More severe and prolonged droughts observed in recent decades require improved methods to predict impacts on agriculture. Crop-growth models estimate yield and plant development variables and are widely used to assess drought impacts; however, they are not explicit forecasting tools, as their accuracy is constrained by physical assumptions, data availability, and multiple sources of uncertainty. To address these limitations, machine learning (ML) models have been increasingly applied for crop yield prediction, typically using drought indices as input, while spatial drought characteristics remain underexplored. This research develops an ML framework that incorporates the spatial extent of drought to predict seasonal crop yield. The framework combines artificial neural network (ANN) and polynomial regression (PR) models, with PR providing baseline estimates and ANN delivering refined predictions. The approach was tested using 50 years of historical crop yield data and drought areas derived from the Standardised Precipitation Evapotranspiration Index at multiple aggregation periods (1–12 months). Results show ANN models consistently outperform PR models, achieving lower prediction errors, with root mean square error values as low as 48.1 kg/ha in best-performing cases. The results demonstrate that spatiotemporal drought area dynamics and their temporal aggregation provide an effective preprocessing strategy for ML-based drought impact prediction.

Optimized water resource allocation is critical for promoting sustainable regional development. However, the intensification of climate change impacts has increased the variability of water availability, thereby compounding uncertainty and complexity in water allocation decision-making. Meanwhile, mismatches in the spatial distribution of water supply and demand have also become an essential constraint to effectiveness and fairness of water allocation. Prior studies show weak correspondence between modeling frameworks to real world conditions and insufficiently describe regional balance, as well as the dynamic interaction between water inflow and demand under varying hydrological regimes. To address these challenges, this study develops a multi-objective water resource allocation model guided by spatial equilibrium principles, ensuring not only fair inter-regional allocation but also balanced co-development among water, socio-economic, and ecological subsystems. Regional balance is quantified with the Gini coefficient. Subsystem co-development is enforced via the coupling coordination degree. To address uncertainty, a nested multi-scenario robust optimization framework is proposed. It applies copula function to generate realistic encounter scenarios and applies a multi-objective probabilistic robustness evaluation to test solution stability and adaptability across scenarios. Applied to the upper and middle reaches of the Huaihe River Basin (HRB), the approach reduces the water-deficit rate by 45.42 % under extreme dry scenarios, lifts the coordination index to > 0.75, and increases reliability by 45.3 % compared with a traditional robust optimization baseline, demonstrating effectively optimize complex water resource systems while keeping both fairness and stability. This research offers a theoretical and practical framework for optimizing water resource allocation in complex and uncertain environments, contributing to the advancement of resilient and equitable water governance.

Model predictive control (MPC) is an optimal control strategy suited for flood control of water resources infrastructure. Despite many studies on reservoir flood control and their theoretical contribution, optimisation methodologies have not been widely applied in real-time operation due to disparities between research assumptions and practical requirements. To address this gap, we include practical objectives, such as minimising the magnitude and frequency of changes in the existing outflow schedule. Incorporating these objectives transforms the problem into a multi-objective nonlinear optimisation problem that is difficult to solve in real time. Additionally, it is reasonable to assume that the weights and some parameters, considered the operators’ preferences, vary depending on the system state. To overcome these limitations, we propose a framework that converts the original intractable problem into parameterised linear MPC problems with dynamic optimisation of weights and parameters. This is done by introducing a model-based learning concept. We refer to this framework as Parameterised Dynamic MPC (PD-MPC). The effectiveness of this framework is demonstrated through a numerical experiment for the Daecheong multipurpose reservoir in South Korea. We find that PD-MPC outperforms standard MPC-based designs without a dynamic optimisation process for the objective weights and model parameter. Moreover, we demonstrate that the weights and parameters vary with changing hydrological conditions.

Study region: Madeira River basin, southwestern Amazonia Study focus: This study investigates spatial and temporal changes in precipitation, evaporation, and streamflow, and their relationship with deforestation in the Madeira River basin, the largest Amazonian sub-basin. We applied Mann-Kendall trend analysis, change-point detection, and correlation analysis across multiple spatial scales, using satellite, reanalysis, and observed data from 1981 to 2015. These methods enabled us to detect long-term trends, identify shifts, and quantify the relationships between forest loss and hydrological changes New hydrological insights for the region: The basin experienced an average deforestation rate of 2810 km² per year from 2001 to 2020, predominantly in the Brazilian portion. Between 1981 and 2016, we observed statistically significant negative trends in precipitation, evaporation, and streamflow, especially in the most deforested areas during the wet season. Correlation analysis (2001–2015) showed a statistically significant and positive relationship between forest area and evaporation in wet months (r = 0.73, p < 0.1) and a negative correlation between forest area and streamflow during the same season (r = –0.6, p < 0.1). These findings highlight the critical role of forests in modulating hydrological processes, supporting the hypothesis that deforestation may reduce evaporation, alter moisture recycling, and slow the water cycle. While our results are robust, we acknowledge that factors such as climate variability and land management practices may also influence hydrological changes and should be considered in future research.

Effective reservoir flood control demands real-time decision-making that balances multiple objectives. However, traditional optimization approaches are often too computationally intensive and become intractable when considering dynamically changing preferences of operators, modelled as weights of different objectives. This study aims to develop tractable real-time flood control strategies that maintain performance while reducing computational complexity. We propose two data-driven approaches based on Model Predictive Control (MPC): (1) an explicit MPC using deep neural networks to directly determine optimal outflow schedules, and (2) a switched MPC that produces optimal weights of objectives based on hydrological conditions. Both methods leverage offline learning from an online Parameterized Dynamic MPC framework incorporating state-dependent weights. We tested these approaches on South Korea’s Daecheong multipurpose reservoir using historical flood events with various patterns. The explicit MPC demonstrated reliable performance under conditions similar to its training data. However, it showed frequent changes in outflow schedules and constraint violations for scenarios outside training data. In contrast, the switched MPC maintained robustness across all test scenarios due to a linear optimization process in a receding horizon manner, though with slightly reduced performance compared to the explicit MPC under scenarios inside the range of training data. Most significantly, both approaches reduced computation time from approximately 10 minutes to less than one second, making real-time implementation feasible. This dramatic improvement enables prompt decision-making during rapidly evolving flood events while maintaining near-optimal control performance.

In recent years, there has been a surge of interest in machine learning (ML) and artificial intelligence (AI) due to the effectiveness of deep learning algorithms and the increasing availability of large data sets. This chapter provides a brief overview of the applications of AI and ML techniques in hydroinformatics, a field that deals with advanced information technology, data analytics, and modeling for aquatic environment management. Data-driven models are becoming more common in water management as they can reveal hidden patterns in data and offer improved accuracy in certain situations. This chapter highlights the importance of spatiotemporal data analysis, pattern recognition, and optimization approaches in water resources management under uncertainty. It does not offer a comprehensive review of all methods but rather focuses on selected ML techniques widely used in water-related problems. Additionally, the chapter discusses the challenges associated with using ML models, such as black-box criticisms, and the potential of hybrid models that combine the strengths of ML and physically based process models for more robust solutions in hydroinformatics.

In its three-dimensional (3-D) characterization, drought is an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time (i.e., drought dynamics). Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small artifact droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.

Contemporary distributed hydrological models are detailed and mathematically rigorous, but their calibration and testing can be still an issue. Often it is based on the quadratic measure of the calculated and observed hydrographs proximity at one outlet gauge station, typically on the Nash-Sutcliffe model efficiency coefficient (NSE). This approach seems insufficient to calibrate a model with hundreds of spatial elements. This paper presents using a multi-dimensional estimator of modeling quality, being a natural generalization of the traditional NSE but which would aggregate data from several hydrological stations using Principal Component Analysis (PCA). The method was tested on the ECOMAG model developed for a sub-basin (24,400 km2, with 15 gauges) of the Ussuri River in Russia. The results show that the presented version of the multi-dimensional NSE with PCA in calibration of spatially-distributed hydrological models has a number of advantages compared to other methods: the reduced dimensionality without loss of important information, straightforward data analysis and the automated calibration procedure; objective separation of the deterministic signal from the noise, calibration using the “informational kernel” of data, leading to more accurate parameters’ estimates. Additionally, the introduced notion of the “compact” dataset allow to interpret physical-geographical homogeneity of the basins in mathematic manner, which can be valuable for hydrological zoning of the basins, hydrological fields analysis, and structuring the models of large basins. There is no doubt that further development and testing of the proposed methodology is advisable in solving spatial hydrological problems based on distributed models, such as managing a cascade of reservoirs, creating hydrological reanalyses, etc.

Water resources managers need to make decisions in a constantly changing environment because the data relating to water resources are uncertain and imprecise. The Robust Optimization and Probabilistic Analysis of Robustness (ROPAR) algorithm is a well-suited tool for dealing with uncertainty. Still, the failure to consider multiple uncertainties and multi-objective robustness hinders the application of the ROPAR algorithm to practical problems. This paper proposes a robust optimization and robustness probabilistic analysis method that considers numerous uncertainties and multi-objective robustness for robust water resources allocation under uncertainty. The copula function is introduced for analyzing the probabilities of different scenarios. The robustness with respect to the two objective functions is analyzed separately, and the Pareto frontier of robustness is generated. The relationship between the robustness with respect to the two objective functions is used to evaluate water resources management strategies. Use of the method is illustrated in a case study of water resources allocation in the Huaihe River basin. The results demonstrate that the method opens a possibility for water managers to make more informed uncertainty-aware decisions.

Kazakhstan is recently experiencing an increase in drought trends. However, low-capacity probabilistic drought forecasts and poor dissemination have led to a drought crisis in 2021 that resulted in the loss of thousands of livestock. To improve drought forecasting accuracy, this study applies Machine Learning and Deep Learning (ML and DL) algorithms to capture the sequences of drought events using a non-contiguous drought analysis (NCDA). Precipitation, 2-m temperature, runoff, solar radiation, relative humidity, and evaporation were collected from the ERA5 database as input variables. Combinations of inputs were used to build ML models, including seven classifiers (Logistic, K-NN, Kernel SVM, Decision Tree, Random Forest, XGBoost, and GRU). The output events were defined by standardized precipitation index (SPI) and SPEI indicators as binary classes. Weekly time series from 1991 to 2021 for each cell were used to forecast a lead time from 1 week to 6 months. GRU provided 97–99% accuracy in more volatile regions while Random Forest and XGBoost showed 94–99% accuracy at a lead time of 6 months. The accuracy evaluation was based on the confusion matrix and F1 score to analyze the stage change capture. This study demonstrates the effectiveness of using ML and DL algorithms for drought forecasting, with potential applications for other regions.

Long-term water resource management involving multipurpose coordination requires robust decision-making in water infrastructure cases to cope with various types of uncertainties. Traditional robust optimization methods generally do not explicitly propagate input or parametric uncertainties into estimates of the robustness of solutions, which limits their ability to address uncertainty comprehensively across solution spaces. In this study, we introduce an explicit robust decision-making framework that blends multiobjective search, probabilistic analysis of robustness, and diagnostic verification tools to identify robust optimal solutions to external uncertainty. The proposed framework is illustrated on four diverse robustness formulations, which capture a wide variety of stakeholder attitudes from highly risk-averse to risk-neutral, for the primary operating objectives (hydropower production, water diversion, and hydrological alteration degree) in China's Hanjiang cascade reservoir system. By analyzing the Pareto front propagated from inflow uncertainty, it is found that optimal robust policies with a significantly higher degree of hydrological alteration are preferred in most formulations to achieve relatively lower joint uncertainty of hydropower and water diversion. These policies also yield sufficiently stable model performance in the case of an out-of-sample streamflow set during diagnostic verification. Furthermore, a comparative analysis of four different formulations suggests that a composite normalized robustness indicator (NRI) developed in this study to integrate various robustness metrics can achieve an effective balance for all considered objectives. These findings highlight the benefits of explicit robust optimization for managing hydrological uncertainties in multipurpose cascade reservoirs.

While drought impacts are widespread across the globe, climate change projections indicate more frequent and severe droughts. This underscores the pressing need to increase resistance and resilience to drought. The strategic application of Preventive Drought Management Measures (PDMMs) is a suitable avenue to reduce the likelihood of drought and ameliorate associated damages. In this study, we use an optimisation approach with a multicriteria decision-making method to allocate PDMMs for reducing the severity of agricultural and hydrological droughts. The results indicate that implementing PDMMs can reduce the severity of agricultural and hydrological droughts, and the obtained management scenarios (solutions) highlight the utility of multi-objective optimisation for PDMMs planning. However, examined management scenarios also illustrate the trade-off between managing agricultural and hydrological droughts. PDMMs can alleviate the severity of agricultural droughts while producing opposite effects for hydrological droughts (or vice versa). Furthermore, the impact of PDMMs displays temporal and spatial variabilities. For instance, PDMMs implementation within a specific subbasin may mitigate the severity of one type of drought in a given month yet exacerbate drought conditions in preceding or subsequent months. In the case of hydrological droughts, the PDMMs may intensify streamflow deficits in the intervened subbasins while alleviating the hydrological drought severity downstream (or vice versa). These complexities emphasise a customised implementation of PDMMs, considering the basin characteristics (e.g., rainfall distribution over the year, soil properties, land use, and topography) and the quantification of PDMMs' effect on the severity of each type of drought.

This research paper presents a systematic literature review on the use of remotely sensed and/or global datasets in distributed hydrological modelling. The study aims to investigate the most commonly used datasets in hydrological models and their performance across different geographical scales of catchments, including the micro-scale (<10 km²), meso-scale (10 km²–1000 km²), and macro-scale (>1000 km²). The analysis included a search for the relation between the use of these datasets to different regions and the geographical scale at which they are most widely used. Additionally, co-authorship analysis was performed on the articles to identify the collaboration patterns among researchers. The study further categorized the analysis based on the type of datasets, including rainfall, digital elevation model, land use, soil distribution, leaf area index, snow-covered area, evapotranspiration, soil moisture and temperature. The research concluded by identifying knowledge gaps in the use of each data type at different scales and highlighted the varying performance of datasets across different locations. The findings underscore the importance of selecting the right datasets, which has a significant impact on the accuracy of hydrological models. This study provides valuable insights into the use of remote sensed and/or global datasets in hydrological modelling, and the identified knowledge gaps can inform future research directions.

The typical drivers of drought events are lower than normal precipitation and/or higher than normal evaporation. The region's characteristics may enhance or alleviate the severity of these events. Evaluating the combined effect of the multiple factors influencing droughts requires innovative approaches. This study applies hydrological modelling and a machine learning tool to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts. The Soil Water Assessment Tool (SWAT) is used for hydrological modelling. Model outputs, soil moisture and streamflow, are used to calculate two drought indices, namely the Soil Moisture Deficit Index and the Standardized Streamflow Index. Then, drought indices are utilised to identify the agricultural and hydrological drought events during the analysis period, and the index categories are employed to describe their severity. Finally, the multivariate regression tree technique is applied to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts.

Our research indicates that multiple parameters influence the severity of agricultural and hydrological droughts in the Cesar River basin. The upper part of the river valley is very susceptible to agricultural and hydrological drought. Precipitation shortfalls and high potential evapotranspiration drive severe agricultural drought, whereas limited precipitation influences severe hydrological drought. In the middle part of the river, inadequate rainfall partitioning and an unbalanced water cycle that favours water loss through evapotranspiration and limits percolation cause severe agricultural and hydrological drought conditions. Finally, droughts are moderate in the basin's southern part (Zapatosa marsh and the Serranía del Perijá foothills). Moderate sensitivity to agricultural and hydrological droughts is related to the capacity of the subbasins to retain water, which lowers evapotranspiration losses and promotes percolation. Results show that the presented methodology, combining hydrological modelling and a machine learning tool, provides valuable information about the interplay between the hydroclimatic factors that influence drought severity in the Cesar River basin.

Extreme peak runoff forecasting is still a challenge in hydrology. In fact, the use of traditional physically-based models is limited by the lack of sufficient data and the complexity of the inner hydrological processes. Here, we employ a Machine Learning technique, the Random Forest (RF) together with a combination of Feature Engineering (FE) strategies for adding physical knowledge to RF models and improving their forecasting performances. The FE strategies include precipitation-event classification according to hydrometeorological criteria and separation of flows into baseflow and directflow. We used ∼ 3.5 years of hourly precipitation information retrieved from two near-real-time satellite precipitation databases (PERSIANN-CCS and IMERG-ER), and runoff data at the outlet of a 3391-km² basin located in the tropical Andes of Ecuador. The developed models obtained Nash-Sutcliffe efficiencies varying from 0.86 to 0.59 for lead times between 1 and 6 h. The best performances were obtained for peak runoffs triggered by short-extension precipitation events (<50 km²) where infiltration- or saturation-excess runoff responses are well learned by the RF models. Conversely, the forecasting difficulty is associated with extensive precipitation events. For such conditions, a deeper characterization of the biophysical characteristics of the basin is encouraged for capturing the dynamic of directflow across multiple runoff responses. All in all, the potential to employ near-real-time satellite precipitation and the use of FE strategies for improving RF forecasting provides hydrologists with new tools for real-time runoff forecasting in remote or complex regions.

Preventive Drought Management Measures (PDMMs) aim to reduce the chance of droughts and minimize drought-associated damages. Selecting PDMMs is not a trivial task, and it can be asserted that actual contributions to drought alleviation still need to be adequately researched. This study evaluates the effects of three potential PDMMs, namely, rainwater harvesting ponds, forest conservation, and check dams, on agricultural and hydrological drought severity. The Soil Water Assessment Tool is used for hydrological modeling and representing PDMMs. The threshold level method is applied to analyze droughts and evaluate the impact of PDMMs on drought severity. Findings show that rainwater harvesting ponds applied on agricultural land reduce the severity of agricultural droughts and hydrological droughts, particularly during the first months of the drought events observed in the rainy season. Results also reveal that forest conservation contributes to reducing the severity of hydrological droughts by up to 90%. Finally, check dams and ponds in upstream subbasins considerably reduce agricultural and hydrological drought severity in the areas where the structures are applied; however, they exacerbate drought severity downstream. The analysis was developed in the Torola River Basin (El Salvador) for the period spanning 2004 to 2018.

Flow control flushing water from reservoirs has been imposed in South Korea for mitigating harmful cyanobacterial blooms (CyanoHABs) in rivers. This measure, however, can cause water shortage in reservoirs, as the measure adopting this flow control may require an additional amount of water which exceeds the water demand allocated to the reservoirs. In terms of sustainability, a trade-off between improving water quality and alleviating water shortage needs to be considered. This study aimed at establishing a practical framework for a decision support system for optimal joint operation of the upstream reservoirs (Andong and Imha) to reduce the frequency of CyanoHABs in the Nakdong River, South Korea. Methodologically, three models were introduced: (1) a machine learning model (accuracy 88%) based on the k-NN (k-Nearest Neighbor) algorithm to predict the occurrence of CyanoHABs at a selected downstream location (the Chilgok Weir located approximately 140 km downstream from the Andong Dam), (2) a multiobjective optimization model employing NSGA-II (Nondominated Sorting Genetic Algorithm II) to determine both the quantity and quality of water released from the reservoirs, and (3) a river water quality model (R2 0.79) using HEC-RAS to simulate the water quality parameter at Chilgok Weir according to given upstream boundary conditions. The applicability of the framework was demonstrated by simulation results using observational data from 2015 to 2019. The simulation results based on the framework confirmed that the frequency of CyanoHABs would be decreased compared with the number of days when CyanoHABs were observed at Chilgok Weir. This framework, with a combination of several models, is a novelty in terms of efficiency, and it can be a part of a solution to the problem of CyanoHABs without using an additional amount of water from a reservoir.

Water utilities are urged to decrease their real water losses, not only to reduce costs but also to assure long-term sustainability. Hardware- and software-based techniques have been broadly used to locate leaks; within the latter, previous works that have used data-driven models mostly focused on single leaks. This paper presents a methodology to locate multiple leaks in water distribution networks employing pressure residuals. It consists of two phases: one is to produce training data for the data-driven model and cluster the nodes based on their leak-flow-rate-independent signatures using an adapted hierarchical agglomerative algorithm; the second is to locate the leaks using a top-down approach. To identify the leaking clusters and nodes, we employed a custom-built k-nearest neighbor (k-NN) algorithm that compares the test instances with the generated training data. This instance-to-instance comparison requires substantial computational resources for classification, which was overcome by the use of high-performance computing. The methodology was applied to a real network located in a European town, comprising 144 nodes and a total length of pipes of 24 km. Although its multiple inlets add redundancy to the network increasing the challenge of leak location, the method proved to obtain acceptable results to guide the field pinpointing activities. Nearly 70% of the areas determined by the clusters were identified with an accuracy of over 90% for leak flows above 3.0 L/s, and the leaking nodes were accurately detected over 50% of the time for leak flows above 4.0 L/s.

Successful modelling of the groundwater level variations in hydrogeological systems in complex formations considerably depends on spatial and temporal data availability and knowledge of the boundary conditions. Geostatistics plays an important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is inhomogeneous. This study combines geostatistics with machine learning approaches to solve problems in complex aquifer systems. Herein, the emphasis is given to cases where the available dataset is large and randomly distributed in the different aquifer types of the hydrogeological system. Self-Organizing Maps can be applied to identify locally similar input data, to substitute the usually uncertain correlation length of the variogram model that estimates the correlated neighborhood, and then by means of Transgaussian Kriging to estimate the bias corrected spatial distribution of groundwater level. The proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological area. The obtained results have shown a significant improvement compared to the ones obtained by classical geostatistical approaches.

Conceptual hydrological models imply a simplification of the complexity of the hydrological system; however, they lack the flexibility in reproducing a wide range of the catchment responses. Usually, a trade-off is done to sacrifice the accuracy of a specific aspect of the system behavior in favor of the accuracy of other aspects. This study evaluates the benefit of using a modular approach, “The fuzzy committee model” of building specialized models to reproduce specific responses of the catchment. We also assess the applicability of using predicted runoff from specialized models to form a fuzzy committee model. In this paper, weighting schemes with power parameter values are investigated. A thorough study is conducted on the relation between the fuzzy committee variables (the membership functions and the weighting schemes), and their effect on the model performance. Furthermore, the Fuzzy committee concept is applied on a conceptual distributed model with two cases, the first with lumped catchment parameters and the latter with distributed parameters. A comparison between different combinations of the fuzzy committee variables showed the superiority of all Fuzzy Committee models over single models. Fuzzy committee of distributed models performed well, especially in capturing the highest peak in the calibration data set; however, it needs further study of the effect of model parameterization on the model performance and uncertainty.