Search Results (6,424)

Accurate and robust runoff simulation is crucial for effective reservoir regulation. Although it is clear that enhancing runoff simulation or optimizing reservoir operation strategies can improve the management of hydropower resources, the specific impact of enhanced simulated runoff on reservoir operation under optimized regulation has not been thoroughly examined. To investigate how high-precision runoff simulation influences reservoir performance, this study proposed a unidirectional coupling framework of the distributed hydrological model CREST and the LSTM model, incorporating the seasonal characteristics of the satellite-based precipitation product CHIRPS. The influence of simulated runoff on hydropower generation was examined from two perspectives: metrics’ accuracy and process-based analysis. The results showed that, following the unidirectional coupling, the Coupling scheme achieved improvements in NSE and R² by 6% and 4%, respectively, while RMSE decreased by 24%. Additionally, it accurately captured the seasonal variations and amplitude of runoff at the annual scale, and was able to reliably detect the periodic signals within runoff across various scales. After reservoir optimization operation, the simulated runoff derived from the Coupling scheme produced hydropower and surplus water values close to those obtained from observed runoff, with errors of 1.09% and −21.64%, respectively. Moreover, the Coupling scheme corrected the prominent peaks in hydropower generation seen in the CREST model across multiple periods, demonstrating a stronger capability for temporal runoff simulation closely aligned with observed runoff in terms of temporal structure. Full article

Stochastic weather generators are commonly employed to create synthetic sequences of daily weather variables across diverse fields, including hydrological, ecological, and agricultural studies. Realistic precipitation sequences, in particular, serve as essential inputs in numerous modeling frameworks. Generalized linear models (GLMs) that incorporate covariates to capture seasonality and teleconnections represent one effective approach for stochastic weather generation. However, these models often underestimate the interannual variability of seasonally aggregated variables, notably precipitation intensity during wet seasons. Recent methods developed to mitigate the issue of overdispersion have nevertheless struggled to adequately replicate observed precipitation intensities in wet seasons. To overcome this limitation, we propose integrating a modified hybrid gamma and generalized Pareto distribution into the GLM-based weather generator. This enhanced method was evaluated using daily precipitation data from Seoul, Korea, and successfully reproduced realistic precipitation intensities while effectively addressing the overdispersion issue. Full article

Floods represent one of the most significant climate-related hazards, particularly in regions with complex topographies and variable precipitation patterns. This study assesses flood-prone areas within the Mantaro watershed, Peru, using Geographic Information Systems (GISs) and morphometric analysis. The methodology integrates digital elevation models (DEMs) with hydrological parameters, applying weighted sum analysis to classify 18 sub-watersheds into different flood priority levels. Morphometric parameters, including basin relief, drainage density, and slope, were analyzed to establish correlations between watershed morphology and flood susceptibility. The results indicate that approximately 74.38% of the watershed exhibits high to very high flood risk, with the most vulnerable sub-watersheds characterized by steep slopes, high drainage densities, and compact morphometric configurations. The correlation matrix confirms that watershed topography significantly influences surface runoff behavior, underscoring the necessity of incorporating geospatial analysis into flood risk assessment frameworks. The classification of sub-watersheds into priority levels provides a scientific basis for optimizing resource allocation in flood mitigation strategies. This study highlights the importance of integrating advanced geospatial technologies, such as GISs and remote sensing, into hydrological risk assessments. The findings emphasize the need for proactive watershed management, including the use of real-time monitoring and digital tools for climate adaptation. Future research should explore the influence of land-use changes and climate variability on flood dynamics to enhance predictive modeling. These insights contribute to evidence-based decision-making for disaster risk reduction, reinforcing resilience in climate-sensitive regions. Full article

Urban trees play a crucial role in regulating hydrological processes within urban ecosystems by intercepting rainfall to effectively reduce surface runoff and mitigate urban flooding. Current research lacks a systematic quantification of rainfall interception capacity and its community-level impacts at the urban scale. This study adopts a city-scale perspective, integrating field survey data with the i-Tree Eco model to systematically explore the contributions of 20 factors to the average annual rainfall interception of tree species and the average annual rainfall interception efficiency of communities. The study revealed that Deciduous broadleaf trees (1.28 m³ year⁻¹) and Pure coniferous forests (90.7 mm year⁻¹) exhibited substantial rainfall interception capacity. Relative Height, Average Tree Height, Average Crown Width, and Planting Density of trees significantly influence interception capacity. Urban planning can optimize the selection of tree species (e.g., Paulownia, Populus tomentosa, etc.) and community structure (e.g., mixed planting of conifers and deciduous broadleaf trees) to improve rainfall interception capacity, thereby effectively reducing stormwater runoff, mitigating the risk of urban flooding. These findings provide a scientific basis for designing urban vegetation to mitigate flooding, support water management, and advance sponge city development. Full article

Urban flooding presents increasingly complex challenges exacerbated by climate change, rapid urbanization, and aging infrastructure. This investigation combines planning theories and socio-hydrological modelling to create a planning-adaptable urban flood management strategy. The case study of Chiba Prefecture, Japan, demonstrates this approach in depth. By applying the Social-Ecological-Technological Systems (SETS) framework in combination with planning theories, the study has identified the relationship between the conventional engineered methods and the newly introduced environmentally friendly (nature-based) solutions. Our findings, which are based on content analysis of 23 official statutory planning documents, indicate that there is a significant focus on the conservation of ecosystems and green infrastructure balanced with issues of emergency planning and community engagement. One of the points that the results highlight is integrating the ecological, social and technological aspects in order to create flood management policies that are both robust and fair. This integrated approach offers a robust framework for mitigating flood risks while promoting sustainable urban development and long-term community resilience. Full article

Runoff prediction plays a critical role in water resource management and flood mitigation. Traditional runoff prediction methods often rely on single-layer optimization frameworks that process the data without decomposition and employ relatively simple prediction models, leading to suboptimal performance. In this study, a novel two-layer optimization framework is proposed that integrates data decomposition techniques with multi-model combination strategies, establishing a closed-loop feedback mechanism between decomposition and prediction processes. The framework employs the Snow Ablation Optimizer (SAO) to optimize combination weights across both layers. Its adaptive fitness function incorporates three evaluation metrics—Mean Absolute Percentage Error (MAPE), Relative Root Mean Square Error (RRMSE), and Nash–Sutcliffe Efficiency (NSE)—to enable adaptive data processing and intelligent model selection. We validated the framework using observational data from Huangzhuang Hydrological Station in the Hanjiang River Basin. The results demonstrate that, at the decomposition layer, optimal performance was achieved by combining non-decomposition, Singular Spectrum Analysis (SSA), and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) (with coefficients 0.4061, 0.6115, and −0.0063), paired with the long short-term memory (LSTM) model. At the prediction layer, the proposed algorithm achieved a 32.84% improvement over the best single decomposition method and a 30.21% improvement over the best single combination optimization approach. These findings confirm the framework’s effectiveness in enhancing runoff data decomposition and optimizing multi-model selection. Full article

Groundwater resource management involves complex socio-hydrological systems characterized by dynamic feedback, uncertainty, and common misconceptions among decision-makers. While deterministic models like MODFLOW simulate physical hydrology effectively, they fall short in capturing the social, legal, and behavioral dynamics shaping groundwater use. System dynamics (SD) modeling offers a robust alternative by incorporating feedback loops, delays, and nonlinearities. Yet, model conceptualization remains one of the most challenging steps in SD practice. Experienced modelers often apply a “Lego-like” approach—assembling new models from pre-validated sub-models. However, this strategy depends on access to well-documented sub-model libraries, which are typically unavailable to newcomers. To address this barrier, we systematically review and classify socio-economic sub-models from existing groundwater SD literature, organizing them by system archetypes and generic structures. The resulting modular library offers a practical resource that supports newcomers in building structured, scalable models. This approach improves conceptual clarity, enhances model reusability, and facilitates faster development of SD models tailored to groundwater systems. The study concludes by identifying directions for future research, including expanding the sub-model library, clarifying criteria for base-model selection, improving integration methods, and applying these approaches through diverse case studies to further strengthen the robustness and utility of groundwater SD modeling. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article

Accurate and up-to-date morphometric data on lakes are crucial for hydrological modeling, ecosystem monitoring, and sustainable water resource management. This study presents the first centimeter-scale, high-resolution bathymetric model of Lake Markakol (eastern Kazakhstan), generated using advanced hydroacoustic and geospatial techniques. The primary objective was to reassess key morphometric parameters—surface area, depth, volume, and shoreline configuration—more than six decades after the only existing survey from 1962. High-density depth data were acquired with a Lowrance HDS-12 Live echo sounder, achieving vertical precision of ±0.17 m, and processed using ReefMaster and ArcGIS to produce a three-dimensional, hydrologically correct model of the lake basin. Compared with archival data, results show that while the surface area (455.365 ± 0.005 km²), length (38.304 ± 0.002 km), and width (19.138 ± 0.002 km) have remained stable, the maximum depth is lower (24.14 ± 0.17 m vs. 27 m), and the total water volume is slightly higher (6.667 ± 0.025 km³ vs. 6.37 km³). These differences highlight both the limitations of historical lead-line surveys and the enhanced accuracy of modern hydroacoustic and GIS-based methods. The workflow developed here is transferable to other remote alpine lakes, providing an invaluable baseline for limnological research, ecological assessment, hydrodynamic modeling, and long-term water resource management strategies in data-scarce mountain regions. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

The frequency and intensity of paleofloods reveal long-term hydrological changes and their responses to regional climate variations. This study focuses on sediment core HDZ04 from the desert section of the upper Yellow River, analyzing sediment grain size and elemental characteristics to reconstruct paleoflood events over the past 30,000 years. Using the EMMA end-member model, four end-member components were extracted, and the proportion of the two coarser end-members was used as a proxy for flood dynamics. Pearson correlation analysis indicated that ln(Zr/Ti) correlates more significantly with grain size value than ln(Zr/Rb), establishing Zr/Ti as a reliableproxy for paleoflood reconstruction. Integrating physical and chemical indicators with OSL dating, the reconstructed paleoflood sequence shows high frequency and intensity from 30~12 ka, lower values during the early and middle Holocene, and a significant increase in the late Holocene (3~0 ka). Comparison with regional climate records indicates that cold and dry periods correspond to higher paleoflood frequency and intensity. This multi-proxy approach provides a transferable framework for reconstructing past flood events in other alluvial systems worldwide, enhancing our understanding of hydrological responses to climatic forcing. Full article

This study proposes a hardware-based approach to address agricultural water shortages by directly improving water supply operations, rather than estimating agricultural water demand or supply. Unlike previous studies that focus on evaluating water supply capacity or predicting reservoir inflows through modeling or data-driven methods, this work proposes an operational strategy involving the physical interconnection of reservoirs. Specifically, the study investigates the coordinated use of surplus storage capacity from reservoirs with high watershed ratios to support those with low watershed ratios, thereby enhancing overall water supply reliability. Reservoir inflows were estimated using the Hydrological Operation Model for Water Resources Systems (HOMWRS). The analysis was conducted on reservoirs managed by the Korea Rural Community Corporation (KRC), selected based on data accessibility and availability. Full article

Accurate precipitation data are essential for hydrological planning in mountainous regions with sparse opportunities for observation, such as the Ambato River basin in Ecuador. In this study, CHIRPS and IMERG satellite precipitation products were compared against six automatic rain gauges from 2014 to 2023, using both categorical metrics (to assess daily rainfall detection skill) and continuous validation (to evaluate rainfall amount), complemented by bias decomposition and spatiotemporal analysis. Our results show that IMERG demonstrated higher skill in detecting daily rainfall, while CHIRPS delivered a more stable performance during dry conditions, with fewer false alarms. Both products capture the main seasonal precipitation patterns but differ in bias behavior: CHIRPS tends to under-estimate daily rainfall less, whereas IMERG provides more reliable volumetric estimates overall. These findings suggest that IMERG may be best suited for flood risk and hydrological modelling, while CHIRPS could be preferred for drought monitoring and climatological studies in Andean catchments. Full article

This study applies the Integrated Modification Methodology (IMM) to assess how morphology-driven, nature-based solutions reduce urban heat island (UHI) effects and flooding in Rio de Janeiro’s Cidade Nova. Multi-scale GIS diagnostics identify green continuity and vertical permeability as critical weaknesses. Simulations (Ladybug/Dragonfly) and hydrological modelling (rational method) quantify the intervention’s impact, including greening, material retrofits, and drainage upgrades. Results show a 38% increase in albedo, a 13% reduction in volumetric heat capacity, and a 30% drop in thermal conductivity. These changes reduce the peak UHI by 0.2 °C hourly, narrowing the urban–rural temperature gap to 3.5 °C (summer) and 4.3 °C (winter). Hydrologically, impervious cover decreases from 22% to 15%, permeable surfaces rise from 9% to 29%, and peak runoff volume drops by 27% (16,062 to 11,753 m³/h), mitigating flood risks. Green space expands from 7.8% to 21%, improving connectivity by 50% and improving park access. These findings demonstrate that IMM-guided interventions effectively enhance thermal and hydrological resilience in dense tropical cities, aligning with climate adaptation and the Sustainable Development Goals. Full article