International Journal of Hydrology Science and Technology (29 papers in press)

Regular Issues

• Analysis of flood severity using intelligent deep networks and sentinel image for the Kerala region  
  by Supriya Kamoji, Mukesh Kalla 
  Abstract: Accurate flood prediction and the classification of severity levels are vital for assessing the impact of floods and ensuring the safety of affected populations. Despite introducing numerous systems, many existing models require significant time to generate prediction results. Intelligent techniques like neural networks have been proposed to enhance prediction accuracy to address this. However, the complexity of data sources, such as satellite or sentinel data, poses challenges due to their unstructured nature and noisy attributes. This study introduces a pioneering approach known as the virtual bee-based recurrent model (VBRM) for flood prediction and severity classification. Specifically, the model categorises floods into low, medium, and high severity levels. The initial step involves gathering sentinel data related to the floods in Ernakulum, Kerala. This data is then used to train the VBRM, followed by a pre-processing stage that effectively filters out irrelevant features and noise. Subsequently, the refined data is fed into the classification layer, where the model extracts pertinent features and determines the severity level of floods. The ultimate objective is to achieve high prediction accuracy with minimal errors. Various performance metrics are employed, and a comparative analysis is conducted against other existing models to evaluate the model’s performance.
  Keywords: deep networks; feature extraction; flood severity classification; optimisation; sentinel data; virtual bee-based recurrent model; VBRM.
  
  
• Long-range forecasting of daily rainfall using machine learning techniques  
  by Syamantak Bhunia, Ujjwal Saha 
  Abstract: Around the world, awareness of the value of rainfall forecasting and its social and economic advantages is rapidly growing. Nation like India, where agriculture is one of the main economic drivers, accurate rainfall forecasting is essential for managing water resources and reducing hydrological extremes. To predict daily rainfall for a year in the drought-prone Kangsabati river basin, long short-term memory (LSTM) and random forest (RF) techniques were utilised using the data of 52 years (1969 to 2020). Finding the right associated variable and a substantial lag in the time series that allows for future value prediction is essential when developing time series forecasting models. The partial auto correlation function and Pearson correlation technique were applied in this context. Furthermore, comparisons were made between the suggested models and the well-known statistical model, the seasonal auto-regressive integrative moving average (SARIMA). The study shows that for this region, the proposed LSTM model and SARIMA model had higher accuracy than the RF model. Additionally, this research suggests that machine learning algorithms may be used to analyse daily rainfall for a particular catchment or station and, to a certain extent, forecast extreme hydrological events.
  Keywords: hyperparameter; long short-term memory; LSTM; machine learning; rainfall forecasting; RF; SARIMA.
  DOI: 10.1504/IJHST.2024.10061937
   
  
• Investigating the effect of watershed management on land use, groundwater recharge, and irrigation potential in Tigray region, northern Ethiopia  
  by Tsige Yenalem, Yilma Kidanie, Ahmed Mohammed Degu, Teame Yisfa 
  Abstract: This study was conducted to investigate the effect of watershed management on groundwater recharge and irrigation expansion in northern Ethiopia. The GIS-based water and energy transfer between soil, plants, and atmosphere under the quasi-steady state (WetSpass) hydrological model was implemented. Two scenarios, before watershed management (1997-2007) and after watershed management (2008-2018), were investigated. After watershed management, groundwater recharge increased from 9.7, 44.4, and 54.15 mm for dry, rainy, and annual conditions, respectively, to 9.9, 96.2, and 106.13 mm for these conditions. The relationship between calculated and observed groundwater depths results in a coefficient of determination of 0.81. After watershed management, the water balance system had evapotranspiration, surface runoff, and groundwater recharge of 83.7%, 3.0%, and 13.3%, respectively, of total precipitation. Excess groundwater recharge of 6.51% resulted in extending the irrigation area by 12.7 hectares. Results show that WetSpass is effective in estimating groundwater recharge and irrigation area expansion.
  Keywords: groundwater recharge; hydrology; water balance; watershed management; WetSpass; Ethiopia.
  
  
• Modeling of scour depth in the existing bridge piers with and without an adjacent parallel bridge  
  by Biswajit Dalal, Subhrajyoti Deb 
  Abstract: This paper presents a hydraulic simulation conducted to assess the stability of the Netaji Subhas Bridge on the Gomti River in Tripura. The simulation takes into account different scour profiles and numerous flood events. The calculation of local scour at various piers takes into account four different flow profiles: PF1 (1,495 m3 /s), PF2 (1,147 m3 /s), PF3 (711 m3 /s) and PF4 (475 m3 /s). The analysis of the HEC-RAS model reveals that the scour depth (Ys) and the Froudes number (Fr) exhibit a decrease for all flow profiles when a parallel bridge is constructed upstream of the existing bridge. But, if the new bridge is constructed on the downstream side then Ys and Fr are increased for PF2, PF3 and PF4 but in the case of PF1, only the Ys decreased. Therefore, the safer and more serviceable option is to construct the parallel bridge on the upstream side of the existing bridge.
  Keywords: Gomti River; parallel bridges; HEC-RAS; modelling; scour depth.
  DOI: 10.1504/IJHST.2024.10062372
   
  
• Estimation of groundwater recharge in Southern Ghana  
  by Delaiah Antwi Nyarko, Larry Pax Chegbeleh, Elikplim Abla Dzikunoo, Edward Kofi Ackom, Sandow Mark Yidana 
  Abstract: The rainfall infiltration breakthrough (RIB) model has been applied to estimate groundwater recharge over parts of the saprolite aquifer unit in southern Ghana. This method relies on rainfall and groundwater level data monitored simultaneously over a period, and properties of the aquifer material. The water table fluctuations (WTF) technique was applied independently to validate the results of RIB technique. Both methods were executed based on specific yield (Sy) values in the range of 1%-5%. The results suggest a wide range of variations in groundwater recharge rates over the terrain. Groundwater recharge rates fall in the range of 0.58%-21.36% of annual precipitation based on the RIB. The results indicate that the lag period between rainfall and eventual groundwater recharge ranges between 0 and 9 months, depending on the thickness and content of the unsaturated zone. Estimates of groundwater recharge suggest variably good fortunes for groundwater.
  Keywords: RIB model; WTF method; groundwater level fluctuation; groundwater recharge; shallow unconfined aquifer; saprolite aquifer system; lag time; lag length.
  DOI: 10.1504/IJHST.2024.10062601
   
  
• Evaluation of mesoscale physical habitats in sediment and water quality improvement - a mesocosm study for urban canals  
  by Sanjana De Zoysa, Kurugama A. T. Chandeep, Pathirathne H. D. R. Pathirathne, Pattiyage I.A. Gomes 
  Abstract: This study investigated the applicability of different types of attenuation processes (i.e., aeration and stirring) with and without dilution in nutrients (nitrogen and phosphorous) and sulphide-polluted sediment cleanup via laboratory mesocosms. Attenuation refers to the decline in contaminant concentration, a phenomenon driven by processes like dilution, mixing, and dispersion. Dilution, a remedial method involving the blending of contaminated water with uncontaminated often happens with uncontaminated runoff or a tributary. Regardless of the seasons, aeration, stirring, combined aeration and stirring, and dilution generally resulted in better removal efficiency of pollutants. Aeration combined with stirring showed notable improvements across multiple water quality parameters, and parameters seemed to be treatment type dependent, but without any significant differences. Dilution reduced electrical conductivity and increased dissolved oxygen but did not influence ammoniacal nitrogen and phosphate. The energy consumption for a unit percentage improvement via aeration and stirring was 0.04-0.25 and 0.03-0.15 USD, respectively. Therefore, relying solely on attenuation processes without dilution is deemed economically infeasible in real or prototype applications. This research sheds light on potential applications including pros and cons, emphasising the need for a balanced approach, and setting the stage for future studies.
  Keywords: aeration; dilution; energy; mesocosms; mesoscale physical habitats; sediment; stirring; water quality.
  
  
• Performance evaluation of commonly used infiltration models  
  by Redahegn Sileshi, Robert Pitt, Shirley Clark 
  Abstract: Soil infiltration rate is a critical parameter in the design and evaluation of stormwater control facilities. The aim of this research was to evaluate selected infiltration models (Horton’s, Kostiakov’s, and Green-Ampt’s) used to estimate final soil infiltration rate. Parameters of the models were estimated. The goodness of fit of the three equations for infiltration was tested using the root mean squared error (RMSE). Comparison of the field and predicted infiltration rate evaluated at each time interval indicated that the infiltration rate predicted by the Green-Ampt’s and Horton’s models were much closer to the measured data.
  Keywords: infiltration rate; stormwater; infiltration models; model parameter; root mean squared error; RMSE.
  DOI: 10.1504/IJHST.2024.10063206
   
  
• Minimising greenhouse gas emissions from small and medium-sized wastewater treatment plants  
  by Jerzy Mikosz 
  Abstract: Municipal wastewater treatment plants often use the activated sludge process in single-stage reactors with nitrification or in two-stage reactors with nitrification and denitrification. Both processes, if operated under unfavourable environmental conditions, can lead to excessive production of nitrous oxide (N2O), a gas with a high global warming potential (GWP) of about 300). In turn, the decomposition of organic pollutants leads to the release of CO2 and CH4 into the atmosphere. When analysing greenhouse gas emissions from wastewater treatment, it is also necessary to consider indirect emissions, mainly related to the use of energy from external sources. Estimating the magnitude of these emissions based on actual measurements is difficult. This article presents the results of simulation studies on the potential for reducing greenhouse gas emissions from small and medium-sized wastewater treatment plants with aerobic sludge digestion (AD) through operational optimisation of existing plants or the application of new technologies.
  Keywords: wastewater treatment plant; WWTP; activated sludge; multiphase reactors; nitrification; enitrification; aerobic digestion; greenhouse gases; GHG; GHG emission; nitrous oxide; N2O; computer simulation.
  DOI: 10.1504/IJHST.2024.10063324
   
  
• Using artificial neural network with clustering techniques to predict the suspended sediment load  
  by Abdelghafour Dellal, Abdelouahab Lefkir, Yamina Elmeddahi, Samir Bengherifa 
  Abstract: Rivers are natural water channels that are influenced by a variety of factors, including erosion and sedimentation, which have a detrimental impact on the ecosystem’s health and water quality. Recently, researchers resorted to using an artificial neural network (ANN) to model the suspended sediment load. This study addressed the application of a multi-layer ANN model. Feed-forward with a backpropagation algorithm based on five different collection methods for the input data. To model the daily suspended sediment load in the Sacramento River, California, USA, current and delayed Ql flow discharge data and solid flow Qs data were used. The accuracy of the five methods was compared in 10 different input groups based on proficiency criteria: standard deviation ratio RSR, coefficient of determination R2, percentage bias (PBIAS), and Nash-Sutcliffe efficacy (NSE). The ANN model with the k-mean clustering technique provides the best results. The RSR values varied between 0.30 to 0.42, and the R2 values ranged from 0.82 to 0.91. While the range of NSE values was from 0.79 to 0.90.
  Keywords: artificial neural network; ANN; clustering technique; flow discharge; suspended sediment.
  DOI: 10.1504/IJHST.2024.10063467
   
  
• Site selection and hydropower potential assessment in Upper Sutlej River Basin: a case study of Indian and Tibetan regions using RS-GIS, SWAT and T-F models  
  by Nikki Chanda, Madhusudana Rao Chintalacheruvu, Anil Kumar Choudhary 
  Abstract: This research explores the untapped hydropower potential in India and Tibets Upper Sutlej River Basin (USRB). By leveraging remote sensing and GIS methods, the study identifies 901 new sites in India and 95 sites in Tibet for potential hydropower generation. The present study estimates that the Indian sites have 2,923 megawatt (MW), 1,830 MW and 1,274 MW, while the Tibetan sites have 289 MW, 178 MW and 98 MW hydropower potential at 50%, 75% and 90% dependability flows, respectively for medium or high-head in the USRB. The future hydropower generation potential is also assessed at the Bhakra dam site, a significant project in the USRB region, utilising historical streamflow data (19932013) and projected streamflows (20142053) through calibrated SWAT and T-F models results. This research offers valuable insights for sustainable development and highlights the immense hydroelectric power possibilities in the USRB region.
  Keywords: hydropower; Upper Sutlej River Basin; USRB; RS-GIS; soil water assessment tool; SWAT; Thomas-Fiering model; T-F model.
  DOI: 10.1504/IJHST.2024.10063572
   
  
• Application of CE-QUAL-W2 model for optimal selective withdrawal of reservoir concerning water quality (case study: Shahr Bijar Dam Reservoir, Iran)  
  by Seyed Abolfazl Ebrahimi 
  Abstract: Reservoirs, essential for human water supply, experience thermal stratification affecting water quality. Utilising the CE-QUAL-W2 model, this study simulates thermal stratification and water quality in Shahr Bijar Reservoir, Iran, spanning September 2019 to 2020. Simulations identify the optimal intake gate for selective withdrawal, considering parameters such as dissolved oxygen, phosphate, ammonium, and total dissolved solids. Calibration and validation demonstrate a reasonable correlation between simulated and observed data. Thermal stratification leads to variable water quality at different intake gate levels. Three seasonal management scenarios determine the optimal outlet for selective withdrawal. Results indicate that the third intake gates, with IRWQISC indices of 83.1, 85.5, and 79.8 in fall, spring, and summer, deliver superior-quality water. In winter, all three intake gates, each with an index of 87, exhibit identical quality. This study introduces an innovative approach to reservoir withdrawal optimisation, emphasising seasonal variations and practical implications for water resource management.
  Keywords: water quality modelling; CE-QUAL-W2; selective withdrawal; dam operation; reservoir management.
  DOI: 10.1504/IJHST.2024.10063982
   
  
• Predictive hydraulic conductivity modeling of wide gradation spectrum sandy soils using stepwise multiple linear and LASSO regression  
  by Mohammad Aasif Khaja, Shagoofta Rasool Shah, Ramakar Jha 
  Abstract: Accurate estimation of soil hydraulic conductivity (K) is crucial in groundwater hydrology and geo-environmental engineering applications. This study introduces novel K predictive modelling for wide gradation spectrum sandy soils, employing stepwise multiple linear regression (SMLR) and least absolute shrinkage and selection operator (LASSO) techniques. Using an 81-sample dataset with key variables, including particle sizes, gradation parameters, porosity, and dry density, this study addresses limitations in existing K prediction methods. Correlation analysis reveals variable associations and multicollinearity issues, necessitating feature selection and the development of SMLR and LASSO regression models. While both models perform well on the training dataset, LASSO excels in mitigating overfitting, achieving a high coefficient of determination (R2) of 0.82 and 0.87 on the training and testing datasets. Comparative analyses with existing models in the literature underscored LASSO’s superiority in approximating laboratory-measured K-values, establishing it as the preferred choice for hydraulic conductivity estimation.
  Keywords: hydraulic conductivity; sandy soils; predictive modelling; stepwise multiple linear regression; SMLR; LASSO regression.
  DOI: 10.1504/IJHST.2024.10064217
   
  
• Accessing the accuracy of modified Schlumberger (Hummel) array of vertical electrical sounding: a pivotal to groundwater exploration  
  by Abayomi Adesola Olaojo, Prekibina Tamunoimama Oti, Kehinde David Oyeyemi 
  Abstract: Space constraints encountered in areas with well-developed housing layouts impede geophysical exploration for groundwater. The study aims to evaluate the effectiveness of the modified Hummel array in groundwater exploration. 32 vertical electrical sounding (VES) data were obtained using both the full and half Hummel and Schlumberger arrays on a migmatite gneiss, revealing its applicability in a complex geology. The data were processed by curve matching and computer iteration. The curve types (H, KH, and QH) and geoelectric sequences inferred from the arrays showed similar layer distributions. The correlated raw data and geoelectric parameters generated moderate to strong coefficients (0.5224-0.9999), indicating a high degree of similarity. The T-values (>2) confirmed the reliability of the coefficients used as predictors. The closeness of data around 1:1 line revealed half Hummel to be a better substitute for Schlumberger, especially in built-up areas where mirror electrode spread is not achievable due to space constraints.
  Keywords: Hummel; Schlumberger; sounding; geoelectric; groundwater; resistivity; correlation; exploration; gneiss; curve-type.
  DOI: 10.1504/IJHST.2024.10064540
   
  
• Analysis of groundwater resources in the Majene coastal aquifer - Indonesia based on a hydrogeological conceptual model  
  by Muhammad Ramli, Aryanti Virtanti Anas 
  Abstract: Many cities do not have sufficient groundwater availability information. As a result, meeting the communitys water needs can become a problem, as in Majene City - Indonesia. Therefore, it is necessary to gain an understanding of groundwater through a hydrogeological conceptual model. The conceptual model was developed based on rock resistivity, hydro-chemical, and climatological data. The rocks that make up this area form an unconfined aquifer which receives a recharge of 277 mm/year. The resistivity data characterises the rock layer/aquifer as being relatively uniform down to a depth of 150 metres. The hydro-chemical groundwater data in the aquifer was indicated to have been contaminated with seawater intrusion. An interpretive model of 2D areal simulation using the developed conceptual model to figure out the current condition based on estimated model parameters and boundary conditions. The numerical results could provide groundwater flow patterns according to the field observations and indicate significant river influence on seawater intrusion into the aquifer.
  Keywords: groundwater; hydro-chemical; seawater intrusion; salinity; aquifer; resistivity; numerical model; recharge; water balance; Indonesia.
  DOI: 10.1504/IJHST.2024.10064608
   
  
• Comparative assessment of LSTM approaches for enhanced prediction of rainfall climatology with minimum uncertainty  
  by Akshay Kumar, Saumitra Rai, Gaurav Kumar, Rajiv Gupta 
  Abstract: Forecasting precipitation is highly challenging for scientific modellers due to the complexity and uncertainty of atmospheric data and weather prediction models. To investigate the hydrological alternations such as rising sea levels, increasing floods and evaporation, and changes in snowpack caused by climate change, it is essential to accurately predict precipitation, a function of several interrelated climatic variables. This study presents a unique approach to predicting precipitation with minimum uncertainty by performing a comparative assessment of long-short-term memory (LSTM) approaches. The LSTM prediction models were run using quarterly, semi-annual, annual, and biannual precipitation data and other data such as temperature, vapour pressure, cloud cover, rainy days, and potential evaporation. Bivariate models using potential evaporation and temperature produced equivalent results to the multivariate model as the mean absolute error (MAE) was found to be 23.89% and 26.35%, respectively, compared to the univariate model (MAE 76.29%).
  Keywords: precipitation prediction; machine learning; LSTM; climate change.
  DOI: 10.1504/IJHST.2024.10064994
   
  
• Economic and hydraulic outcomes in storm water collection networks - application of momentum equation  
  by Masih Zolghadr, Farzan Jahanbakhsh, Hector Martin, Mohammad Rafie Rafiee, Hazi Md. Azamathulla 
  Abstract: The efficacy of existing stormwater collection systems and the costs associated with safely transmitting floodwater remain uncertain due to climate change. A case study employing one-dimensional hydraulic flood routing with a five-year return period was conducted in Qader Abad, Iran. The study compared the hydraulic characteristics, dimensions and construction cost of stormwater collection systems using dynamic, diffusive, kinematic, and constant flow techniques. The findings of the study emphasised that ignoring the inertia and pressure terms could lead to an overestimation of the flow depth and conduit diameter. In particular, the dynamic wave method demonstrated a lower average maximal discharge than other methods. Despite their higher computational costs, the dynamic and diffusive wave methods had significantly lower construction costs than the kinematic and constant flow methods.These findings highlight the importance of incorporating inertia and pressure terms into the momentum equation when estimating the construction costs of urban drainage projects.
  Keywords: urban drainage system; numerical simulation; rainfall-runoff model; SWMM software; stormwater; climate change.
  DOI: 10.1504/IJHST.2024.10065598
   
  
• Effect of different discharges and type of soils on scour under bridge piers by using statistical analysis  
  by Basima Abbas Jabir Al-Humairi, Fatima Asaad Tayeb, Noor S. Mahdi 
  Abstract: The effect of different water discharges on soil scour bridge piers was investigated in this study, which used cylindrical shape piers and experiments on sand soil and river sand soil, with different water discharges and conducted on rectangular laboratory channel with length 12 m and width 0.5 m.The results were statistically treated by using DataFit software package depending on parameters like the flow velocity, depth of water, area of water section, diameter of pier, medium particle size, Froude number, discharge of water, intensity of flow. Several parameters such as coefficient of determination (R2 ), standard estimated error (SEE), root mean squared error (RMSE), mean absolute error (MAE) and relative error (RE) were determined using the strength of the relationship between estimated and observed values of scour depth under bridge pier and used to verify the generated model.
  Keywords: soil scour; bridge pier; velocity of water; statistical analysis; type of soil; scour; statistical analysis; soils; velocity; piers.
  DOI: 10.1504/IJHST.2024.10065828
   
  
• Machine learning-based approach coupled to SWAT model to dynamically quantify the natural groundwater recharge  
  by Khaoula Khemiri, Anis Chkirbene, Constantinos F. Panagiotou, Catalin Stefan 
  Abstract: Limited understanding of aquifers’ responses to global warming and human activities challenges scientists and water professionals. This study, an early attempt to simulate natural groundwater recharge in the Chiba watershed, assesses the impact of human activities and climate change using Google Earth Engine and machine learning. With a Kappa coefficient of about 90%, the study produced reliable results. From 1985 to 2021, the SWAT model effectively replicated hydrological dynamics. Findings show that a 12% increase in agricultural land and a 2% decrease in precipitation result in a 16% rise in evapotranspiration and a 33% decline in natural recharge. Hydrological processes are sensitive to precipitation and land use changes. Spatial distribution of annual recharge indicates low groundwater recharge with upstream-downstream variance. Landsat images and machine learning enhance land use/land cover classification in Tunisia’s semiarid context. This research calls for deeper investigations into groundwater levels for comprehensive groundwater resource management and sustainability.
  Keywords: climate change; groundwater; natural recharge; human activities; random forest; soil and water assessment tool; SWAT; modelling.
  DOI: 10.1504/IJHST.2024.10065829
   
  
• Sustainable solutions for brackish water desalination: a comprehensive investigation into reverse osmosis efficiency enhancement  
  by Moumni Mohammed, Massour El Aoud Mohamed 
  Abstract: Desalination of brackish and saltwater is one of the most important freshwater resources in water stressed regions. The reverse osmosis process is an efficient and successful water desalination technology that requires a significant amount of electrical power and accordingly, it prevents the diffusion of this technology worldwide. The objective of this study is to reduce energy consumption in a Moroccan brackish water reverse osmosis desalination plant. Currently, this plant operates without energy recovery and the production rate reaches 1,017 m3 /h with a maximum SEC of 3.2 kWh/m3 . The organisation plans to increase production rates by adding more reverse osmosis trains which has a direct impact on the SEC. This study aims to optimise SEC through this plant in its future design. Both configurations with and without ERD were simulated to analyse the variation of SEC using a range of feed water parameter fluctuations, particularly its temperature and salinity. The results showed that the implementation of ERD in the future design will reduce the SEC by 30%.
  Keywords: desalination; brackish water; reverse osmosis; energy recovery.
  
  
• Free surface flow assessment through a homogeneous earth-fill dam using a feed-forward neural network model  
  by Mohamed Khorchani, Noureddine Rhayma, Sandra Pereira, Pierre Breul 
  Abstract: Seepage flow through the body of an earth-fill dam adversely affects dam’s stability. Therefore, a better understanding of the seepage phenomenon is required to detect early signs of abnormal behaviour and to plan intervention strategies when needed. In this study, a feed-forward neural network model is used to evaluate the free surface flow through a homogenous earth-fill dam. Data collected from the monitoring system were first analysed using principal component analysis (PCA) to identify the relationships between the selected variables. The model inputs were then pre-processed using Min-Max scaling transformation, and a trial-and-error approach has been used in order to achieve the best neural network architecture. As a next step, the delayed effects of reservoir water level fluctuations on the seepage phenomenon were investigated. Additionally, the effect of the dataset size on the prediction capabilities of the neural network model has also been explored. According to the selected performance criteria, the proposed neural network model was shown to be a powerful tool for predicting piezometric levels in dam’s body. Such a result can be used for a continuous simulation of earth dam’s behaviour when coupled with a numerical model.
  Keywords: earth-fill dam; neural networks; free surface flow; delayed effects.
  DOI: 10.1504/IJHST.2024.10066191
   
  
• Energy dissipation downstream multi opening sluice gate  
  by Mohammad Y. Hamid, Ahmed Y. Mohammed 
  Abstract: Sluice gates are structures, which are used to regulate the water level in open channels. Many studies examined the hydraulic properties of these gates to reach the best performance, but studies that dealt with multi-opening sluice gates are still few and need more investigations, this study focuses on gate operating scenarios and the effect on energy dissipation. The energy dissipation equation for a multi-opening sluice gate is predicted and compared with actual data, with maximum error not exceed 15%. The results showed that the maximum E/E1 values reached 70.18% for the same gate openings, and this value reached 68.46% when the summation of intermediate openings is greater than that for side operating. These values decrease to 44.1% in the opposite operation, so this operating system must be avoided.
  Keywords: sluice gate; open channel flow; energy dissipation; multi-opening gate; operating scenarios.
  DOI: 10.1504/IJHST.2024.10066353
   
  
• Enhancing bridge pier resilience against scouring during extreme flood condition using vegetation as a countermeasure-experimental approach  
  by Sajjad Hussain, Nadir Murtaza, Zaka Ullah Khan, Naeem Ejaz, Inzimam Ul Haq, Diyar Khan 
  Abstract: In the current investigation, scour mitigation around the bridge pier has been explored using vegetation of different densities (G/d) and aspect ratios (A/R) under various flow conditions. Initially, scour depth around the bridge pier was measured without vegetation elements, and afterwards vegetation elements were installed on the upstream side of the bridge pier to investigate maximum scour reduction. The result demonstrates that denser vegetation elements with a lower A/R value reduce scour depth significantly because of greater resistance to the flow. However, scour depth decreased by decreasing the G/d and A/R values from 2.85 to 0.71 and 18.5 to 7.45, respectively. A maximum scour reduction of 71.7% was observed by placing denser vegetation with A/R = 7.46 on the upstream side of the pier. The findings of the present research help to provide an eco-friendly solution for bridge pier scour mitigation.
  Keywords: bridge pier; scour; vegetation; aspect ratio; climate change.
  DOI: 10.1504/IJHST.2024.10066354
   
  
• In-situ bathymetry and discharge-based modelling for potential GLOF assessment of Gepang Gath Lake, Lahaul Himalaya, India  
  by Parmod Kumar, Swati Sharma, I.M. Bahuguna, Sanjay Deswal, Partibha, Jogender Singh 
  Abstract: The present study covers a vulnerable glacial lake known as Gepang Gath Lake in the Lahaul area of Himachal Pradesh, India. The field investigation was carried out to measure depth and discharge rate for analysing the probability of lake outburst hazard. The lake depth ranges between a minimum of 2m to a maximum of 48 m with an average depth of 19 m with a standard deviation of 13 as per the field surveys. The stream discharge of 10 m/s was measured near Sissu village valley lying downslope from the Gepang Gath Lake on 21 June 2021 in the afternoon. Snowmelt discharge from the temperature index model was compared with the stream discharge on a clear weather day plus the rainfall data of monsoon months (when the possibility for discharge is higher) was analysed and modelled in HEC-HMS software for peak precipitation discharge based on rainfall data of July and August. 1D flood modelling using glacial lake volume in HEC-RAS software was performed and finally, the resultant discharge (a combination of all three discharges from snowmelt, precipitation and lake volume) is 3,696 m3 /s. This resultant discharge is considered huge enough to affect the downstream and valley area with a high-intensity flood that can reach up to the Tandi village area.
  Keywords: Gepang Gath Lake; snowmelt; lake depth; discharge rate; HEC-HMS; HEC-RAS; India.
  DOI: 10.1504/IJHST.2024.10066536
   
  
• Modelling drought as a climate change indicator in the southwest coastal region of Bangladesh  
  by Noor-E-Ashmaul Husna, Sheikh Hefzul Bari, G.M. Tarekul Islam, A.K.M. Saiful Islam, Md. Manjurul Hussain 
  Abstract: In this study, we conducted a comprehensive analysis of meteorological drought in the southwest region of Bangladesh using the reconnaissance drought index (RDI) and the Mann-Kendall trend test. Furthermore, trends in the initial form of RDI (α_k) are assessed using the Mann-Kendall and sequential Mann-Kendall tests to find any indication of climate change. The results show that the study area has experienced moderate to extreme droughts in the past, with an increasing frequency in recent years showing a seasonal nature. The negative trend in the annual and most seasonal standardised RDI (RDI_st) series suggests that drought severity will increase in the future. At the same time, trends in the initial RDI (α_k) suggest a potential change in the climate, especially after 1990. The resulting change in precipitation, or potential evapotranspiration (PET), may significantly affect surface water sources during dry seasons.
  Keywords: climate change; meteorological drought; Southwest Bangladesh; reconnaissance drought index; RDI; drought management; drought monitoring; sequential Mann-Kendall test.
  DOI: 10.1504/IJHST.2023.10059979
   
  
• Identification of flood hazard zones in Afghanistan using GIS and multi-criteria decision approach  
  by Hamidullah Tani, Gokmen Tayfur 
  Abstract: This study assessed Afghanistan's potential flood hazard zones using the geographic information systems (GIS) and the analytical hierarchy process (AHP). Six different thematic layers were selected, and the AHP was applied to estimate the influence weights of each parameter. The final flood hazard zones map (FHZM) was reclassified into five zones. Sensitivity analysis was employed to create the flood hazard sensitivity map (FHSM) based on 'effective weights'. It was found that the land use land cover (LULC) and rainfall are less sensitive compared to the other parameters. The FHZM and FHSM comparatively indicate the same regions regarding flood hazard levels. The methodology was tested against the recorded flood events in the region. The results showed that about 44% of the study area is under low and very low flood hazards, whereas 56% is subjected to high and very high. Low-lying areas are highly prone to flooding.
  Keywords: flood prone regions; flood hazard; sensitivity analysis; geographic information systems; GIS; analytical hierarchy process; AHP; Afghanistan.
  DOI: 10.1504/IJHST.2023.10059873
   
  
• Smart irrigation planning in water deficit area (case study: Pench Irrigation Project, India)  
  by Narendra Bonde, Vivek Manekar, Dinesh V. Morankar 
  Abstract: As the climate is changing, more and more area is turning into water deficit for irrigation. It is always challenging for the authority to allocate crop area and water resources in such a way that farmers should not be in the loss. Genetic algorithm (GA) optimisation model is used in this for optimisation of surface and groundwater (GW) resources in the Pench Irrigation Project. The results show that the command area's GW allotment is significantly reduced, and GW withdrawal may also be limited to recharge in order to preserve the equilibrium between rivers and aquifers. The optimisation problem for constrained conjunctive use was solved using a GA. To enhance the net benefit to the farmers and keep the water demand within the volume of water available, the results show that paddy, cotton, and wheat should be grown less, and sugarcane, other perennial crops, and chillies should be grown more.
  Keywords: conjunctive use; irrigation; crop area optimisation; linear programming; India.
  DOI: 10.1504/IJHST.2023.10061259
   
  
• Assessment of recharge dam performance in arid regions based on satellite data, case study: Jalajil and Rawdah Dams  
  by Raied Saad Alharbi, Jerome Arunakumaren 
  Abstract: Effective water resources management in arid regions necessitates accurate information on their current condition and how they change due to resource use and development, land-use patterns, and climate change. Due to a lack of data collected on the ground, it is challenging to evaluate the condition of the resources and implement effective management strategies. Recent developments in earth observation and satellite remote sensing offer valuable data for water resource management and better knowledge and control of the natural and anthropogenic processes that affect changes in water resources. This paper discusses how historical surface water area variations at dam sites, derived from remote sensing data, can be used to assess current conditions and long-term trends in water resource availability. The Jalajil Dam's water balance model was developed using the GoldSIM modelling framework by linking precipitation-runoff inflow components and reservoir storage variation processes. Based on the model results, the dam recharge component is much higher than the evaporation component, and the dam act to save collected water from evaporation. Enhancement of recharge at dam sites has become necessary in arid regions to develop water resources from availability to times of need, reduce water supply issues, and even mitigate climate change.
  Keywords: remote sensing; water balance model; GoldSIM; managed aquifer recharge; modified normalised difference water index; MNDWI.
  DOI: 10.1504/IJHST.2023.10059786
   
  
• Mauritius oil spill detection using transfer learning approach for oil spill mapping and wind impact analysis using Sentinel-1 data  
  by Koushik Das, Prashanth Janardhan, Harish Narayana 
  Abstract: The oil spill detection and mapping using Sentinel-1 (S-1) data for the Mauritius oil spill event have been done in this study. The convolutional neural network (CNN)-based on pre-trained models such as AlexNet, VGG-16, and VGG-19, have been used to classify the S-1 images by the transfer learning approach. The S-1 images are classified into two classes: with and without the oil spill. Then, the oil spill detection was done in the sentinel application platform (SNAP), and the oil spill mapping was done in ArcGIS. The VGG-16 network performs the best among the other pre-trained networks with an accuracy of 96.88%, precision of 95.92%, and recall of 97.92%. The impact of wind on the spreading of oil is also analysed using remote sensing and GIS techniques. It has been observed that the spreading of oil doesn't only depend on sea wind but also other environmental factors.
  Keywords: transfer learning; convolutional neural network; CNN; Sentinel-1; sentinel application platform; SNAP; oil spill; image classification; remote sensing; GIS.
  DOI: 10.1504/IJHST.2023.10059874
   
  
• Electrolised oxidised water using rainwater synthesis as an environmentally friendly disinfectant  
  by Agus Maryono, Daniel Timotius, Muhammad Sulaiman, Andhika Puspito Nugroho, Heni Wahyu Sartika, Cristina M. Talakua, Pratama Tirza Surya Sembada, Yosef Prihanto, Eko Handoko, Agus Prasetya, Himawan Tri Bayu Murti Petrus 
  Abstract: Electrolytic oxidising water (EOW) is a disinfectant and sanitary material that is classified as easy to obtain, inexpensive, safe, and environmentally friendly. This study aims to determine the effect of electrodes and mineral salt concentration on changes in the properties of EOW such as potential hydrogen (pH), oxidation-reduction potential (ORP), and total dissolved solids (TDS) at various times, determine the toxicity of EOW to earthworms (Lumbricus sp.), and the disinfecting ability against bacteria (both gram-positive and gram-negative) was measured with disc diffusion method. The water used for EOW is rainwater in Yogyakarta. The study results show that EOW water is successfully produced by an electrolysis device and the EOW water used as a disinfectant is environmentally friendly. Furthermore, the use of EOW water as a disinfectant shows the ability to inhibit gram-positive and gram-negative bacteria, which is comparable to 70% ethanol.
  Keywords: electrolytic oxidising water; EOW; rainwater; disinfectant; toxicity; disinfection.
  DOI: 10.1504/IJHST.2024.10066240