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Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments. [ABSTRACT FROM AUTHOR]

In many regions globally, snowmelt‐recharged mountainous karst aquifers serve as crucial sources for municipal and agricultural water supplies. In these watersheds, complex interplay of meteorological, topographical, and hydrogeological factors leads to intricate recharge‐discharge pathways. This study introduces a spatially distributed deep learning precipitation‐runoff model that combines Convolutional Long Short‐Term Memory (ConvLSTM) with a spatial attention mechanism. The effectiveness of the deep learning model was evaluated using data from the Logan River watershed and subwatersheds, a characteristically karst‐dominated hydrological system in northern Utah. Compared to the ConvLSTM baseline, the inclusion of a spatial attention mechanism improved performance for simulating discharge at the watershed outlet. Analysis of attention weights in the trained model unveiled distinct areas contributing the most to discharge under snowmelt and recession conditions. Furthermore, fine‐tuning the model at subwatershed scales provided insights into cross‐subwatershed subsurface connectivity. These findings align with results obtained from detailed hydrogeochemical tracer studies. Results highlight the potential of the proposed deep learning approach to unravel the complexities of karst aquifer systems, offering valuable insights for water resource management under future climate conditions. Furthermore, results suggest that the proposed explainable, spatially distributed, deep learning approach to hydrologic modeling holds promise for non‐karstic watersheds. Key Points: An explainable spatially distributed deep learning hydrologic model is built using a spatial attention mechanismThe model trained with streamflow at the watershed outlet simulates discharge at subwatershed scales reasonably wellRecharge‐discharge pathways suggested by attention weights are mostly consistent with hydrogeochemical tracer studies [ABSTRACT FROM AUTHOR]

Coastal cities face increasing flood risks due to urban expansion and climate change. This study simulates flood hydrographs in the Netravathi River watershed using the HEC-HMS hydrological model to improve flood management in Mangalore, Karnataka, which has experienced severe floods recently. The SCS curve number (CN) method was selected for its efficacy in estimating surface runoff across diverse land use and soil types. GIS tools analyzed spatial data on soil types, drainage, and land cover changes from 1990 to 2021, enhancing runoff forecast accuracy. Model calibration optimized parameters with historical flood events, and validation used independent past flood events. Validation showed a strong correlation between observed and simulated runoff hydrographs, particularly during peak discharge periods. A high Nash–Sutcliffe Efficiency (0.89) and low Percentage Bias (0.65%) demonstrate the model's accuracy. The coefficient of determination (0.86) confirms the model's predictive capability. The HEC-HMS model effectively forecasts streamflows in ungauged catchments within the Netravathi sub-basin using measured rainfall data, enabling more precise planning and management of water resource developments. [ABSTRACT FROM AUTHOR]

This study models the rainfall-runoff relationship in the Kebir-Rhumel River watershed in the Constantine Highlands, Algeria, using data from three concomitant rainfall and hydrometric stations. Statistical tests confirmed the absence of breaks in the series. We applied four conceptual models (GR4J, IHAC6, MORDOR, TOPMO8) and neural network models (RNN, NARX, LSTM) over three- and ten-year periods. Among the conceptual models, GR4J provided the best fit, highlighting the non-stationary nature of the relationship. The PMC neural network model performed well over three years but was less effective over ten years due to low flow influence. Notably, the NARX-RNN and RNN-LSTM models showed excellent predictive accuracy, with NARX-RNN perfectlycapturing flow dynamics and RNN-LSTM achieving minimal RMSE and high correlation coefficients. This study lies the comparative analysis of conceptual and neural network models, specifically the NARX-RNN and RNN-LSTM models, which have not been extensively applied in this context. This research fills the gap in understanding the effectiveness of neural network models in modelling non-stationary rainfall-runoff relationships in the region. [ABSTRACT FROM AUTHOR]

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies. [ABSTRACT FROM AUTHOR]

The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak‐sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash‐Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision‐making process, three primary flood‐inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation‐dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics. Plain Language Summary: The formation of floods is often dynamically influenced by multiple driving factors. Traditional methods based on statistics or hydrological models struggle to clearly understand flood mechanisms due to their limitations. Although deep learning has become an effective tool for flood modeling, its black‐box nature makes it difficult to enrich understanding of flood processes through it. Our proposed interpretable deep learning offers a perspective to unveil the dynamic drivers of floods by extracting patterns from deep learning in a reverse deduction approach. We started with models that perform best on flood extremes and identified the dominant factors and variations of floods across 482 watersheds in the United States. These variations exhibit regional characteristics, with precipitation playing a more significant role and showing more pronounced trends in watersheds where floods are primarily driven by precipitation. We found that variables from 2 days before a flood could have a greater impact than those from the previous days. Furthermore, we revealed the combined impact of variables on flooding through deep learning, showcasing their dynamic changes. Interpretable deep learning explores a new avenue to derive new hydrological insights from inverse data, helping hydrologists better understand natural physical processes. Key Points: Proposed a framework for interpretable time series deep learning based on optimal performance to reveal the dynamic drivers of floodsUnveiled the changes in the dominant driving factors of floods and the roles of driving factors at different moments before a floodElucidated the dynamic responses of floods to the interactions among driving factors [ABSTRACT FROM AUTHOR]

The aim of this article was hydrological modeling using chicken swarm optimization algorithm. Interactions between meteorological and hydrometric data been identified as a major conducting factor stationarity in rainfallrunoff relationships. The methodology employed in this work is rainfall-runoff modeling using chicken swarm optimization algorithm. This approach based on randomly selection and chicken society behavior. For the efficiency evaluation of chicken swarm optimization algorithm, a variety of statistical parameters have been used with acceptable values of Nash–Sutcliffe Efficiency (NSE) with 0.38% and index of agreement (IA) with 0.91%. The important and new results are the hydrological models (empirical models) adaptation to Algerian conditions especially in ungagged basins. Therefore, the proposal of the present modeling is a technique using the CSO method to model the rainfall-runoff relationship and discharge forecasting in the Oued El Melah basin located in Guelma city. CSO proves to be a valuable model for studies of the rainfall and flood runoff response to protection of agglomerations against flooding risks projections. [ABSTRACT FROM AUTHOR]

This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson's r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson's r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.

Rainfall–runoff modeling is essential for the hydrological analysis of basins; however, the traditional modeling approach does not incorporate geological features such as fractures and fissures in the modeling task. These features are significant in the water loss during a rainstorm, which should be incorporated to obtain realistic rainfall–runoff results. A novel approach is presented here in to quantify the geological features and link them to the curve number (CN) method. The proposed methodology has not been applied in the literature. This approach is validated on five gauged basins, namely, Yiba, Al Lith, Liyya, Habawnah, and Tabalah, in the southwest part of Saudi Arabia. Four major stages are conducted. The first stage is the extraction of the geological lineaments using remote sensing and geographical information system technology; the second stage is estimating CN from rainfall–runoff data; the third stage is developing a relationship between CN and lineament density (LD); and the final stage is evaluating the developed equations on hydrological response. The least-squares method is employed to minimize the difference between observed and predicted runoff and determine the optimum range of CN. The research provides a comprehensive understanding of hydrological processes in fractured geologic systems and explores the influence of fractures on curve number. This study identifies two major lineament trends aligned with the Arabian trend direction, namely, north-northwest (NNW)–south-southeast (SSE) and north-northeast (NNE)–south-southwest (SSW). Furthermore, a moderate inverse correlation is established between LD and CN, highlighting the significance of geologic fractures on the hydrological response. The findings of this study provide insight into how the geological fissures in the mountainous region affected the rainfall–runoff response that leads to a low value of CN due to the water loss in the fissures and faults. As a result, this study clearly demonstrates the importance of the geological structures on rainfall–runoff responses.

Urban flooding problem has been exacerbated in recent times, especially in developing nations, due to haphazard changes in land use and land cover (LULC) resulting from rapid urban expansion, coupled with river encroachments and inadequately engineered river management structures. Kathmandu Valley Watershed (KVW), encompassing Kathmandu, Bhaktapur and Lalitur districts, the fastest growing cities in South Asia, is constantly growing, with a significant increase in urban areas. Due to urbanization, the watershed's water storage capacity is diminishing, while surface runoff volume and rate are accelerating. We evaluated the isolated as well as the integrated impact of multiple scenarios of LULC change and river encroachment on flood inundation characteristics in KVW. LULC prediction revealed an increase in built-up areas by 113% between 1990 and 2020, which are further projected to increase by 29% by 2050. Inundation modeling using Rainfall-Runoff Inundation (RRI) model showed that rather than the increase in inundation extent, the depth of inundation is projected to increase in future as a result of increasing urban areas. Furthermore, our research highlighted that the impact of river width encroachment had a more substantial effect on flooding compared to changes in LULC alone. Similarly, integrated impact of LULC change and river encroachment was more pronounced than the impact of change in LULC alone. The aggregate of observations leads to the conclusion that the encroachment of rivers is the predominant factor contributing to the flooding issue within the KVW. The findings of the study is anticipated to assist policymakers in effective land use planning and in proposing appropriate development initiatives concerning the river environment. [ABSTRACT FROM AUTHOR]

Fluoroquinolone antibiotics are pseudo-persistence in aquatic environments as emerging contaminants. In this study, the Shitoukoumen Reservoir and its four inflow rivers were selected as a typical river-reservoir system. The factors, including river input, rainfall, flow regulation, population, antibiotic half-life, and antibiotic emission path were investigated to find out the reasons for antibiotic variations in the river-reservoir system during the reservoir discharge and filling periods. The results showed that ofloxacin was the most prevalent antibiotic in both the reservoir and inflow-river water, with median concentrations of 18.6 ± 2.4 and 39.7 ± 6.2 ng L−1, respectively. The concentrations of enrofloxacin, norfloxacin, and ofloxacin in the reservoir fluctuated during the reservoir discharge and filling periods. Enrofloxacin was only detected in the reservoir during the discharge period due to its input from the inflow rivers. Artificial reservoir discharge decreased the dissolved concentrations of norfloxacin and ofloxacin in the reservoir, whereas reservoir filling increased their accumulation. The Shuangyang, Yinma, and Chalu Rivers contributed relatively high individual antibiotic mass loads to the reservoir, ranging from 66.7% to 100%. Antibiotic-containing waste runoff during rainfall events increased input contributions of antibiotics from the Shuangyang and Chalu Rivers. The risk assessments indicated that norfloxacin posed a high risk to the most sensitive algae and therefore required greater attention. The study provides a comprehensive insight into fluoroquinolone antibiotic residues in the river-reservoir system during the periods of reservoir discharge and filling, and is of importance for their future monitoring and management. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, the change in hydrological characteristics and the response of the urban watershed to rainfall and flooding has become increasingly evident in terms of quantity and quality for human life and resources. Preventing the waste of these effluents and consequently compensating for the lack of water resources is an effective step in this regard. In this study, rainfall-runoff simulations in SWMM were used to investigate the hydrological response of the Neyshabur urban catchment to production runoff using low-impact development methods under six different scenarios. The inflow data corresponding to the hyetograph of five precipitation events at the outlet point were collected in the field using the float method. Statistical evaluations (Nash-Sutcliffe, RMSE, percent bias, and coefficient of determination) during the calibration and validation phases proved the good performance of the model (peak-discharge and its time). The percentage of impervious surfaces was found to be the most effective parameter for peak runoff discharge. Overall, the implementation of the following scenarios was evaluated: BioRetention-Cell, Porous-Paver-Systems, Infiltration-Trench, Vegetative-Swale and the Combination-of-the-Scenarios resulted in a reduction in runoff of 8.51, 9.58, 15.79, 10.25 and 43 percent, respectively. Finally, in the return periods of 2, 5 and 10 years, the best performance has been achieved by Combination-of-the-Scenarios, Infiltration-Trench, Vegetative-Swale, Porous-Paver-Systems and Bio-Retention-Cell. The results of the present study have shown that the implementation of low-impact development strategies together with the conventional urban drainage network in the SWMM is a good estimate for testing the performance and implementation methods of different scenarios for experts and managers. [ABSTRACT FROM AUTHOR]

Abstract The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak‐sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash‐Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision‐making process, three primary flood‐inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation‐dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics.

Abstract Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km2. Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash–Sutcliffe Efïciency (NSE), Coefficient of Determination (R2), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions. [ABSTRACT FROM AUTHOR]

The computation of hydrographs in large watersheds necessitates utilizing channel routing, which calculates the movement of hydrographs along channel branches. Routing methods rely on an implicit scheme to facilitate numerical resolution, which requires more computational time than the explicit scheme. This study presents an explicit scheme channel routing model that offers a versatile approach to open channel flow analysis. The model is based on mass conservation principles and Manning equations, and it can accommodate varying bed slopes, making it highly adaptable to diverse hydraulic scenarios. In addition, the proposed model considers backwater effects, which enhances its applicability in practical scenarios. The model was tested in a practical application on a rectangular channel with a width of 7 m, and the results showed that it can accurately predict outflow hydrographs and handle different flow conditions. Comparative analyses with existing models revealed that the proposed model's performance in generating water flow oscillations was competitive. Moreover, sensitivity analyses were performed, which showed that the model is highly responsive to parameter variations, such as Manning's coefficient, bed slope, and channel width. The comparison of peak flows and peak times between the proposed model and existing methods further emphasized the model's reliability and efficiency in simulating channel routing processes. This research introduces a valuable addition to the field of hydrology by proposing a practical and effective channel routing model that integrates essential hydraulic principles and parameters. The results of the proposed model (lumped routing) are comparable with the solution provided by the Muskingum–Cunge method (distributed routing). It is of utmost importance to note that the proposed model applies to channel branches with bed slopes below 6°. [ABSTRACT FROM AUTHOR]

Woodlands can reduce downstream flooding, but it is not well known how the extent and distribution of woodland affects reductions in peak flow. We used the spatially distributed TOPMODEL to simulate peak flow during a 1 in 50 year storm event for a range of broadleaf woodland scenarios across a 2.6 km2 catchment in Northern England. Woodland reduced peak flow by 2.6%–15.3% depending on the extent and spatial distribution of woodland cover. Cross slope and riparian woodland resulted in larger reductions in peak flow, 4.9% and 3.3% for a 10‐percentage point increase in woodland cover respectively, compared to a 2.7% reduction for woodland randomly located across the catchment. Our results demonstrate that increased woodland cover can reduce peak flows during a large storm event and suggest that targeted placement of woodland can maximise the effectiveness of natural flood management interventions. [ABSTRACT FROM AUTHOR]

The present study aims to identify potential locations for small‐scale hydroelectric power (HEP) stations in hilly regions for the purpose of generating renewable energy. A rainfall‐runoff (R‐R) model of the Beas River catchment was established using the MIKE 11 NAM to estimate the available discharge. The model was calibrated and validated over the period of June‐2015–May‐2018 and June‐2018–May‐2020, respectively, using daily observed discharge data at the Pandoh Dam site. The model exhibited good performance with a coefficient of determination (R2) of 0.82 during calibration and 0.70 during validation and a water balance of −0.01% and −18%, respectively. However, Lmax, CK1, CK2 and CQOF are found most sensitive parameters during the calibration. Further, thirteen major streams of order five or higher were selected for the assessment of hydropower potential, resulting in the identification of 131 potential run‐of‐river (ROR) hydropower sites. The hydropower potential at two proposed sites, Bhang SHEP (9 MW) and Raison SHEP (18 MW), was estimated to be 11 and 15 MW, respectively, using 90% dependable flow. The results demonstrate the effectiveness of using Digital Elevation Model (DEM) and Geographic Information System (GIS) techniques for determining hydropower potential in ungauged basins in the Himalayas. [ABSTRACT FROM AUTHOR]

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling. [ABSTRACT FROM AUTHOR]

Rainfall-runoff simulation models require calibration to ensure accurate results. The present study assesses the efficacy of the Melody Search algorithm (MeS) in auto-calibration of the SWAT, by comparing it to other commonly used calibration methods in SWAT-CUP, namely, SUF12, GLUE, ParaSol, and PSO. In order to assess the MeS algorithm's performance, the continuous rainfall-runoff simulation was implemented using daily time-step data from the Taleghan Basin in northern Iran, spanning a 10-year period. The calibration of parameters was established through developing a FORTRAN program. The Nash–Sutcliffe efficiency (NSE) index indicator in SUFI2, GLUE, ParaSol, PSO, and MeS algorithms was found to be 0.647, 0.6, 0.628, 0.625, and 0.66, respectively. Thus, based on these results, the proposed MeS algorithm outperforms all the native calibrating methods in SWAT in all iterations. MeS specifically outperforms all native approaches in reaching a solution after 100 iterations. The results imply that MeS is an algorithm that quickly converges and functions as a reliable calibration technique for SWAT applications. [ABSTRACT FROM AUTHOR]

Low‐gradient coastal watersheds are susceptible to flooding caused by various flows such as rainfall‐runoff, astronomical tides, storm surges, and riverine flows. Compound flooding occurs when at least one coastal flood driver occurs simultaneously or in close succession with a pluvial and/or fluvial flood driver, such as during a tropical cyclone event. This study presents a one‐dimensional (1‐D), reduced‐order physics compound inundation model tested over an idealized coastal watershed transect under various forcing conditions (e.g., coastal and pluvial) that varied in magnitude, time, and space. This study aims to evaluate each flooding mechanism and the associated hydrodynamic responses by performing a sensitivity analysis and developing a non‐linear equation that could correlate the flood drivers with the severity of its flood. Compound inundation levels are affected by the magnitude and timing of each flooding mechanism. Results highlight the need to consider momentum exchange during a compound event and the importance of reduced‐physics approaches that can improve the interaction between flood drivers when paired with a moving coupling node approach. The desire is a more holistic compound inundation model that can be a critical tool for decision‐makers, stakeholders, and authorities who provide evacuation planning to save human lives and enhance resilience. Plain Language Summary: Intense rainfall, tides, and hurricane storm surges can flood coastal regions with mild sloping terrain. When multiple flooding events occur at the exact location, the inundation effects can be aggravated by the interaction of these physical processes. This study aims to disentangle the complexities of interactive physics according to fundamental concepts. Thus, identifying regions affected by hydrological and coastal processes can improve flood mapping techniques for coastal communities in future efforts. Results show that each flood event's strength and time of occurrence affect the total inundation levels. Furthermore, the flow velocity from these flood processes is essential when they interact. Our research can serve as a foundation for further studies to enhance tools that accurately predict these flood events, resulting in better planning and preparedness for worldwide coastal communities to reduce property damage and loss of lives. Key Points: A reduced‐physics one‐dimensional flood model was developed to assess compound floods at an idealized coastal watershed transectA moving coupling node approach can capture the momentum exchange between pluvial and coastal flood driversA non‐linear equation correlating storm characteristics with flood severity was developed to examine the driver's interaction [ABSTRACT FROM AUTHOR]

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]

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research. [ABSTRACT FROM AUTHOR]

Study region: Flood formation involves complex nonlinear processes and numerous variables, with data-driven models becoming a key non-engineering approach to flood prevention and mitigation. Yet, single machine learning models are insufficient to fully capture the complex dynamics of the flood process. Study focus: We propose an interpretable flood forecasting hybrid model based on Transformer, LSTM, and Adaptive Random Search Algorithm (AGRS), termed as AGRS-LSTM-Transformer. Investigating the predictive performance of the hybrid model, this study compares it against AGRS-LSTM, AGRS-Transformer, AGRS-BP, and AGRS-MLP models, utilizing flood data from 1971 to 2013 years in the Jingle watershed. New hydrological insights for the region: The AGRS-LSTM-Transformer model demonstrates superior performance over benchmark models, achieving accurate runoff forecasts with a lead time of 1–6 hours. It achieves a Nash-Sutcliffe Efficiency (NSE) greater than 0.905, and root mean squared error (RMSE), mean absolute error (MAE), Bias, and relative error (RE) values for the runoff process below 34.891 m³/s, 25.125 m³/s, 9.537 %, and 8.025 %, respectively. The coupling between the LSTM layers and the Transformer input components plays a crucial role in the architecture of the AGRS-LSTM-Transformer model. Rainfall from stations situated near the main river channel and downstream flow sections exerted a positive influence. Runoff from the preceding moment significantly impacts the predicted flow, with the contribution of runoff inputs exceeding that of rainfall. Inputs nearer to the forecast moment do not invariably improve forecasting accuracy, and historical rainfall and runoff volumes with extended lag times may detrimentally impact the model prediction. The study highlights the potential of hybrid data-driven models in enhancing the accuracy of flood forecasting, offering insights for reducing uncertainty in flood prediction and interpreting machine learning flood forecasting models. This provides a scientific basis for flood early warning systems and water resource management.

Introduction Rainfall-runoff modeling is one of the most important components of hydrological processes in water resources management and accurate estimation of runoff and river flow in the short and long term can be of great help to various sectors of water engineering. In several types of rainfall-runoff models, the unit hydrograph methods are still a useful tool for flood estimation in many, except non-gauged, basins. The unit hydrograph is the same as the unit pulse response function of a linear hydrological system. The tank model is one of the hydrological models for analyzing river flow characteristics. In hydrological analysis, simulation models are often used to describe and predict basin response to rainfall events based on mathematical and physical knowledge. In this article, using the concept of linear system theory, the pulse response functions of the runoff components (surface runoff and base flow) using the reservoir model for several flood events related to the two Navrood basin in Gillan province and Liqvan basin in the East Azerbaijan province has been extracted. Since Simulink can schematically show the dynamic relationship between hydrological components such as rainfall, runoff, storage, evapotranspiration, and runoff, it can be useful for rainfall-runoff modeling. Therefore, Modeling is done in the Simulink MATLAB environment. The modular design and block library can help users focus on hydrological analysis, including modeling strategy development, parameter estimation, and model application. Materials and Methods In the present study, to evaluate the capability of the tank model in different climates, the model has been implemented for two regions with dry and wet climates to evaluate the effectiveness of the tank model by comparing the results. Therefore, the Navrood representative basin in Gilan province was selected for a wet climate, and the Liqvan representative basin in East Azerbaijan province for a dry and semi-arid climate. The response of the entire basin to the rainfall that fell on its surface has been determined using the conceptual model of the tank. In order to extract the unit pulse response function for the runoff caused by precipitation, the model of three tanks in series with holes on the side and bottom has been used to show the types of currents prevailing in the process of forming runoff. The internal dependence of reservoirs is described using exponential functions of model parameters. Estimating the model parameters was performed using the cluttered evolution optimization method or SCE-UA for short, a conceptual optimization method. In addition, to extract unit pulse response functions and evaluate the model's efficiency in predicting flood events, it was tried to select events that have rained in the entire basin and correspond to flood events in terms of occurrence time. Results and Discussion The results obtained in this research showed that the tank model provides good results in estimating the peak discharges and the time to reach the peak discharges in the two representative basins of Navrood and Liqvan with two different climates. The parameters of the model, which actually reflect the geomorphological characteristics of the basin, are almost constant, and only the changes in soil moisture storage are variable in the runoff calculations. Response functions as exponential functions of model parameters have simulated the different roles of flow components (quick surface, quick subsurface, delayed subsurface, and underground flow) in relation to the precipitation process. As can be deduced from the results obtained for the Navrood basin, the slow runoff accounts for a major part of the total runoff in the falling limb of the hydrograph. The relations extracted for the response functions of the unit pulse for ru as a unit input that happened in the duration of ∆t have been obtained parametrically, therefore, for different combinations of ru and ∆t, several unit pulse response functions can be extracted. Examining the parameters of the model obtained from the SCE-UA algorithm shows that due to the large value of the b2 parameter compared to other parameters, a major part of the volley losses is penetration losses. In fact, the high permeability of the surface layers of the soil justifies the high value of the b1 parameter compared to other parameters. Conclusion The performance results of the tank model showed that the model has a relatively good capability in predicting the runoff affected by the rainstorm. The comparison of the computational and observed hydrographs shows that the mentioned values have a good correlation. According to the results, it can be seen that the fast runoff, which usually appears in the form of surface flow and waterways in the basin, occupies a significant part of the entire flood hydrograph during the flooding process, and in terms of durability has a shorter duration than slow runoff. Slow runoff hydrograph is slowly affected by precipitation during the flooding process, but in terms of durability, it has a longer duration.

Introduction Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, It is crucial to have a deep understanding of the future condition of water resources within the country's watersheds (Fathollahi et al., 2015). By utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. Support vector machine (SVM) and Random Forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (Kisi & Parmarm, 2016). The most recent study conducted by Zarei et al. (2022) evaluated the risk of flooding using data mining models of SVM and RF (case study: Frizi watershed). By analyzing the results, it was found that both the SVM algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. The purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the Maragheh plain (Khormazard station on the Mahpari chai river and Bonab station on the Sufichai river) in East Azerbaijan province using support vector machine and random forest modeling algorithms. This study has been conducted over a period of 43 years, making it one of the few research cases in this area. Materials and Methods The Maragheh Sufi chai basin is situated in the eastern region of Lake Urmia, within the East Azarbaijan province. It covers an area of 611.89 square kilometers and is located between longitudes 45° and 40´ to 46° and 25´and latitudes from 37° and 15´ to 37° and 55´ north. The average height of the basin is 1767 meters above sea level (Sharmod et al., 2015). Based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. The first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. To simulate rainfall-runoff, first the average rainfall of Maragheh plain was calculated by polygonal method. Subsequently, this data was combined with the discharge output from Bonab and Khormazard stations, with a one-day time lag. These inputs were then utilized in two models, SVM (kernel function) and RF. For this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. Then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. Several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. The inputs consist of the monthly Q and P values that were recorded previously (Pt, Qt-1), while the output represents the current runoff data (Qt), with the subscript t indicating the time step. As a result, two input combinations were constructed from Q and P data (as seen in Table 3) and SVM and RF models were used for rainfall-runoff modeling to determine the optimal input combination. Calculating average rainfall through the Thiessen Polygons method Thiessen polygons, which are Voronoi cells, are used to define rainfall polygons that correspond to the surface area (Ai). These polygons are used to weight the rainfall measured by each rain gauge (ri). Consequently, the area-weighted rainfall is equivalent to: (1) Random Forest Algorithm Random forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. This algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks. Support Vector Machine (SVM) algorithm Support vector machines works like other artificial intelligence methods based on data mining algorithm. The most important functions of the support vector machine model are classification and linearization or data regression. Evaluation Criteria To evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (RMSE), correlation coefficient (r), explanation coefficient (R2) and Nash-Sutcliffe efficiency coefficient (NS) are used. Below are the relationships among these criteria: (2) (3) (4) (5) Results and Discussion Figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.The analysis of the figures showed that for Bonab station, during the two study periods, the value of Kendall's statistic for precipitation variable was 0.044 and 0.028, respectively. For Khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. However, these values are not significant at the 95% level. This indicates that the annual rainfall for the two studied stations during these years is not statistically significant. Therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. The variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. However, it should be noted that there were instances of both increasing and decreasing trends in certain years Examining the time series reveals varying trends Initially, the outflow from Bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. However, in recent years, there has an increase in outflow from this station. The Mann-Kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. These values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. The reason for this trend at the Bonab station, compared to other entrance stations to Lake Urmia, is the lower demand for water in the Sofichai basin for agricultural and industrial purposes, in contrast to other rivers. To explore the root cause of this issue, studies should be conducted to examine both underground and surface water sources, as well as the utilization of water in the agricultural and industrial sectors of this region. On the contrary, the trend observed at Khormazard station (c and d) is different. Unlike Bonab station, the discharge from Khormazard station exhibited a complete downward trend. The Mann-Kendall test statistic for the discharge variable during our two research periods were -0.269 and -0.412, respectively. At the 95% level, the decreasing trend of discharge in this station was found to be significant. On the other hand, it is apparent that the volume of discharge in this hydrometric station has decreased drastically since 1976 (d). Apart from 2007, when there was a sudden increase in discharge volume, the water inflow into lake Urmia has remained at its lowest level throughout the years. To analyze the Bonab and Khormazard stations during two distinct periods, rainfall and runoff statistics (average, minimum, maximum) for the first period (1976-1994) and the second period (1995-2019) are presented in Tables 4 and 5. Based on the data presented in both tables, the Bonab station displays the highest average rainfall and runoff values in the total data column, while the Khormazard station has the lowest average rainfall and runoff values. As mentioned, in order to model rainfall-runoff data using SVM and RF models, a portion of the data was used for training purposes, while another portion was used for validation. Tables 5 and 6 present the values of the calculated statistical indicators associated with the results obtained from the training and validation sections for both SVM and RF models. According to the results of Tables 6 and 7, it is clear that in both study periods, the SVM model outperformed the RF model at the Bonab station. The SVM model demonstrated superior accuracy in simulating both flow rate and monthly rainfall. Conversely, at the Kharmazard station during these periods, the RF model displayed better performance compared to the SVM model. The modeling results in the test set for both stations revealed that the mutual correlation values for the first and second study periods at the Bonab station were 0.85 and 0.84, respectively. For the Kharmazard station, these values were 0.79 and 0.75, respectively. Conclusion The results indicate that for both periods at the Bonab station, the SVM model exhibited higher efficiency compared to the RF model. Conversely, at the Khormazard station, the RF model outperformed the SVM model for both periods. Mutual correlation values for the test sets were 0.85 and 0.84 for the first and second study periods at the Bonab station, respectively, for the SVM model test set. For the Khormazard station, these values were 0.79 and 0.75, respectively, for the RF model test set. Other notable findings of this research include the analysis of the time series data for rainfall and runoff over 43 years. Graphs obtained for both stations, along with the Mann-Kendall statistic for precipitation and flow parameters, revealed no discernible trend in precipitation during the two study periods. Instead, precipitation in these areas displayed fluctuating patterns However, the analysis of the time series and statistical values for the discharge of Sofichai and Mahpari chai rivers at the Bonab and Khormazard stations showed different results. In the Bonab station, the discharge exhibited fluctuations, with an increase observed in the second period. Conversely, at the Khormazard station, the discharge trend was downward in both study periods. The volume of Mahpari chai River outflow notably decreased in recent years, as evidenced by the Mann-Kendall statistic showing a decreasing trend.

Given the changing climate, understanding the recent variability in large-scale rainfall patterns is a crucial task in order to better understand the underlying hydrological processes that occur within a watershed. This study aims to investigate how rainfall events in western Japan have changed due to climate change and how these changes have affected runoff–turbidity dynamics during the rainy season. To address the research objectives, we analyzed two decades of precipitation records in the Gōno River watershed and examined the associated runoff–turbidity dynamics during floods using turbidity–discharge (T-Q) loops, quantified using an enhanced hysteresis index. Our findings revealed a kind of intense rainfall event occurring every 3 to 4 years. Additionally, spatial pentad analysis showed varying intensities of accumulated precipitation, indicating that extreme rainfall is not confined to a specific spatial zone. Regarding turbidity–discharge behavior, we found that clockwise hysteresis patterns were caused by sediment sources from near-channel areas, while anticlockwise patterns were caused by soil erosion from nearby areas. Another notable finding was that turbidity peaks during floods may represent the earlier (or later) arrival of turbid water from distant upstream sources due to intense precipitation. One of the key challenges in quantifying hysteresis patterns is that there is no agreed-upon definition for how to determine the start and end of a flood event. This can lead to bias in the quantification of these patterns.

Rainfall-runoff modelling is a critical component of hydrological studies, and its accuracy is essential for water resource management. Recent advances in machine learning have led to the development of more sophisticated rainfall-runoff models, but there is still room for improvement. This study proposes a novel approach to streamflow modelling that uses the artificial hummingbird algorithm (AHA) to optimize the boosted tree algorithm. the AHA-boosted tree algorithm model was compared against two established methods, the support vector machine (SVM) and the Gaussian process regression (GPR), using a variety of statistical and graphical performance measures. The results showed that the AHA-boosted tree algorithm model significantly outperformed the SVM and GPR models, with an R2 of 0.932, RMSE of 5.358 m3/s, MAE of 2.365 m3/s, and MSE of 28.705 m3/s. The SVM model followed while the GPR model had the least accurate performance. However, all models underperformed in capturing the peak flow of the hydrograph. Evaluations using both statistical and graphical performance measures, including time series plots, scatter plots, and Taylor diagrams, were critical in this assessment. The results suggest that the AHA-boosted tree algorithm could potentially be a superior alternative for enhancing the precision of rainfall-runoff modelling, despite certain challenges in predicting peak flow events. HIGHLIGHTS Introduced artificial hummingbird algorithm (AHA) to optimize boosted tree algorithm in rainfall-runoff modelling.; AHA-boosted model significantly outperforms SVM and GPR methods.; Enhances precision in hydrological studies using advanced machine learning.; Challenges in peak flow prediction were not adequately addressed by the models.; Aligns with Journal of Hydroinformatics’ focus on computational hydrosystem advancements.;

This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins.

The Flood Studies Report (FSR) summer and winter design profiles are a key component of rainfall design guidance in the United Kingdom (UK). We have examined the rainfall profiles of over 70,000 extreme rainfall events with the original FSR profile methodology. This analysis reveals that rainfall profiles change with rainstorm duration but not season, contradicting one of the key assumptions in current UK rainfall design guidance. By using a method that does not artificially generate symmetrical and centred profiles we show that profile shapes are highly variable and strongly related to event duration and magnitude. Short events tend towards front‐loaded profiles, while heavy long‐duration events tend towards centred profile shapes. Finally, manual, automatic and mixed methods of deriving new design profiles for use in the UK were trialled, with consistent results. These could be used to derive new design profiles to supersede the FSR profiles. Notably, peak intensities in observed profiles and trialled summary profiles often exceeded those found in both FSR profiles. We conclude that current design profile guidance for the UK fails to account for the observed variability in event profile shapes and peak intensities and may lead to significant under‐ or over‐design of flood risk management solutions. [ABSTRACT FROM AUTHOR]

Given the changing climate, understanding the recent variability in large-scale rainfall patterns is a crucial task in order to better understand the underlying hydrological processes that occur within a watershed. This study aims to investigate how rainfall events in western Japan have changed due to climate change and how these changes have affected runoff–turbidity dynamics during the rainy season. To address the research objectives, we analyzed two decades of precipitation records in the Gōno River watershed and examined the associated runoff–turbidity dynamics during floods using turbidity–discharge (T-Q) loops, quantified using an enhanced hysteresis index. Our findings revealed a kind of intense rainfall event occurring every 3 to 4 years. Additionally, spatial pentad analysis showed varying intensities of accumulated precipitation, indicating that extreme rainfall is not confined to a specific spatial zone. Regarding turbidity–discharge behavior, we found that clockwise hysteresis patterns were caused by sediment sources from near-channel areas, while anticlockwise patterns were caused by soil erosion from nearby areas. Another notable finding was that turbidity peaks during floods may represent the earlier (or later) arrival of turbid water from distant upstream sources due to intense precipitation. One of the key challenges in quantifying hysteresis patterns is that there is no agreed-upon definition for how to determine the start and end of a flood event. This can lead to bias in the quantification of these patterns. [ABSTRACT FROM AUTHOR]

Introduction Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, It is crucial to have a deep understanding of the future condition of water resources within the country's watersheds (Fathollahi et al., 2015). By utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. Support vector machine (SVM) and Random Forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (Kisi & Parmarm, 2016). The most recent study conducted by Zarei et al. (2022) evaluated the risk of flooding using data mining models of SVM and RF (case study: Frizi watershed). By analyzing the results, it was found that both the SVM algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. The purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the Maragheh plain (Khormazard station on the Mahpari chai river and Bonab station on the Sufichai river) in East Azerbaijan province using support vector machine and random forest modeling algorithms. This study has been conducted over a period of 43 years, making it one of the few research cases in this area. Materials and Methods The Maragheh Sufi chai basin is situated in the eastern region of Lake Urmia, within the East Azarbaijan province. It covers an area of 611.89 square kilometers and is located between longitudes 45° and 40' to 46° and 25'and latitudes from 37° and 15' to 37° and 55' north. The average height of the basin is 1767 meters above sea level (Sharmod et al., 2015). Based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. The first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. To simulate rainfall-runoff, first the average rainfall of Maragheh plain was calculated by polygonal method. Subsequently, this data was combined with the discharge output from Bonab and Khormazard stations, with a one-day time lag. These inputs were then utilized in two models, SVM (kernel function) and RF. For this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. Then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. Several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. The inputs consist of the monthly Q and P values that were recorded previously (Pt, Qt-1), while the output represents the current runoff data (Qt), with the subscript t indicating the time step. As a result, two input combinations were constructed from Q and P data (as seen in Table 3) and SVM and RF models were used for rainfall-runoff modeling to determine the optimal input combination. Calculating average rainfall through the Thiessen Polygons method Thiessen polygons, which are Voronoi cells, are used to define rainfall polygons that correspond to the surface area (Ai). These polygons are used to weight the rainfall measured by each rain gauge (ri). Consequently, the area-weighted rainfall is equivalent to: ... Random Forest Algorithm Random forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. This algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks. Support Vector Machine (SVM) algorithm Support vector machines works like other artificial intelligence methods based on data mining algorithm. The most important functions of the support vector machine model are classification and linearization or data regression. Evaluation Criteria To evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (RMSE), correlation coefficient (r), explanation coefficient (R²) and Nash-Sutcliffe efficiency coefficient (NS) are used. Below are the relationships among these criteria: ... Results and Discussion Figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.The analysis of the figures showed that for Bonab station, during the two study periods, the value of Kendall's statistic for precipitation variable was 0.044 and 0.028, respectively. For Khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. However, these values are not significant at the 95% level. This indicates that the annual rainfall for the two studied stations during these years is not statistically significant. Therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. The variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. However, it should be noted that there were instances of both increasing and decreasing trends in certain years Examining the time series reveals varying trends Initially, the outflow from Bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. However, in recent years, there has an increase in outflow from this station. The Mann-Kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. These values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. The reason for this trend at the Bonab station, compared to other entrance stations to Lake Urmia, is the lower demand for water in the Sofichai basin for agricultural and industrial purposes, in contrast to other rivers. To explore the root cause of this issue, studies should be conducted to examine both underground and surface water sources, as well as the utilization of water in the agricultural and industrial sectors of this region. On the contrary, the trend observed at Khormazard station (c and d) is different. Unlike Bonab station, the discharge from Khormazard station exhibited a complete downward trend. The Mann-Kendall test statistic for the discharge variable during our two research periods were -0.269 and -0.412, respectively. At the 95% level, the decreasing trend of discharge in this station was found to be significant. On the other hand, it is apparent that the volume of discharge in this hydrometric station has decreased drastically since 1976 (d). Apart from 2007, when there was a sudden increase in discharge volume, the water inflow into lake Urmia has remained at its lowest level throughout the years. To analyze the Bonab and Khormazard stations during two distinct periods, rainfall and runoff statistics (average, minimum, maximum) for the first period (1976-1994) and the second period (1995-2019) are presented in Tables 4 and 5. Based on the data presented in both tables, the Bonab station displays the highest average rainfall and runoff values in the total data column, while the Khormazard station has the lowest average rainfall and runoff values. As mentioned, in order to model rainfall-runoff data using SVM and RF models, a portion of the data was used for training purposes, while another portion was used for validation. Tables 5 and 6 present the values of the calculated statistical indicators associated with the results obtained from the training and validation sections for both SVM and RF models. According to the results of Tables 6 and 7, it is clear that in both study periods, the SVM model outperformed the RF model at the Bonab station. The SVM model demonstrated superior accuracy in simulating both flow rate and monthly rainfall. Conversely, at the Kharmazard station during these periods, the RF model displayed better performance compared to the SVM model. The modeling results in the test set for both stations revealed that the mutual correlation values for the first and second study periods at the Bonab station were 0.85 and 0.84, respectively. For the Kharmazard station, these values were 0.79 and 0.75, respectively. Conclusion The results indicate that for both periods at the Bonab station, the SVM model exhibited higher efficiency compared to the RF model. Conversely, at the Khormazard station, the RF model outperformed the SVM model for both periods. Mutual correlation values for the test sets were 0.85 and 0.84 for the first and second study periods at the Bonab station, respectively, for the SVM model test set. For the Khormazard station, these values were 0.79 and 0.75, respectively, for the RF model test set. Other notable findings of this research include the analysis of the time series data for rainfall and runoff over 43 years. Graphs obtained for both stations, along with the Mann-Kendall statistic for precipitation and flow parameters, revealed no discernible trend in precipitation during the two study periods. Instead, precipitation in these areas displayed fluctuating patterns However, the analysis of the time series and statistical values for the discharge of Sofichai and Mahpari chai rivers at the Bonab and Khormazard stations showed different results. In the Bonab station, the discharge exhibited fluctuations, with an increase observed in the second period. Conversely, at the Khormazard station, the discharge trend was downward in both study periods. The volume of Mahpari chai River outflow notably decreased in recent years, as evidenced by the Mann-Kendall statistic showing a decreasing trend. [ABSTRACT FROM AUTHOR]

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Introduction Rainfall-runoff modeling is one of the most important components of hydrological processes in water resources management and accurate estimation of runoff and river flow in the short and long term can be of great help to various sectors of water engineering. In several types of rainfall-runoff models, the unit hydrograph methods are still a useful tool for flood estimation in many, except non-gauged, basins. The unit hydrograph is the same as the unit pulse response function of a linear hydrological system. The tank model is one of the hydrological models for analyzing river flow characteristics. In hydrological analysis, simulation models are often used to describe and predict basin response to rainfall events based on mathematical and physical knowledge. In this article, using the concept of linear system theory, the pulse response functions of the runoff components (surface runoff and base flow) using the reservoir model for several flood events related to the two Navrood basin in Gillan province and Liqvan basin in the East Azerbaijan province has been extracted. Since Simulink can schematically show the dynamic relationship between hydrological components such as rainfall, runoff, storage, evapotranspiration, and runoff, it can be useful for rainfall-runoff modeling. Therefore, Modeling is done in the Simulink MATLAB environment. The modular design and block library can help users focus on hydrological analysis, including modeling strategy development, parameter estimation, and model application. Materials and Methods In the present study, to evaluate the capability of the tank model in different climates, the model has been implemented for two regions with dry and wet climates to evaluate the effectiveness of the tank model by comparing the results. Therefore, the Navrood representative basin in Gilan province was selected for a wet climate, and the Liqvan representative basin in East Azerbaijan province for a dry and semi-arid climate. The response of the entire basin to the rainfall that fell on its surface has been determined using the conceptual model of the tank. In order to extract the unit pulse response function for the runoff caused by precipitation, the model of three tanks in series with holes on the side and bottom has been used to show the types of currents prevailing in the process of forming runoff. The internal dependence of reservoirs is described using exponential functions of model parameters. Estimating the model parameters was performed using the cluttered evolution optimization method or SCE-UA for short, a conceptual optimization method. In addition, to extract unit pulse response functions and evaluate the model's efficiency in predicting flood events, it was tried to select events that have rained in the entire basin and correspond to flood events in terms of occurrence time. Results and Discussion The results obtained in this research showed that the tank model provides good results in estimating the peak discharges and the time to reach the peak discharges in the two representative basins of Navrood and Liqvan with two different climates. The parameters of the model, which actually reflect the geomorphological characteristics of the basin, are almost constant, and only the changes in soil moisture storage are variable in the runoff calculations. Response functions as exponential functions of model parameters have simulated the different roles of flow components (quick surface, quick subsurface, delayed subsurface, and underground flow) in relation to the precipitation process. As can be deduced from the results obtained for the Navrood basin, the slow runoff accounts for a major part of the total runoff in the falling limb of the hydrograph. The relations extracted for the response functions of the unit pulse for ru as a unit input that happened in the duration of ∆t have been obtained parametrically, therefore, for different combinations of ru and ∆t, several unit pulse response functions can be extracted. Examining the parameters of the model obtained from the SCE-UA algorithm shows that due to the large value of the b2 parameter compared to other parameters, a major part of the volley losses is penetration losses. In fact, the high permeability of the surface layers of the soil justifies the high value of the b1 parameter compared to other parameters. Conclusion The performance results of the tank model showed that the model has a relatively good capability in predicting the runoff affected by the rainstorm. The comparison of the computational and observed hydrographs shows that the mentioned values have a good correlation. According to the results, it can be seen that the fast runoff, which usually appears in the form of surface flow and waterways in the basin, occupies a significant part of the entire flood hydrograph during the flooding process, and in terms of durability has a shorter duration than slow runoff. Slow runoff hydrograph is slowly affected by precipitation during the flooding process, but in terms of durability, it has a longer duration. [ABSTRACT FROM AUTHOR]

Abstract Woodlands can reduce downstream flooding, but it is not well known how the extent and distribution of woodland affects reductions in peak flow. We used the spatially distributed TOPMODEL to simulate peak flow during a 1 in 50 year storm event for a range of broadleaf woodland scenarios across a 2.6 km2 catchment in Northern England. Woodland reduced peak flow by 2.6%–15.3% depending on the extent and spatial distribution of woodland cover. Cross slope and riparian woodland resulted in larger reductions in peak flow, 4.9% and 3.3% for a 10‐percentage point increase in woodland cover respectively, compared to a 2.7% reduction for woodland randomly located across the catchment. Our results demonstrate that increased woodland cover can reduce peak flows during a large storm event and suggest that targeted placement of woodland can maximise the effectiveness of natural flood management interventions.

Background: The slash disposal-burning forest-in high-intensity management Eucalyptus grandis × urophylla plantation has accelerated soil degradation. Statement of the problem: Slash disposals is a contributing factor, but its specific role in the correlation between rainfall-runoff and soil erosion remains elusive. Objectives: his study investigated the characteristics of rainfall-runoff and soil erosion resistance in different methods of slash disposals in plantation. Methods: Three methods of slash disposal, namely burning forest (BF), moving away (MA), and spreading evenly (SE), were established. A field simulation experiment of rainfall was conducted, and path analysis was used. Results: The findings revealed that the water holding, infiltrating properties and the time the rainfall-runoff generated of SE were increased by approximately 10∼20 %, 100 %, and 80 %, respectively, compared with BF and MA. Water loss, soil loss and nutrient loss were significantly reduced by 62.23 % and 61.56 %, 69.06 % and 49.55 %, and 58.8 % and 65.42 % in SE and BF compared to MA. Path analysis suggested that different from BF and MA, the correlation between soil water properties and rainfall-runoff factors in SE was weakened, simultaneously considering the result that SE had the lower proportions of silt for sediment component (75.31 %), it stabilized the soil structure. Conclusions and prospect: Consequently, SE mitigated the erosion force by reducing rainfall-runoff and enhancing the anti-erosion of soil through improved water properties, making it a viable slash disposal. This work provides a detailed description of the soil erosion characteristics of plantation, including water, soil, and nutrient losses caused by rainfall-runoff, as well as the soil anti-erosion due to different slash disposals. These findings offer valuable insights for the management of high-intensity Eucalyptus grandis × urophylla plantations.

We use a Generalized Watershed Loading Function (GWLF) model to simulate streamflow in the Gualí River Basin. The model's performance is assessed using metrics such as percentage of bias (PBIAS), Nash–Sutcliffe efficiency (NSE), RMSE–observations standard deviation ratio (RSR), and Kling–Gupta efficiency (KGE) to indicate good performance. Furthermore, we analyze projections of precipitation and streamflow using several global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5). Despite the uncertainties and coarse resolution, our results show that increases in the mean streamflow and significantly decreasing trends in projected precipitation and streamflow are observed from 2015 to 2099 under the SSP5-8.5 scenario. Furthermore, our findings suggest an increase in long-term mid-flow and low-flow. Moreover, this work provides a methodological framework for hydrological modeling in small tropical river basins, by incorporating data from GCMs while raising concerns and caveats. This study offers valuable insights into the potential effects of climate change on streamflow in an Andean river basin characterized by volcanic activity and significant human impacts. The findings reported here provide useful information for future decisions related to water supply for the social, environmental, and productive sectors in the seven towns within the catchment. HIGHLIGHTS There are few studies of streamflow projections for tropical river basins with volcanic activity.; We provide new information about streamflow modeling using data from CMIP6 models.; The Gualí River Basin has a high population with different economic activities, so this research is important to water resources management there.; This work sheds light on challenges in hydrological processes in complex river basins.;