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This paper aims to evaluate the performance of a hydrological model by using satellite image classification (SIM) to extract land use (LU) information. Four methods, namely naive Bayes (NB), classification and regression trees (CART), support vector machine (SVM), and random forest (RF), are assessed for SIM using satellite images. CART demonstrated the highest overall accuracy (OA) of 0.996, followed by RF (OA = 0.994), SVM (OA = 0.797), and NB (OA = 0.543). The Soil and Water Assessment Tool (SWAT) model was employed in this study to construct and refine the hydrological model for simulating the streamflow in the basin. For this purpose, the ground-based layer was replaced by LU generated from the classification algorithms. Integrating the four classification approaches yielded a significant improvement in Nash-Sutcliffe efficiency of the SWAT model, increasing it from 0.37 to approximately 0.76. These findings highlight the effectiveness of using satellite image classification in enhancing the efficiency of hydrological models. [ABSTRACT FROM AUTHOR]

This study aimed to assess the hydrological response and resilience of watersheds in a neotropical region to identify regions sensitive to climate variations, enabling the development of adaptive strategies in response to global environmental changes. This study applied Budyko's framework using Fuh's hydrological model rewritten by Zhou to estimate hydrological response and Budyko's metrics (deviation and elasticity) to estimate hydrological resilience to climatic changes in 26 watersheds in southeastern Brazil. The proposed modeling was able to capture the differences among the watersheds, with "m" values ranging from 1.79 to 3.63. It was possible to rank the hydrological resilience from low to high across watersheds using Budyko's metrics, where the highest values of elasticity were found in watersheds with a higher percentage of forest cover. The sensitive analyses showed that watersheds with higher "m" values are more sensitive to changes in precipitation and potential evapotranspiration. The results also demonstrate that mean elevation and stream density were two key variables that influence the "m" value; these physiographic characteristics may alter the water and energy balance of the watershed affecting the water yield. A relationship between watershed's hydrological response and resilience was proposed to identify critical areas for the stability of water yield in the watersheds, providing a guide for public policy and suggesting ways to help the management of water resources in watersheds. [ABSTRACT FROM AUTHOR]

Flow rate is the basic parameter for calculating the energy capacity and investment costs of small hydropower plants. Because energy costs are directly related to energy capacity. While the energy capacity increases, the investment cost of the hydropower plant increases also. However, if the energy capacity is high, the energy production will be high and the investment cost will be amortized in a short time. In this study, small rivers in the Euphrates and Tigris Basins with previously determined hydropower capacities were selected, and the investment costs of small hydroelectric power plants to be designed on these rivers were analysed. For this analysis, 10 streams were selected from the Euphrates and Tigris Basins, and only Baskoy and Celebiyan streams have flow observation stations. The flow values of the remaining 8 streams were found by regression analysis using the basin and flow values of Baskoy and Celebiyan streams. Therefore, cost–benefit analyses were carried out separately for these flows in this study. In this study, because it includes the most effective hydrological parameters as well as many technical and economical parameters, the SMART Mini Hydro Tool Program was used. [ABSTRACT FROM AUTHOR]

Today, hydrological models are important tools for successfully managing water resources and understanding the hydrologic response of watersheds, especially in arid regions with limited water resources. The purpose of this study is to evaluate the potential of the Soil Water Assessment Tools model (SWAT) for predicting the hydrological regime of El Chique Dam watershed, in a semi-arid region of the state of Zacatecas, Mexico. The model was calibrated and validated on a daily and a monthly scale using the sequential uncertainty fitting algorithm (SUFI-2) within the Calibration and Uncertainty Program SWAT-CUP. The performance of the SWAT model was evaluated by means of four statistical measures: the Nash–Sutcliffe coefficient (NSE), the determination coefficient (R2), the Percentage of Bias (PBIAS) and the mean square error (RSR). The results showed a better performance of the model in the simulation on a monthly scale, obtaining values of NSE, R2, PBIAS and RSR of 0.78, 0.79, -14.00 and 0.47 during the calibration period, and 0.60, 0.61, -18.70 and 0.63 during the validation period, respectively. The most sensitive parameters that affect the behavior of runoff in the watershed are: ALPHA_BF, CH_K2, CN2, SOL_BD and OV_N. The overall hydrological water balance analysis showed that more than 90% of precipitation is lost by evapotranspiration, while 8.6% of precipitation contributed to streamflow. Thus, the SWAT model can predict runoff on a monthly scale very well and therefore, these results are expected to assist in parameter selection for calibration processes in other semi-arid regions, as well as in decision-making related to water resource management and planning in the watershed under study. [ABSTRACT FROM AUTHOR]

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation. [ABSTRACT FROM AUTHOR]

Climate change is a major threat to river basins and ecosystems, leading to changes in ecosystems due to rising temperatures, expansion or contraction of specific habitat boundaries, and alterations in the timing of the seasons. This study investigated climate and land use changes to predict future hydrological regime in the Wang River Basin (WRB) and its impact on the ecosystem, focusing on key flow properties such as magnitude, duration, and intensity. The flow properties were studied based on the indicators of hydrologic alteration software and environmental flow components, which were separated into five groups and considered for economic and forest conservation scenarios based on the representative concentration pathway (RCP4.5 and RCP8.5) trajectories. The results showed that future climate change in the WRB will involve severe maximum/minimum temperature increases of 2.09-1.95°C and 4.01-4.05°C for RCP4.5 and RCP8.5 respectively, while the annual rainfall trend will decrease during the 2030s and the 2050s and then increase during the 2070s and the 2090s or a change ranging from -1.96 to 6.10% for RCP4.5 and 1.43 to 6.68% for RCP8.5 from 2030s to 2090s. The projected annual discharges for the combined impacts of climate change and land use change during 2030-2090 indicated that the discharge will tend to decrease in the future, especially in the near future (ranging from -9.75 to -12.32%). Furthermore, there will be an increase in the rise and fall rates (120.24-147.11% and 61.24-62.30%). Consequently, these impacts will eventually affect the livelihoods and ecosystems in this river basin. [ABSTRACT FROM AUTHOR]

Hydrological models are crucial for characterizing large-scale water quantity variations and correcting GNSS reference station vertical displacements. We evaluated the robustness of multiple models, such as the Global Land Data Assimilation System (GLDAS), the Famine Early Warning System Network Land Data Assimilation System (FLDAS), the National Centers for Environmental Prediction (NCEP), and the WaterGAP Global Hydrology Model (WGHM). Inter-model and outer comparisons with Global Positioning System (GPS) coordinate time series, satellite gravity field Mascon solutions, and Global Precipitation Climatology Centre (GPCC) guide our assessment. Results confirm WGHM's 26% greater effectiveness in correcting nonlinear variations in GPS height time series compared to NCEP. In the Amazon River Basin, a 5-month lag between FLDAS, GLDAS, and satellite gravity results is observed. In eastern Asia and Australia, NCEP's Terrestrial Water Storage Changes (TWSC)-derived surface displacements correlate differently with precipitation compared to other models. Three combined hydrological models (H-VCE, H-EWM, and H-CVM) utilizing Variance Component Estimation (VCE), Entropy Weight Method (EWM), and Coefficient of Variation Method (CVM) are formulated. Correcting nonlinear variations with combined models enhances global GPS height scatter by 15%–17%. Correlation with precipitation increases by 25%–30%, and with satellite gravity, rises from 0.2 to 0.8 at maximum. The combined model eliminates time lag in the Amazon Basin TWSC analysis, exhibiting a four times higher signal-to-noise ratio than single models. H-VCE demonstrates the highest accuracy. In summary, the combined hydrological model minimizes discrepancies among individual models, significantly improving accuracy for monitoring large-scale TWSC.

Abstract Flow rate is the basic parameter for calculating the energy capacity and investment costs of small hydropower plants. Because energy costs are directly related to energy capacity. While the energy capacity increases, the investment cost of the hydropower plant increases also. However, if the energy capacity is high, the energy production will be high and the investment cost will be amortized in a short time. In this study, small rivers in the Euphrates and Tigris Basins with previously determined hydropower capacities were selected, and the investment costs of small hydroelectric power plants to be designed on these rivers were analysed. For this analysis, 10 streams were selected from the Euphrates and Tigris Basins, and only Baskoy and Celebiyan streams have flow observation stations. The flow values of the remaining 8 streams were found by regression analysis using the basin and flow values of Baskoy and Celebiyan streams. Therefore, cost–benefit analyses were carried out separately for these flows in this study. In this study, because it includes the most effective hydrological parameters as well as many technical and economical parameters, the SMART Mini Hydro Tool Program was used.

High-quality precipitation data input and the selection of reasonable and applicable hydrological models are the main ways to improve the accuracy of runoff simulation, and are crucial for flood control, drought resistance and comprehensive water resource management in the basin. This study takes the Jingle Basin as the research area, establishing a transformer model that integrates rainfall data from multiple sources considering environmental factors. It combines six types of remote sensing data with rainfall data, which are then used as inputs for the XAJ model, LSTM model, and Prophet model, respectively. The output results are further separately using the ensemble mean method and the Bayesian mean method for ensemble forecasting. The results show that: Compared with a single precipitation product, the fusion model considering environmental factors significantly enhances the correlation between the predicted rainfall and the observed rainfall, with the CC value reaching 0.72; Compared with the other two models, the LSTM model has the NSE value of 0.89, showing a better runoff prediction effect; Compared with the LSTM model with the NSE value of 0.85 and the ensemble average method with the NSE value of 0.76, the Bayesian model averaging method demonstrates the best runoff prediction and simulation effect, with the NSE value of 0.88. [ABSTRACT FROM AUTHOR]

This study presents novel mathematical formulations of the Soil Conservation Service curve number (SCS-CN) method that incorporate both temporally decaying retention parameters and storm intensity/duration. To evaluate its performance, we compared it with the existing versions of the SCS-CN model using a large data set of 35,546 storm events of 113 different US watersheds. Obtained results indicate that the proposed model outperforms other models in almost all 113 US watersheds with the highest Nash-Sutcliffe efficiency (NSE). Furthermore, the results are supported by the percent bias (PBIAS) being close to 0 and the lowest root mean square error (RMSE), RMSE-observations standard deviation ratio (RSR), normalized root mean square error (NRMSE), and mean absolute error (MAE) statistics. The general form of the proposed model performed particularly well in clayey and sandy soils with different land uses and catchment areas larger than 1 ha. Rainfall (P) and the coefficient (β) parameters are identified as the most and least sensitive parameters of the proposed model, respectively. [ABSTRACT FROM AUTHOR]

In this research, the authors aim to evaluate the damage caused by floods in Vinh Phuc Province. The study incorporates the construction of hydro-hydraulic models and the development of damage functions for the main elements at risk. In order to ensure the accuracy of the calculated results, the hydro-hydraulic models and damage curves are verified for their reasonableness. The outcomes of this study give us an overview of flood damage in the research area. It indicates that the potential level of flood damage is a matter of significant concern, with an annual damage estimate of approximately 6.9 million USD. The research also reveals the distribution of damage by sector and by space. According to the research findings, the greatest damage was in Vinh Tuong District, where most of the damage was caused by flooding of agricultural land. In contrast, residential buildings in the area suffered relatively little damage compared to agricultural land. This information is crucial for decision-makers to implement effective and targeted mitigation measures. HIGHLIGHTS Developing a hydrological and hydraulic model to flooding in Phan-Ca Lo basin. Mathematical models are effective tools for hazard assessment.; Establishing two specific damage functions for residential area and agricultural land of Vinh Phuc Province.; Assessing flood damage in Vinh Phuc Province through damage–frequency curve and the expected annual damage.;

The Bekaa Plain in Lebanon, is a major agricultural region known for its fertile soil and extensive crops cultivation using groundwater resources which is recently became under stress due to population growth, urbanization, and climate change. The main aim of this study is to investigate the rate of groundwater recharge (GwR) into this region using APLIS Model which principally depends on altitude, slope, lithology, infiltration, and soil type. Using GIS-based for a multicriteria mapping approach, in this study additional factors were added to the model, by incorporating rainfall rates, stream density, and fracture density. Results revealed various recharge potential area in the Bekaa Plain, with 52 % classified as high to very high GwR zones, equivalent to approximately 42.5 % of precipitated water, which is a testament to the exceptional capacity of the region to replenish groundwater aquifers. The geographic distribution of GwR categories primarily depends on altitude and lithological characteristics. The exceptional GwR potential of the Bekaa Plain holds immense promise for mitigating water scarcity, fostering agricultural productivity, and ensuring the socioeconomic well-being of communities reliant on groundwater resources.

Seasonally managed wetlands in the San Joaquin River (SJR) watershed in California provide important benefits to wildlife and humans but are threatened through anthropogenic activity. Wetlands in the SJR are subject to salinity regulation, which poses challenges for wetland management. Salinity management in the SJR basin is supported by a process-based model, the Watershed Analysis Risk Management Framework (WARMF). Wetlands are simulated with a "bathtub" analog where water levels are assumed to be the same over one model compartment and the storage volume depends on depth. The complexity and extent of hydrological features pose challenges for input data acquisition. Two approaches to estimating inflow and pond depth and determining water sources were assessed. Approach 1 used mostly monitored data, while Approach 2 used wetland manager knowledge. Approach 2 predicted outflow and salinity better than Approach 1, and an important benefit was the simulation of water reuse within the wetland complex, which was previously not implemented. Approach 1 is generally suited for estimating pond depth when a model compartment represents one wetland, while Approach 2 is suited for wetlands with large spatial extent, many hydrological features, and managed flows. The improved model will support wetland management. [ABSTRACT FROM AUTHOR]

Characterization of debris flow is critical to both risk assessment and hazard mitigation. Recent technologies enable onsite environmental monitoring sensors for geological disaster monitoring. However, the spatiotemporal understanding of debris flow in remote mountainous areas is still limited due to difficulties in observation networks and its complex driving conditions. Here we apply a coupled surface–subsurface hydrological model to examine the characteristics of the water movements near debris flow sites in southwest China. Our approach captured the temporal dynamics of infiltration, redistribution of soil moisture, groundwater storage, and lateral groundwater fluxes. The lateral groundwater flux and groundwater storage were informative indicators in identifying debris flow location. Such informativeness was only effective when hourly dynamics were analysed. Our findings provide new insight into quantifying debris flow susceptibility. This study suggests that the coupled surface–subsurface watershed modelling approach can be informative for preliminary monitoring, planning and management of debris flow. [ABSTRACT FROM AUTHOR]

This study utilizes the Simulator of Hydrologic Unstructured Domains (SHUD) model and the China Meteorological Forces Dataset (CMFD) to investigate the hydrological dynamics of the Buha River watershed, a critical tributary of Qinghai Lake, from 1979 to 2018. By integrating high-resolution terrestrial and meteorological data, the SHUD model simulates streamflow variations and other hydrological characteristics, providing valuable insights into the region's water balance and runoff processes. Key findings reveal a consistent upward trend in precipitation and temperature over the past four decades, despite minor deviations in daily precipitation intensity and relative humidity data. The SHUD model demonstrates high accuracy on a monthly scale, with Nash–Sutcliffe Efficiency (NSE) values of 0.72 for the calibration phase and 0.61 for the validation phase. The corresponding Kling–Gupta Efficiency (KGE) values are 0.73 and 0.49, respectively, underscoring the model's applicability for hydrological forecasting and water resource management. Notably, the annual runoff ratios for the Buha River fluctuate annually between 0.11 and 0.21, with significant changes around 2007 correlating with a shift in Qinghai Lake's water levels. The analysis of water balance indicates a net leakage over long-term periods, with spatial alterations in leakage and replenishment along the river. Furthermore, snow accumulation, which increases with altitude, significantly contributes to streamflow during the melting season. Despite the Buha River basin's importance, research on its hydrology remains limited due to data scarcity and minimal human activity. This study enhances the understanding of the Buha River's hydrological processes and highlights the necessity for improved dataset accuracy and model parameter optimization in future research. [ABSTRACT FROM AUTHOR]

Mountain railroad tunnels often need to cross deep and large active fracture zones during construction and tunnel site areas can be crisscrossed with underground water systems that carry huge volumes of water. The risk of sudden water inrush during the construction of mountain railroad tunnels is high, and uncontrolled inrush water runoff can negatively impact the tunnel site area. Therefore, risk assessments and prevention of inrush related surface runoff damage is key to reducing the ecological impact of tunnel construction. In this paper, the inrush water runoff of a typical mountainous railroad tunnel in the southwest of China was taken as the research object and the hydrological model and hierarchical analysis (AHP) were used to build a comprehensive risk assessment algorithm for surface runoff. Then the risk level of each section of the runoff channel was assessed under different flow scenarios and different inrush water runoff environmental risk prevention measures were proposed. [ABSTRACT FROM AUTHOR]

Aquatic macroinvertebrates, including many aquatic insect orders, are a diverse and ecologically relevant organismal group yet they are strongly affected by anthropogenic activities. As many of these taxa are highly sensitive to environmental change, they offer a particularly good early warning system for human-induced change, thus leading to their intense monitoring. In aquatic ecosystems there is a plethora of biotic monitoring or biomonitoring approaches, with more than 300 assessment methods reported for freshwater taxa alone. Ultimately, monitoring of aquatic macroinvertebrates is used to calculate ecological indices describing the state of aquatic systems. Many of the methods and indices used are not only hard to compare, but especially difficult to scale in time and space. Novel DNA-based approaches to measure the state and change of aquatic environments now offer unprecedented opportunities, also for possible integration towards commonly applicable indices. Here, we first give a perspective on DNA-based approaches in the monitoring of aquatic organisms, with a focus on aquatic insects, and how to move beyond traditional point-based biotic indices. Second, we demonstrate a proof-of-concept for spatially upscaling ecological indices based on environmental DNA, demonstrating how integration of these novel molecular approaches with hydrological models allows an accurate evaluation at the catchment scale. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

The Bekaa Plain in Lebanon, is a major agricultural region known for its fertile soil and extensive crops cultivation using groundwater resources which is recently became under stress due to population growth, urbanization, and climate change. The main aim of this study is to investigate the rate of groundwater recharge (GwR) into this region using APLIS Model which principally depends on altitude, slope, lithology, infiltration, and soil type. Using GIS-based for a multicriteria mapping approach, in this study additional factors were added to the model, by incorporating rainfall rates, stream density, and fracture density. Results revealed various recharge potential area in the Bekaa Plain, with 52 % classified as high to very high GwR zones, equivalent to approximately 42.5 % of precipitated water, which is a testament to the exceptional capacity of the region to replenish groundwater aquifers. The geographic distribution of GwR categories primarily depends on altitude and lithological characteristics. The exceptional GwR potential of the Bekaa Plain holds immense promise for mitigating water scarcity, fostering agricultural productivity, and ensuring the socioeconomic well-being of communities reliant on groundwater resources. [Display omitted] [ABSTRACT FROM AUTHOR]

The natural hydrological cycle of basins has been significantly altered by climate change and human activities, leading to considerable uncertainties in attributing runoff. In this study, the impact of climate change and human activities on runoff of the Ganjiang River Basin was analyzed, and a variety of models with different spatio-temporal scales and complexities were used to evaluate the influence of model choice on runoff attribution and to reduce the uncertainties. The results show the following: (1) The potential evapotranspiration in the Ganjiang River Basin showed a significant downward trend, precipitation showed a significant upward trend, runoff showed a nonsignificant upward trend, and an abrupt change was detected in 1968; (2) The three hydrological models used with different temporal scales and complexity, GR1A, ABCD, DTVGM, can simulate the natural distribution of water resources in the Ganjiang River Basin; and (3) The impact of climate change on runoff change ranges from 60.07% to 82.88%, while human activities account for approximately 17.12% to 39.93%. The results show that climate change is the main driving factor leading to runoff variation in the Ganjiang River Basin. [ABSTRACT FROM AUTHOR]

This study aims to identify suitable sites for Corrigiola litoralis (Strapwort) using hydrological and hydrodynamic models and geographical information techniques. It was conducted on the lower Elbe River in Czechia for 1981–2020 at six research sites. Hydrological models BILAN, GR4J and TUW were used together with HEC‐RAS hydrodynamic model. In addition to hydrological model simulations, we also used observed flows. We addressed identifying sites suitable for Corrigiola litoralis by introducing conditions reflecting the ecological requirements of this species. We compared the results with the findings recorded in the literature. The accuracy of this approach was 0.750 when the source of hydrological data was the observations, and the accuracy was, on average, 0.776 when using model simulations. The determined size of a suitable area had a significant variance. The closest to the size determined from the observed data were results based on the simulation of the TUW model. The GR4J model and BILAN model overestimated the size of suitable areas. The presented approach proved to be reasonable for modelling spatio‐temporal dynamics of sites suitable for Corrigiola litoralis. [ABSTRACT FROM AUTHOR]

An hourly hydrological forecasting model (GRP), used for flood forecasting in France, has been enhanced by developing a semi-distributed version (GRPS). This new model addresses some limitations of the original lumped approach by integrating flow observations from upstream stations to improve downstream flood predictions. A comparison of GRP and GRPS was conducted on a large set of flood events in nested catchments in France. Results indicate that GRPS slightly outperforms GRP up to the lead time of the last upstream flow observations reaching the downstream station, though performance gains vary widely across events. Event classification identified detrimental interactions between data assimilation methods and river routing model errors. A sensitivity test suggested that enhancing both the propagation model and the assimilation scheme could reduce poor performance at short lead times for some events. The study also demonstrated that upstream forecast quality significantly impacts downstream forecast accuracy. Overall, the findings confirm the benefits of incorporating multiple flow observations into a semi-distributed model and suggest several avenues for further improvement.

With rapid urbanization, the conflicts between economic development and environmental protection have become increasingly complex and exhibit multiscale characteristics. Constructing a green space ecological network (EN) is an effective approach to alleviate urban ecological issues and maintain regional ecological security. However, traditional EN research often focuses on a single scale, neglecting multiscale nesting and collaborative optimization of landscape elements, particularly the importance of spatial granularity. To address this deficiency, this study proposed a multiobjective, multiscale nested green space EN framework for Chengdu, China. Using landscape pattern index fitting functions, morphological spatial pattern analysis, the connectivity index, circuit theory, hydrological analysis model, scale nesting, and hierarchical transmission theories, a multiscale coupled synergistic and hierarchical optimization regional green space ecological security pattern (ESP) was developed. Analysis of the derived results revealed the following. ① With increasing granularity, the landscape pattern indices showed overall increasing, overall decreasing, and overall fluctuating trends. The optimal granularity threshold for large, medium, and small scales was 9, 6, and 3 m, respectively. ② Overall, 92, 66, and 88 ecological sources; 403, 278, and 321 ecological corridors; 72, 77, and 47 pinch points; 96, 94, and 88 barriers; and 182, 120, and 87 ecological nodes were identified in the city, central city, and old city areas, respectively. ③ The ENs demonstrated reasonable hierarchical nesting characteristics, essential for interscale material and energy circulation. There were 9 overlapping ecological sources with total area of 18.34 km2 and 47 overlapping corridors with total length of 53.37 km. These overlapping areas were key regions for ensuring stability in the overall regional ESP and continuity in biological processes, and should be given priority protection. In addition, there were also 19 overlapping pinch points, 12 overlapping barriers, and 4 ecological nodes that accounted for 26.39 %, 24.68 %, and 40.43 % of the total pinch points; 12.50 %, 12.77 %, and 13.64 % of the total barriers; and 2.20 %, 3.33 %, and 4.60 % of the total ecological nodes in the city area, central city area, and old city area, respectively. The high overlap of ecological strategic points across scales indicated cross-scale continuity of biological processes. ④ Enhancing the habitat quality of land types such as forest land, green spaces, grassland, and water bodies is crucial for restoration of ecological corridors and strategic points. Constructing a multiscale hierarchical linkage framework and formulating cross-scale pattern optimization and coordination schemes can effectively address ecological issues. The multiscale nested and synergistic green space ESP, constructed from the “source–corridor–strategic-point–network” framework, enhances the connectivity of regional landscape elements, increases energy flow efficiency, and strengthens spatial stability. The results provide methodological support for strategic policymaking focused on ecological spatial governance of an urban ecological protection at multiple levels.

Palmer Drought Severity Index (PDSI) is a widely used indicator to assess the intensity of drought based on a similar two-layer bucket (TLB) model, especially in arid and semi-arid regions. However, the TLB model cannot quantify surface water balance, effectively, which may lead to the mis-assessment of drought. Therefore, an optimized PDSI indicator named RW-PDSI based on the water balance is constructed. RW-PDSI is developed using the output of a newly proposed remote-sensing-based water balance assessment tool (RWBAT) model. The RWBAT model is constructed to depict the detailed hydrological processes on a fine spatial scale using high-resolution remote sensing data. To test its performance, we collected tree-ring data on the Loess Plateau (LP) to show the drought conditions in past decades (1991–2020). Tree-ring data provide a useful continuous multi-year data archive for drought assessment. Our results showed that the correlation between RW-PDSI and tree-ring data was statistically significant (p

Study region: The River Frome, a chalk stream in West Dorset, UK. Study focus: High-resolution hydrological models are required to integrate with the current wave of high-frequency data and advance our understanding of pollutant sources, pathways, and sinks. This presents several challenges in chalk streams, as their high-permeability and unique hydrogeological characteristics often leads to complex models that are overparameterized and computationally burdensome. In this article, we develop a novel and parsimonious modelling approach to describe the surface hydrology for a chalk stream in high resolution (15-minute frequency, ≤ 100 m cross-section spacing), using groundwater levels as a proxy for spring discharges. New hydrological insights for the region: Our results show that chalk stream dry-weather flows can be simulated accurately and parsimoniously at high-resolution (Nash-Sutcliffe efficiency = 0.97, mean relative error = 2.86 %, for a five-year period). We also show that spring discharges are the dominant form of flow accretion in all seasons and are critical to dilute sewage treatment inputs during the ecological growing season, whilst runoff and quick-flow pathways in the river valley corridor contribute a small proportion to annual flow accretion (< 5.2 %). Due to its simplicity and few parameters to calibrate, this modelling approach has broad applicability in lowland permeable catchments. Management implications include expeditious investigations of high-resolution freshwater quality responses to pollution and informing abstraction limits to sustain robust ecological conditions.

AbstractAn urban hydrological model (UHM) forced by radar-observed rainfalls was developed for producing rainstorm-related urban inundation maps for obtaining flash flood forecasts. The parameter sets of the land use and land cover (LULC) and urban drainage capacity derive from satellite multispectral images and high spatial resolution GIS datasets, relating to the urban hydrology and hydraulic properties. The hydrodynamics model was based on the simplified shallow water equation (SWE) neglecting the convective acceleration and pressure terms in the St. Venant equation. The intense convective rainstorm that induced heavy flash floods on 21 Jul. 2012 and the large-scale stratiform precipitation that occurred from 19 Jul. to 21 Jul. 2016 in Beijing were deliberately selected to perform flood-simulation and model-validation case studies. The simulation of severe flooding scenarios in the 2012 event was fairly reproduced and verified using media reports. In the case studies, the sensitivity tests verified that the flood intensity (1) would be enhanced slightly on pure impervious surfaces, (2) would be reduced significantly on pure pervious surfaces, and (3) would increase by 30%–60% without a drainage system. No false alarm could be perceived in the simulation of the 2016 event in a prolonging stratiform precipitation. The case studies confirmed the perspectives of using the hydrological model fed by QPEs/QPFs for flash flood forecasting and concluded that the hourly precipitation surpassing 30 mm/h would be indicative in impending flash floods in Beijing metropolis.

In the recent past, low-lying areas close to riverbanks and urban agglomerations have witnessed frequent floods due to inadequate mapping of flood zones and rapidly growing impervious areas in cities/towns. This inadequacy is due to the use of design storms for run-off estimates, which do not account accurately for antecedent moisture conditions. Furthermore, when using daily rainfall data, capturing accurate run-off estimates becomes challenging as rainfall characteristics such as duration and intensity are not accounted for. These problems can be addressed with long-term hourly rainfall data. However, most gauging stations in India have long-term daily rainfall data and hourly data for the last few years. There are various global methods to overcome this data limitation by disaggregating daily data into hourly data. However, well-established, peer-reviewed research on this process is still in infancy in India. Therefore, a methodology has been developed to disaggregate long-term daily rainfall data into hourly rainfall using statistical and probabilistic principles based on sample hourly data. In this study, the run-off estimates derived from disaggregated data closely match those obtained from actual hourly data with similar characteristics when simulated over the Belagavi city catchment area in Karnataka, India. The methodology developed relies on sample hourly rainfall, making it scalable across various locations. It holds promise for resilient urban stormwater infrastructure planning in the absence of long-term hourly rainfall data. [ABSTRACT FROM AUTHOR]

BACKGROUND: Irrigation is used extensively to enhance grain production and ensure food security. Many studies have used crop models and global climate models to study the variation of irrigated crop yield in the context of climate change. But most considered the influence of direct climate change but neglected the influence of irrigation water availability, which is affected by land‐use/cover change (LUCC) and indirect climate change, on irrigated crop yield. This study therefore developed a framework including Patch‐generating Land Use Simulation model, Soil and Water Assessment Tool, Agricultural Production Systems sImulator Model, and global climate models for exploring the impacts of LUCC, direct climate change, and indirect climate change on wheat yield in a typical watershed. RESULTS: Both LUCC and climate change caused increased runoff from October to May, and thus increased the irrigation water availability, by 51.6 and 30.7 mm per growing season under 1.5 and 2.0 °C warming, respectively. The combined influence of LUCC, direct, and indirect climate change increased wheat yield by about 18.5% and 15.5% in the context of 1.5 and 2.0 °C warming, respectively. The relative contribution of LUCC, indirect climate change and direct climate change to yield was 4.7%, 41.2%, and 54.1% under 1.5 °C warming, and 13.1%, 28.7%, and 58.2% under 2.0 °C warming, respectively. CONCLUSION: We suggest that changes in irrigation water availability should be considered from a watershed perspective when simulating the influence of climate change on crop yield, especially regional crop production estimation. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Water resources variability and availability in a basin affect river flows and sustain river ecosystems. Climate change and human activities disrupt runoff sequences, causing water environmental issues like river channel interruptions. Therefore, determining ecological flow in changing environments is challenging in hydrological research. Based on an analysis of long-term changes in hydrological and meteorological variables and interruption conditions in the semi-arid Liu River Basin (LRB), this study summarizes the controlling factors of river interruption at different temporal and spatial scales and proposes a framework to determine ecological flow under changing environments. Hydrological model and the monthly optimal probability distribution were used to determine the optimal ecological runoff of LRB. The results showed that from 1956 to 2017, precipitation and potential evapotranspiration in the basin showed no significant decreasing trend, but the streamflow significantly decreased, and the downstream interruption worsened, with an average annual interruption duration of 194 days at Xinmin Station from 1988 to 2017. The controlling factors of river interruption are as follows: (1) soil and water conservation measures in the upstream significantly reduce the runoff capacity; (2) the operation mode of the controlling reservoir in the middle reaches changes from "all-year discharge" to "winter storage and spring release" to "combined storage and supply," severing the hydraulic connection between upstream and downstream; and (3) siltation in the downstream river channel coupled with over-extraction of groundwater increases the seepage capacity of the river. The monthly ecological flow of Naodehai Reservoir was determined by considering the monthly seepage losses after reconstructing the natural runoff using the SWAT model and determining the optimal probability distribution function for monthly runoff. The findings are important for downstream LRB ecological restoration and for determining the ecological flow of other river basins in changing environments. [ABSTRACT FROM AUTHOR]

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The motivation of the present study is to study and address the limitations imposed on hydrological models and the spatial and temporal distribution of streamflow in the Watershed Diyala River (WDR) from January 1996 to April 2023, which has an area of 25,652 km², which is considered an important source of water for central and southern Iraq, to enable decision-makers to make a future management plan for streamflow. To achieve this motivation, the SWAT model was used and fed by input data such as a digital elevation model with a 30 m resolution, a soil map, the land use and land cover map with a spatial resolution of 30 m, weather data, and the daily streamflow data. Using the SUFI-2 algorithm and SWAT-CUP, the model was calibrated automatically. Statistically, using the R², NSE, and Pbs, the performance of the model was evaluated. The statistical analysis displays a perfect match between simulated and observed values when calibrating and verifying the model for streamflow. The results indicate that the monthly evapotranspiration rate and streamflow constitute 65% and 37% of the rainfall in WDR, respectively, and that the annual average surface runoff ranged from 320.358 to 8.325 m3/s. Therefore, it is possible to rely on the calibrated model, successfully verified with high reliability, to simulate the hydrological model in WDR. [ABSTRACT FROM AUTHOR]

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Background and Objectives: Runoff is the main variable for the hydrological analysis of the watershed, and due to its importance, for several decades, hydrological research has focused on the simulation of rainfall-runoff relationships, which has led to the presentation of many models. Due to the multiplicity of hydrological models, choosing an optimal model among various models is not a simple process. For this purpose, in the present research, after selecting the Galikash watershed from the most flood-prone basins in Golestan province, the performance of three hydrological models AWBM, Tank, and IHACRES were evaluated. The parameters of the models were also analyzed for sensitivity, and finally, the efficiency of the models in wet and dry periods was examined. Materials and Methods: The amount of daily runoff from the watershed for a period of 30 years (1989-2019) was simulated using each of the mentioned models and using four criteria Nash-Sutcliffe evaluation coefficient, root mean square error, coefficient of determination, and mean absolute percentage error, the performance of each model has been checked in two periods of calibration and validation. After optimizing the values of all the parameters, the sensitivity of the parameters of each model has been analyzed. Finally, after specifying the drought condition with the SPI index, the performance of each model for two wet and dry periods has been investigated and evaluated. Results: The results indicate that two rainfall-runoff models, IHACRES and AWBM, have almost similar performance. IHACRES model with Nash-Sutcliffe coefficients of 0.73 and 0.75 and RMSE of 2.97 and 2.94, respectively, in two calibration and validation periods and the AWBM model with Nash-Sutcliffe coefficients of 0.74 and 0.69 and RMSE of 2.92 and 3.24 for the calibration and validation periods have shown good performance. Still the Tank model was not successful in simulating the watershed runoff and its performance is lower than the two other models. The sensitivity analysis of the model parameters also showed that Kbase, H11, and f parameters are the most sensitive to the change of their values in AWBM, Tank, and IHACRES models, respectively. Finally, the comparison of the performance of the models in wet and dry periods showed that all the models have succeeded in simulating the watershed runoff with high accuracy in the wet period, so that the Nash-Sutcliffe coefficient is 0.79, 0.74 and 0.78 for the three AWBM, Tank and IHACRES models, respectively, shows the acceptable performance of the models in simulating the runoff in the wet period. The evaluation of the results has shown the poor performance of all models in the dry period, and the Nash-Sutcliffe coefficient obtained for the models is -0.05, -0.45, and 0.12, respectively, which shows the weakness of the models in simulation of the low flow. Conclusion: In the evaluation of the three hydrological models AWBM, Tank, and IHACRES in daily runoff simulation, it was found that in general, with a small difference, the IHACRES model shows better results than the AWBM model. Also, in wet periods, according to the evaluations, the AWBM model led to good accuracy, while the IHACRES model showed better performance than other models in dry periods. Considering this issue, it can be said that the models performed weaker in simulating low flows that occur during dry periods, while the knowledge of streamflow conditions during dry periods can play an effective role in managing water resources. Therefore, to increase their accuracy, a solution should be found. [ABSTRACT FROM AUTHOR]

Deep neural networks has been widely used in runoff forecasting and has achieved better performance than of conceptual hydrological models. However, most existing studies only use a single type of neural network model to build runoff forecasting models, which fails to fully explain the role of different types of neural networks in runoff forecasting. In this study, the convolutional neural networks (CNN), long short-term memory (LSTM) networks, and convolutional LSTM (ConvLSTM) were used to design a hybrid deep neural network model (HydroDL) for runoff prediction by referring to the structure of the conceptual hydrological model. The proposed model was used to predict the daily runoff at the Xinlong and Daofu hydrological stations in the upper reaches of the Yalong River, and several other runoff prediction models based on neural networks and conceptual hydrological models were developed for comparative study. Daily scale meteorological, hydrological and topographic data from January 2011 to December 2020 were used to train and validate the above models. The results show that: (1) the proposed HydroDL model has higher prediction accuracy and more stable prediction performance than runoff prediction models based on a single type of neural network. (2) effects of different parts in the HydroDL model are distinct, among which the terrain feature extraction and runoff conversion has the most significant and least significant effect, respectively, on improving the forecast results. [ABSTRACT FROM AUTHOR]

Hydrological models are traditionally calibrated against observed discharge. However, for a given model, similar performance for discharge simulation can be achieved through a variety of parameter combinations, some of which produce unrealistic simulations of non‐discharge variables. Thus, considering non‐discharge variables in calibration can help to reduce equifinality and give more realistic simulations. Few studies considered non‐discharge variables in calibration and evaluation and across a large sample of catchments. In this study, we calibrate the lumped SIMHYD model with several combinations of different hydrological variables such as discharge, actual evapotranspiration, soil wetness, and total water storage. The various calibration scenarios are conducted across 550 catchments in Australia spanning arid, tropical and temperate regions. We show that the performance of the discharge‐calibrated model for actual evapotranspiration, soil wetness, and total water storage is strongly related to the co‐seasonality of discharge and the respective variable. Considering actual evapotranspiration, soil wetness, or total water storage in addition to discharge in calibration improves the simulation of the added variable on average by 53%, 58%, and 41%, respectively, which comes at the expense of decrease in discharge performance by 5%. Cross‐benefits, that is, the improved simulation of a variable by considering another variable in calibration, mainly occurs between actual evapotranspiration and total water storage and are most pronounced in humid catchments with highly seasonal soil wetness. We also find that when using all four variables in model calibration, the trade‐off in model performance between discharge and the other variables gets more pronounced with increasing catchment wetness. Key Points: Using four different hydrological variables, multivariate calibration and evaluation were undertaken in 550 Australian catchmentsBenefit of multivariate calibration across non‐discharge variables is higher in humid than in (semi‐) arid climatesPronounced evaluation cross‐benefits between evapotranspiration and total water storage in temperate climates with seasonal soil wetness [ABSTRACT FROM AUTHOR]

Human activities have significantly altered the hydrological processes of rivers. In recent years, the increased focus on global water resource exploitation and land use changes has heightened the significance of related ecological and environmental issues. To investigate the land use changes in Hulan River Basin between 1980 and 2020, and the corresponding flow under various ecological standards, a quantitative assessment of land use changes in Hulan River Basin was conducted by analyzing the Land Use Dynamic Degree (LUD) index and the land use change matrix. Two types of models, namely natural runoff models and status quo runoff models, were developed to evaluate alterations in basin runoff. Various hydrological techniques were utilized to calculate the ecological water deficit in Hulan River Basin. The results suggest the following: (1) human consumption comprises approximately 40% of surface water resources, with Hulan River Basin exhibiting a moderate consumption level; (2) when determining the minimum ecological flow, the Distribution Flow Method (DFM) method yielded slightly higher outcomes compared to alternative methodologies; both the variable Q90 method and DFM (Q2) method satisfy 10% of the natural river flow, however, in terms of capturing the hydrological pattern, DFM exhibits a slightly lower fitting degree compared to the variable Q90 (monthly average flow with 90% guarantee rate) method; (3) DFM is identified as scientifically reasonable for determining the most suitable ecological flow in comparison to other hydrological methods; (4) despite the widespread water scarcity in Hulan River Basin, the variance between most periods and the ideal ecological flow remains minimal, indicating that severe water shortages are uncommon. [ABSTRACT FROM AUTHOR]

Distributed hydrological models based on shallow water equations have gained popularity in recent years for the simulation of storm events, due to their robust and physically based routing of surface runoff through the whole catchment, including hill slopes and water streams. However, significant challenges arise in their calibration due to their relatively high computational cost and the extensive parameter space. This study presents a surrogate-assisted evolutionary algorithm (SA-EA) for the calibration of a distributed hydrological model based on 2D shallow water equations. A surrogate model is used to reduce the computational cost of the calibration process by creating a simulation of the solution space, while an evolutionary algorithm guides the search for suitable parameter sets within the simulated space. The proposed methodology is evaluated in four rainfall events located in the northwest of Spain: one synthetic storm and three real storms in the Mandeo River basin. The results show that the SA-EA accelerates convergence and obtains superior fit values when compared to a conventional global calibration technique, reducing the execution time by up to six times and achieving between 98% and 100% accuracy in identifying behavioral parameter sets after four generations of the SA-EA. The proposed methodology offers an efficient solution for the calibration of complex hydrological models, delivering improved computational efficiency and robust performance. [ABSTRACT FROM AUTHOR]

Leaky barriers have become an important mitigation option within natural flood management to reduce downstream flood risk. Modelling is a key tool to aid in the design of leaky barrier installations for flood mitigation, but there is limited evidence about the accuracy of model representations of the system. Here, the hydrological model SWAT was combined with a water routing model that simulates multiple leaky barriers as permeable sluice gates. Storage behind individual barriers was quantified using barrier dimensions and LIDAR topography. The model was applied to a series of 27 leaky barriers installed as part of a long‐term manipulation experiment into a 11‐km2 intensive lowland agricultural catchment in Leicestershire, England. Evaluation of the model against flow data collected before and after leaky barrier installation and time‐lapse photography taken across storm events at individual barriers demonstrated robust model performance (Nash‐Sutcliffe efficiency and R2 across 19 validation events were 0.84 ± 0.14 and 0.91 ± 0.08, respectively). Empirical and modelling data were then combined to demonstrate that the 17,700 m3 of water storage provided by the 27 leaky barriers reduced peak flows at the catchment outlet by 22 ± 6% and delayed the peak in flow by up to 5 h for 11 storm events recorded after all barriers had been installed. The volume of storage utilised prior to the flood event was a key factor influencing the reduction in peak flow, and a sensitivity analysis indicated that barriers should be permeable to accelerate drain‐down of the barrier and help to mitigate risk from multiple storm events occurring in sequence. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop artificial neural network (ANN) models to predict floods in the Branco River, Amazon basin. The input data for the models included the river levels and the average rainfall within the drainage area of the basin, which was estimated from the remotely sensed rainfall product PDIRnow. The hourly water level data used in the study were recorded by fluviometric telemetric stations belonging to the National Agency of Water. The multilayer perceptron was used as the neural framework of the ANNs, and the number of neurons in each layer of the model was determined via optimization with the SCE-UA algorithm. Most of the fitted ANN models showed Nash–Sutcliffe efficiency index values greater than 0.9. It is possible to conclude that the ANNs are effective for predicting the flood levels of the Branco River, with horizons of 6, 12 and 24 h; thus, constituting a viable option for use in river-flood warning systems in the Amazon basin. For the forecast with a 24-h horizon, it is essential to include the average rainfall of the basin that accumulated over the last 48 h as input data into the ANNs, along with the levels measured by the streamflow stations. The indirect rainfall estimates provided by PDIRnow are an excellent alternative as input data for ANN models used to predict floods and constitute a viable solution for regions where the density of rain gauge stations is low, as is the case in the Amazon basin. [ABSTRACT FROM AUTHOR]

Water storage is one of the key components of terrestrial water balance, therefore its accurate assessment is necessary for a sufficient description of hydrological processes within river basins. Here we compare terrestrial water storage using calibrated hydrological model ECOMAG forced by gauge observations, uncalibrated INM RAS–MSU land surface model forced by reanalysis and GRACE satellite-based data over Northern Dvina and Pechora River basins. To clearly identify differences between the datasets long-term, seasonal and residual components were derived. Results show a predominance of the seasonal component variability over the region (~64% of the total) by all datasets but INM RAS–MSU shows a substantial percentage of long-term component variability as well (~31%), while GRACE and ECOMAG demonstrate the magnitude around 18%. Moreover, INM RAS–MSU shows lowest magnitude of annual range. ECOMAG and INM RAS–MSU is distinguished by earliest begin of TWS decline in spring, while GRACE demonstrates latest dates. Overall, ECOMAG has shown the lowest magnitude of random error from 9 mm for Northern Dvina basin to 10 mm for Pechora basin, while INM RAS–MSU has shown largest one.

Understanding hydrological processes is achieved using modeling approaches due to the extensive and complex interactions between various environmental elements. Hydrological modeling is based on empirical equations that require parameter calibration and model validation to improve performance and evaluate results. This process requires the implementation of absent or lacking data in many ungauged catchments. Therefore, Hydrological Modeling in Ungauged Catchments (HMUC) is an important research area in hydrology. Many researchers tried to develop appropriate technology for this purpose. This review article describes regionalization, satellite observation and machine learning based technologies used for this purpose and presents relevant issues. Key studies worldwide using regionalization, satellite observations and machine learning approaches to develop HMUC have been reviewed here. This study promotes research on HMUC by describing the performances of these methods in different climatic, and geographic conditions. It identifies potential application limitations to guide the framing of future requirements and opportunities for HMUC.

This paper presents a hydrological assessment of the 113,981 km2 Meta River basin in Colombia using 13 global climate models to predict water yield for 2050 under two CMIP6 scenarios, SSP 4.5 and SSP 8.5. Despite mixed performance across subbasins, the model was notably effective in the upper Meta River subbasin. This study predicts an overall increase in the basin's annual water yield due to increased precipitation, especially in flatter regions. Under the SSP 4.5, the Meta River basin's water flow is expected to rise from 5141.6 m3/s to 6397.5 m3/s, and to 6101.5 m3/s under the SSP 8.5 scenario, marking 24% and 19% increases in water yield, respectively. Conversely, the upper Meta River subbasin may experience a slight decrease in water yield, while the upper Casanare River subbasin is predicted to see significant increases. The South Cravo River subbasin, however, is expected to face a considerable decline in water yield, indicating potential water scarcity. This study represents a pioneering large-scale application of the InVEST–AWY model in Colombia using CMIP6 global climate models with an integrated approach to produce predictions of future water yields. [ABSTRACT FROM AUTHOR]