiple linear regression, and the results were tested at a level of significance α = 0.05. Model performance was evaluated using a coefficient of determination (R2) and the ratio of the root mean square error to the standard deviation of measured data (RSR). The results revealed a coefficient of determination (R2) of 0.743 and a ratio of the root mean square error to the standard deviation of measured data (RSR) of 0.67. The constructed model will provide essential information to stakeholders on drought preparedness and mitigation, including water conservation measures and the management of water releases during critical periods.

Preparedness for recurrent drought disaster: insights from the Sudano-Sahelian zone of Cameroon

ABSTRACT This study examined the drought preparedness of drought-prone communities in the Sudano-Sahelian zone of Cameroon. The study employed a mixed-method approach, using qualitative and quantitative data collection methods, including interviews, household surveys, focus group discussions, and field observations. The data were then analysed using SPSS for quantitative data and content analysis for qualitative data. The results reveal that the respondents primarily rely on their personal and community abilities to prepare for droughts. A significant proportion (χ 2 = 11.676, P = 0.020) of the respondents depend on community leaders for drought information, while others rely on family and friends. They also use indigenous knowledge and construct boreholes/wells to prepare for droughts. However, limited government support, inadequate extension/scientific support services, and insufficient formal training limited drought preparedness. Age, household size, and income significantly influenced perceived preparedness for drought. Based on the findings, the study concludes that informal/locally-driven strategies are important in drought preparedness and should be strengthened. The study recommends the establishment of drought management committees at the grassroots level and the effective use of indigenous knowledge in combination with scientific knowledge for drought planning and adaptation. Given the geographical variation in climatic stressors, a focus on a specific event such as drought has enhanced the understanding of drought disaster preparedness at the community level for effective planning and policy interventions. This study contributes to the literature in the disaster preparedness discipline with a lens on community drought preparedness.

Modeling the influence of small reservoirs on hydrological drought propagation in space and time

To increase drought preparedness in semi-arid regions across the world many small and medium reservoirs have been built in recent decades. Together these reservoirs form a Dense Reservoir Network (DRN) and its presence generates numerous challenges for water management. Most of the reservoirs that constitute the network are unmonitored and unregistered, posing questions on their cumulative effects on strategic reservoirs and water distribution at watershed scale. Their influence on hydrological drought propagation is thus largely unexplored. The objective of this study is then to assess DRN effects on droughts both in time and space. A modeling-analytical framework is proposed to achieve this goal. A mesoscale semi-distributed hydrological model was utilized to simulate both a network of large strategic reservoirs and a DRN in a large-scale tropical semiarid watershed. To investigate the effects in time and space generated by the network’s presence, the differences between multiple network scenarios were analyzed. Results show that the presence of the DRN accelerates the transition from meteorological to hydrological drought phases by 20% on average and slows down the recharge in strategic reservoirs by 25%, leading to a 12% increase of periods in hydrological drought conditions in a highly strategic basin and 26% without strategic reservoirs. This is because the DRN increases the hydrological dysconnectivity at catchment scale, reducing inflow to strategic reservoirs. In space, the DRN shifts upstream the basin’s water storage capacity by 8%, but when both large and small reservoirs are present the stored volume distribution behavior is not straightforward. The findings confirm the need to consider the effect of small reservoirs when developing and implementing drought management policies and reservoir-management approaches at regional scale.

Equity, Justice, and Drought: Lessons for Climate Services from the U.S. Southwest

Abstract Drought is a complex hazard, with many interconnected impacts on environment and society. Droughts are difficult to monitor as they are slow-moving events with impacts that are not always visible. There is an increasing call to study and monitor droughts as a human–environment process and to provide climate services that can inform proactive decision-making on drought. While climate services strive to make droughts visible and therefore manageable for society, many of the equity issues that arise during periods of drought remain largely invisible. In this article we explore inequity in drought impacts in the U.S. Southwest, focusing on agriculture, household water security, and wildfires. Drawing from lessons in the literature on equity, environmental justice, and climate services as well as our experience researching drought impacts in the Southwest, we recommend that climate services can support drought decision-making that addresses equity issues by 1) integrating both physical and social dimensions of drought in climate services, 2) investing in engagement and trust building with diverse communities, and 3) better integrating place-based knowledge to reconcile scaling challenges. With the acceleration of the warming and drying of many parts of the world, there is an ever-increasing need to focus on reducing inequities in drought preparedness and response, which we propose starts with production of drought information that is more reflective of how droughts are experienced across all parts of society.

Effectiveness of Early Warning Systems in Enhancing Food Security in Pokot Central Sub County, West Pokot County, Kenya

Pokot Central Sub County in West Pokot County, Kenya, is the most drought-stricken and food-insecure region as of 2022. The region faces severe threats to agriculture and food accessibility, exacerbated by delayed, reactive drought response strategies and limited resources. This study evaluated the effectiveness of early warning systems in enhancing food security during drought periods. The study used vulnerability and resilience and complex adaptive systems theories to explore these interrelationships. A mixed-method approach with a convergent parallel design was adopted, targeting 22,041 households in Pokot Central. A sample of 396 households was selected through stratified random sampling and 20 key informants were chosen using purposive sampling. Descriptive analysis outlined drought preparedness and food security, while thematic and content analysis were used for qualitative data. Regression and correlation analysis determined relationships between early warning systems and food security. Key findings revealed a positive association between early warning awareness and food security (r=0.155, p=0.000). Recommendations include enhancing drought risk management and improving early warning dissemination. An integrated approach involving government agencies, NGOs and local communities is crucial for effective drought response.

FIND: A Synthetic weather generator to control drought Frequency, Intensity, and Duration

Water systems worldwide are experiencing climate change-induced shifts in drought properties like frequency, intensity, and duration, affecting water security and reliability. To develop and test effective drought preparedness plans, researchers often use synthetic weather generators to create hydrological scenarios that explore drought variability beyond historical records. Existing weather generators typically allow users to adjust streamflow statistics like percentiles or temporal correlation but do not directly control drought properties of frequency, intensity, and duration. To fill this gap, we propose FIND (Frequency, INtensity, and Duration) synthetic weather generator. FIND incorporates a standardized drought index to directly and independently control drought frequency, intensity, and duration in generated streamflow time series while preserving observed hydrological variability. Use cases for FIND include (i) water systems analysis applications that seek to train and test drought strategies under historical and plausible future drought conditions, and (ii) bottom-up vulnerability studies relating system vulnerability outcomes to specific changes in drought properties of frequency, intensity, and duration. We demonstrate FIND’s versatility through three experiments: replicating historically observed drought properties, generating streamflow scenarios for multiple sites preserving correlation between their drought conditions, and generating a set of scenarios with direct and independent changes in drought properties. FIND source code is openly available for applications beyond the scope of this paper.

Indigenous knowledge systems and drought preparedness in rural Umzingwane in Zimbabwe

Indigenous Knowledge Systems (IKS) are an essential survival tool for humans since time immemorial and have always been connected to the management of weather related hazards such as drought. However, the advancement in technology and dominant application of the western version of science in rural Africa continues to masks the significance of IKS resulting in the shrinkage of its use. Against this background, the article examines existing indigenous knowledge and the influence they have on the preparedness of vulnerable groups against drought shocks. Further, the study focuses on the perceptions of rural Umzingwane on the application of indigenous knowledge in predicting and responding to drought shocks in contemporary settings. In this context, the Drought Cycle Management Model anchored theoretical arguments on IKS and drought preparedness. The study gathered data using in-depth interviews purposively targeting spirit mediums, traditional leaders, Agriculture Technical and Extension Workers, District Development Coordinator to gain insights on IKS and its relevance in drought preparedness. The study found that IKS informs early detection of drought and response strategies in rural Umzingwane. In predicting drought hazards, communities rely on observing animal behaviour, fruiting of trees and growth of certain species in certain seasons. The study recommends documentation of IKS and the establishment of platforms to disseminate indigenous knowledge to strengthen capacities of the drought exposed to proactively deal with the hazard.

From creeping crisis to policy change: The adoption of drought preparedness policy in Brazil

Abstract This paper aims to understand the national-level policy change that occurred in Brazilian drought management, whereby the policy shifted from reactive crisis management to a drought preparedness approach. We found that a combination of factors supported the policy change, such as the interplay of multiple drought events in different regions of the country, the length and timing of these events, attention paid to the issue, and the role of policy entrepreneurs and political entrepreneurs in connecting solutions to the problem. The analysis is based on the multiple streams framework (MSF), which includes two windows of opportunity: an agenda window, to account for the juncture at which the drought issue appeared on the political and public agenda; and the decision window, when a drought preparedness instrument was designed and adopted. We contribute to the literature on policy change in the wake of a disaster by showing how a long-duration event sparked policy change and by shedding light on the role of creeping crises as focusing events; and we contribute to the MSF literature by analytically distinguishing the features of the agenda and the decision window and by applying the theory to a Latin American context.

Multivariate Drought Risk Analysis for the Weihe River: Comparison between Parametric and Nonparametric Copula Methods

This study analyzed the multivariate drought risks for the Wei River basin by characterizing the interdependence between the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). Both parametric and nonparametric copulas were adopted to quantify the dependence between the SPI and SPEI. The results indicated that the Gaussian copula demonstrated the best fit in most cases, while the nonparametric copula method showed superiority over the parametric models at only one out of eighteen meteorological stations. The joint return periods (TOR, TAND, and TKendall) were computed through copula modeling, providing valuable insights into the co-occurrence of extreme drought events. For the SPI and SPEI with a 50-year return period, the TOR values range from 25.5 to 37.9 years, the TAND values fluctuate between 73.4 and 1233 years, and the TKendall values range from 60.61 to 574.71 years, indicating a high correlation between the SPI and SPEI in the study area. The spatial analysis revealed varying patterns across the basin with some regions more prone to experiencing simultaneous drought conditions characterized by both the SPI and SPEI. Furthermore, our results indicated that the SPEI exhibited more severity in drought characterization than the SPI due to its consideration of temperature effects. The disparities in the spatial features of the SPI and SPEI underscore the importance of incorporating multiple meteorological factors for a comprehensive drought risk analysis. This research contributes to a better understanding of the drought patterns and their joint risks in the Wei River basin, offering valuable information for drought preparedness and water resource management.

Higher vegetation sensitivity to meteorological drought in autumn than spring across European biomes

Europe has experienced severe drought events in recent decades, posing challenges to understand vegetation responses due to diverse vegetation distribution, varying growth stages, different drought characteristics, and concurrent hydroclimatic factors. To analyze vegetation response to meteorological drought, we employed multiple vegetation indicators across European biomes. Our findings reveal that vegetation sensitivity to drought increases as the canopy develops throughout the year, with sensitivities from −0.01 in spring to 0.28 in autumn and drought-susceptible areas from 18.5 to 57.8% in Europe. Soil water shortage exacerbates vegetation-drought sensitivity temporally, while its spatial impact is limited. Vegetation-drought sensitivity strongly correlates with vapor pressure deficit and partially with atmospheric CO2 concentration. These results highlight the spatiotemporal variations in vegetation-drought sensitivities and the influence of hydroclimatic factors. The findings enhance our understanding of vegetation response to drought and the impact of concurrent hydroclimatic factors, providing valuable sub-seasonal information for water management and drought preparedness.

Early Warning Indicators of Groundwater Drought in Mountainous Regions

AbstractAquifers in mountainous regions are susceptible to drought. However, the diverse hydroclimatology, the small and responsive aquifers, and the varied nature of interactions between groundwater and surface water lead to complex groundwater level responses that are challenging to interpret for understanding groundwater drought. In this study, generalized additive models (GAMs) are used to explore the sensitivity of summer groundwater levels to various climate and hydrological predictor variables (indicators) in each of a snowmelt‐ and rainfall‐dominated hydroclimatic regime in British Columbia, Canada. A sensitivity analysis explores individual seasonal predictor variables, station versus gridded climate data, teleconnection indices, and time series length. GAMs are then generated for different combinations of predictor variables to identify the best combination for each region. In the snowmelt‐dominated regime, maximum spring temperature, maximum snow water equivalent, and the winter Nino 3.4 index is the best combination of variables for predicting summer groundwater levels. In the rainfall‐dominated regime, maximum spring temperature, winter precipitation, and spring streamflow is the best combination. The unique combinations of predictor variables for each region can be used as early warning indicators for groundwater drought preparedness by water managers prior to the beginning of the summer. The Standardized Groundwater Level Index (SGI) is also effective at indicating which wells had pronounced responses to periods of drought in each region. However, the SGI differed among aquifers of similar type, suggesting other factors such as aquifer response mechanism and groundwater pumping may have an important influence on the SGI.

Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level.

Groundwater level prediction is important for effective water management. Accurately predicting groundwater levels allows decision-makers to make informed decisions about water allocation, groundwater abstraction rates, and groundwater recharge strategies. This study presents a novel model, the self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM), for groundwater level prediction. The SATCN-LSTM model combines the advantages of the SATCN and LSTM models to overcome the limitations of the LSTM model. By utilizing skip connections and self-attention mechanisms, the SATCN model addresses the vanishing gradient problem, identifies relevant data, and captures both short- and long-term dependencies in time series data. By demonstrating the improved performance of the SATCN-LSTM model in terms of mean absolute error and root mean square error (RMSE), and by comparing these results with those reported in previous papers, we have highlighted the advancements and contributions of the proposed model. By improving prediction accuracy, the SATCN-LSTM model enables decision-makers to make informed choices regarding water allocation, groundwater abstraction rates, and drought preparedness. The SATCN-LSTM model contributes to the sustainable and efficient use of groundwater resources by providing reliable information for decision-making processes. The SATCN-LSTM model combines the temporal convolutional network (TCN) architecture with LSTM. TCN is known for its ability to capture short-term dependencies in time series data, while LSTM is effective at capturing long-term dependencies. By integrating both architectures, the SATCN-LSTM model can capture the complex temporal relationships at different scales, leading to improved prediction accuracy. Meteorological data were used to predict GWL. The SATCN-LSTM model outperformed the other models. The SATCN-LSTM model had the lowest mean absolute error (MAE) of 0.09, followed by the self-attention (SA) temporal convolutional network (SATCN) model with an MAE of 0.12. The SALSTM model had an MAE of 0.16, while the TCN-LSTM, temporal convolutional network (TCN), and LSTM models had MAEs of 0.17, 0.22, and 0.23, respectively. The SATCN-LSTM model had the lowest root mean square error of 0.14, followed by SATCN with an RMSE of 0.15. The study results indicated that the SATCN-LSTM model was a robust tool for predicting groundwater level.

Indicator-to-impact links to help improve agricultural drought preparedness in Thailand

Abstract. Droughts in Thailand are becoming more severe due to climate change. Developing a reliable drought monitoring and early warning system (DMEWS) is essential to strengthen a country's resilience to droughts. However, for a DMEWS to be valuable, the drought indicators provided to stakeholders must have relevance to tangible impacts on the ground. Here, we analyse drought indicator-to-impact relationships in Thailand, using a combination of correlation analysis and machine learning techniques (random forest). In the correlation analysis, we study the link between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for crop yield and forest growth impacts. Our analysis shows that this link varies depending on land use, season and region. The random forest models built to estimate regional crop productivity allow a more in-depth analysis of the crop- and region-specific importance of different drought indicators. The results highlight seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effects are somewhat attenuated in irrigated regions. Integration of the approaches provides new, detailed knowledge of crop- and region-specific indicator-to-impact links, which can form the basis of targeted mitigation actions in an improved DMEWS in Thailand and could be applied to other parts of Southeast Asia and beyond.

Skill and lead time of vegetation drought impact forecasts based on soil moisture observations

Timely and skilful forecasts of vegetation drought impacts should enable more proactive drought preparedness, management, and mitigation. Numerous previous studies found a temporal correlation between soil moisture and vegetation condition. However, a correlation across the full range of soil moisture and vegetation condition does not automatically translate into skill in forecasting infrequent events such as agricultural droughts. Here, we develop a threshold- or impact-based forecasting framework to assess early warning capability (EWC). We analysed the skill and lead time achieved using soil moisture observations at multiple depths as predictors of subsequent vegetation drought impacts inferred from MODIS satellite observations at 93 sites across the United States. Forecast thresholds were expressed in terms of seasonally-adjusted standardised anomalies (z-scores) to distinguish climate-related impacts from any seasonal vegetation cycle. Different combinations of soil moisture integration depth, satellite vegetation indices and threshold levels were tested. Near-Infrared Reflectance of vegetation (NIRv) yielded a marginally better EWC than other indicators of vegetation drought impact. The optimal soil moisture integration depth varied between land cover types, from 0 to 10 cm for cropping systems to 0–100 cm for grassland and savanna. The greatest skill improvements were achieved using similar z-score threshold values for the soil moisture trigger and vegetation impact, producing typical lead times of two to four weeks. Further research is recommended to combine the framework developed here with spatially continuous soil moisture analyses or forecasts available from remote sensing and land surface models.

Drought trigger thresholds for different levels of vegetation loss in China and their dynamics

Frequent meteorological droughts can negatively impact terrestrial ecosystems by controlling the opening and closing of vegetation stomatal and altering vegetation structure to limit the ability of vegetation to sequester carbon. Due to the lagging and accumulation effects of drought on vegetation growth, drought trigger thresholds for different levels of vegetation loss are still unclear, which is very important for accurately assessing the future impacts of drought on terrestrial ecosystem. Therefore, this study proposed a framework to investigate drought trigger thresholds under various vegetation losses based on copula theory and conditional probabilities, and assessed the dynamics of drought trigger thresholds and possible causes, based on the random forest model. In addition, we used multiple GPP and soil water datasets for the analysis to ensure the robustness of relevant findings. The results show that: (1) there is a generally positive correlation between GPP and SPEI in China, and the response time of vegetation to drought is mostly on a short time scale (less than or equal to 4 months); (2) drought trigger thresholds are also higher in eastern China, with lower vegetation resistance and significantly higher risk of vegetation productivity loss than in other regions; (3) the trigger thresholds in northeastern China show a decreasing trend, with vegetation resistance gradually increasing. CO2 fertilization enhances vegetation drought resistance, but the magnitude of resistance increase is reduced due to the adverse effects of water stress and VPD on vegetation. The findings of this study may advance our comprehension of terrestrial ecosystem vulnerability and response to drought, and further provide scientific guidance for watershed water allocation, drought preparedness and risk management.

Assessment of meteorological drought in Balod district, India through GIScience and Remote sensing

The present study was conducted in Balod district to assess meteorological drought events for prevention and future water resource management planning. The study area has a deficit in rainfall with a higher rainfall variation. From 1990 to 2020, the rainfall anomaly index (RAI) was used to calculate meteorological drought and monitor dry and wet periods in the area. Rainfall datasets from IMD daily gridded data of 0.25º × 0.25º spatial resolution were combined with the Geographic information system (GIS) environment. The results from the index revealed that the highest negative values were observed in the years 2000, 2002, 2008 and 2009 which have faced severe drought events. The findings from the study can assist in making a drought preparedness plan to mitigate the risk with effective water resources management.

Integrated proactive drought management in hydrosystems and cities: building a nine-step participatory planning methodology

Drought is a natural hazard with complex socioeconomic impacts and influences on human experiences. Preparedness is the only way a society can mitigate drought impacts but integrating participatory decision-making with hydrological modeling into a more comprehensive planning process is still a challenge. Here, we present a step-by-step methodology to guide the implementation of a participatory drought preparedness plan (DPP), specially designed for hydrosystems and cities scales. We highlight strategies to engage local stakeholders in constructing such plans and build trust in the process. We propose two types of drought preparedness plans: (1) Socio-technical–built only from the tacit knowledge of the system operators, which needs only two days to be ready; and (2) Socio-technical with modeling-intensive simulation—a more robust methodology that adds hydrologic and hydraulic modeling to the existing tacit knowledge. The participatory DPP methodology was developed and applied to hydrosystems and cities in a drought-prone area of Brazil. Our findings suggest that modeling was important, but not essential to assessing vulnerability scenarios and strategies. However, the simplified version can achieve satisfactory results even when data and resources are limited. We present the methodology as a nine-step participatory planning methodology developing a meaningful and convincing narrative that speaks to theory and practice.