Search Results (57)

Continuous use of agricultural chemicals and fertilizers, sporadic sewer overflow events, and an increase in urbanization have led to significant nutrient/pollutant loadings into the semi-arid Arroyo Colorado River basin, which is located in South Texas, U.S. Priority nutrients that require reduction include phosphorus and nitrogen and to mitigate issues of low dissolved oxygen, in some of its river segments. Consequently, the river’s potential to support aquatic life has been significantly reduced, thus highlighting the need for restoration. To achieve this restoration, a watershed protection plan was developed, comprising several preventive mitigation measures, including installing green infrastructure (GI) practices. However, for effective reduction of excessive nutrient loadings, there is a need to study the effects of different combinations of GI practices under current and future land use scenarios to guide decisions in implementing the cost-effective infrastructure while considering factors such as the existing drainage system, topography, land use, and streamflow. Therefore, this study coupled the Soil and Water Assessment Tool (SWAT) model with the System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) model to determine the effects of different combinations of GI practices on the reduction of nitrogen and phosphorus under changing land use conditions in three selected Arroyo Colorado subwatersheds. Two land use maps from the U.S. Geological Survey (USGS) Forecasting Scenarios of land use (FORE-SCE) model for 2050, namely, A1B and B1, were implemented in the coupled SWAT-SUSTAIN model in this study, where the urban area is projected to increase by 6% and 4%, respectively, with respect to the 2018 land use scenario. As expected, runoff, phosphorus, and nitrogen slightly increased with imperviousness. The modeling results showed that implementing either vegetated swales or wet ponds reduces flow and nutrients to meet the Total Maximum Daily Loads (TMDLs) targets, which cost about USD 1.5 million under current land use (2018). Under the 2050 future projected land use changes (A1B scenario), the cost-effective GI practice was implemented in vegetated swales at USD 1.5 million. In contrast, bioretention cells occupied the least land area to achieve the TMDL targets at USD 2 million. Under the B1 scenario of 2050 projected land use, porous pavements were most cost effective at USD 1.5 million to meet the TMDL requirements. This research emphasizes the need for collaboration between stakeholders at the watershed and farm levels to achieve TMDL targets. This study informs decision-makers, city planners, watershed managers, and other stakeholders involved in restoration efforts in the Arroyo Colorado basin. Full article

Runoff prediction is a complex hydrological, nonlinear time-series problem. Many machine learning methods have been put forth in recent years to predict runoff. A sliding window method is typically used to preprocess the data in order to rebuild the time series of runoff data into a standard machine learning dataset. The size of the window is a variable parameter that is commonly referred to as the time step. With developments in computer and AI technology, data-driven models have demonstrated tremendous potential for runoff prediction. And AI technology has opened up a new avenue for weather prediction, with Pangu-Weather demonstrating considerable improvements in both accuracy and processing efficiency. This study creates two novel prediction models, LSTM-Pangu and GRU-Pangu, by combining Pangu with Long Short-Term Memory (LSTM) and the Gate Recurrent Unit (GRU). We concentrated on the Beipanjiang River Basin in China, using Guizhou Qianyuan Power Company Limited’s daily runoff data and meteorological satellite data from the Climate Data Store platform to forecast daily runoff. These models were used to anticipate runoff on various future days (known as the lead time). The results show that regardless of time step, both LSTM-Pangu and GRU-Pangu outperform the LSTM and GRU models. Furthermore, this advantage is more evident as the advance time increases. When the time step is 7 and the lead time is 5, the Nash–Sutcliffe Efficiency (NSE) of the LSTM-Pangu model improves by 8.1% compared to the LSTM model, while the NSE of the GRU-Pangu model improves by 11.7% compared to the GRU model. Furthermore, LSTM-Pangu and GRU-Pangu outperform LSTM and GRU models in terms of the predictive accuracy under high-flow conditions, highlighting their significant advantages in flood forecasting. This integration strategy displays great transferability and may be expanded to other typical data-driven models. Full article

This study, based on hydrological station data and wavelet analysis, explores the periodic variation characteristics and trends of the two main tributaries (Buha River and Shaliu River) in the Qinghai Lake Basin from 1960 to 2016. Wavelet transform is used to analyze the runoff data, revealing long-term periodic fluctuations and their correlation with precipitation changes. The study finds that, from 2003 to 2016, the daily peak flow and daily minimum flow of the two rivers increase compared to the period from 1960 to 2003, though the magnitude and trends of the increase differ. At the monthly scale, runoff patterns show that June to October is the main period for concentrated runoff in the basin, with July and August being the peak months. Additionally, interannual runoff changes for both rivers show a gradually increasing trend amid fluctuations, with varying fluctuation intensities observed in different years. Wavelet analysis results indicate that the main periodicity of runoff is 23 years, closely linked to changes in precipitation. This study reveals the periodic variation patterns of runoff in the Qinghai Lake Basin, providing valuable insights for watershed water resource management and hydrometeorological forecasting. Full article

It is well understood that climate change is a major cause of the environmental shifts that are significantly impacting human lives. The floods caused by climate change are not only occurring more frequently each year, but they also bring up the problem of access to clean water for drinking and other daily usage for the affected communities. The Swat district of the Khyber Pakhtunkhwa province in Pakistan is one of the impacted regions and the growing concern for clean water access is yet to be resolved. This study aims to propose a sustainable solution to water access during the emergencies, particularly in flood and drought situations. While the roof water harvesting system (RWHS) is well established and functional in many developed regions, its potential remains underexplored in Pakistan. This research study analyzed the climate change projection data for the Saidu Sharif region of Swat. The regional climate data are gathered from the Shared Socio-economic Pathways (SSPs) for the period from 2015 to 2045. Five general circulation models (GCMs) were selected based on their performance in South Asian climate simulations. Analysis of the regional forecasted climate data indicates that almost all of the five climate models have predicted the periods of excessive rainfall to occur in the months of July, August, and September, while prolonged dry seasons may last between 271 and 325 days annually. Hydrological modeling was used to estimate RWHS performance, which incorporated the key parameters such as catchment area, runoff coefficient, and rainfall intensity. The findings suggest that the proposed RWHS could meet basic drinking water needs during the floods and even during the drought periods near around 100% satisfaction of water demand under certain conditions. For example, for an average drought period of 273 days, a household of seven people with a per capita daily water demand of 17 L requires a storage capacity of 33 m³. On the other hand, for a maximum drought duration of 325 days, the required storage volume increases to 39 m³. Demand satisfaction calculations are also used to evaluate the effectiveness of the proposed model. This research contributes to addressing the growing water scarcity challenge posed by climate change in the Swat region and offers a sustainable and practical solution. Full article

Hydrological runoff prediction plays a crucial role in water resource management and sustainable development. However, it is often constrained by the nonlinearity, strong stochasticity, and high non-stationarity of hydrological data, as well as the limited accuracy of traditional forecasting methods. Although Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) have been widely used for data augmentation to enhance predictive model training, their direct application as forecasting models remains limited. Additionally, the architectures of the generator and discriminator in WGAN-GP have not been fully optimized, and their potential in hydrological forecasting has not been thoroughly explored. Meanwhile, the strategy of jointly optimizing Variational Autoencoders (VAEs) with WGAN-GP is still in its infancy in this field. To address these challenges and promote more accurate and sustainable water resource planning, this study proposes a comprehensive forecasting model, VXWGAN-GP, which integrates Variational Autoencoders (VAEs), WGAN-GP, Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), Gated Recurrent Units (GRUs), and Attention mechanisms. The VAE enhances feature representation by learning the data distribution and generating new features, which are then combined with the original features to improve predictive performance. The generator integrates GRU, BiLSTM, and Attention mechanisms: GRU captures short-term dependencies, BiLSTM captures long-term dependencies, and Attention focuses on critical time steps to generate forecasting results. The discriminator, based on CNN, evaluates the differences between the generated and real data through adversarial training, thereby optimizing the generator’s forecasting ability and achieving high-precision runoff prediction. This study conducts daily runoff prediction experiments at the Yichang, Cuntan, and Pingshan hydrological stations in the Yangtze River Basin. The results demonstrate that VXWGAN-GP significantly improves the quality of input features and enhances runoff prediction accuracy, offering a reliable tool for sustainable hydrological forecasting and water resource management. By providing more precise and robust runoff predictions, this model contributes to long-term water sustainability and resilience in hydrological systems. Full article

Accurate forecasting of river flows is essential for effective water resource management, flood risk reduction and environmental protection. The ongoing effects of climate change, in particular the shift in precipitation patterns and the increasing frequency of extreme weather events, necessitate the development of advanced forecasting models. This study investigates the application of long short-term memory (LSTM) neural networks in predicting river runoff in the Velika Morava catchment in Serbia, representing a pioneering application of LSTM in this region. The study uses daily runoff, precipitation and temperature data from 1961 to 2020, interpolated using the inverse distance weighting method. The LSTM model, which was optimized using a trial-and-error approach, showed a high prediction accuracy. For the Velika Morava station, the model showed a mean square error (MSE) of 2936.55 and an R² of 0.85 in the test phase. The findings highlight the effectiveness of LSTM networks in capturing nonlinear hydrological dynamics, temporal dependencies and regional variations. This study underlines the potential of LSTM models to improve river forecasting and water management strategies in the Western Balkans. Full article

This study assesses the effectiveness and limitations of publicly accessible runoff and river discharge reanalysis datasets through an intercomparison in the Upper Jinsha River, an alpine region with substantial hydropower potential on the eastern edge of the Tibetan Plateau. The examined datasets are the European Centre for Medium-Range Weather Forecast Reanalysis version 5 (ERA5-Land), the Global Flood Awareness System (GloFAS), the Global Reach-Level Flood Reanalysis (GRFR), and the China Natural Runoff Dataset (CNRD). These datasets are created using various meteorological forcing, runoff generation models, river routing models, and calibration methods. To determine the causes of discrepancies, additional simulations were carried out. One simulation, driven by meteorological forcing similar to that of ERA5-Land and GloFAS but utilizing the uncalibrated NoahMP land surface model at a higher spatial resolution, was included to evaluate the effects of meteorological inputs, spatial resolution, and calibration on runoff estimation. Runoff from all datasets was rerouted on a high-resolution river network derived from the 3-arcsecond Multi-Error-Removed Improved-Terrain Hydrography (MERIT-Hydro) dataset, allowing for a comparison between vector- and grid-based river routing models for discharge estimates. The intercomparison is grounded in observations from three gauging stations—Zhimenda, Gangtuo, and Benzilan—at monthly, daily, and hourly scales. The results suggest that model calibration has a more significant influence on runoff and discharge estimates than meteorological data. Calibrated datasets, such as GloFAS and GRFR, perform better than others, despite variations in the forcing data. The runoff characteristics-based calibration method used in GRFR exhibits superior performance at Zhimenda and Benzilan. However, at Gangtuo, GRFR’s performance is unsatisfactory, highlighting the limitation of the machine learning-based method in regions with rugged terrain and limited observations. Vector-based river routing models demonstrate advantages over grid-based models. GloFAS, which uses a grid-based routing model, encounters difficulties in simultaneously producing accurate runoff and discharge estimates. The intercomparison shows that GRFR’s river routing is sub-optimally configured. However, when GRFR’s runoff rerouted, the performance of discharge improves substantially, attaining a Kling–Gupta efficiency of approximately 0.9. These findings offer valuable insights for the further development of reanalysis datasets in this region. Full article

The Australian Bureau of Meteorology provides flood forecasting and warning services across Australia for most major rivers in Australia, in cooperation with other government, local agencies and emergency services. As part of this service, the Bureau issues a flood watch product to provide early advice on a developing situation that may lead to flooding up to 4 days prior to an event. This service is based on (a) an ensemble of available Numerical Weather Prediction (NWP) rainfall forecasts, (b) antecedent soil moisture, stream and dam conditions, (c) hydrological forecasts using event-based models and (d) expert meteorological and hydrological input by Bureau of Meteorology staff, to estimate the risk of reaching pre-specified river height thresholds at locations across the continent. A flood watch provides information about a developing weather situation including forecasting rainfall totals, catchments at risk of flooding, and indicative severity where required. Although there is uncertainty attached to a flood watch, its early dissemination can help individuals and communities to be better prepared should flooding eventuate. This paper investigates the utility of forecasts of daily gridded national runoff to inform the risk of riverine flooding up to 7 days in advance. The gridded national water balance model (AWRA-L) runoff outputs generated using post-processed 9-day Numerical Weather Prediction hindcasts were evaluated as to whether they could accurately predict exceedance probabilities of runoff at gauged locations. The approach was trialed over 75 forecast locations across North East Australia (Queensland). Forecast 3-, 5- and 7-day accumulations of runoff over the catchment corresponding to each location were produced, identifying whether accumulated runoff reached either 95% or 99% historical levels (analogous to minor, moderate and major threshold levels). The performance of AWRA-L runoff-based flood likelihood was benchmarked against that based on precipitation only (i.e., not rainfall–runoff transformation). Both products were evaluated against the observed runoff data measured at the site. Our analysis confirmed that this runoff-based flood likelihood guidance could be used to support the generation of flood watch products. Full article

Rainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall from space. In Algeria, to identify a relevant modeling approach using this new source of rainfall information, the present research aims to (i) compare a conceptual model (GR4J) and seven machine learning algorithms (FFNN, ELM, LSTM, LSTM2, GRU, SVM, and GPR) and (ii) compare different types of precipitation inputs, including four satellite products (CHIRPS, SM2RAIN, GPM, and PERSIANN), one reanalysis product (ERA5), and observed precipitation, to assess which combination of models and precipitation data provides the optimal performance for river discharge simulation. The results show that the ELM, FFNN, and LSTM algorithms give the best performance (NSE > 0.6) for river runoff simulation and provide reliable alternatives compared to a conceptual hydrological model. The SM2RAIN-ASCAT and ERA5 rainfall products are as efficient as observed precipitation in this data-scarce context. Consequently, this work is the first step towards the implementation of these tools for the operational monitoring of surface water resources in Algeria. Full article

Erosive rainfall is essential for initiating surface runoff and soil erosion to occur. The analysis on its temporal and spatial distribution characteristics is crucial for calculating rainfall erosivity, predicting soil erosion, and implementing soil and water conservation. This study utilized daily rainfall observation data from 90 meteorological stations in Henan from 1981 to 2020, and conducted geostatistical analysis, M-K mutation test analysis, and wavelet analysis on erosive rainfall to reveal the spatiotemporal distribution characteristics over the past 40 years. Building on this foundation, the correlation between erosive rainfall, rainfall, and rainfall erosivity were further explored. The findings indicated that the average annual rainfall in Henan Province varied between 217.66 mm and 812.78 mm, with an average yearly erosive rainfall of 549.24 mm and a standard deviation of 108.32 mm. Erosive rainfall constitutes for 77% of the average annual rainfall on average, and the analysis found that erosive rainfall is highly correlated with rainfall volume. The erosive rainfall increased from northwest to southeast, and had the same spatial distribution characteristics as the total rainfall. The number of days with erosive rainfall was 20.5 days and the annual average sub-erosive rainfall was 26.86 mm. The average annual rainfall erosivity in Henan Province ranged from 1341.81 to 6706.64 MJ·mm·ha⁻¹·h⁻¹, averaging at 3264.63 MJ·mm·ha⁻¹·h⁻¹. Both the erosive rainfall and the rainfall erosivity are influenced by the monsoon, showing a unimodal trend, with majority of the annual total attributed to rainfall erosivity from June to September, amounting to 80%. The results can provide a basis for forecasting of heavy rainfall events, soil conservation and planning, ecological treatment, and restoration. Full article

The inherent uncertainties in traditional hydrological models present significant challenges for accurately simulating runoff. Combining machine learning models with traditional hydrological models is an essential approach to enhancing the runoff modeling capabilities of hydrological models. However, research on the impact of mixed models on runoff simulation capability is limited. Therefore, this study uses the traditional hydrological model Simplified Daily Hydrological Model (SIMHYD) and the machine learning model Long Short Term Memory (LSTM) to construct two coupled models: a direct coupling model and a dynamically improved predictive validity hybrid model. These models were evaluated using the US CAMELS dataset to assess the impact of the two model combination methods on runoff modeling capabilities. The results indicate that the runoff modeling capabilities of both combination methods were improved compared to individual models, with the combined forecasting model for dynamic prediction effectiveness (DPE) demonstrating the optimal modeling capability. Compared with LSTM, the mixed model showed a median increase of 12.8% in Nash Sutcliffe efficiency (NSE) of daily runoff during the validation period, and a 12.5% increase compared to SIMHYD. In addition, compared with the LSTM model, the median Nash Sutcliffe efficiency (NSE) of the hybrid model simulating high flow results increased by 23.6%, and compared with SIMHYD, it increased by 28.4%. At the same time, the stability of the hybrid model simulating low flow was significantly improved. In performance testing involving varying training period lengths, the DPE model trained for 12 years exhibited the best performance, showing a 3.5% and 1.5% increase in the median NSE compared to training periods of 6 years and 18 years, respectively. Full article

The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Lake Nokoué in Benin. This paper aims to provide an effective and reliable method to enable the reproduction of the future daily water level of Lake Nokoué, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Lake Nokoué up to a forecast horizon of t + 10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash–Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t + 3 days. The values of these metrics remain stable for forecast horizons of t + 1 day, t + 2 days, and t + 3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Lake Nokoué basin. Based on the evaluation indices used to assess the model’s performance for the appropriate forecast horizon of water level in the Lake Nokoué basin, the forecast horizon of t + 3 days is chosen for predicting future daily water levels. Full article

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R²) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R² and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R² and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction. Full article

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. Full article

Runoff forecasting is crucial for sustainable water resource management. Despite the widespread application of deep learning methods in this field, there is still a need for improvement in the modeling and utilization of multi-scale information. For the first time, we introduce the Neural Basis Expansion Analysis with Exogenous Variable (NBEATSx) model to perform runoff prediction for a full exploration in rich temporal characteristics of runoff sequences. To harness wavelet transform (WT) multi-scale information capabilities, we developed the WT-NBEATSx forecasting model, integrating WT and NBEATSx. This model was further enhanced by incorporating a Long Short-Term Memory (LSTM) model for superior long-term dependency detection and a Random Forest (RF) model as a meta-model. The result is the advanced multi-model fusion forecasting model WT-NBEATSx-LSTM-RF (WNLR). This approach significantly enhances performance in runoff prediction. Utilizing a daily scale runoff and meteorological dataset from the Yangtze River Source region in China from 2006 to 2018, we systematically evaluated the performance of the WNLR model in runoff prediction tasks. Compared with LSTM, Gated Recurrent Units (GRUs), and NBEATSx models, the WNLR model not only significantly outperforms the original NBEATSx model but also surpasses other comparison models, particularly in accurately extracting cyclical change patterns, with NSE scores of 0.986, 0.974, and 0.973 for 5-, 10-, and 15-day forecasts, respectively. Additionally, compared to the standalone LSTM and GRU models, the introduction of wavelet transforms to form WT-LSTM and WT-GRU notably improved prediction performance and robustness, especially in long-term forecasts, where NSE increased by 32% and 1.5%, respectively. This study preliminarily proves the effectiveness of combining the cyclical characteristics of NBEATSx and wavelet transforms and creatively proposes a new deep learning model integrating LSTM and RF, providing new insights for further considering multi-scale features of complex runoff time series, thereby enhancing runoff prediction effectiveness. Full article