Role In Water Resources Management Research Articles

Rolling forecast of snowmelt floods in data-scarce mountainous regions using weather forecast products to drive distributed energy balance hydrological model

Snowmelt flood forecasting plays a crucial role in water resource management and utilization in arid regions. However, the accuracy of current snowmelt flood forecasts falls short of requirements due to complex mountainous terrain and limited observational data. In this study, We conducted a snowmelt flood simulation and forecasting experiment in a mountainous watershed in northern Xinjiang. First, we used site observations, ERA5-land reanalysis data and WorldClim data to produce historical meteorological forcing, calibrated the VIC-CAS model and improved routing model using historical observed runoff data, and conducted a detailed simulation of the hourly snowmelt flood process in the basin. Then, the latest grid forecast weather product developed by the China Meteorological Administration was integrated into the system to conduct a rolling forecast test of the snowmelt floods in the next 72 h during the snowmelt period in 2023. The remotely sensed snow cover and the freezing level measured by the sounding balloon were assimilated into the model in real time to correct the deviation of runoff simulation. The results show that during the model calibration period, the KGE of the simulated monthly runoff in the Kayertes River basin (KRB) from 2006 to 2011 was 0.83 and the NSE was 0.86. In the continuous hourly runoff simulation during the 2022 snowmelt period, the model well captured the intraday fluctuations of snowmelt runoff in the KRB, with the daily peak flow error within ± 20 % and the peak occurrence time controlled within ± 4 h. In the rolling forecast test in 2023, the daily peak flow error is within ± 20 % (the peak occurrence error during the peak period is even controlled within ± 5 %), and the peak occurrence time is within ± 4 h. Real-time assimilation of freezing level measured by sounding balloons and remotely sensed snow cover can effectively correct the deviations in simulated temperature and snow cover. This study can provide insights for runoff prediction in other high-altitude mountainous areas where data are scarce.

Breach Analysis of Earthen Dam

Failure of a levee or dam breach can result in severe economic losses and loss of human life. To minimize these consequences, it's imperative to assess the risks associated with levee breaches. This involves accurately predicting the volume of water released during a breach and simulating the propagation of flood waves across the floodplain to create inundation maps. Despite extensive observation and research into representing dam breaches and deriving crucial breach parameters like width, height, and side slope, there is no universally accepted method for determining these parameters. This study aims to evaluate existing methodologies for dam breach analyses, highlighting their advantages, limitations, and the challenges that lie ahead in this field. Earthen dams play a crucial role in water resource management and flood control. However, breaches in these structures pose significant risks to surrounding communities and infrastructure. This study explores the causes of earthen dam breaches, focusing on hydraulic, geotechnical, and structural factors. Through a comprehensive analysis of historical case studies and numerical modeling techniques, we elucidate the underlying mechanisms leading to dam failures. Furthermore, this research investigates effective mitigation strategies to enhance the resilience of earthen dams against breach incidents. By identifying key vulnerabilities and implementing targeted interventions, we aim to minimize the likelihood and impact of future breaches, ensuring the safety and sustainability of earthen dam infrastructure. Keywords— Breach analysis; Geotechnical Engineering; Earthen dam and Gangapur Dam

A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP.

Streamflow is a crucial variable for assessing the available water resources for both human and environmental use. Accurate streamflow prediction plays a significant role in water resource management and assessing the impacts of climate change. This study explores the potential of coupling conceptual hydrological models based on physical processes with machine learning algorithms to enhance the performance of streamflow simulations. Four coupled models, namely SWAT-Transformer, SWAT-LSTM, SWAT-GRU, and SWAT-BiLSTM, were constructed in this research. SWAT served as a transfer function to convert four meteorological features, including precipitation, temperature, relative humidity, and wind speed, into six hydrological features: soil water content, lateral flow, percolation, groundwater discharge, surface runoff, and evapotranspiration. Machine learning algorithms were employed to capture the underlying relationships between these ten feature variables and the target variable (streamflow) to predict daily streamflow in the Sandu-River Basin (SRB). Among the four coupled models and the calibrated SWAT model, SWAT-BiLSTM exhibited the best streamflow simulation performance. During the calibration period (training period), it achieved R2 and NSE values of 0.92 and 0.91, respectively, and maintained them at 0.90 during the validation period (testing period). Additionally, the performance of all four coupled models surpassed that of the calibrated SWAT model. Compared to the tendency of the SWAT model to underestimate streamflow, the absolute values of PBIAS for all coupled models are below 10%, which indicates that there is no significant systematic bias evident. SHapley Additive exPlanations (SHAP) were used to analyze the impact of different feature variables on streamflow prediction. The results indicated that precipitation contributed the most to streamflow prediction, with a global importance of 29.7%. Hydrological feature variable output by the SWAT model played a dominant role in the Bi-LSTM's prediction process. Coupling conceptual hydrological models with machine learning algorithms can significantly enhance the predictive performance of streamflow. The application of SHAP improves the interpretability of the coupled models and enhances researchers' confidence in the prediction results.

The multiscale nexus among land use-land cover changes and water quality in the Suquía River Basin, a semi-arid region of Argentina

Agricultural intensification and urban sprawl have led to significant alterations in riverscapes, and one of the critical consequences is the deterioration of water quality with significant implications for public health. Therefore, the objectives of this study were the assessment of the water quality of the Suquía River, the assessment of LULC change at different spatial scales, and the analysis of the potential seasonal correlation among LULC change and Water Quality Index (WQI). The Sample Sites (SS) 1 and 2 before Cordoba city had the highest WQI values while from SS3 the WQI decreased, with the lowest WQI close to the wastewater treatment plant (SS7) after Cordoba city. From SS8 in a agricultural context, the WQI increases but does not reach the original values. In light of analysis carried out, the correlation between water quality variables and the different LULC classes at the local and regional scales demonstrated that WQI is negatively affected by agricultural and urban activities, while natural classes impacted positively. The spatialization of the results can help strongly in assessing and managing the diffusion of point and non-point pollution along the riverscape. The knowledge gained from this research can play a crucial role in water resources management, which supports the provision of river ecosystem services essential for the well-being of local populations.

Hydrological risk assessment for Mangla Dam: compound effects of instant flow and precipitation peaks under climate change, using HEC-RAS and HEC-GeoRAS

Dams play a pivotal role in water resource management by storing and supplying water for a multitude of purposes. However, the looming threat of dam breach floods necessitates meticulous research and the simulation of potential failure scenarios. These endeavors are essential not only for comprehending the gravity of dam break floods but also for identifying vulnerable regions and informing emergency response strategies and land-use planning initiatives. This study employs a two-dimensional hydraulic model within the HEC-RAS (Hydrologic Engineering Center and River Analysis System) software to conduct an extensive dam breach analysis specifically focusing on the Mangla Dam located in Azad Kashmir, Pakistan. The analysis encompasses the prediction of various breach parameters, including the hydrograph of the breach flood, peak flow rates, arrival times of the flood, and the creation of inundation maps. Of primary concern is the Probable Maximum Floo, which drives the dam collapse model under unsteady flow conditions, accounting for both piping and overtopping failure scenarios. This study discerns the breach outflow hydrograph through the utility of HEC-RAS tools and evaluates hydraulic conditions at critical downstream locations. To dynamically route flood waves, the breach outflow hydrographs are harnessed. Furthermore, the HEC-RAS model is executed with breach parameters derived from five distinct empirical approaches, with ensuing outcomes subjected to rigorous comparative analysis. A comprehensive sensitivity study pertaining to breach parameters is also carried out to ascertain the sensitivity of peak flow and maximum stage. The results reveal peak flow rates of 174,850 m3/s and 177,850 m3/s in the downstream vicinity adjacent to the dam, translating into corresponding flooded areas of 379 km2 and 394 km2 attributable to piping and overtopping failures, respectively. The analysis of Land Use Land Cover data demonstrates that in the event of piping failure, 217 km2 of agricultural land and 56 km2 of urban areas would be completely submerged. Conversely, overtopping failure would inundate 220 km2 of agricultural land and 59 km2 of urban areas. The utilization of advanced remote sensing data, combined with flood modeling insights, equips engineers and stakeholders with invaluable knowledge. This knowledge, in turn, underpins strategic planning and well-informed decision-making processes, essential for addressing the potential global repercussions of similar catastrophes.

Estimation of the deep drainage for irrigated cropland based on satellite observations and deep neural networks

The deep drainage of irrigated cropland, which is one of the main causes of water losses in irrigation, plays an important role in water resource management. However, its magnitudes and variations remain unclear at regional scales. In this study, we established a remote sensing deep drainage model for irrigated cropland (ICDD_RS model) based on satellite observations and a deep neural network (DNN). First, deep drainage for natural vegetation was calculated by the water balance equation. Then, the ICDD_RS model was established by linking the calculated deep drainage and remote sensing data for natural vegetation based on a DNN. Finally, the model was used to predict the deep drainage of irrigated cropland through remote sensing data. The model was tested in the Heihe River Basin of China and the results showed the following: (1) The ICDD_RS model can well describe the relationship between deep drainage and selected influencing factors (0.83 < R2 < 0.86). At the site scale, it is highly consistent with in-situ observations with R2 values of 0.86, 0.74, and 0.71 for the Daman, Yingke, and Pingchuan stations, respectively. At the catchment scale, a low bias of 12% is obtained. (2) Deep drainage is large, accounting for 6–17% of the irrigation water. This model should be valuable in providing more detailed and effective information on agricultural water resource management.

Novel Threshold Self-Regulating Water Extraction Method

Water resources are crucial for human activities and sustainable socioeconomic development. Understanding surface water information can play a key role in water resource management, which affects the global water cycle and ecological environments. Considering the Hailar River water body as an example, this study proposes a new threshold self-learning water body extraction method (TSLWEM) based on modified normalized difference water index (MNDWI) data. The optimal water extraction thresholds determined by the TSLWEM algorithm for four test images were −0.0030, 0, 0.1990, and −0.0800. The TSLWEM algorithm effectively identified the target water body with recognition accuracies of 98.08%, 99.93%, 93.39%, and 93.20% for the four test images. Moreover, it can accurately identify small tributaries, such as lakes and rivers. The TSLWEM algorithm is suitable for Landsat 8 Operational Land Imager (OLI) data, which can effectively monitor and map complex surface water in temperate and semiarid regions while improving the accuracy of water body identification. The study’s findings provide technical support for the protection of water resources as well as their rational utilization and monitoring.

Contamination of the urban river network with perfluoroalkyl acids (PFAAs) introduced during river regulations

River regulation has a key role in water resource management, but the introduced pollutants cannot be underestimated. This study reported spatiotemporal variations of perfluoroalkyl acids (PFAAs) significantly affected by river regulations in a standard example of urban river network with bidirectional flow in China. Perfluoroalkyl sulfonic acids (PFSAs), mostly of domestic origin, dominated during discharge, and perfluoroalkyl carboxylic acids (PFCAs), industrial pollutants, during diversion. The estimated PFAA flux into the Yangtze River during discharge was 1.22 × 102 kg with 62.5 % from Taihu Lake and 37.5 % from the river network. And that from the Yangtze River during diversion was 90.2 kg with 72.2 % into Taihu Lake and 27.8 % into the river network. Our findings show that PFAAs can exert pressure on regional water security that most of the urban river network was at medium risk. This study improves understandings of the role of river regulations in urban water networks and provides solid reference for risk assessment.

Virtual Water and Its Role in Water Resource Management

Considering the future water scarcity issues, the control and management of water resources have become critically important, particularly with the rapid population growth in various countries and the rising standards of living. Agriculture, especially in developing countries, is the largest consumer of water. Therefore, the concept of virtual water gains significant importance. Virtual water refers to the hidden water embedded in agricultural products, considering the substantial amount of water required for their growth. For instance, the production of one kilogram of wheat consumes more than 1000 liters of water, termed as virtual water, as it's actually used and, in case of agricultural exports and imports, transferred from one country to another. Nations can reduce the cultivation of crops that consume a lot of water through the management of virtual water, controlling the input and output of water concerning this definition.&#x0D; Virtual water management allows countries to control their water input and output concerning their crops. This concept plays a crucial role in areas facing water scarcity. With efficient management of virtual water, demand for water in different regions (both dry and semi-arid) can be significantly reduced. The term "virtual water" indicates the amount of water used in the production of food items or commercial goods. By minimizing the production of water-intensive products, especially in water-scarce regions, the need for water consumption in those areas decreases significantly. Storing water virtually is a practical way to overcome periods of scarcity, more efficient than artificial reservoirs. The rise in competition for water resources and the increase in demand have made water management a prevalent topic. The transfer of embedded water in agricultural products is a vital component of global water management, particularly in water-scarce regions. The trade of virtual water not only saves water in importing countries but is also considered a global water conservation measure. This article aims to introduce the concept of virtual water and its role in water resource management.

Machine Learning Framework with Feature Importance Interpretation for Discharge Estimation: A Case Study in Huitanggou Sluice Hydrological Station, China

Accurate and reliable discharge estimation plays an important role in water resource management as well as downstream applications such as ecosystem conservation and flood control. Recently, data-driven machine learning (ML) techniques showed seemingly insurmountable performance in runoff forecasting and other geophysical domains, but they still need to be improved in terms of reliability and interpretability. In this study, focusing on discharge estimation and management, we developed an ML-based framework and applied it to the Huitanggou sluice hydrological station in Anhui Province, China. The framework contains two ML algorithms, the ensemble learning random forest (ELRF) and the ensemble learning gradient boosting decision tree (ELGBDT). The SHapley Additive exPlanation (SHAP) was introduced into our framework to interpret the impact of the model features. In our framework, the correlation analysis of the dataset can provide feature information for modeling, and the quartile method was utilized to solve the outlier problem of the dataset. The Bayesian optimization algorithm was adopted to optimize the hyperparameters of the ensemble ML models. The ensemble ML models are further compared with the traditional stage–discharge rating curve (SDRC) method and the single ML model. The results show that the estimation performance of the ensemble ML models is superior to that of the SDRC and the single ML model. In addition, an analysis of the discharge estimation without considering the flow state was performed. This analysis reveals that the ensemble ML models have strong adaptability. The ensemble ML models accurately estimate the discharge, with a coefficient of determination of 0.963, a root mean squared error of 31.268, and a coefficient of correlation of 0.984. Our framework can prove helpful to improve the efficiency of short-term hydrological estimation and simultaneously provide the interpretation of the impact of the hydrological features on estimation results.

Machine learning models combined with wavelet transform and phase space reconstruction for groundwater level forecasting

Reliable forecasting of groundwater levels plays an important role in water resource management and the prevention of environmental problems. This study aims to introduce a novel hybrid model combining machine learning with wavelet transform (WT) and phase space reconstruction (PSR) for groundwater level forecasting. The development of the proposed hybrid model consists of two levels. Firstly, a popular data-driven nonlinear artificial neural network (ANN) model was selected as the base model. Thereafter, the WT is used to decompose the groundwater level time series. In addition, the PSR method is further adopted to select the suitable sub-series components resulting from the WT decomposition processes. At the first level, the hybrid model only combines the WT with ANN (WT-ANN). At the second level, a two-step preprocessing process, WT coupled with PSR, is implemented before the ANN, namely WT-PSR-ANN. Meanwhile, three mother wavelets (Daubechies, Symlets, and Dmeyer) are selected to further investigate the effect of wavelet type. Finally, the model's performance is evaluated using visual analysis and statistical metrics (root means square error, correlation coefficient, and the Nash-Sutcliffe efficiency coefficient). The results show that the WT-ANN model outperformed the vanilla ANN model. Furthermore, the WT-PSR-ANN model achieved the best accuracy for all selected mother wavelets. The most suitable mother wavelet is specific to each well, and there is no one-size-fits-all answer. This study highlights the effectiveness of PSR combined with WT as a data preprocessing step when the ANN model is applied for groundwater level forecasting.

Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran

Seasonal hydrological forecasts play a critical role in water resources management. The Copernicus Climate Change Service (C3S) data store provides open access to monthly hydrological forecasts for up to six-months. This study aims to evaluate, for the first time, 1- to 3-month runoff forecasts using the European Centre for Medium-Range Weather Forecasts (ECMWF) ensembles of precipitation, runoff, and temperature in 1981–2015 period over a total of 30 s-level basins in Iran. We adopted the 5th, 50th and 95th ECMWF ensemble quantiles for each variable that represent low, medium and high probability of occurrence, respectively. Pearson correlation analysis (Pca), Recursive Feature Elimination (RFE) via random forest (RF) model, and Bayesian Networks (BN) feature selection algorithms were used in order to reduce input variable dimension and select potential predictors to be fed to the machine learning models. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) machine learning models were used with Repeated K-Fold cross validation (rK-Fold CV) while model efficiency was evaluated using modified Kling-Gupta efficiency coefficient (KGE’), Nash-Sutcliffe Efficiency coefficient (NSE), and Normalized Root Mean Square Error (NRMSE). Results of this study revealed that C3S runoff ensembles have the highest impact on forecast accuracy of streamflow, followed by precipitation and temperature. Overall, model performance yield a best-to-worst ranking of ANN, XGBoost, RF, MLR, and SVR with KGE’ values of 0.70, 0.68, 0.66, 0.57, and 0.41, respectively. The predictive performance of all models decreased with lead times beyond 1-month, where ANN and XGBoost outperformed other models with KGE’ of 0.65 for 2-month lead time and 0.60 for 3-month lead time. The three superior models of XGBoost, ANN, and RF, were employed with RFE and BN FSAs most frequently across Iran’s 30 s level basins in all lead times. Almost all models in the arid central region of Iran showed the lowest performance while highest skills were achieved in the western regions of Iran. Finally, for all models and over all regions, the model performance reduced by increase in lead-time.

Interpretation of spatio-temporal variation of precipitation from spatially sparse measurements using Bayesian compressive sensing (BCS)

ABSTRACT Precipitation might change rapidly and vary spatially, therefore, knowledge on spatio-temporal variation of precipitation plays a pivotal role in water resources management, hydrogeological hazard and risk assessment, and city resilience enhancement. However, precipitation monitoring data are collected through a limited number of precipitation stations in practice, and they are often sparse and discontinuous, particularly in spatial domain. Furthermore, regional precipitation data exhibits characteristics of seasonality, periodicity and highly non-stationarity on a long-time scale. Therefore, it is challenging to obtain a spatio-temporal variation of precipitation with high spatial resolution from monitoring data measured at a limited number of precipitation stations. To address these challenges, this study develops a non-parametric spatio-temporal Bayesian compressive sensing (ST-BCS) method for interpolation of spatio-temporally varying, but sparsely measured precipitation data in the spatial domain. The proposed method is able to not only provide precipitation interpolation results with high spatial resolution from a limited number of monitoring stations, but also quantify the associated interpolation uncertainty simultaneously. In addition, ST-BCS is directly applicable to the non-stationary spatio-temporal meteorological data. Furthermore, real precipitation datasets are established to benchmark different spatio-temporal interpolation methods. The benchmarking results show that the proposed ST-BCS method performs well and outperforms the spatial BCS method.

Data division effect on machine learning performance for prediction of streamflow

Accurate estimation of streamflow has an important role in water resources management, disaster preparedness and early warning, reservoir operation, and sizing of water structures. In this study, Extreme gradient boosting (XGBoost) and K-Nearest Neighbours (KNN) algorithms are used for the estimation of streamflow. In order to reveal the appropriate model, the raw model and models with optimized parameters were evaluated while the models were being built. In the setup of the models, various training test rates were also tried, and it was investigated which data division showed more effective results. For this purpose, the data were divided into ratios such as 60-40, 70-30, 80-20, and 90-10, respectively, and the model results were compared. Various statistical indicators such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) were used when comparing the models. As a result of the analysis, it was determined that the most suitable model for monthly streamflow estimation was obtained by using the optimized Xgboost algorithm and 60-40% data division. The obtained outputs constitute a vital resource for decision-makers regarding water resources planning and flood and drought management.

Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT).

In recent days, the quality of water in inland water bodies has been threatened by various natural and anthropogenic activities. Henceforth, the continuous monitoring of water quality is mandatory to control the pollution level in surface water bodies. In this work, remote sensing technology integrated with an Artificial Intelligence (AI) algorithm, a new technique called Spatio-Temporal Hybrid Novel Technique (STHNT), was used to predict, and monitor the chemical water quality pollution level through the Water Quality Index (WQI). The Two Bands Regression Empirical (TBRE) water quality model has been used to retrieve water quality parameters from multi-resolution satellite imagery (Sentinel-2 MSI). The Nonlinear Auto-regressive Neural Network (NARNET), which is an Artificial Neural Network (ANN), was set up to predict the water quality index. Based on the model performed on the remote sensing water quality data, it is inferred that NARNET (Coefficient of determination-R2:0.9911, Root Mean Square Error-RMSE:1.693 and Sum of Squares of Error-SSE:14.33) provides significant results in predicting WQI. Therefore, the combined remote sensing technology with artificial intelligence plays a pivotal role in water resource management, which helps in reducing the pollution level in surface water bodies.

Retrieval of total suspended solids concentration from hyperspectral sensing using hierarchical Bayesian model aggregation for optimal multiple band ratio analysis

Water quality monitoring plays an essential role in water resource management and water governance. At present, the monitoring is commonly conducted via in-situ sampling and/or by setting up gauging stations, which can be labour intensive and costly. Recently, the possibility of monitoring water quality through remote sensing with Unmanned Aerial Vehicles (UAVs) and hyperspectral sensors has shown great promise, with the key advantages of larger spatial coverage and possibly higher accuracy enabled by higher spectral resolution and more extensive data. Correspondingly, more advanced methods need to be established for hyperspectral analysis for water quality determination to capitalize on this wealth of information. In this study, a new method called Hierarchical Bayesian Model Aggregation for Optimal Multiple Band Ratio Analysis (HBMA-OMBRA) has been developed as a proof-of-concept for estimating Total Suspended Solids (TSS) concentrations from the hyperspectral data. The method leverages on the Bayesian ensembling of competing models because there is not a single best working model for all situations. It also encompasses a new approach called Ensemble Band Ratio Selection (ENBRAS) for the identification of best candidate band ratios (BBRs) via a set of ensembling and “bagging” procedures, followed by a modified Batchelor Wilkin’s algorithm to cluster the candidate band ratios. A laboratory investigation was conducted in the present study to measure the hyperspectral reflectance in different experiments under various environmental conditions to verify the robustness of HBMA-OMBRA. From the experimental results, six distinct clusters of candidate BBRs were identified using ENBRAS. In particular, two clusters in the red, green, and near infrared spectrum showed the largest contribution. The significance of multi-clusters provides an explanation for previously contrasting results reported in the literature and some evidence for reconciling these findings.

Probability Distribution for Water Fluxes in a Heterogeneous Unsaturated Zone Using an Ensemble of 1D Simulations

Groundwater recharge plays an important role in water resource management. In arid and semi-arid areas, the groundwater recharge under agricultural fields constitutes a large portion of the total recharge. Estimating the agricultural recharge is important for groundwater management and assessing contamination risk. However, estimations of the recharge fluxes are difficult due to the soil heterogeneity and the complex nature of the flow processes in the soil. Here, we suggest a new method for estimating the recharge flux probability distribution that accounts for the soil heterogeneity. The method is demonstrated using a unique data set of long-term continuous measurements of water content under an agricultural field. The measurements are used to assign weights to a large ensemble of hydraulic parameter sets. The weights are then used to derive the probability distribution of flux under different agricultural practices. We found that the distribution of the recharge fluxes varies considerably between one annual crop and a cycle of three crops over 2 years. However, the distribution of the fraction of the input water that infiltrates does not vary much between the two practices. Under natural conditions, with no irrigation, the recharge flux is much smaller, and its distribution is narrower. Our approach provides the full probability distribution of fluxes rather than a single expected value of the flux or its mean.

Radium isotope assessment of submarine groundwater discharge and associated nutrient inputs in Eastern Liaodong Bay, China

The accurate assessment of submarine groundwater discharge (SGD) and associated nutrient fluxes plays a significant role in water resources management and ecological environment protection in the coastal area. Currently, Liaodong Bay has become one of the most polluted marine areas in China. However, the nutrient fluxes carried by the SGD and its environmental effects have not yet been thoroughly reported in eastern Liaodong Bay, China. In this study, the distribution of 224Ra/223Ra activity ratio and its influencing factors under the geochemical conditions were explored. The activity of radium was affected by the water type and the geochemical factors. The radium isotopes in seawater were positively correlated with the ratios of Mg2+/Na+ and Ca2+/Na+ but negatively correlated with salinity and SO42−. The average apparent water age was evaluated to be 13.0 days. Furthermore, based on the radium mass balance model, the total average SGD flux was calculated to be 1.31 × 108 m3 day–1. The nutrient fluxes carried by the SGD were 7.16 × 107, 1.01 × 106, 1.61 × 107, 0.92 × 106, and 5.41 × 107 mol day−1 for DIN, DIP, NH4+, NO2−, and NO3−, respectively. The nutrient inputs through the SGD have an average DIN : DIP ratio of 70, which was able to seriously affect the ecological environment. The SGD-derived nutrient fluxes were higher than the local river input, which demonstrated that the SGD was a major source of nutrients affecting regional marine ecosystems.

Women’s role in water resource management: a case study on upper catchment area of Kangsabati river basin under Purulia district, West Bengal

Women’s role in water resource management: a case study on upper catchment area of Kangsabati river basin under Purulia district, West Bengal

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

The research into rainfall-runoff plays a very important role in water resource management. However, runoff simulation is a challenging task due to its complex formation mechanism, time-varying characteristics and nonlinear hydrological dynamic process. In this study, a nonlinear autoregressive model with exogenous input (NARX) is used to simulate the runoff in the Linyi watershed located in the northeastern part of the Huaihe river basin. In order to better evaluate the performance of NARX, a distributed hydrological model, TOPX, is used to simulate the discharge as a reference, and runoff classification by cluster analysis is used to further improve the accuracy of runoff simulation. Based on the four statistics indexes of the Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean square error (RMSE) and mean relative bias (Bias), the NARX model is capable of simulating the rainfall-runoff dynamic process satisfactorily, although there is a little underestimation of the peak flow. After runoff classification, underestimation has been improved, and discharge simulation driven by NARX based on runoff classification (C-NARX) is well consistent with the observation. It is feasible to take it as a promising method, which also can be seen as a good reference and replacement for the current rainfall-runoff simulation.