In response to pressing global challenges like climate change, rapid population growth, and an urgent need for sustainable infrastructure, cities face an immediate and crucial necessity to transition swiftly toward an integrated approach to managing urban water resources. This shift is not merely an option but an imperative, driven by the rapidly evolving urban landscape. In addressing this imperative, a crucial decision support tool that has emerged as an asset in the domain of urban water planning and management is the Urban Water Use (UWU) tool. This tool offers an integrated approach for strategic planning, promoting urban water conservation and environmental health through the investigation of interventions in urban infrastructure under different scenarios. In this study, the latest version of this UWU tool was deployed in a case study conducted in Almirante Tamandaré, Brazil. The objective was to evaluate how an integrated decision-making approach concerning urban water systems influences the efficiency and effectiveness of interventions, ultimately contributing to achieve widespread adoption, accessibility, and relevance of urban water services. The refined UWU tool evaluates a spectrum of measures across diverse scenarios, incorporating various drivers, focusing on the stakeholders' visions for the locality. These visions are composed of sustainability indicators, specifying different sets of target values and importance weights for each indicator. The approach followed in this study demonstrates how the effectiveness indexes can vary based on stakeholders' perception. Measures under Water Sensitive Urban Design and Water Demand Management strategies were deployed to simulate the response of urban water systems under three distinct scenarios, embracing the complexities of social dynamics and of climate change. The findings of the study emphasize that realizing a desired vision through selected measures relies significantly on the adoption of an integrated approach within the decision-making process. The stakeholders' perception of how indicators should be weighted while defining the vision was found to significantly impact the effectiveness range of these measures.

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.

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.

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.

Microbial pathogens in urban floodwaters pose risks to human health, potentially causing diseases such as diarrhea. However, the disease burden related to urban traffic exposure from citizens passing through floodwaters is not easily quantified and therefore not included in many studies. Notably, this problem has received little attention in low-to-middle-income countries, with frequent flood events and the heavy diarrheal disease burden. This article calculates the infection risks and disease burden, considering traffic associated with exposure to floodwater contaminated with rotavirus for the first time in Ninh Kieu District, Can Tho city. Can Tho city in the Vietnamese Mekong Delta is well known to have many flood events every year, with many diarrheal cases during the flood season. The methodology comprises two steps. First, we applied quantitative microbial risk assessment that proposes the inclusion of exposure to traffic due to rotavirus in floodwater. Second, the disease burden was expressed in disability-adjusted life years (DALYs). The exposed groups are child pedestrians, adult pedestrians, motorcyclists, and cyclists. We used video footage to monitor the traffic. The results show that total DALYs per flood event were 1.35 × 10⁴ for 63,390 exposed people (i.e., 2129 DALYs per 10,000 cases). Motorcyclists are the strongest contributors to the DALYs (95%), followed by cyclists (2.8%), adult pedestrians (2%), and child pedestrians (0.2%). The population in Ninh Kieu District may suffer from waterborne diseases through traffic activities during flooding times. Our approach can be applied in other areas worldwide and helps identify main risk groups and focus areas for interventions.

Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index (SEDI) for drought characterization and drought hazard index (DHI) calculation. Although SEDI’s applicability is theoretically proven, it has been rarely applied. Drought risk is generally derived from the interactions between drought hazard (DHI) and vulnerability (DVI) indices but neglects resilience’s inherent impact. Accordingly, we propose incorporating DHI, DVI, and drought resilience index (DREI) to calculate drought risk index (DRI). Since system factors are not equally vulnerable, i.e., they are heterogeneous, our methodology applies the Analytic Hierarchy Process (AHP) to find the weights of the selected factors for the DVI computation. Finally, we propose a geometric mean method for DRI calculation. Results show a rise in DHI during 2006–2010 that affected DRI. We depict the applicability of SEDI via its relationship with El Nino-La Nina and El Salvador’s cereal production. This research provides a systematic drought risk assessment approach that is useful for decision-makers to allocate resources more smartly or intervene in Drought Risk Reduction (DRR). This research is also useful for those interested in socioeconomic drought.

Many grid‐based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model‐building to be taken into account: spatial resolution, the routing equation limitations, and calibration of spatial parameters, and their influence on modeling results, all are decisions that are often made without adequate analysis. In this research, an experimental analysis of grid discretization level, an analysis of processes integration, and the routing concepts are ana-lyzed. The HBV‐96 model is set up for each cell, and later on, cells are integrated into an interlinked modeling system (Hapi). The Jiboa River Basin in El Salvador is used as a case study. The first concept tested is the model structure temporal responses, which are highly linked to the runoff dynam-ics. By changing the runoff generation model description, we explore the responses to events. Two routing models are considered: Muskingum, which routes the runoff from each cell following the river network, and Maxbas, which routes the runoff directly to the outlet. The second concept is the spatial representation, where the model is built and tested for different spatial resolutions (500 m, 1 km, 2 km, and 4 km). The results show that the spatial sensitivity of the resolution is highly linked to the routing method, and it was found that routing sensitivity influenced the model performance more than the spatial discretization, and allowing for coarser discretization makes the model sim-pler and computationally faster. Slight performance improvement is gained by using different pa-rameters’ values for each cell. It was found that the 2 km cell size corresponds to the least model error values. The proposed hydrological modeling codes have been published as open‐source.

A spatiotemporal object-based rainfall analysis method is used to evaluate the hydrological response of two systematic satellite error sources for storm estimation in the Capivari catchment, Brazil. This method is called Spatiotemporal Contiguous Object-based Rainfall Analysis (ST-CORA) specifically evaluates the error structure of satellite-based rainfall products using a 3D pattern clustering algorithm. Errors due to location and magnitude in the Near Real-time (NRT) CMORPH product are subtracted by adjusting the shift and the intensity distribution with respect to a storm object obtained from gauge-adjusted weather radar. Synthetic scenarios of each error source are used as forcing for hourly calibrated distributed hydrological ‘wflow-sbm’ model to evaluate the main sources of systematic errors in the hydrological response. Two types of storm events in the study area are evaluated: short-lived and a long-lived storm. The results indicate that the spatiotemporal characteristics obtained by ST-CORA clearly reflect the main source of errors of the CMORPH storm detection. It is found that location is the main source of error for the short-lived storm event, while volume is the main source in the long-lived storm event. The subtraction of both errors leads to an important reduction of the simulated streamflow in the catchment. The method applied can be useful in bias correction schemes for satellite estimations especially for extreme precipitation events.

Drought is a complex natural phenomenon. The description of the way in which drought changes (moves) in space may help to acquire knowledge on its drivers and processes to improve its monitoring and prediction. This research presents the application of an approach to characterise the dynamics of drought. Tracks, severity, duration, as well as localisation (onset and end position), and rotation of droughts were calculated. Results of calculated droughts were compared with documented information. Data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor was used to identify droughts in India as an example for the period 1901–2013. Results show regions where droughts with considerable coverage tend to occur. Paths, i.e. consecutive spatial tracks, of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Results of this research are being used to build a model to predict the spatial drought tracks, incl. India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).

This study evaluates the performance of four Near Real-Time (NRT) satellite rainfall products in estimating the spatiotemporal characteristics of different extreme rainfall events in a subtropical catchment in south-eastern Brazil. The Climate Prediction Centre Morphing algorithm (CMORPH), Tropical Rainfall Measuring Mission, Multisatellite Precipitation Analysis in real time (TMPA-RT), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Global Cloud Classification System (PERSIANN-GCCS), and the Hydro-Estimator are evaluated for monsoon seasons, based on their capability to represent four types of rainfall events distinguished for: (1) local and short duration, (2) long-lasting event, (3) short and spatial extent, and (4) spatial extent and long lasting. Since the events are defined relative to a percentile, the relative performance variation at different threshold levels (75th, 90th, and 95th) is also evaluated. The data from the 13 Automatic Weather Stations (AWSs) for the period from 2007 to 2014 are used as the reference. The results show that the product performance highly depends on the spatiotemporal characteristics of rainfall events. All four products tend to overestimate intense rainfall in the study area, especially in high altitude zones. CMORPH had the best overall performance to estimate different types of extreme spatiotemporal events. The results allow for developing a better understanding of the accuracy of the NRT products for the estimation of different types of rainfall events.

A sufficient data length can play an important role in a proper estimation drought index, leading to a better appraisal for drought risk reduction. The South Central Region of Vietnam is one of drought prone areas but it has poor data conditions. A collection of meteorological data in the study area during a period of 38 years 1977-2014 found out a fact that there existed missing values in 10 out of 30 collected rainfall stations and 4 out of 13 collected temperature stations. Therefore, this study aims at evaluating the influence of three different infilling techniques (Inverse Distance Weighting, Multi-Linear Regression, and Artificial Neural Network) on 1-month Standardized Precipitation Evapotranspiration Index (SPEI1) drought indicator for the given region. The performance on rainfall and temperature infilling indicated that ANN technique achieved lower errors between observations and predictions than others. Infilled rainfall and temperature generated from different infilling techniques were then combined with the available data to calculate SPEI1. The results showed that infilling techniques seem to create the SPEI1 time series which have higher number of drought events, in comparison with that time series containing the observation values only. Otherwise, drought index seems to be insensitive to different infilling techniques.