International Journal of Hydrology Science and Technology (36 papers in press)

Regular Issues

• Precipitation prediction in Bangladesh using machine learning approaches  
  by Md. Ariful Islam, Mosa. Tania Alim Shampa 
  Abstract: In the assessment of different hydrological activities, the prediction of rainfall is essential. As agriculture is critical to survival in Bangladesh, rainfall or precipitation is most important. This study shows how a machine learning approach can be used to make a reliable model for predicting rain. This way, people can know when rain is coming and take the steps they need to protect their crops. Many techniques have been applied so far to predict rainfall. But machine learning algorithms can provide more accuracy in this case. Nine machine learning algorithms have been used to find a good model that can be used to predict rain in Bangladesh. The prediction models were evaluated by dint of evaluation metrics such as coefficient of determination (R 2 ), root mean squared error (RMSE), and mean absolute error (MAE). Among nine algorithms and eight models, the model H including all meteorological exogenous inputs with gradient boosting regressor algorithm led to the best predictions (R 2 = 0.78, RMSE = 134, MAE = 92) for Sylhet division. The model G excluding wind speed with gradient boosting regressor algorithm shows the best predictions (R 2 = 0.76, RMSE = 147, MAE = 89) for both Chittagong and Rangpur divisions.
  Keywords: rainfall; machine learning algorithms; precipitation; gradient boosting regressor; GBR; Bangladesh.
  DOI: 10.1504/IJHST.2023.10055268
   
  
• Groundwater recharge and its response to land use - land cover dynamics in Biji catchment of Marodi-Jeeh, Somaliland  
  by Ridwan Ahmed Abdirahman, Dagnachew Daniel Molla, Tarun Kumar Lohani 
  Abstract: The land cover dynamics in Biji catchment of Somaliland was undertaken to evaluate groundwater (GW) recharge response to LULC change using integrated geospatial and distributed modelling approach. Satellite imagery (Land sat TM and ETM+ 30 m) classification of 1986, 2005 and 2019 using Arc GIS software image processing techniques were employed. Land use change of agriculture over the last 33 years was assessed as 16.8%-24.8%, bare land 15.8%-14.9%, shrub 67.3%-59.7%, and built-up 0.06%-0.5%. The water balance indicates evapotranspiration loss of 61.1%, runoff 4.7%, and GW-recharge 34.2%. GW recharge response of land use dynamics in the last three periods was about 135.8, 142.5, and 137.7 mm, respectively which is predominantly due to agricultural expansion and urbanisation. During the same period, WetSpass model was used to estimate total flow with a correlation coefficient of 0.82 and 0.88 respectively at a statistically significant level.
  Keywords: Biji catchment; Somaliland; groundwater recharge; LULC; WetSpass model.
  DOI: 10.1504/IJHST.2023.10055362
   
  
• The effect of reactor H/D ratios on the cultivation of aerobic granular sludge  
  by Fengli Zhao, Shenjie Zhou 
  Abstract: Height to diameter ratios (H/D ratios) of the sequencing batch reactors (SBRs) are of great significance in the cultivation of the aerobic granular sludge (AGS). In this study, the gas-liquid two-phase flow patterns in the reactors with two different H/D ratios of 5 and 3 were simulated based on computational fluid dynamics (CFD). The results showed that higher H/D ratios of the reactors provided smaller liquid velocity and shear rate but more small vortices and longer circulations compare with the lower H/D ratio reactor. Moreover, the cultivation of AGS in the two H/D ratio reactors was further investigated. It was found that reactors with higher H/D ratios can accelerate sludge granulation and retain more sludge than reactors with lower H/D ratios. However, the two reactors achieved the similar pollutant removal efficiency during the stable period, demonstrating that a high H/D ratio can enhance granulation but is not necessary for practical applications, which can reduce construction difficulties.
  Keywords: aerobic granular sludge; AGS; H/D ratios; computational fluid dynamics; CFD; flow patterns; granular performance.
  DOI: 10.1504/IJHST.2023.10055671
   
  
• Grouting - an effective method for reducing the permeability of sandy soils  
  by A.C. Sekhar, Benny Mathews Abraham, T.G. Santhosh Kumar, A. Sridharan 
  Abstract: Grouting is normally undertaken to reduce the permeability of rock or soil formations and this process is used extensively in the construction of hydraulic structures such as dams, tunnels and in a wide variety of special cases. Even though the application of the grouting technique to reduce the permeability of rock formations has been reported in literature, no serious attempts are reported about the effective use of this technique to reduce the permeability of soil formations. In this paper, an attempt has been made to study the effectiveness of grouting in reducing the permeability of the granular medium. Constant head permeability tests were carried out on the sand medium treated with different grouting materials such as cement, bentonite, lime, locally available clay and different combinations of the above materials. By grouting with different grout materials (e.g., in the case of cement - bentonite grout) the permeability of the medium sand got reduced from 10-4 m/s to 10-9 m/s. The present study undoubtedly proves the effectiveness of using grouting as an efficient technique in reducing the permeability of sandy soils.
  Keywords: ground improvement; ground water management; grouting; soil permeability; sub-surface barriers.
  DOI: 10.1504/IJHST.2023.10055876
   
  
• Identifying the optimum location of the dewatering pumping wells around excavations using numerical FEFLOW model: a theoretical study  
  by Hayder H. Kareem 
  Abstract: A theoretical study has been explored the dewatering process in an excavation pit has dimensions (600 x 600) m2 and depth 6m in an area (4,000 x 4,000) m2 with a total depth 30 m using FEFLOW programme. A mathematical model is created to reduce the water table at least 1 m below the bottom level of the excavation. The model is run in the steady state (one year) and transient flow (45 days, 60 days, 90 days and 120 days) conditions. Pumping wells are installed at distances of 50 m, 100 m, 150 m, 300 m, 450 m and 600 m away from the excavation. The total tested scenarios are 27: 15 (steady state) and 12 (transient state). Only 13 scenarios succeeded in achieving the required condition: 4 (steady state) and 9 (transient state). Economically, over the succeeded scenarios, scenario (S16) is considered the best dewatering one with the lowest extraction rate (157,500 m3/45 days) and minimal distance away from the excavation (50 m).
  Keywords: construction excavation pit; dewatering; FEFLOW; optimum location; mathematical modelling.
  DOI: 10.1504/IJHST.2023.10056098
   
  
• Urban expansion based on remote sensing extraction of impervious surface information  
  by Weifeng Kang, Dayong Chen 
  Abstract: Impervious surface is an important evaluation factor for the study of urban regional expansion and its social and environmental effects using remote sensing technology and the city has expanded rapidly, which is obviously reflected in the great changes in the impervious surface. Relying on 3S technology to study the classification method of urban impervious surface in this area and to explore the dynamic change law of urban impervious surface and its driving force has dual value of scientific research and social economy. This paper explores the computer automatic extraction method of impervious surface in my country in the past 20 years by using the time series Landsat TM images and object-based image analysis and processing technology and laws to explore the changes and internal causes of urban expansion. Moreover, this method can be utilised for hydrological modelling, flood mapping and monitoring, water quality monitoring, and urban heat island analysis.
  Keywords: impervious surface; improved normalised difference impervious surface index; object-based image analysis; OBIA; image segmentation.
  DOI: 10.1504/IJHST.2023.10056161
   
  
• Bias correction of regional climate model simulation for hydrological climate change over Bouregrag watershed in Morocco  
  by F. Gadouali, T. Benabdelouahab, A. Boudhar, R. Hadria, N. Semane, A. Fadil, K. Elrhaz 
  Abstract: General and regional climate models (GCMs/RCMs) exhibit many systematic biases, which affects the simulation accuracy of the real precipitation patterns and consequently the associated hydrological changes. The common approach used to reduce errors in the climate model output is to apply bias correction methods (BCMs) which attempt to adjust the climate simulation by its observation counterpart. In this study, we applied BCM on simulated rainfall over the Bouregrag basin (Morocco) from CanRCM4 CORDEX RCM using linear SCALING method (SCALING), gamma quantile mapping (GQM) and empirical quantile mapping (EQM). Owing to its high performance compared to the others, we applied the EQM method on climate projections under the RCP4.5 scenario. Results showed a decrease up to -50% in the monthly rainfall for the period 20412060 with the exception of August and December exhibiting an increase between +20% and +78%. This study supports the need to bias correct climate data before their use in hydrological models where the bias could be irreversible.
  Keywords: regional climate model; RCM; bias correction methods; BCMs; hydrological models; quantile mapping; Morocco.
  DOI: 10.1504/IJHST.2023.10056362
   
  
• Impact of large dam reservoirs on slight local season shifting (case study: Three Gorges Dam)  
  by Yongzhen Deji, Jihui Fan, Majid Galoie, Artemis Motamedi 
  Abstract: The monthly data (19802018) from 54 stations, which were classified into three altitude levels, were extracted. The analysis showed TGD had a minor effect on the variation of the precipitation data. Also, due to the variation in dominant wind direction and speed, the spatial distribution of rainfall was altered significantly. The Standardised Precipitation Evapotranspiration Index (SPEI) analysis in 1, 3, and 12 months showed degradation in the positive drought index downstream of the dam after impounding. The data analysis showed that TGD had a strong impact on the increase in monthly temperature and relative humidity both upstream and downstream of the dam such that for the regions with altitudes of higher than 400 m.a.s.l., the month with the maximum monthly RH and temperature was shifted from December to June and from July to June, respectively. This shifting in lower altitude regions was not seen.
  Keywords: Three Gorges Dam; TGD; meteorological data analysis; season shifting; local climate change.
  DOI: 10.1504/IJHST.2023.10056527
   
  
• Anomalous behaviour of Indian summer monsoon rainfall due to the change in some of the monsoon semi-permanent features in surplus and deficit years  
  by Ghulam G. Zahid, Nuzhat Fatima, Aparna Sinha, Ajhar Hussain, Firoz Ahmad 
  Abstract: The purpose of this research is to better understand the Indian summer monsoons insufficient rainfall in 2002 and excess rainfall in 2007, as well as their relationship with large-scale circulation features and land-sea heating contrast. Zonal and Meridional wind at 850 Hpa and mean sea level pressure and monthly mean rainfall are the parameters used to describe the semi-permanent features and anomalous behaviour of ISMR. Over extensive portions of the Indian Ocean, Arabian Sea, and much of India, significant mean sea level pressure and rainfall anomalies are present. Negative rainfall anomalies prevailed over some parts in the centre of the monsoon core zone (MCZ). By evaluating the Somali jet, the Mascarene high and ISMR observed that heavy rainfall occurs in the MCZ. Mascarene high show stronger anticlockwise circulation in deficit year but due to the effects of El Nino wind and moisture transferred towards the East-Pacific region.
  Keywords: Indian summer monsoon rainfall; ISMR; monsoon semi-permanent features; ENSO; convection; cross-equatorial low-level jet; monsoon core zone; MCZ.
  DOI: 10.1504/IJHST.2023.10056541
   
  
• Rainfall trend analysis in Gujarat’s semi-arid zone: a modified approach with autocorrelation consideration  
  by Ramiz F. Tasiya, Naveena K. Kannegowda, Shilpesh C. Rana 
  Abstract: Precise identification of rainfall movement is very essential during climate change scenarios for better water resources management. Erratic and violent monsoon rainfall movements are observed in the coastal region of Maharashtra, Gujarat, and Kerala within a very short period. The aim of the research is to identify the changes that occurred in the South-West monsoon rainfall trend using advance statistical package of R-software, for the semi-arid region of Gujarat. From the Wallis and Moore phase-frequency test, it is found that 6 out of 29 stations are having significant auto-correlation. Modified Mann-Kendall and Sen’s slope tests are used to carry out rainfall trend analysis. Spatial representation of mean SW-monsoon rainfall and Sen’s slope shows lower magnitude near coastal region and higher magnitude at higher elevation range. As a result, it is concluded that in monsoon two stations (7%) and particularly during June month seven (24%) stations are showing a significantly decreasing trend.
  Keywords: R-software; Sen’s Slope test; Wallis and Moore phase-frequency test (Z- test); modified Mann-Kendall test.
  DOI: 10.1504/IJHST.2023.10056997
   
  
• Determining the most suitable Sentinel-2 indices for turbidity and chlorophyll-a concentration for an oligotrophic to mesotrophic reservoir in Brazil  
  by Fernanda Mara Coelho Pizani, Camila Costa De Amorim, Philippe Maillard 
  Abstract: Water quality is a major issue for agencies responsible for the management and maintenance of reservoirs. In this article, Sentinel-2 (S-2) data is tested to produce reliable estimates of turbidity and chlorophyll-a concentrations in an oligotrophic to mesotrophic reservoir in Brazil. The spectral bands were all tested individually and jointly to determine the best and most stable features. Many indices are also tested. In situ data was acquired over a period of three years with synchronous S-2 data. The models were evaluated on their robustness and stability using a bundle of in situ dataset (all field campaigns). The results show that no model can be directly applied to other reservoirs or different dates without calibration. Conversely, calibrating the S-2 indices with two in situ measurementsa greatly reduces the errors. Average root mean square errors of 0.518 FNU and 1.1 _g/l were obtained for turbidity and chlorophyll-a concentrations respectively.
  Keywords: chlorophyll-a; turbidity; empirical models; Sentinel-2; reservoir.
  DOI: 10.1504/IJHST.2023.10057236
   
  
• Effect of hydrological model selection in climate change impact assessment in the Dano tropical catchment (Burkina-Faso)  
  by Yacouba Yira, Aymar Y. Bossa, Ngague Hisseine Ganda, Djigbo F. Badou, Kpade O. Laurentin Hounkpatin, Jean Hounkpè, Luc Ollivier Sintondji 
  Abstract: Applying a validated hydrological model is a common approach in climate change impact studies. The current study used an ensemble of five regional climate models and two hydrological simulation models (HBV-light and GR4J). Both models were successfully calibrated and validated with coefficients of determination-R 2 and Nash and Sutcliffe efficiencies-NSE ranging between 0.64 and 0.88. Compared to the reference period (1976-2005), the projected temperature shows an increase for the future periods 2021-2100, whereas for the projected precipitation change, a mixed trend is expected. The projected discharge change is very similar to the precipitation signal. The results further indicate for some climate datasets that, the climate-induced discharge change is lower than 50 mm per year for both HBV-light and GR4J, while the inter-comparison of discharges between the two hydrological models indicates differences exceeding 100 mm per year. Therefore, the choice of the hydrological model overscores the impact of the projected climate change.
  Keywords: climate change; CORDEX; discharge; HBV-light; GR4J; hydrological model selection; climate change impact; tropical catchment; Burkina Faso.
  DOI: 10.1504/IJHST.2023.10057775
   
  
• Integrated rainfall-runoff modelling using fuzzy logic considering soil moisture: case study of Damanganga Basin  
  by Vrushti C. Kantharia, Darshan J. Mehta 
  Abstract: The present study aims to develop integrated rainfall-runoff modelling considering soil moisture at three different depths (5 cm, 100 cm, and bedrock) for the Damanganga basin. To simulate the daily discharge Mamdani fuzzy-logic model and adaptive neuro-fuzzy inference system (ANFIS) are used. The analysis is carried out for the South-West Monsoon season, i.e., (June, July, August, and September) for 39 years, i.e., 1983-2022. Mamdanis fuzzy-logic and adaptive neuro-fuzzy inference system (ANFIS) model uses soil moisture and rainfall data as the input variables to simulate daily discharge. The model application result reveals that soil moisture at bedrock gives more precise results as compared to soil moisture at 5 and 100 cm. Comparison between both the models is also carried out on the basis of regression and the results demonstrate that the adaptive neuro-fuzzy inference system (ANFIS) model gives a more precise value of daily discharge as compared to the Mamdani fuzzy model. This study can be helpful to future research scholars and to hydrological scientists in selecting appropriate rainfall-runoff models.
  Keywords: ANFIS; Damanganga; discharge; fuzzy logic; Mamdani; rainfall; soil moisture.
  DOI: 10.1504/IJHST.2023.10058036
   
  
• Infiltration characteristics and hydraulic properties of soil as affected by tillage practices and biochar-poultry manure application.  
  by Chukwuebuka Vincent Azuka 
  Abstract: The study investigated the effect of biochar and poultry manure (PM) applications on infiltration characteristics and hydraulic properties of degraded Ultisols in Nsukka under two tillage practices. The treatments were tillage practices (conventional tillage; zero-tillage) and organic amendments (10 t/ha biochar, 10 t/ha PM, 10 t/ha biochar + 200 kg/ha NPK and 400 kg/ha NPK). The results showed significant (p < 0.05) higher values of cumulative and steady-state infiltration, saturated hydraulic conductivity (Ksat), microporosity and total porosity (TP) in tilled soils (1,266 mm, 0.772 cm/min, 25.88 cm/hr, 40.53% and 49.23% respectively) than in no-tilled soils (799 mm, 0.502 cm/min, 20.05 cm/hr, 36.88% and 45.81% respectively). Organic amendments had a significant (p < 0.05) effect on BD, cumulative and steady-state infiltration, Ksat and available water capacity (AWC). The highest values of cumulative and steady-state infiltration, Ksat and AWC (1,302 mm, 0.800 cm/min, 25.23 cm/hr and 0.290 respectively) and lowest values of BD (1.49 g/cm3) were obtained in biochar-amended soil.
  Keywords: organic amendment; soil degradation; soil quality; biochar; soil productivity.
  DOI: 10.1504/IJHST.2023.10058434
   
  
• Extreme value modelling using the peaks over threshold method, an inverse cumulative uniform distribution approach  
  by Justin Chirima 
  Abstract: This paper proposes an inverse continuous uniform distribution approach for selecting the threshold when performing the peaks over threshold (POT) method to data with outliers. Applying results from the mean residual life plot (MRLP) and the threshold stability plot (TSP), the threshold is assumed to follow a continuous uniform distribution between values a and b. An inverse uniform distribution algorithm for threshold selection is developed and applied to hypothetical data and Zimbabwean precipitation data. The proposed method is capable of determining optimal thresholds which give reasonable fits for historical data.
  Keywords: uniform distribution; threshold; generalised Pareto distribution; GPD; peaks over threshold; POT.
  DOI: 10.1504/IJHST.2023.10058520
   
  
• Estimation of rainfall-runoff potential using SCS-CN and geospatial approach for Loktak Lake watershed, India  
  by Ranu Jajo Laishram, Wazir Alam 
  Abstract: Rainfall and runoff are important hydrologic variables that determine the spatio-temporal availability and distribution of water resources. The Loktak Lake watershed is an important ungauged catchment of Manipur, India, where the runoff behaviour has not been well documented due to constraints involved in its direct estimation. Therefore, the present study uses the soil conservation service curve number (SCS-CN) method by integrating geoinformatics tools to estimate the potential runoff from Loktak watershed. The assessed land use/land cover changes of the catchment area during 2000 to 2020 illustrate significant increase in urbanisation/built-up area (10.67% to 19.95%) and decrease in forest cover (25.86% to 24.49%). The estimated total potential runoff depths during the study period ranged from 118.21 mm to 996.88 mm, with highest runoff observed in 2017. Average annual proportion of rainfall converted to runoff was around 25.9%, and highest average monthly runoff was observed during June and lowest in January.
  Keywords: runoff; watershed; SCS-CN; land use/land cover; LULC; geographic information system; GIS; remote sensing; India.
  
  
• 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
   
  
• Identification of flood hazard zones in Afghanistan using GIS and multi-criteria decision approach  
  by Hamidullah Tani, Gokmen Tayfur 
  Abstract: This study assessed Afghanistans 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
   
  
• 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
   
  
• 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 (?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 (RDIst) series suggests that drought severity will increase in the future. At the same time, trends in the initial RDI (?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
   
  
• 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
   
  
• 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
   
  
• Evaluation of artificial intelligence models in calculating daily and monthly reference evapotranspiration (case study: Khorramabad station)  
  by Yaser Sabzevari, Ali Heidar Nasrolahi, Majid Sharifipour, Babak Shahinejad 
  Abstract: The FAO-penman-monteith method requires a lot of input data, which is difficult in many cases to access, it is necessary to replace the simpler models with low initial inputs and appropriate accuracy. Based on the effect of input parameters on reference evapotranspiration, six input patterns for modelling were determined. This result indicates the appropriate selection of input parameters and their effectiveness in modelling and increasing the number of effective variables in the input causes the expansion of the model memory to estimate the output values, which increases the number of data for network training and the network is well generalised.
  Keywords: reference evapotranspiration; regression; gene expression programming; GEP; support vector machine; SVM; Bayesian network; BN.
  DOI: 10.1504/IJHST.2024.10063502
   
  
• Effects of the 2018 and 2019 floods in Kerala, India on the existing multivariate statistical models  
  by P.G. Dileep Kumar, Narayanan Viswanath, Sobha Cyrus, Benny Mathews Abraham 
  Abstract: The state of Kerala, India, experienced severe flood events during August 2018 and 2019. The aim of this paper was to study the post-flood relevance of the multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) models formed before floods, for Kozhikode city, Kerala, India. For this, water samples were collected from 49 different locations in the above city, in September 2019. Both the existing MLR and ANFIS models were found to be less effective on post-flood data. Hence, new MLR, structural equation (SE) and ANFIS models were formed separately for severely and less severely flood-affected samples by performing bootstrapping to address the problems caused by the small datasets. The root mean square error (RMSE) and Lorenz curve were used to analyse the performance of the models. It was observed that ANFIS models performed better than MLR models.
  Keywords: flood events; statistical modelling; multiple linear regression; adaptive neuro-fuzzy inference system; structural equation modelling.
  DOI: 10.1504/IJHST.2023.10053898
   
  
• Performance assessment of two semi-distributed and lumped hydrological models in the Peruvian Andes  
  by Víctor Oscar Rendón Dávila, Albert Johan Mamani Larico, Gabriel Fabricio Mejia Medina, Ángela Milagros Figueroa Tapia, Deysi Ivani Yana Quispe, Lady Nathaly Constancio Vilca, Vanessa Rossa Hilario Coaguila, Sebastián Adolfo Zúñiga Medina 
  Abstract: The high demand for water resources in the Peruvian Andes requires simple and available tools for decision-making. In our study, the lumped Témez model and soil and water assessment tool (SWAT) model were used in the El Pañe Basin. The performance of both the models was reasonably good. The SWAT model showed less uncertainty, whereas the Témez model showed a better percent bias. Using the Penman-Monteith actual evapotranspiration model from SWAT, the evapotranspiration/precipitation ratio showed an increase in variability in the last decade and a decreasing trend during drought periods. The soil water content was well-represented by the normalised difference vegetation index and the land surface temperature index owing to the high correlations between them. The Témez model was appropriate for studying limited data of the El Pañe Basin, while the SWAT model allowed more detailed studies. Both the models were found to be useful tools for water management.
  Keywords: soil and water assessment tool; SWAT; Témez; soil water; evapotranspiration; normalised difference vegetation index; NDVI; land surface temperature; LST.
  DOI: 10.1504/IJHST.2023.10054441
   
  
• Dam break analysis using hydrodynamic modelling and geospatial techniques: a case of Damanganga Reservoir, Gujarat, India  
  by Kishanlal Darji, Uttamkumar Vyas, Dhruvesh Patel 
  Abstract: Flooding is a recurring phenomenon which can reduce certain extent by storing the water in large dam. However, dam operation is the most challenging part of flood resilience in recent climatic uncertainty and extremes. Furthermore, old dams have the potential to fail due to extreme rainfall, earthquakes, and structural failure. Recently, dam break analysis for prior threat detection has been used to strengthen resilience and improve decision-making by guiding emergency action plans (EAP) preparation. This paper describes the analysis of overtopping and piping failure of the earthen part of the Damanganga Reservoir, Gujarat, India using geospatial techniques and 2D hydrodynamic inundation modelling. The Froehlich's guideline is utilised for dam breach analysis. Water surface elevation, water depth, water velocity, arrival time, and water inundation map were developed using 2D HEC-RAS based hydrodynamic modelling. The generated maps will utilise for flood decision making activity to reinforce the EAP for large dam.
  Keywords: dam break analysis; hydrodynamic modelling; Dam Rehabilitation and Improvement Program; DRIP; geospatial techniques; emergency action plans; EAP; digital elevation model; DEM; India.
  DOI: 10.1504/IJHST.2023.10056492
   
  
• Estimation of storage capacity of the proposed rainwater harvesting structures (check dam) using geospatial technique: a case study in Tripura, India  
  by Bajitborlang L. Chyne, Ranjit Das, Naveen Ramachandran, Shanbor Kurbah 
  Abstract: The state of Tripura receives 2,380 mm rainfall annually. Due to the topography, most of the surface runoff drains off. Rainwater harvesting is a straightforward technique that collects and stores runoff from open spaces for future use. Check dam is one of the structures for harvesting rainwater. For preparing a proper water management scheme, estimation of the storage capacity of the reservoir is essential. Remote sensing and geographic information system is effectively utilised for estimation the storage capacity of the reservoirs. The inundated areas of the dams were estimated using digital elevation model in GIS environment and used finite difference method to develop the depth area curve and then elevation-area-capacity curve. The storage capacities of the proposed structures were estimated to be 3.19 and 0.35 million m³ at Buri Gang and Ghoramara Cherra respectively. This study provides a methodology to estimate the storage volume of reservoirs using geospatial technique.
  Keywords: reservoir; capacity; geographic information system; GIS; digital elevation model; DEM; topography; India.
  DOI: 10.1504/IJHST.2023.10054787