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Application of Advanced Computational Methods in Hydrological and Environmental Modelling
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Application of Advanced Computational Methods in Hydrological and Environmental Modelling

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 21701

Special Issue Editors

Dr. Rana Muhammad Adnan

E-Mail Website
Guest Editor
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Interests: sustainable development; water resources management; hydrological modeling; artificial intelligence; time series analysis; rainfall–runoff relationship; wind energy; sediment load; evaporation; evapotranspiration; hydro-meteorological droughts; groundwater; water quality parameters modeling; novel meta-heuristic approaches applications; trend analysis; clustering; watershed planning and management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ozgur Kisi

grade E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Natural Sciences and Engineering Ilia State University, 0162 Tbilisi, Georgia
Interests: developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling; suspended sediment modeling; forecasting; estimating; spatial and temporal analysis of hydro-climatic variables such as precipitation; streamflow; suspended sediment; evaporation; evapotranspiration; groundwater; lake level and water quality parameters; hydro-informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water management within a catchment remains an important problem which has several influencing variables. The accurate and deterministic forecasting of water resource variables such as flood, drought, lake, groundwater levels, water temperature, evaporation, discharges, and water quality is very difficult. Simulated hydrological responses of river basins remain highly uncertain, due to the presence of a broad variety of schematizations, erroneous measurements, and prior assumptions. Accurate and reliable runoff predictions by rainfall–runoff models should be a core component of flood risk management. However, since most catchments around the world remain ungauged, identifying the parameters of the rainfall–runoff models is still a challenge that may lead to the use of advance computational methods to overcome uncertainty in runoff predictions. In addition, in water resources management, there are currently different challenges and uncertainties due to climate change and man-made interferences, so it is very difficult to decide and select the best decisions. Mismanagement and the sustainability of the current and future water resource allocation are other concerns. Thus, it is important to use the newest technologies and tools to improve and properly develop sustainable management. The use of metaheuristic techniques in modeling water resource variables is a growing field of work in hydrology. This Special Issue invites authors to contribute new and original research findings that can add new knowledge to the effort toward understanding water resource systems, patterns, behaviors, and tools for hydrological prediction. Contributors are encouraged to present state-of-the art research to help the wider user community to include uncertainty in hydrological forecasts and to use such forecasts in supporting the decision-making process.

Dr. Rana Muhammad Adnan
Prof. Dr. Ozgur Kisi
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Ground water modelling
  • Water quality modelling
  • Flood modelling
  • Drought modelling
  • Hydrological modelling
  • Environmental modelling
  • Spatial modelling
  • Extreme events modelling
  • Lake level modelling
  • Rainfall–runoff modelling Predictions in ungauged catchments
  • Reservoir inflow modelling

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Published Papers (6 papers)

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Research

25 pages, 6521 KiB  
Article
Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu
by Sellaperumal Pazhanivelan, Vellingiri Geethalakshmi, Venkadesh Samykannu, Ramalingam Kumaraperumal, Mrunalini Kancheti, Ragunath Kaliaperumal, Marimuthu Raju and Manoj Kumar Yadav
Water 2023, 15(7), 1435; https://doi.org/10.3390/w15071435 - 6 Apr 2023
Cited by 7 | Viewed by 3734
Abstract
The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. [...] Read more.
The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. For effective drought monitoring/assessment, satellite-based precipitation products offer more reliable rainfall estimates with higher accuracy and spatial coverage than conventional rain gauge data. The present study on satellite-based drought monitoring and reliability evaluation was conducted using four high-resolution precipitation products, i.e., IMERGH, TRMM, CHIRPS, and PERSIANN, during the northeast monsoon season of 2015, 2016, and 2017 in the state of Tamil Nadu, India. These four precipitation products were evaluated for accuracy and confidence level by assessing the meteorological drought using standard precipitation index (SPI) and by comparing the results with automatic weather station (AWS) and rain gauge network data-derived SPI. Furthermore, considering the limited number of precipitation products available, the study also indirectly addressed the demanding need for high-resolution precipitation products with consistent temporal resolution. Among different products, IMERGH and TRMM rainfall estimates were found equipollent with the minimum range predictions, i.e., 149.8, 32.07, 80.05 mm and 144.31, 34.40, 75.01 mm, respectively, during NEM of 2015, 2016, and 2017. The rainfall data from CHIRPS were commensurable in the maximum range of 1564, 421, and 723 mm in these three consequent years (2015 to 2017) compared to AWS data. CHIRPS data recorded a higher per cent of agreement (>85%) compared to AWS data than other precipitation products in all the agro-climatic zones of Tamil Nadu. The SPI values were positive > 1.0 during 2015 and negative < −0.99 for 2016 and 2017, indicating normal/wet and dry conditions in the study area, respectively. This study highlighted discrepancies in the capability of the precipitation products IMERGH and TRMM estimates for low rainfall conditions and CHIRPS estimates in high rainfall regimes. Full article
(This article belongs to the Special Issue Application of Advanced Computational Methods in Hydrological and Environmental Modelling)
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18 pages, 7532 KiB  
Article
Water Quality Prediction of the Yamuna River in India Using Hybrid Neuro-Fuzzy Models
by Ozgur Kisi, Kulwinder Singh Parmar, Amin Mahdavi-Meymand, Rana Muhammad Adnan, Shamsuddin Shahid and Mohammad Zounemat-Kermani
Water 2023, 15(6), 1095; https://doi.org/10.3390/w15061095 - 13 Mar 2023
Cited by 11 | Viewed by 4649
Abstract
The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle swarm optimization, genetic algorithm, harmony search, and teaching–learning-based optimization algorithm, was investigated in this study in estimating the water quality of the Yamuna River in Delhi, India. A cross-validation approach was employed by [...] Read more.
The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle swarm optimization, genetic algorithm, harmony search, and teaching–learning-based optimization algorithm, was investigated in this study in estimating the water quality of the Yamuna River in Delhi, India. A cross-validation approach was employed by splitting data into three equal parts, where the models were evaluated using each part. The main aim of this study was to find an accurate prediction model for estimating the water quality of the Yamuna River. It is worth noting that the hybrid neuro-fuzzy and LSSVM methods have not been previously compared for this issue. Monthly water quality parameters, total kjeldahl nitrogen, free ammonia, total coliform, water temperature, potential of hydrogen, and fecal coliform were considered as inputs to model chemical oxygen demand (COD). The performance of hybrid neuro-fuzzy models in predicting COD was compared with classical neuro-fuzzy and least square support vector machine (LSSVM) methods. The results showed higher accuracy in COD prediction when free ammonia, total kjeldahl nitrogen, and water temperature were used as inputs. Hybrid neuro-fuzzy models improved the root mean square error of the classical neuro-fuzzy model and LSSVM by 12% and 4%, respectively. The neuro-fuzzy models optimized with harmony search provided the best accuracy with the lowest root mean square error (13.659) and mean absolute error (11.272), while the particle swarm optimization and teaching–learning-based optimization showed the highest computational speed (21 and 24 min) compared to the other models. Full article
(This article belongs to the Special Issue Application of Advanced Computational Methods in Hydrological and Environmental Modelling)
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19 pages, 5787 KiB  
Article
Development of a One-Parameter New Exponential (ONE) Model for Simulating Rainfall-Runoff and Comparison with Data-Driven LSTM Model
by Jaenam Lee and Jaekyoung Noh
Water 2023, 15(6), 1036; https://doi.org/10.3390/w15061036 - 9 Mar 2023
Cited by 4 | Viewed by 1801
Abstract
Runoff information can be used for establishing watershed water management plans. However, hydrological models with complex parameters make it difficult to quickly estimate runoff. This study developed a one-parameter new exponential (ONE) model for simulating rainfall-runoff using a single parameter, which was designed [...] Read more.
Runoff information can be used for establishing watershed water management plans. However, hydrological models with complex parameters make it difficult to quickly estimate runoff. This study developed a one-parameter new exponential (ONE) model for simulating rainfall-runoff using a single parameter, which was designed based on a nonlinear exponential function and watershed water balance that varies according to the soil water storage. The single parameter was included in the runoff function and implemented to continuously track the state of the soil water storage based on the watershed water balance. Furthermore, to validate the model’s effectiveness, it was applied to two multipurpose dams in Korea and the results showed that the daily results of the ONE model were better than those of a learning-based long short-term memory model in terms of the quantitative evaluation indices, monthly heatmap and annual runoff rate. This study demonstrated that rainfall-runoff can be simulated using only one parameter and that minimizing the number of parameters could enhance the practical utility of a hydrological model. The use of a single parameter is expected to maximize user convenience for simulating runoff, which is essential in the operation of water resource facilities. Full article
(This article belongs to the Special Issue Application of Advanced Computational Methods in Hydrological and Environmental Modelling)
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23 pages, 3516 KiB  
Article
Hybridized Adaptive Neuro-Fuzzy Inference System with Metaheuristic Algorithms for Modeling Monthly Pan Evaporation
by Rana Muhammad Adnan Ikram, Abolfazl Jaafari, Sami Ghordoyee Milan, Ozgur Kisi, Salim Heddam and Mohammad Zounemat-Kermani
Water 2022, 14(21), 3549; https://doi.org/10.3390/w14213549 - 4 Nov 2022
Cited by 16 | Viewed by 2232
Abstract
Precise estimation of pan evaporation is necessary to manage available water resources. In this study, the capability of three hybridized models for modeling monthly pan evaporation (Epan) at three stations in the Dongting lake basin, China, were investigated. Each model consisted of an [...] Read more.
Precise estimation of pan evaporation is necessary to manage available water resources. In this study, the capability of three hybridized models for modeling monthly pan evaporation (Epan) at three stations in the Dongting lake basin, China, were investigated. Each model consisted of an adaptive neuro-fuzzy inference system (ANFIS) integrated with a metaheuristic optimization algorithm; i.e., particle swarm optimization (PSO), whale optimization algorithm (WOA), and Harris hawks optimization (HHO). The modeling data were acquired for the period between 1962 and 2001 (480 months) and were grouped into several combinations and incorporated into the hybridized models. The performance of the models was assessed using the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), coefficient of determination (R2), Taylor diagram, and Violin plot. The results showed that maximum temperature was the most influential variable for evaporation estimation compared to the other input variables. The effect of periodicity input was investigated, demonstrating the efficacy of this variable in improving the models’ predictive accuracy. Among the models developed, the ANFIS-HHO and ANFIS-WOA models outperformed the other models, predicting Epan in the study stations with different combinations of input variables. Between these two models, ANFIS-WOA performed better than ANFIS-HHO. The results also proved the capability of the models when they were used for the prediction of Epan when given a study station using the data obtained for another station. Our study can provide insights into the development of predictive hybrid models when the analysis is conducted in data-scare regions. Full article
(This article belongs to the Special Issue Application of Advanced Computational Methods in Hydrological and Environmental Modelling)
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20 pages, 4534 KiB  
Article
Soil Moisture Data Assimilation in MISDc for Improved Hydrological Simulation in Upper Huai River Basin, China
by Zhenzhou Ding, Haishen Lü, Naveed Ahmed, Yonghua Zhu, Qiqi Gou, Xiaoyi Wang, En Liu, Haiting Xu, Ying Pan and Mingyue Sun
Water 2022, 14(21), 3476; https://doi.org/10.3390/w14213476 - 31 Oct 2022
Cited by 3 | Viewed by 2316
Abstract
In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown that the assimilation of remotely sensed soil moisture [...] Read more.
In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown that the assimilation of remotely sensed soil moisture (SM) data could help improve the simulation and forecasting capability of hydrological models. Still, very few studies have attempted to assimilate SM data from land surface process models into hydrological models to improve model simulation and forecasting accuracy. Therefore, in this study, we used the ensemble Kalman filter (EnKF) to assimilate the China Land Data Assimilation System (CLDAS) SM product into the MISDc model. We also corrected the CLDAS SM and assimilated the corrected SM data into the hydrological model. In addition, the effects of the 5th and 95th percentiles of flow were evaluated to see how SM DA affected low and high flows, respectively. Additionally, we tried to find an appropriate size for the number of ensemble members of the EnKF for this study. The results showed that the EnKF SM DA improved the runoff simulation ability of the hydrological model, especially for the high flows of the model; however, the simulation for the low flows deteriorated. In general, SM DA positively affected the ability of the MISDc model runoff simulation. Full article
(This article belongs to the Special Issue Application of Advanced Computational Methods in Hydrological and Environmental Modelling)
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19 pages, 2039 KiB  
Article
Flood Inundation Modeling by Integrating HEC–RAS and Satellite Imagery: A Case Study of the Indus River Basin
by Muhammad Adeel Afzal, Sikandar Ali, Aftab Nazeer, Muhammad Imran Khan, Muhammad Mohsin Waqas, Rana Ammar Aslam, Muhammad Jehanzeb Masud Cheema, Muhammad Nadeem, Naeem Saddique, Muhammad Muzammil and Adnan Noor Shah
Water 2022, 14(19), 2984; https://doi.org/10.3390/w14192984 - 22 Sep 2022
Cited by 11 | Viewed by 5550
Abstract
Floods are brutal, catastrophic natural hazards which affect most human beings in terms of economy and life loss, especially in the large river basins worldwide. The Indus River basin is considered as one of the world’s large river basins, comprising several major tributaries, [...] Read more.
Floods are brutal, catastrophic natural hazards which affect most human beings in terms of economy and life loss, especially in the large river basins worldwide. The Indus River basin is considered as one of the world’s large river basins, comprising several major tributaries, and has experienced severe floods in its history. There is currently no proper early flood warning system for the Indus River which can help administrative authorities cope with such natural hazards. Hence, it is necessary to develop an early flood warning system by integrating a hydrodynamic model, in situ information, and satellite imagery. This study used Hydrologic Engineering Center–River Analysis System (HEC–RAS) to predict river dynamics under extreme flow events and inundation modeling. The calibration and validation of the HEC–RAS v5 model was performed for 2010 and 2015 flood events, respectively. Manning’s roughness coefficient (n) values were extracted using the land use information of the rivers and floodplains. Multiple combinations of n values were used and optimized in the simulation process for the rivers and floodplains. The Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09A1, and MOD09GA products were used in the analysis. The Normalized Difference Water Index (NDWI), Modified NDWI1 (MNDWI1), and MNDWI2, were applied for the delineation of water bodies, and the output of all indices were blended to produce standard flood maps for accurate assessment of the HEC–RAS-based simulated flood extent. The optimized n values for rivers and floodplains were 0.055 and 0.06, respectively, with significant satisfaction of statistical parameters, indicating good agreement between simulated and observed flood extents. The HEC–RAS v5 model integrated with satellite imagery can be further used for early flood warnings in the central part of the Indus River basin. Full article
(This article belongs to the Special Issue Application of Advanced Computational Methods in Hydrological and Environmental Modelling)
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