Flooding is commonly approached as a hydrological disaster, followed by emergency relief, disease surveillance, and environmental clean-up. This approach appears incomplete because floodwater rapidly connects contaminated land, failed sanitation systems, agricultural areas, industrial facilities, water-supply infrastructure, ecosystems, and households. Flood-driven water contamination should therefore be understood as a preventable public-health infrastructure failure rather than an unavoidable aftermath of inundation. This Perspective argues that the health consequences of flooding are shaped not only by the intensity of rainfall or flood depth, but also by the pollutant burden already present in a catchment, the condition of water and sanitation systems, the security of drinking-water sources, and the ability of institutions to translate environmental warnings into timely protective action. The paper proposes flood-ready water and health systems as an integrated public-health model that links upstream pollution prevention, flood-triggered water-quality surveillance, water-safety planning, equitable risk communication, healthcare preparedness, and environmental-health governance. The model treats aquatic ecosystem integrity and safe drinking-water access as interconnected determinants of population health. It also recognizes that vulnerable communities are disproportionately exposed when flooding damages wells, sanitation facilities, water-treatment infrastructure, health facilities, and housing. Strengthening flood resilience therefore requires more than post-flood disinfection and relief supplies. It requires preventive investments that reduce contaminant mobilization, preserve essential water services, identify unsafe sources rapidly, and protect communities before exposure becomes a disease emergency.
Contamination of urban groundwater from faecal effluent in sewered and unsewered areas is a widespread constraint to its use for water-supply provision. Here, we present the first multi-annual (2018–2024) assessment of the hydrogeochemistry and irrigation suitability of urban groundwater from the Thiaroye Aquifer. Groundwater samples (n = 225) were collected from 25 sites comprising boreholes, dug wells, hand pumps, and piezometers prior to, and following the monsoon over a seven-year period (2018–2024). Dominant facies groups are Na-K-SO4-Cl and Ca-Mg-SO4-Cl; chloride largely derives from anthropogenic sources including wastewater and on-site sanitation. Application of standard indices of water quality for irrigation (RSC, SAR, and %Na) reveal marked spatial heterogeneity, with 57% of groundwater samples classified as suitable for irrigation. Several wells (PD1, Pts 58, NP3, PAWS, F30), located in densely populated peri-urban areas with agricultural activity, exhibited elevated sodium concentrations limiting their suitability for use in irrigation. Recharge from monsoonal precipitation does not consistently result in the dilution of solute concentrations in shallow groundwater due, in places, to high pollutant loads from the surface and shallow subsurface, and impeded, lateral groundwater flow. Critically, nitrate concentrations reach up to 813 mg/L, significantly surpassing the agronomic thresholds for nitrate-sensitive crops like tomatoes, carrots, and onions cultivated in the Niayes. Coupled with the well-documented faecal contamination of the Thiaroye aquifer, this study suggests that pre-treatment methods such as biological denitrification or reverse osmosis are necessary before this urban groundwater can be safely utilized for irrigation at the most contaminated sites. These findings highlight the spatial heterogeneity of groundwater quality within the Thiaroye aquifer and suggest that targeted management strategies, combining controlled pumping with appropriate treatment and land-use planning, are essential to enhance irrigation potential while mitigating urban groundwater flooding in Dakar.
IntroductionUrban drainage systems in flood-prone riverine cities are often designed mainly for pluvial flooding, while pluvial-fluvial interactions and river backwater are neglected. This limitation is critical in developing cities where stormwater is conveyed through open-channel and roadside drains whose performance depends on terrain elevation, road elevation, and channel geometry. Existing optimization approaches have largely focused on closed conduit systems or low-impact development placement and can become computationally expensive for large networks.MethodsThis study develops a rank based sequential SWMM simulation optimization framework for large-scale open drainage design under a specified compound rainfall and river-stage scenario. The framework minimizes total channel excavation volume using a constraint-based rank sequential search. Three strategies are included: (i) hierarchical network ranking from upstream to downstream to enforce non-decreasing capacity and reduce the search space; (ii) hydraulic feasibility constraints keeping peak water depth below design channel depth; and (iii) river backflow constraints for elevated river stages.ResultsThe framework is applied to two watersheds in North Guwahati, Assam, India, affected by Brahmaputra backwater. The optimized design covers a 128.33 km drainage network over a 43.50 km2 urban area under a scenario-based compound flood boundary comprising a 5-year design storm and a fixed river stage of 49.5 m. Optimization completed in 11.7 h after 3,156 SWMM evaluations.DiscussionAn independent HEC-RAS 2D rain-on-grid cross-check confirmed the hydraulic consistency of the optimized design under the same compound rainfall-river-stage scenario. The framework provides a computationally tractable, compound-flood-aware approach for open-drain master planning in data-scarce riverine cities.
IntroductionIndia's water dynamics are characterized by intricate challenges arising from rapid population growth, institutional deficiencies, and environmental degradation. These factors lead to significant disparities in Water, Sanitation, and Hygiene (WASH), further entrenching social inequalities and obstructing sustainable development. The study emphasizes that securing fundamental water needs is critical for enabling broader community empowerment, governance innovation, and technological interventions, which are essential components for achieving a resilient and sustainable WASH system.MethodsTo address WASH challenges holistically, this study adopts Maslow's Theory of Human Motivation and a theoretical conceptual policy review, recognizing access to safe water as a primary physiological need essential for societal advancement. These theoretical perspectives help unravel how unmet basic needs impede higher-level social, economic, and environmental progress. Maslow's model offers a compelling way to analyze and enhance Indian government programmes related to water and sanitation by linking people's motivations with water governance.ResultsThe study creates a comprehensive and people-centred policy framework. This research identifies comprehensive pathways that stakeholders can leverage to address India's evolving water and sanitation concerns while promoting sustainable development.DiscussionThis study discusses how Maslow's model provides a powerful lens to interpret why multifaceted solutions like community engagement and institutional reform are essential for building resilience and ensuring equitable water access and addressing water disparities in the country.
Floods remain among the most frequent and destructive natural hazards, requiring spatially explicit tools to support risk-reduction planning and emergency preparedness. This study develops an integrated flood susceptibility and exposure assessment for São Paulo State, Brazil, combining machine learning, explainable artificial intelligence, and census-based demographic information. Flood Susceptibility Maps (FSMs) were generated at 30 m spatial resolution using the Random Forest (RF) algorithm, while the contribution of each conditioning factor was interpreted using SHapley Additive exPlanations (SHAP). Human exposure was assessed by integrating the susceptibility map with the spatial granularity of the 2022 Brazilian Institute of Geography and Statistics (IBGE) Census. Based on 15 conditioning factors and a balanced dataset of 2,000 flood-prone and 2,000 non-flood-prone samples, the RF model achieved high predictive performance (accuracy = 0.939; AUC = 0.982), with river proximity, slope, Curve Number, and altitude emerging as the most influential conditioning factors. Integrating susceptibility patterns with census data revealed marked spatial inequalities in flood exposure: the 6.2% of the state territory classified as Very High flood susceptibility concentrated 22.3% of the population, corresponding to approximately 9.89 million people, and 23.5% of all housing units. The municipal-level analysis further showed that 24.6% of people younger than 17 years and 26.8% of people older than 65 years were located in municipalities classified as High exposure. To support exposure-informed planning, a Combined Prioritization Index (CPI) was developed by integrating dominant municipal susceptibility, relative population exposure, and absolute population exposure. The CPI identified higher-priority municipalities not only along the coastal region but also in inland areas, including Guaruj, Rio Claro, Santos, Itanham, Praia Grande, Cubatão, São Vicente, and Caraguatatuba. Overall, the findings reveal spatial inequalities in flood exposure by showing where elevated flood susceptibility overlaps with concentrated human exposure, providing evidence for more transparent and anticipatory flood-risk planning.
Hydrological modelling in snow-dominated Himalayan Basins faces critical challenges under accelerating cryospheric change and hydro-climatic non-stationarity. The Jhelum River Basin (JRB), located in the northwestern Himalayas and spanning the India-Pakistan border region, presents modelling complexity because of its dual atmospheric forcing, steep elevation gradients, sparse observations, and strong temperature sensitivity of runoff. Despite decades of modelling activity in the basin, reported model performance has never been statistically synthesised across frameworks, nor has performance been interpreted relative to model structure, simulation objective, or uncertainty treatment. This leaves cross-study variability unexplained and predictive reliability under climate change difficult to assess. This study addresses this gap through the systematic quantitative synthesis of hydrological modelling applications in the JRB across 41 peer-reviewed studies published between 2000–2024, identified using a PRISMA-based search protocol across scholarly databases. The synthesis compares model performance across dominant frameworks, evaluates structural process representation, assesses uncertainty treatment and propagation, and identifies future modelling pathways under climate change. From 29 retained Nash-Sutcliffe Efficiency (NSE) values, the Soil and Water Assessment Tool (SWAT; mean NSE = 0.732, CV = 0.219, N = 13) showed the broadest performance dispersion, consistent with long-term multi-process simulation. The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS; mean NSE = 0.763, CV = 0.159, N = 8) showed a tighter distribution, reflecting its event-based flood simulation focus. The Snowmelt Runoff Model (SRM) achieved the highest mean NSE (0.802, N = 3) within its narrow snowmelt-estimation domain. Performance divergence across frameworks reflected structural specialisation and simulation objective, not inherent model superiority. Only 26.8% of reviewed studies applied formal probabilistic uncertainty analysis, a critical gap under non-stationary hydro-climatic forcing. Widespread reliance on temperature-index snowmelt formulations also introduces systematic uncertainty in runoff timing and magnitude. This concern intensifies under projected warming of 1.5–3.8 °C by 2041–2070. Transition toward hybrid, cryosphere-coupled, ensemble-based, and uncertainty-aware modelling frameworks is essential to strengthen predictive reliability and support climate adaptation across cryosphere-influenced Himalayan basins.
IntroductionA key insight into the long-term interaction between humans and floods is that historical faith-based facilities built over many years often embody the physical characteristics of extreme disasters in their respective regions and the disaster-resilience strategies developed to address them as socio-hydrological markers. In this study, we focused on shrines in Nagoya located in areas with high flood-exposure potential to explore the traditional flood-resilience strategies of shrine spaces established over several centuries.MethodsTarget shrine selection was conducted from two complementary perspectives: flood-exposure potential simulated using the Rainfall-Runoff-Inundation (RRI) model and historical continuity investigated from the literature. Consequently, 11 shrines were selected from 129 candidate shrines as the target shrines of this study.ResultsThe results demonstrated that most target shrines maintained the safety of their spaces even for a 100-year return-period precipitation. Furthermore, this study revealed distinct differences among target shrines in their traditional flood-resilience strategies. Before the Meiji era (1868), shrines were maintained by local communities and had achieved flood-resilience across the entire shrine space by selecting landforms with low flood-exposure potential and/or by raising the shrine space's ground level. In contrast, shrines established after the Meiji era were managed by local administrations and have achieved only functional flood-resilience by raising the floor of the main hall. Although the management of target shrines shifted from local communities to local governments, the safety of the shrine spaces against a 100-year return-period precipitation scenario was ensured across all 11 shrines.DiscussionThis continuity suggests that the administration inherited local communities' awareness of flood risk. Ultimately, this study provided empirical evidence that faith-based facilities serve as important socio-hydrological markers of the long-term interaction between humans and floods.
IntroductionFlood Frequency Analysis (FFA) plays a crucial role in the design of hydraulic structures and flood risk management. Conventional estimates of flood exceedance quantiles are highly dependent on the underlying flood frequency distribution, especially its upper tail, which is particularly difficult to estimate from observed data.MethodsWe use a simulation-based approach to investigate the transformation in upper tail behavior of flood peak distributions when dikes are overtopped in the large and complex river network of the River Rhine. We account for the effects of dike overtopping, floodplain storage, upstream-downstream interactions and interplay of tributaries. A coupled hydrologic-hydraulic modeling system, driven by long synthetic time series of meteorological fields from many realizations of a stochastic weather generator, is used to simulate dike overtopping and inundation.ResultsOvertopping results in a consistent reduction of the shape parameters of the Generalized Extreme Value distribution at mainstream and tributary gauges. At the same time, the variability of shape parameters between model realizations increases in most casesDiscussionOn average, reaches with large overtopping volumes and storage capacities show stronger reduction of shape parameters, whereas, in individual model realizations, the timing of overtopping and the rate at which overtopping volumes are generated seem to control shape parameter reduction.
BackgroundIn semi-arid, data-scarce regions, the convergence of climate change and rapid urbanization poses a severe threat to groundwater-dependent cities. This study evaluates the impact of hydro-climatic stressors and demographic growth on urban water security and aquifer depletion in Borama City, Somaliland.MethodsEmploying a mixed-methods design, quantitative data were collected through structured questionnaires from 362 randomly stratified urban households to assess socio-economic vulnerabilities. Concurrently, qualitative data were gathered via 22 Key Informant Interviews with water engineers and utility managers, which were triangulated with historical institutional hydrogeological records (2002–2024) from the local water utility.ResultsFindings reveal a systemic physical water scarcity driven by erratic rainfall (cited by 95.3% of respondents) and explosive urban expansion (93.4%), leading to severe aquifer drawdown. Consequently, 76.4% of household’s experience strict water rationing, and 80.5% bear a high financial burden from reliance on unregulated private water trucking. Despite the utility’s high administrative efficiency reducing non-revenue water to 10.6% empirical borehole data confirm that extraction vastly exceeds natural recharge.ConclusionBorama City has reached an ecological tipping point. Overcoming this invisible crisis requires an urgent transition from reactive coping strategies to proactive infrastructural investments, specifically the implementation of Managed Aquifer Recharge (MAR) and the exploration of alternative well-fields.
Groundwater is the primary drinking water source for communities in Wassa East, Ghana, but faces increasing threats from intensive farming, illegal artisanal mining, and improper waste disposal. This study integrates hydrogeochemical characterisation (Piper, Chadha, Gaillardet, Gibbs diagrams), multivariate statistics (PCA, hierarchical clustering), an Integrated Weighted Water Quality Index (IWQI) combining entropy and CRITIC weighting, and a USEPA-based human health risk assessment (carcinogenic and non-carcinogenic) to evaluate groundwater quality from 139 boreholes across the district. Results show that groundwater chemistry is controlled primarily by silicate weathering, with Ca-Mg-HCO₃, Ca-Mg-Cl, and Na-HCO₃-Cl as the dominant water types. Most physicochemical parameters are within WHO permissible limits, but iron (48 samples, mean 0.53 mg/L) and chromium (8 samples) exceed thresholds in localized areas. The IWQI classifies most groundwater as “good” for drinking, with poorer quality in southern/central zones driven by geogenic iron and colour. However, health risk assessment reveals a more concerning pattern: 12 samples exceed the non-carcinogenic hazard quotient for cyanide (maximum HQ = 4.56), 5 for arsenic, and 8 for chromium. Carcinogenic risks for arsenic and chromium exceed the 1 × 10−4 threshold in several samples. Microbial contamination is widespread (89 samples positive for total coliforms; 35 for E. coli). Hazard Index mapping shows unacceptable cumulative non-carcinogenic risks (HI > 1) concentrated in northern and central communities where mining and agricultural land use intensity is highest. The study concludes that while Wassa East’s groundwater is generally suitable for drinking based on IWQI classification, significant localised health risks exist from cyanide (artisanal mining), chromium, arsenic, and microbial pathogens. Routine monitoring and targeted interventions in hotspot communities (Daboase, Odumase, New Subri Zongo) are recommended.
Field-scale soil water assessment can be improved by coordinated satellite and ground observations, yet differences in spatial and vertical support complicate their comparison. This study assessed the practical feasibility and limitations of a pilot protocol integrating fully polarimetric ALOS-2 retrievals, in situ probe observations, and gravimetric measurements in a 344 ha rainfed agricultural field in Northern Kazakhstan. Six collocated sectors were observed on May 5 and September 8, 2025, under bare or near-bare soil conditions. ALOS-2 estimates were averaged within 50 m radius sectors, while ground observations were synchronized with the satellite overpasses and expressed as volumetric water content. The protocol enabled the three data streams to be assembled at common locations and dates. Descriptive comparisons, however, showed weak and inconsistent cross-source relationships. These differences are attributed to limited sampling, untested spatial representativeness of the 50 m aggregation radius, vertical support mismatch, and sensitivity of the SAR inversion to roughness and residual vegetation. The study evaluates whether the three source data types based workflow can be implemented under real agricultural conditions and identifies the sampling, support, and surface state constraints that must be resolved before a definitive validation campaign.
Groundwater recharge is a critical process for sustaining aquifer systems, water security, agricultural productivity, and ecosystem functioning. However, rapid land-use change, urbanisation, infrastructure expansion, and climate variability increasingly threaten groundwater replenishment worldwide. This study systematically reviewed the impacts of land use and infrastructure development on groundwater recharge across diverse environmental and hydrogeological settings. The review aimed to synthesise current scientific evidence, identify key drivers of recharge processes, and evaluate management strategies to enhance groundwater sustainability. A systematic literature review was conducted following the PRISMA 2020 framework. Relevant studies published between 2021 and 2025 were retrieved from Scopus, Web of Science, ScienceDirect, Google Scholar, SpringerLink, PubMed, ASCE Library, and Wiley Online Library. From 1,248 initially identified records, 40 studies met the predefined inclusion criteria and were subjected to qualitative thematic synthesis. The findings revealed that land-use change significantly alters groundwater recharge by modifying infiltration, evapotranspiration, runoff generation, and soil-water interactions. Urbanisation and the expansion of impervious surfaces consistently reduced recharge by limiting infiltration opportunities and increasing surface runoff. Infrastructure development, including roads, drainage systems, dams, and urban utilities, further modified natural hydrological pathways, although green infrastructure and managed aquifer recharge systems demonstrated potential to enhance groundwater replenishment. Climate variability amplified uncertainty in recharge through changes in precipitation patterns, temperature, and extreme weather events. Regional analyses indicated that groundwater recharge responses are highly context-specific and influenced by local climatic, geological, and land-management conditions. The review highlights the need for integrated land-use planning, groundwater-sensitive infrastructure development, recharge-zone protection, and climate-resilient water management strategies to ensure long-term groundwater sustainability and water security.
The rapid development of UAV-based river surveying is creating new opportunities to efficiently survey river reaches and collect high-resolution topographic, hydraulic, and surface velocity data for hydraulic rating-curve modeling and discharge estimation. Hydraulic rating-curve modeling provides a physically based approach that can reduce extrapolation uncertainty and constrain rating curves with fewer stage–discharge gaugings. However, despite these advances, how such UAV-derived data can be integrated into rating-curve uncertainty estimation frameworks and the impact of different calibration-gauging datasets on uncertainty in derived hydrological signatures is largely unexplored, limiting understanding of the method's operational potential. This study provides the first application of the RUHM framework (Rating curve Uncertainty estimation using Hydraulic Modeling) using UAV-derived data and the first quantification of how RUHM-modeled rating-curve and discharge uncertainties propagate to hydrological signatures under different gauging strategies. We applied RUHM at two sites in northern Sweden. At the Rakkurijärvi site, RUHM was applied using UAV-derived data from LiDAR, Structure from Motion (SfM) photogrammetry, and surface-velocimetry videos together with bathymetric and water-surface slope data. At the Röån site, RUHM was evaluated under nine gauging scenarios to assess their impacts on uncertainty in hydrological signatures. RUHM constrained rating-curve uncertainty across the full flow range at Rakkurijärvi using only three calibration gaugings, with similar results for SfM and LiDAR topography; using UAV-derived discharge for calibration increased uncertainty but still yielded a reliable rating curve. At the Röån site, rating-curve, discharge, and hydrological signature uncertainties were generally well constrained when calibration included low- to mid-flow stage–discharge gaugings, including for extreme-flow and flow-variability metrics, but the resulting signature distributions did not necessarily overlap for all metrics. These results show that RUHM, combined with UAV-based surveys, can support rapid and cost-effective estimation of rating curves and their uncertainty using relatively few gaugings, while also demonstrating how gauging strategy influences the uncertainty in hydrological signatures derived from hydraulically modeled rating curves and discharge estimates. The approach is best suited to sites with near one-dimensional hydraulic conditions and a stable stage–discharge relation, and we found that careful flight planning was needed to obtain high quality UAV data.
Extreme droughts are increasing in severity and frequency due to climate change, highlighting the need for reliable early-warning information at seasonal time scales. Bias-corrected seasonal forecasts offer a promising basis for anticipating drought conditions several months in advance, yet their predictive skill and potential operational value for the most severe droughts at the global scale remains insufficiently quantified. Here, we assess the performance of seasonal drought forecasts derived from the SEAS5-BCSD dataset using the Standardized Precipitation Evapotranspiration Index (SPEI). Thirty-six of the most extreme drought events between 1981 and 2024 are identified from ERA5 reanalysis, selecting two events per continent and accumulation period (SPEI-1, SPEI-3, and SPEI-6), and assessing forecast performance using probabilistic, spatial, and impact-oriented verification metrics, including the Continuous Ranked Probability Skill Score (CRPSS), the Fractions Skill Score (FSS), and the Potential Economic Value (PEV). The results demonstrate that SEAS5-BCSD drought forecasts outperform climatology for nearly all analyzed events. Across all verification metrics, forecast skill is generally highest for SPEI-1 events and for the selected African droughts, whereas the selected North American events exhibit comparatively lower skill. While the exact location and intensity of the most severe drought cores remain difficult to predict, useful probabilistic information is often preserved within the ensemble distribution. Maximum PEV values frequently exceed 0.7, indicating substantial potential value for decision-making under suitable cost-loss assumptions. These findings highlight the benefits of probabilistic, ensemble-based interpretation of seasonal drought forecasts. Rather than relying solely on the ensemble mean, considering the full forecast distribution enables more risk-informed drought preparedness by accounting for both the likelihood and the potential severity of extreme events.
This study explores strategies for water conservation by repurposing agricultural land in California’s San Joaquin Valley (SJV) for alternative uses like habitat restoration, groundwater recharge, and solar energy development. Water availability in the region has become increasingly constrained due to climate variability and regulatory restrictions under the Sustainable Groundwater Management Act (SGMA), which mandates groundwater sustainability through reduced extraction and enhanced recharge. Climate change and expansion of water intensive high-value crops are further increasing agricultural water demand and exacerbating water scarcity. One proposed water demand management strategy is land fallowing, but leaving land idle can have significant financial and environmental trade-offs. Repurposing fallowed land for alternative uses can address these trade-offs and deliver benefits beyond water savings. Using the VIC-CropSyst, an agrohydrology model, this study assesses water savings across various land-use and climate change scenarios. Results show annual water savings between 370 and 3,740 million m3, requiring the fallowing of 838 to 4,360 km2 of farmland, with estimated revenue losses ranging from $0.87 billion to $4.4 billion. Habitat restoration and groundwater recharge are identified as the most effective strategies for reducing water demand, while solar development offers moderate savings. Perennial crops, particularly almonds, demonstrate the highest water demand reductions, while corn shows the least. The findings underscore the importance of crop-specific and farm-centric strategies to optimize water use. These insights will aid policymakers in developing multi-benefit land repurposing policies to support sustainable water management in the SJV amidst climate challenges.
Today, the usage of plastic has been normalized across a wide range of products from essential medical apparatuses to daily convenience-seeking behaviors, thereby achieving a ubiquitous presence. While plastic waste management became one of top policy agendas in 21st century, concerns have escalated regarding the hazardous risks associated with the degrading nature of plastics and subsequent transport of smaller plastic particles. Given the urgent need for a comprehensive understanding of their occurrence, transport, ecological consequences, and potential remediation strategies, a scoping review of 123 articles selected from Web of Science and Google Scholar was conducted. The review confirmed that the fragmentation of plastics into micro- and nanoplastics facilitates their vertical migration through natural sub-surface filtration barriers to contaminate groundwater aquifers. Yet, the establishment of a systematic framework, encompassing a standardized global definition of plastic particles by size and the harmonization of experimental protocols, remain in its early development stage that information sharing for cross-studies on the detection, analysis, and removal of plastic particles from various environmental compartments is limited. Among the studied physical, chemical, and biological remediation approaches, the biodegradability of plastic particles has emerged as a scalable, economically feasible, and sustainable solution; however, the challenge lies in identifying site-specific microbial strains capable of targeting distinct polymer species prevalent in subject aquifer. While governmental interventions through preventive measures represent a commendable effort, especially since detection and remediation technologies are still under development; their actual contribution to plastic waste reduction remains questionable, as a significant portion of these policies relies on voluntary compliance or limited bans on single-use plastics in daily consumption. Thus, policy implications underscore two primary pillars: first, prioritizing surface-level plastic source control to intercept the migration of contaminants into subsurface ecosystems, and, second, accelerating the standardization of experimental protocols to facilitate a global database for polymer characterization.
Understanding how spatial resolution shapes the interpretation of drought-yield relationships is a fundamental challenge in sociohydrology. This study systematically examines how spatial resolution, from 1-km to 50-km, influences what can be observed about drought-yield sensitivity in Indonesian dry season paddy, and what each scale uniquely suggests about human-water interactions. Using the Standard Precipitation-Evapotranspiration Index at a 3-month accumulation period (SPEI-3) as a drought hazard indicator and crop yield anomalies as a risk indicator across both dry cropping seasons (2001–2021), we conduct an aggregation experiment computing Pearson correlation at five resolutions (1, 5, 10, 25, and 50 km). The overall strength of drought-yield correlation remains stable across resolutions (median |r| ≈ 0.18–0.21), demonstrating that spatial aggregation does not amplify the drought signal. However, each resolution serves a distinct analytical purpose: at 50-km, spatially coherent regional patterns emerge that identify where drought-yield coupling is strongest, with negative correlations dominating across the majority of agricultural regions; at intermediate resolutions (5–10 km), seasonal differences between the second and third cropping seasons become most robustly detectable through all three statistical tests applied (t-test, Mann-Whitney, and Kolmogorov-Smirnov); and at 1-km, the analysis enables separation of dam-based and community-based water resource areas, revealing two contrasting drought response patterns between governance types. In dam-based areas, drought severity is closely associated with yield loss, reflecting the institutional structure of centralized irrigation management. In community-based areas, locally varying human factors, including farmer group organization, supplementary water infrastructure, and livelihood diversification, suggests having mediate drought impacts, producing near-zero aggregate correlation despite higher average yield losses (16.2–25.7% vs. 13.3–20.3% in dam-based areas). This combination of lower sensitivity with higher vulnerability demonstrates that the modest aggregate correlation observed at all scales is not evidence of weak drought influence, but rather reflects the mixing of two fundamentally different response populations. These findings demonstrate that spatial resolution functions as a methodological lens in sociohydrological analysis: each scale answers a different question about drought-yield dynamics, and fine-resolution analysis is essential for revealing the socially mediated pathways through which drought impacts are experienced.
Accurate and rapid prediction of groundwater levels (GWL) is essential for effective groundwater management. Machine learning models are efficient tools for GWL prediction, but individual models often suffer from limited generalization due to inherent randomness. This study proposed a stacking-based GWL prediction framework suitable for arid regions in Northwest China. Feature variables affecting GWL were selected using variable importance in projection (VIP). Then, three machine learning models—artificial neural networks (ANN), random forests (RF), and Light Gradient Boosting Machine (LightGBM)—were developed, and their outputs were integrated using a support vector regression (SVR)-based stacking method to enhance the accuracy of GWL prediction. The results show that the factors of influencing GWL changes vary significantly across different regions, and selecting the most contributive feature variables is beneficial for model construction. Among the individual models, the RF model demonstrated higher accuracy and more stable performance, outperforming the ANN and LightGBM models. However, individual models exhibited poor generalization during validation. In contrast, the stacking model maintained high performance, demonstrating superior generalization. Compared to the best-performing individual model (RF) in validation period, the Nash–Sutcliffe efficiency (NSE) and Kling–Gupta efficiency (KGE) of stacking model improved by 0.11–0.66 and 0.05–0.41, the correlation coefficient (R2) increased by 0.05–0.3, and root mean square error (RMSE) reduced by 0.01–0.1 m. In the stacking simulation, RF had the highest average contribution (80.2%), followed by ANN (13.9%) and LightGBM (5.9%). This study provides a stacking simulation framework based on machine learning methods for precise groundwater level simulation, which can serve as a reference for groundwater level simulation in other regions.
Excess phosphorus in aquatic ecosystems promotes eutrophication, leading to ecological alterations that modify the abundance of phytoplankton and aquatic macrophytes. This study evaluated chemical precipitation with aluminum sulfate as a management strategy for Lake Busa by assessing phosphorus removal and its effects on the aquatic macrophyte Schoenoplectus californicus (C.A.Mey.) Soják and the fish Cyprinus carpio Linnaeus, 1758. Jar tests were performed to determine the optimal aluminum sulfate dose for phosphorus removal. The effects of treated water were subsequently evaluated biologically in a 35-day microcosm experiment using 54 S. californicus individuals distributed among 12 experimental containers. Acute toxicity was assessed in newly hatched C. carpio fry through 96-h laboratory exposure tests using untreated water, treated water, and potable-water controls. Because the experimental design did not include replicated containers for each treatment, the biological responses are presented as observed patterns under the tested conditions rather than statistically validated treatment effects. Under the experimental conditions evaluated, aluminum sulfate doses of 34−40 mg/L removed 88.9−90.9% of phosphorus (from 0.09−0.11 mg/L to 0.01 mg/L), while residual aluminum concentrations remained between 0.061 and 0.071 mg/L. Under the experimental conditions evaluated, S. californicus grown in treated water showed a mean height increase of 5.28 cm and a mean stem diameter increase of 0.12 cm, whereas plants maintained in untreated lake water showed mean increases of 12.5 cm and 0.27 cm, respectively. A mortality rate of 93.33% was observed in newly hatched C. carpio fry after 96 h of exposure to treated water containing residual aluminum. These findings indicate that although aluminum sulfate effectively reduces phosphorus and limits macrophyte growth, the associated residual aluminum may adversely affect aquatic biodiversity. Consequently, chemical precipitation should be evaluated using both water-quality and biological indicators before large-scale application in eutrophic lakes.
