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Application of Various Hydrological Modeling Techniques and Methods in River Basin Management

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 24683

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Special Issue Editors

Dr. Ankur Srivastava

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Guest Editor

College of Engineering, Science and Environment, School of Engineering, Callaghan, Australia
Interests: evapotranspiration; soil moisture; irrigation; hydrological modeling; ecohydrology; remote sensing of vegetation; solar radiation; landscape evolution; water resources; net radiation
Special Issues, Collections and Topics in MDPI journals

Dr. Venkat Sridhar

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Guest Editor

Department of Biological Systems Engineering, Virginia Polytechnic University, Blacksburg, VA 24061, USA
Interests: evapotranspiration; Earth system modeling; climate impacts on hydrology on water resources; land–atmosphere interactions
Special Issues, Collections and Topics in MDPI journals

Dr. Nikul Kumari

E-Mail Website
Guest Editor

Faculty of Science, University of Technology, Sydney, Australia
Interests: hydrology; ecohydrology; ecogeomorphology; remote-sensing

Special Issue Information

Dear Colleagues,

Freshwater scarcity and freshwater mismanagement are increasingly common challenges that poses a serious threat to the socio-economic development of today’s world. Rising demand for water in different parts of the world necessitates better management of freshwater resources for agricultural purposes, irrigation, water resources management, and climate-feedback mechanisms. With advancements in modeling and computing techniques, hydrological models act as a boon for predicting extreme events like floods and droughts. Hydrological models (conceptual, semi-distributed, fully distributed) are valuable and informative tools in determining and finding different ways to combat environment-related problems and stabilize the water balance of the watershed. In addition, machine learning algorithms (MLAs) have great potential and have been promising in simulating hydrologic processes. For instance, streamflow estimation is crucial for efficient water management and decision-making in any given catchment, especially for drought and flood hydrology, crop modeling, flood forecasting, crop water requirement, major reservoir operations, freshwater allocation, as well as freshwater utilization and management. The complex nature of hydrological processes such as evapotranspiration, soil moisture, and baseflow among the land–water–plant ecosystems hinders the accurate streamflow estimation at the watershed scale. The present Special Issue of Water focuses on the developments in new techniques and perspectives in catchment modeling, the adaptation of remotely sensed data for hydrological modeling and the application of MLAs in predicting water balance components.

Dr. Ankur Srivastava
Dr. Venkat Sridhar
Dr. Nikul Kumari
Guest Editors

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Keywords

freshwater
hydrological model
machine learning
streamflow
evapotranspiration
soil moisture
remote sensing
land use/land cover change
drought
flood
crop water requirement
irrigation

Published Papers (14 papers)

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Research

18 pages, 8802 KiB

Open AccessArticle

Improvement and Evaluation of CLM5 Application in the Songhua River Basin Based on CaMa-Flood

by Heng Li, Zhiwei Zhang and Zhen Zhang

Water 2024, 16(3), 442; https://0-doi-org.brum.beds.ac.uk/10.3390/w16030442 - 29 Jan 2024

Viewed by 804

Abstract

This paper optimized the hydrological postprocessing of CLM5 using CaMa-Flood, combining multi-source meteorological forcing datasets and a dynamically changing surface dataset containing 16 PFTs (plant functional types) to simulate the high-resolution runoff process in the SRB from 1996 to 2014, specifically by integrating discharge with flooded area. Additionally, we evaluated the spatiotemporal variations of precipitation data from meteorological forcing datasets and discharge to validate the accuracy of model improvements. Both the discharge and the flooded area simulated by the coupled model exhibit pronounced seasonality, accurately capturing the discharge increase during the warm season and the river recession process in the cold season, along with corresponding changes in the flooded area. This highlights the model’s capability for hydrological process monitoring. The simulated discharge shows a high correlation coefficient (0.65–0.80) with the observed discharge in the SRB, reaching a significance level of 0.01, and the Nash–Sutcliffe efficiency ranges from 0.66 to 0.78. Leveraging the offline coupling of CLM and CaMa-Flood, we present a method with a robust physical mechanism for monitoring and providing a more intuitive representation of hydrological events in the SRB. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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26 pages, 4262 KiB

Open AccessArticle

Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle Swarm Optimization-Extreme Learning Machine Models for the Water Discharge of the Buzău River, Romania

by Liu Zhen and Alina Bărbulescu

Water 2024, 16(2), 289; https://0-doi-org.brum.beds.ac.uk/10.3390/w16020289 - 15 Jan 2024

Viewed by 857

Abstract

Modeling and forecasting the river flow is essential for the management of water resources. In this study, we conduct a comprehensive comparative analysis of different models built for the monthly water discharge of the Buzău River (Romania), measured in the upper part of the river’s basin from January 1955 to December 2010. They employ convolutional neural networks (CNNs) coupled with long short-term memory (LSTM) networks, named CNN-LSTM, sparrow search algorithm with backpropagation neural networks (SSA-BP), and particle swarm optimization with extreme learning machines (PSO-ELM). These models are evaluated based on various criteria, including computational efficiency, predictive accuracy, and adaptability to different training sets. The models obtained applying CNN-LSTM stand out as top performers, demonstrating a superior computational efficiency and a high predictive accuracy, especially when built with the training set containing the data series from January 1984 (putting the Siriu Dam in operation) to September 2006 (Model type S2). This research provides valuable guidance for selecting and assessing river flow prediction models, offering practical insights for the scientific community and real-world applications. The findings suggest that Model type S2 is the preferred choice for the discharge forecast predictions due to its high computational speed and accuracy. Model type S (considering the training set recorded from January 1955 to September 2006) is recommended as a secondary option. Model type S1 (with the training period January 1955–December 1983) is suitable when the other models are unavailable. This study advances the field of water discharge prediction by presenting a precise comparative analysis of these models and their respective strengths Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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19 pages, 7161 KiB

Open AccessArticle

Multi-Station Hydrological Modelling to Assess Groundwater Recharge of a Vast Semi-Arid Basin Considering the Problem of Lack of Data: A Case Study in Seybouse Basin, Algeria

by Cagri Alperen Inan, Ammar Maoui, Yann Lucas and Joëlle Duplay

Water 2024, 16(1), 160; https://0-doi-org.brum.beds.ac.uk/10.3390/w16010160 - 31 Dec 2023

Viewed by 1365

Abstract

Water resource management scenarios have become more crucial for arid to semi-arid regions. Their application prerequisites rigorous hydrological modelling approaches since data are usually exposed to uncertainties and inaccuracies. In this work, Soil Water Assessment Tool (SWAT), an open source semi-distributed, continuous-time, process-based physical hydrological model is used to model hydrological processes and eventually calculate groundwater recharge estimations in Seybouse basin, Northeast Algeria. The model uses estimated rainfall to calibrate the model with observed discharge from hydrometric stations. Model calibration and validation are performed over four hydrometric stations located in the basin. Uncertainty analysis and sensitivity analysis supported the calibration period. SUFI-2 algorithm is used for uncertainty estimations along with a global sensitivity analysis prior to calibration simulations. Simulated flood hydrographs showed generally good accuracy with few misfits on the peaks. The model obtained satisfactory and consistent calibration and validation results for which the Nash score varied from 0.5 to 0.7 for calibration and from −0.1 to 0.6 for validation and R² from 0.6 to 0.7 for calibration and 0.03 to 0.8 for validation. Moreover, estimated water budget values show strong similarities with the observed values found in the literature. The present work shows that the rigorously calibrated and validated SWAT model can simulate hydrological processes as well as major high and low flows using estimated rainfall data. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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26 pages, 7080 KiB

Open AccessArticle

Comparative Analysis of MCDA Techniques for Identifying Erosion-Prone Areas in the Burhanpur Watershed in Central India for the Purposes of Sustainable Watershed Management

by Abhishek Patel, K. V. Ramana Rao, Yogesh A. Rajwade, Chandra Kant Saxena, Karan Singh and Ankur Srivastava

Water 2023, 15(22), 3891; https://0-doi-org.brum.beds.ac.uk/10.3390/w15223891 - 08 Nov 2023

Cited by 1 | Viewed by 1011

Abstract

The degradation of land and increasing water scarcity are existing challenges for agricultural sustainability, necessitating the implementation of improved soil-conservation practices at the watershed scale. The identification and selection of critical/prone areas based on erosion-governing criteria is essential and helps in the execution of the management process for determining priority. This study prioritizes erosion-prone sub-watersheds (alternatives) based on morphometric parameters (multiple criteria) via five Multi-Criteria Decision Analysis (MCDA) approaches, i.e., AHP: Analytical Hierarchy Process; TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; VIKOR: VIseKriterijumska Optimizacija I Kompromisno Resenje; SAW: Simple Additive Weighting; and CF: Compound Factor. Based on their priority score, 19 sub-watersheds were classified into four priority classes: low priority (0–0.25), moderate priority (0.25–0.50), high priority (0.50–0.75), and very high priority (0.75–1). The results revealed that about 8.34–30.15% area of the Burhanpur watershed is critically prone to erosion, followed by 23.38–52.05% area classed as high priority, 7.47–49.99% area classed as moderate priority, and 10.33–18.28% area classed as low priority. Additionally, four indices—percentage of changes (∆P), intensity of changes (∆I), the Spearman rank correlation coefficient test (SCCT), and the Kendall tau correlation coefficient test (KTCCT)—were employed to compare the models. This study confirms the efficacy of morphometric parameters for prioritizing sub-watersheds to preserve soil and the environment, particularly in areas for which limited information is available. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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18 pages, 12723 KiB

Open AccessArticle

Implementation of HydroBID Model with Satellite-Based Precipitation Products in Guadalquivir Basin, Bolivia

by Oliver Saavedra, Jhonatan Ureña and Moisés Perales

Water 2023, 15(18), 3250; https://0-doi-org.brum.beds.ac.uk/10.3390/w15183250 - 13 Sep 2023

Viewed by 1261

Abstract

The use of distributed precipitation data in hydrological models is critically important to simulate processes at a micro-basin scale. However, aerial precipitation at a high resolution is required to run these models. This study aimed to set up the HydroBID tool in the Guadalquivir River basin using satellite-based precipitation products. The employed products included GSMaP gauge version 6, interpolated rain gauges using Kriging, the combined GS product for Bolivia, and the proposed combined product for the Guadalquivir basin. The GS Guadalquivir was generated by combining the satellite-based product GSMaP gauge version 6 with the local rain gauge network. The main difference with GS Bolivia is the improvement of the resolution from 5 km to 250 m. An iteration scheme using 230 micro-basins was employed, reaching a correlation of 0.98 compared to the control dataset. By using the hydrological model with the precipitation products, the daily river discharge was obtained, showing a high correlation of 0.99 and efficiency of 0.96 in relation to observed data between 2000 and 2016 at Obrajes station. Simulated flows with Kriging and GS Guadalquivir products presented similarly high correlations compared to the observed flows. In the case of GSMaP and GS Bolivia, these products showed general underestimations of the simulated flows, reaching correlations between 0.28 and 0.91, respectively. Moreover, annual volumes were analyzed, where the overestimation of GSMaP, Kriging, and GS Guadalquivir showed similar characteristics concerning the distribution of specific river discharges and volumes. Therefore, HydroBID appeared to be a feasible tool with enough adaptability to use distributed precipitation and simulate flows at a micro-basin scale. Therefore, we recommend applying this scheme to other basins to carry out analysis of events, water balance, and floods and similar studies. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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25 pages, 5302 KiB

Open AccessArticle

Monthly Runoff Forecasting Using Particle Swarm Optimization Coupled with Flower Pollination Algorithm-Based Deep Belief Networks: A Case Study in the Yalong River Basin

by Zhaoxin Yue, Huaizhi Liu and Hui Zhou

Water 2023, 15(15), 2704; https://doi.org/10.3390/w15152704 - 27 Jul 2023

Cited by 1 | Viewed by 850

Abstract

Accuracy in monthly runoff forecasting is of great significance in the full utilization of flood and drought control and of water resources. Data-driven models have been proposed to improve monthly runoff forecasting in recent years. To effectively promote the prediction effect of monthly runoff, a novel hybrid data-driven model using particle swarm optimization coupled with flower pollination algorithm-based deep belief networks (PSO-FPA-DBNs) was proposed, which selected the optimal network depth via PSO and searched for the optimum hyper parameters (the number of neurons in the hidden layer and the learning rate of the RBMs) in the DBN using FPA. The methodology was divided into three steps: (i) the Comprehensive Basin Response (COM) was constructed and calculated to characterize the hydrological state of the basin, (ii) the information entropy algorithm was adopted to select the key factors, and (iii) the novel model was proposed for monthly runoff forecasting. We systematically compared the PSO-FPA-DBN model with the traditional prediction models (i.e., the backpropagation neural network (BPNN), support vector machines (SVM), deep belief networks (DBN)), and other improved models (DBN-PLSR, PSO-GA-DBN, and PSO-ACO-DBN) for monthly runoff forecasting by using an original dataset. Experimental results demonstrated that our PSO-FPA-DBN model outperformed the peer models, with a mean absolute percentage error (MAPE) of 18.23%, root mean squared error (RMSE) of 230.45 m³/s, coefficient of determination (DC) of 0.9389, and qualified rate (QR) of 64.2% for the data from the Yalong River Basin. Also, the stability of our PSO-FPA-DBN model was evaluated. The proposed model might adapt effectively to the nonlinear characteristics of monthly runoff forecasting; therefore, it could obtain accurate and reliable runoff forecasting results. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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29 pages, 4398 KiB

Open AccessArticle

Different Effect of Cloud Seeding on Three Dam Basins, Korea

by Jeong-Hyeok Ma, Chulsang Yoo, Sung-Uk Song, Wooyoung Na, Eunsaem Cho, Sang-Keun Song and Ki-Ho Chang

Water 2023, 15(14), 2555; https://0-doi-org.brum.beds.ac.uk/10.3390/w15142555 - 12 Jul 2023

Viewed by 1161

Abstract

This study shows that cloud seeding should be planned by considering the dam reservoir characteristics as well as the dam basin characteristics. First, the collection efficiency of increased rainfall by cloud seeding is compared for three dam basins (Boryeong Dam, Yongdam Dam, and Namgang Dam basins) located in the western part of the Korean Peninsula. Second, the additional runoff volumes in those three basins from cloud seeding are compared with each other. Finally, the change in water supply capacity is evaluated by considering the dam reservoir operation and planned water supply. In this study, cloud seeding is simulated using the WRF−ARW model, and, additionally, four different rainfall data generated by considering the scenarios of a rainfall increase of 5, 10, 15, and 20% are used for more practical evaluation. The results in this study show that the situation in Boryeong Dam basin is better than in the other two dam basins. More active cloud seeding is necessary in the Yongdam Dam and Namgang Dam basins. However, it has also been found that cloud seeding alone cannot solve the water supply problems in those two dam basins. The above findings also indicate that cloud seeding should be carefully planned. It can vary dam-by-dam. Cloud seeding might be effective every season in one dam, but only in Spring in another dam basin, while in other dams, summer or fall season might be the best option. The target increase of rainfall is also an important issue. Just a mild increase could be better in one dam, but it can be important to secure much more rainfall in other dams. Even though the three dams considered in this study are located in practically the same climatic zone, the conditions required for cloud seeding differ completely. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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25 pages, 5205 KiB

Open AccessArticle

Assessing the National Water Model’s Streamflow Estimates Using a Multi-Decade Retrospective Dataset across the Contiguous United States

by Mohamed Abdelkader, Marouane Temimi and Taha B.M.J. Ouarda

Water 2023, 15(13), 2319; https://0-doi-org.brum.beds.ac.uk/10.3390/w15132319 - 21 Jun 2023

Cited by 11 | Viewed by 2292

Abstract

The goal of this study is to evaluate the performance of the National Water Model (NWM) in time and space across the contiguous United States. Retrospective streamflow simulations were compared to records from 3260 USGS gauging stations, considering both regulated and natural flow conditions. Statistical metrics, including Kling–Gupta efficiency, Percent Bias, Pearson Correlation Coefficient, Root Mean Squared Error, and Normalized Root Mean Squared Error, were employed to assess the agreement between observed and simulated streamflow. A comparison of historical trends in daily flow data between the model and observed streamflow provided additional insight into the utility of retrospective NWM datasets. Our findings demonstrate a superior agreement between the simulated and observed streamflow for natural flow in comparison to regulated flow. The most favorable agreement between the NWM estimates and observed data was achieved in humid regions during the winter season, whereas a reduced degree of agreement was observed in the Great Plains region. Enhancements to model performance for regulated flow are necessary, and bias correction is crucial for utilizing the NWM retrospective streamflow dataset. The study concludes that the model-agnostic NextGen NWM framework, which accounts for regional performance of the utilized model, could be more suitable for continental-scale hydrologic prediction. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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17 pages, 3209 KiB

Open AccessArticle

Assessing the Effectiveness of the Use of the InVEST Annual Water Yield Model for the Rivers of Colombia: A Case Study of the Meta River Basin

by Jhon B. Valencia, Vladimir V. Guryanov, Jeison Mesa-Diez, Jeimar Tapasco and Artyom V. Gusarov

Water 2023, 15(8), 1617; https://0-doi-org.brum.beds.ac.uk/10.3390/w15081617 - 21 Apr 2023

Cited by 5 | Viewed by 2505

Abstract

This paper presents the results of one of the hydrological models, the InVEST “Annual Water Yield” (InVEST–AWY), applied to the Meta River basin in Colombia, which covers an area of 113,981 km². The study evaluates the performance of the model in different subbasins of the Meta River basin. The model’s accuracy was assessed using different statistical measures, including Nash–Sutcliffe Efficiency (NSE) coefficient, Root Mean Square Error (RMSE), correlation coefficients for the calibration (r_cal) and validation (r_val) periods. The overall performance of the model in the Meta River basin is relatively poor as indicated by the low NSE value of 0.07 and high RMSE value of 1071.61. In addition, the model explains only a 7% of the variance in the observed data. The sensitivity analysis revealed that a 30% reduction in crop coefficient (Kc) values would result in a 10.7% decrease in water yield. The model estimated, for example, the annual average water yield of the river in 2018 as 1.98 × 10¹¹ m³/year or 6273.4 m³/s, which is 1.3% lower than the reported value. The upper Meta River subbasin shows the highest NSE value (0.49), indicating a good result between observed and simulated water discharge. In contrast, the South Cravo River subbasin shows a negative NSE value of −1.29, indicating poor model performance. The Yucao River subbasin and the upper Casanare River subbasin also show lower NSE values compared to the upper Meta River subbasin, indicating less accurate model performance in these subbasins. The correlation coefficients in calibration (r_cal) and validation (r_val) for the upper Meta River, Yucao River, South Cravo River, and upper Casanare River subbasins were 0.79 and 0.83, 0.4 and 0.22, 0.5 and −0.25, and 0 and 0.18, respectively. These results provide useful insights into the limitations for the proper use of the InVEST–AWY model in Colombia. This study is the first to use the InVEST–AWY model on a large scale in the territory of Colombia, allowing to evaluate its effectiveness in hydrological modeling for water management. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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32 pages, 9909 KiB

Open AccessArticle

Predictive Modelling of Reference Evapotranspiration Using Machine Learning Models Coupled with Grey Wolf Optimizer

by Pangam Heramb, K. V. Ramana Rao, A. Subeesh and Ankur Srivastava

Water 2023, 15(5), 856; https://0-doi-org.brum.beds.ac.uk/10.3390/w15050856 - 22 Feb 2023

Cited by 4 | Viewed by 1863

Abstract

Mismanagement of fresh water is a primary concern that negatively impacts agricultural productivity. Judicious use of water in agriculture is possible by estimating the optimal requirement. The present practice of estimating crop water requirements is using reference evapotranspiration (ET₀) values, which is considered a standard method. Hence, predicting ET₀ is vital in allocating and managing available resources. In this study, different machine learning (ML) algorithms, namely random forests (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB), were optimized using the naturally inspired grey wolf optimizer (GWO) viz. GWORF, GWOXGB, and GWOLGB. The daily meteorological data of 10 locations falling under humid and sub-humid regions of India for different cross-validation stages were employed, using eighteen input scenarios. Besides, different empirical models were also compared with the ML models. The hybrid ML models were found superior in accurately predicting at all the stations than the conventional and empirical models. The reduction in the root mean square error (RMSE) from 0.919 to 0.812 mm/day in the humid region and 1.253 mm/day to 1.154 mm/day in the sub-humid region was seen in the least accurate model using the hyperparameter tuning. The RF models have improved their accuracies substantially using the GWO optimizer than LGB and XGB models. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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21 pages, 10475 KiB

Open AccessFeature PaperArticle

Streamflow and Sediment Yield Analysis of Two Medium-Sized East-Flowing River Basins of India

by Nageswara Reddy Nagireddy, Venkata Reddy Keesara, Venkataramana Sridhar and Raghavan Srinivasan

Water 2022, 14(19), 2960; https://0-doi-org.brum.beds.ac.uk/10.3390/w14192960 - 21 Sep 2022

Cited by 4 | Viewed by 2601

Abstract

With increased demand for water and soil in this Anthropocene era, it is necessary to understand the water balance components and critical source areas of land degradation that lead to soil erosion in agricultural dominant river basins. Two medium-sized east-flowing rivers in India, namely Nagavali and Vamsadhara, play a significant role in supporting water supply and agriculture demands in parts of the Odisha districts of Kalahandi, Koraput and Rayagada, as well as the Andhra Pradesh districts of Srikakulam and Vizianagaram. Floods are more likely in these basins as a result of cyclones and low-pressure depressions in the Bay of Bengal. The water balance components and sediment yield of the Nagavali and Vamsadhara river basins were assessed using a semi-distributed soil and water assessment tool (SWAT) model in this study. The calibrated model performance revealed a high degree of consistency between observed and predicted monthly streamflow and sediment load. The water balance analysis of Nagavali and Vamsadhara river basins showed the evapotranspiration accounted for 63% of the average annual rainfall. SWAT simulated evapotranspiration showed a correlation of 0.78 with FLDAS data. The calibrated SWAT model showed that 26.5% and 49% of watershed area falling under high soil erosion class over Nagavali and Vamsadhara river basins, respectively. These sub watersheds require immediate attention to management practices to improve the soil and water conservation measures. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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21 pages, 5298 KiB

Open AccessArticle

Improving the Prediction of Soil Organic Matter in Arable Land Using Human Activity Factors

by Lixin Ning, Changxiu Cheng, Xu Lu, Shi Shen, Liang Zhang, Shaomin Mu and Yunsheng Song

Water 2022, 14(10), 1668; https://0-doi-org.brum.beds.ac.uk/10.3390/w14101668 - 23 May 2022

Cited by 4 | Viewed by 1674

Abstract

Detailed spatial distribution of soil organic matter (SOM) in arable land is essential for agricultural management and decision making. Based on digital soil mapping (DSM) theory, much attention has been focused on the selection of environmental covariates. However, the importance of human activity factors in SOM prediction has not received enough attention, especially in arable soil. Moreover, due to the insufficient amount of soil sampling data used to train and validate the DSM model, the prediction results may be questionable, and some even contradictory. This paper explores the effectiveness of the human footprint, amount of fertilizer application, agronomic management level, crop planting type, and irrigation guarantee degree in SOM mapping of arable land in Heilongjiang Province. The results show that the model only including environmental covariates accounts for 41% of the variation in SOM distribution. The model combining the five human activity factors increases the SOM spatial prediction by 39% in terms of R² (coefficient of determination), 12% in terms of RMSE (root mean square error), 15% in terms of MAE (mean absolute error), and 11% in terms of LCCC (Lin’s concordance correlation coefficient), showing better prediction accuracy and performance. This indicates that human activity factors play a crucial role in determining SOM distribution in arable land. In the SOM prediction, soil moisture is the most important environmental covariate, and the amount of fertilizer application with a relative importance of 11.36% (ranking 3rd) is the most important human activity factor, higher than the annual average precipitation and elevation. From a spatial point of view, the Sanjiang Plain is a difficult area for prediction. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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22 pages, 3551 KiB

Open AccessArticle

Optimal Operation of Nashe Hydropower Reservoir under Land Use Land Cover Change in Blue Nile River Basin

by Megersa Kebede Leta, Tamene Adugna Demissie and Jens Tränckner

Water 2022, 14(10), 1606; https://0-doi-org.brum.beds.ac.uk/10.3390/w14101606 - 17 May 2022

Cited by 10 | Viewed by 2156

Abstract

Changes in LULC (land use land cover), which significantly influence the spatial and temporal distribution of hydrological processes and water resources in general, have a substantial impact on hydropower generation. The utilization of an optimization approach in order to analyze the operation of reservoirs is an important concern in the planning and management of water resources. The SWAT (Soil and Water Assessment Tool) and the HEC-ResPRM (Hydrologic Engineering Center reservoir evaluation system Prescriptive Reservoir Model) were combined to model and optimize the Nashe hydropower reservoir operation in the Blue Nile River Basin (BNRB). The stream flow into the reservoir was determined using the SWAT model, considering the current and future impacts of LULC changes. The HEC-ResPRM model has been utilized in order to generate the optimal hydropower reservoir operation by using the results of the SWAT calibrated and validated stream flow as input data. This study proposes a method for integrating the HEC-ResPRM and SWAT models to examine the effects of historical and future land use land cover change on the watershed’s hydrological processes and reservoir operation. Therefore, the study aimed to investigate the current and future optimal reservoir operation scenarios for water resources management concerning hydropower generation under the effect of LULC changes. The results reveal that both the 2035 and 2050 LULC change scenarios show the increased operation of hydropower reservoirs with increasing reservoir inflows, releases, storage, and reservoir elevation in the future. The effects of LULC change on the study area’s hydrological components reveal an increase in surface runoff until 2035, and its decrease from 2035 to 2050. The average annual reservoir storage and elevation in the 2050 LULC scenario increased by 7.25% and 2.27%, respectively, when compared to the current optimized scenario. Therefore, changes in LULC have a significant effect on hydropower development by changing the total annual and monthly reservoir inflow volumes and their seasonal distribution. Reservoir operating rule curves have been commonly implemented in the operation of hydropower reservoirs, since they help operators to make essential, optimal decisions with available stream flow. Moreover, the generated future reservoir rule curves can be utilized as a reference for the long-term prediction of hydropower generation capacity, and assist concerned authorities in the successful operation of the reservoir under the impact of LULC changes. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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15 pages, 5312 KiB

Open AccessFeature PaperArticle

Modeling Average Grain Velocity for Rectangular Channel Using Soft Computing Techniques

by Anuradha Kumari, Akhilesh Kumar, Manish Kumar and Alban Kuriqi

Water 2022, 14(9), 1325; https://0-doi-org.brum.beds.ac.uk/10.3390/w14091325 - 19 Apr 2022

Cited by 5 | Viewed by 1846

Abstract

This study was undertaken with the primary objective of modeling grain velocity based on experimental data obtained under the controlled conditions of a laboratory using a rectangular hydraulic tilting channel. Soft computing approaches, i.e., support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR), were applied to simulate grain velocity using four input variables; shear velocity, exposed area to base area ratio (EATBAR), relative depth, and sediment particle weight. Quantitative performance evaluation of predicted values was performed with the help of three different standard statistical indices, such as the root mean square error (RMSE), Pearson’s correlation coefficient (PCC), and Wilmot index (WI). The results during the testing phase revealed that the SVM model has RMSE (m/s), PCC, and WI values obtained as 0.1195, 0.8877, and 0.7243, respectively, providing more accurate predictions than the MLR and ANN models during the testing phase. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management)

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Application of Various Hydrological Modeling Techniques and Methods in River Basin Management

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