Water Resources Management Planning Research Articles

Modified water balance model for groundwater recharge estimations using cloud integration

Groundwater recharge is an essential element of enhancing global water governance. This is conspicuous over areas like West Bengal, India, which face natural and manmade water resource challenges. This particular study aims at improving the estimation of groundwater recharge using the Modified Water Balance Model (MWBM), which has been integrated with Google Earth Engine (GEE) and high-resolution remote sensing data here in application for groundwater. The method used consists of MODIS land surface temperature and CHIRPS precipitation data efficiently maps groundwater recharge estimation for various districts of West Bengal. The MWBM utilizes the geospatial analytic capabilities of GEE and above calculations in MWBM in creating recharge estimations that are geographically referenced. The majority of the results showed significant differences in the spatial recharge characteristics of the aquifers across the study region. High recharge was found in Alipurduar and Jalpaiguri district because of the high rainfall but low and constant recharge potential in Bankura and Purba Bardhaman districts due to less permeable rock layers. Within MWBM, improvements in groundwater management include the use of remote sensing techniques as well as modernization of computational processes to enhance recharge estimates. The present study not only aims at improving the accuracy of recharge estimation methods but also suggests a workable approach in the context of water resource management plans.

Coupling Driving Force–Pressure–State–Impact–Response–Management Framework with Hydrochemical Data for Groundwater Management on Sithonia Peninsula, Greece

Water scarcity in coastal tourist areas constitutes a critical environmental and socioeconomic sustainability issue. Hence, it is crucial to implement an integrated water resource management and protection plan. In this research, the DPSIR framework is coupled with hydrochemical data on groundwater resources in the fractured aquifer of the Sithonia Peninsula in Chalkidiki, North Greece. Geographical and demographic data, together with morphology, geology, hydrology, and groundwater quality data, were collected and evaluated to categorize the hydrosystem’s driving forces, pressures, states, impacts, and responses. The main pressures that affect groundwater quality in the study area are tourism, geological formation, and land use. Based on the analysis of the DPSIR framework, the absence of a landfill site, the inadequate operation of sewage treatment plants and biological wastewater treatment systems, and tourist activity contribute significantly to the degradation of groundwater quality. Additionally, the fractured rock aquifer develops preferential flow paths to pollutants through preexisting faults, which influence groundwater quality. The hydrochemical analysis of groundwater indicates seawater intrusion in the coastal area. The combination of DPSIR analysis and a water quality index based on ion ratios of groundwater samples identifies high-risk areas of seawater intrusion. Thus, it is essential to reinforce groundwater resources by implementing managed aquifer recharge, limiting unnecessary use of groundwater during the tourist season, and storing surface water during the wet period.

Optimized Evaluations of Irrigation Profits and Balance of Irrigation Water Demand and Supply Under Climate Change

Optimizing irrigation profit is essential for enhancing agricultural productivity and ensuring sustainable water resource management, especially under the uncertainties caused by climate variability. It signifies that maximizing irrigation profits has become the primary consideration of current agricultural irrigation systems. By discussing the change in irrigation profits with irrigation fraction f, which represents the proportion of irrigated farmland per unit area of cultivated land, a new method of qualitative diagnosis of irrigation supply and demand balance was proposed by means of the optimal irrigation fraction f0 corresponding to the objective function yf and the correlation coefficient ρ between irrigation water demand anomalies D^ and irrigation water withdrawal anomalies I^ (ρD^,I^=0). It will serve as a new attempt to optimize irrigation profits. We argue that the optimal objective function yf0 represents the state of equilibrium of irrigation supply and demand. This study proposes a reverse optimization idea, which includes two comparison methods, to estimate the comprehensively optimal irrigation fraction f0 that maximizes irrigation profits and irrigation profit per unit area while minimizing risks associated with climatic anomalies. By demonstrating that f0 performs exceptionally well in meeting the objectives of maximizing irrigation profits and minimizing risks across various climatic scenarios, we suggest a robust method for optimizing irrigation profits, which simply calculates ρD^,I^f=0 because ρD^,I^ is the function of f. Since f0 could capture the optimal solution to the greatest extent. It can serve as a simple and effective solution to determine the optimal objective function yf0 and diagnose the state of balance between irrigation water demand and supply. This method offers practical implications for water resource management and agricultural planning in the face of climate change.

Spatial assessment of land use and land cover change impacts on groundwater recharge and groundwater level: A case study of the Hat Yai basin

Study regionHat Yai Basin, Thailand. Study focusThis study aimed to assess the impacts of land use and land cover changes on groundwater recharge and groundwater levels—specifically, the effects of increasing built-up areas and urban expansion. The MOLUSCE (plug-in QGIS) software was used to analyze land use and land cover changes. The groundwater recharge estimation in this study was conducted using the WetSpass model, and the groundwater level was simulated using MODFLOW. New hydrological insights for the regionThe findings indicate that the Hat Yai basin has experienced its highest increase in built-up areas and found a decrease in the average recharge rate for 2017 compared to 2007. Correspondingly, a simulation of groundwater levels indicates that the overall groundwater level has decreased. Spatially, the Hat Yai district, which exhibited the highest increase in built-up areas, experienced a decrease in groundwater levels. In contrast, the Khuan Niang district, which had the largest increase in perennial areas, demonstrated increased groundwater levels. The findings show that increasing built-up areas decreases the recharge rate, consequently lowering groundwater levels. The spatial assessment is proved to be more effective in illustrating positive and negative impacts than the basin's overall average. Such spatial assessment is valuable for water resource management planning and addressing localized water resource issues that may differ across various areas.

Estimation of Future Climate Change in Low Folded Zone, Iraq with the LARS-WG and Five GMS Models under CMIP5 Scenarios

This study investigates the trends of climate change in the Low Folded Zone region of Iraq and provides future projections for daily maximum and minimum temperatures, as well as precipitation. Using the LARS-WG model and five General Circulation Models (GCMs) under three Representative Concentration Pathways (RCP) scenarios, the research aims to provide valuable insights into the anticipated changes in climate variables. The calibration and validation of the model demonstrate its capability to simulate future climatic data, with statistical indices indicating a strong correlation between observed and generated data. The projected future temperatures showed a consistent increase across all selected stations, with average annual maximum temperatures expected to rise by 1.06 to 5.48°C by the end of the twenty-first century. The highest increase in temperatures was predicted under the high-emission scenario RCP8.5. The results also indicated spatial and temporal variations in precipitation patterns, with the annual percentage increase in precipitation ranging from 7.07% to 10.75% for RCP 2.6, 0% to 2.2% for RCP4.5, and 3.7% to 6.8% for RCP8.5. The findings reveal a projected increase in annual temperatures and variable precipitation patterns, highlighting the urgency of proactive measures to address the challenges posed by climate change in the region. The results of this study are crucial for informing decision-makers and planners in developing strategies for climate change adaptation and resource management, particularly in water resource management and agricultural planning.

Remote sensing-based agricultural drought mapping in Northern Jordan using Landsat and MODIS data

Monitoring agricultural drought in a semi-arid environment is critical, especially during the growing season, as it negatively impacts vegetation health and crop yield. This study aimed to monitor the spatiotemporal variation of agricultural drought in Northern Jordan using Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The Spatio-Temporal Image Fusion Model (STI-FM) was used to produce synthetic Landsat images with a high spatiotemporal resolution (30 m / 8 days) by utilizing a pair of successive MODIS images at two points in time (time-1 and time-2) and one Landsat-8 image at time-1. Agricultural drought was mapped and monitored using spectral indices namely, Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Standard Precipitation Index (SPI) -based meteorological rainfall data was used to validate the accuracy of the drought maps. The results revealed significant spatiotemporal variations in drought conditions, with April 2020 showing the least dry conditions, while 2019 was identified as the driest year. Validation through SPI indicated high accuracy, with kappa values ranging from 0.70 to 0.85 and overall accuracy ranging from 80 % to 92 %. Furthermore, the STI-FM data fusion algorithm effectively generated high-resolution Landsat-8 images, demonstrating a strong correlation between original and synthetic images for the red and NIR spectral bands (0.93 and 0.84, respectively). These findings highlight the effectiveness of integrating STI-FM with spectral indices and SPI for accurate and high-resolution agricultural drought monitoring, which can support improved water resource management and agricultural planning in semi-arid regions.

Machine learning for improved drought forecasting in Chhattisgarh India: a statistical evaluation

Meteorological drought is one of the major natural hazards that affects the ecosystem of the Central Indian state of Chhattisgarh. This study delves into the analysis, comparison, and prediction of drought trends spanning the period from 1993 to 2023 in the study area. Employing a comprehensive methodology, utilization of the Modified Mann–Kendall test to analyze drought trends, while assessing drought severity through the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) has been done. Further research entails assessing the link between SPI and SPEI utilizing the Pearson correlation coefficient and simple linear regression techniques. Additionally, the Support Vector Machine (SVM) and Random Forest (RF) methods were used for predictive modelling feasibility. The findings helped to deepen our understanding of drought dynamics in the region, providing important insights for drought mitigation and adaptation efforts. This study emphasizes the importance of using a variety of statistical techniques and machine learning algorithms to thoroughly analyze, compare, and forecast drought patterns, thereby informing evidence-based decision-making for sustainable water resource management and agricultural planning in Chhattisgarh, India.

Changing Rainfall Patterns in the Northeastern South Kivu Region, Democratic Republic of the Congo: A Detailed Analysis Using CHIRPS Rainfall Data (1981–2023)

Understanding changes in rainfall patterns is vital for effective water resource management and agricultural planning in climate-sensitive regions. In Northeastern South Kivu, Democratic Republic of the Congo, limited knowledge of rainfall dynamics poses challenges for local climate assessments. This study analyzed rainfall patterns using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2023 to fill this knowledge gap. The findings revealed significant spatial and temporal changes in rainfall across the regions of Kabare, Bukavu, Kalehe, and Idjwi. Mean annual rainfall ranged from 1136.92 mm to 2048.85 mm, with coefficients of variation between 8.23% and 8.45%. Monthly rainfall fluctuations were substantial, ranging from 16 to 56.35%. Standardized rainfall anomalies indicated a shift from predominantly wet conditions in the 1980s to drier conditions in later decades, with over half the years from 1990 to 2023 recording below-average rainfall. Mann-Kendall trend analysis, supported by Innovative Trend Analysis (ITA), confirmed significant decreases in annual rainfall, with rates between − 3.53 to -5.72 mm/year. Additionally, rainfall intensity indices highlighted a decline in total annual precipitation, suggested fewer extreme events. Spatial variability was observed, with regions like Kalehe and Bukavu showed higher values for indices such as R95P and RX1day compared to Kabare and Idjwi. These results underscored the urgent need for adaptive strategies to mitigate water scarcity and manage changing rainfall patterns in the region.

Construction of a quantitative model for ski resort water demand and preliminary exploration of drainage irrigation pathways

In China, natural snowfall is insufficient, and ski resorts often require artificial snowmaking in winter and turf management in summer, which results in high overall water consumption and considered as one of the high water-consuming service industries, with an urgent need for theoretical foundations related to water management. Based on life cycle theory, we examined the characteristics of the water systems of ski resorts in winter and summer. A Monte Carlo simulation was used to construct a stochastic model to quantify the theoretical water consumption for snowmaking at ski resorts and to investigate the mechanisms by which various factors influence this consumption. We summarize the necessity of summer turf management and irrigation requirements at ski resorts, explore the interaction between ski resorts and agricultural irrigation. The results show that the theoretical water demand of ski resorts is basically consistent with the actual water consumption of ski resorts, that the constructed model is feasible, and that snowmaking water is the most important water use of ski resorts. Exploring the collection and reuse of meltwater or rainwater from ski resorts for agricultural irrigation in the spring and summer could provide more possibilities for the sustainable management of regional water resources. The research findings can provide a theoretical basis for scientifically and reasonably setting the norm of water intake for ski resorts and for developing comprehensive water resource management plans for ski resorts and agricultural irrigation.

Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Climate change impacts on hydrology and water resources in East Africa considering CMIP3, CMIP5, and CMIP6

Climate change impacts on the hydrology and water resources in East Africa require proper planning of regional adaptation measures. Past studies on the subject matter tended to focus on catchment instead of regional scale. To support actionable policy regarding regional planning of water resources management, this paper reviewed studies conducted in East Africa based on CMIP3, CMIP5, and CMIP6. Compared to the historical condition, temperature of East Africa over the mid- to late 21st century will increase by varying extents over the range 0.17–7°C with about 2.6°C on average. Future precipitation over East Africa will increase by 8.5% on average. Changes in discharge based on CMIP3, CMIP5, and CMIP6 will vary from −25.3 to +60.5%, −42.5 to 129%, and −23.4 to 69%, respectively. Ensemble means of the changes in discharge across the study area based on CMIP3, CMIP5, and CMIP6 will be by 25.2, 19.2, and 19.1%, respectively. Future peak river discharge across East Africa will increase by 8% on average. These results are important for planning regional climate change adaptation across East Africa. To support local or catchment adaptation, results of climate change impacts of temperature, precipitation and discharge of about 50 catchments across East Africa can be found summarized in this review paper. It is recommended that both regional and local policies or approaches should be considered for climate change adaptation to ensure a balanced, effective and sustainable framework for climate resilience in support of measures for tackling broad and specific needs cohesively.

Enhancing LSPIV accuracy in low-speed flows and heterogeneous seeding conditions using image gradient

Flow measurement in rivers and channels is crucial for water resource management and infrastructure planning, especially under the context of climate change. However, traditional methods like mechanical current meters and hydroacoustic instruments face limitations in terms of cost, intrusiveness, and accessibility. In recent years, image-based velocimetry techniques have emerged as promising alternatives due to their non-contact nature and cost-effectiveness. Nevertheless, persistent challenges remain, particularly concerning the uniform distribution of surface tracers necessary for precise measurements. These challenges are particularly pronounced in cases involving artificial seeding, where ensuring uniform distribution poses a significant obstacle. To address this issue, this study presents a novel methodology for filtering Large Scale Particle Image Velocimetry (LSPIV) data based on indicators of pixel intensity gradients. The methodology was evaluated across various field measurements under low flow conditions, encompassing a wide range of seeding characteristics. The evaluations demonstrated improvements in mean surface velocity profile estimation, showing reductions of up to 70 % in normalized root mean square error compared to not using filters. Additionally, the results were compared with filters typically employed by experienced LSPIV users, such as background subtraction and cross-correlation coefficient thresholds, showing improvements with the proposed filter. Implementation of the proposed strategy reduces the subjectivity in LSPIV implementation, particularly for users with limited knowledge of the technique, but also require minimal post-processing efforts. The methodology is anticipated to be integrated into existing software tools, thereby enhancing the accessibility of LSPIV for individuals with limited expertise in image velocimetry. Overall, this methodology facilitates cost-effective expansion of hydrological information availability, particularly in resource-constrained regions.

Could California's groundwater resource management lessons be beneficial to England?

In California, Groundwater Sustainability Agencies (GSAs) are basin/catchment-specific water organizations mandated by the Sustainable Groundwater Management Act of 2014 to manage groundwater sustainably by 2040/2042. A GSA can charge replenishment fees for pumped groundwater and these fees can be used to: (a) monitor basin hydrologic conditions; (b) improve the water budget accuracy; (c) increase water availability through management actions and projects; (d) mitigate undesirable results; and/or (e) pay for activities including issuing allocations and limiting production to allocations. In England, housing is frequently proposed in river basin catchments that face water resource challenges. High profile examples are the Arun Valley and Cambridge. The current National Planning Policy Framework manages water resource planning strategically through water company Water Resource Management Plans or Environment Agency River Basin Management Plans. These processes can be slow to respond to development requirements. England might look to California's practices that require water users and developers to pay for ‘water credits’ or fees. This could involve a ‘Watermaster’ charged with local or catchment scale resource management and project implementation. This Watermaster could engage all stakeholders including the local water company, farmers, the Environment Agency and the local authority, none of whom is well positioned to act in isolation.

Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin

Rainfall erosivity is a crucial factor in the evaluation of soil erosion, significantly influencing the complex relationships among water, soil, and the environment. Understanding its attributes and variations in space and time is essential for effective water resource management, erosion mitigation, and land-use planning. This paper utilizes daily precipitation data from 123 grid points in the Niyang River Basin, spanning from 2008 to 2016, to calculate rainfall erosivity using a straightforward algorithmic model. Ordinary Kriging was used to examine the spatial and temporal variations in rainfall erosivity, while Spearman’s correlation analysis was employed to examine the relationships between annual rainfall erosivity and various factors, including multi-year average precipitation, erosive rainfall, dry-season rainfall, wet-season rainfall, temperature, and elevation. The results indicate a year-by-year increase in rainfall erosivity in the basin, with a trend towards stabilization. The average annual rainfall erosivity over the years is 711 MJ·mm·hm−2·h−1, peaking at 1098 MJ·mm·hm−2·h−1 in 2014. A significant 93.9% of rainfall erosivity is concentrated in the wet season, with a maximum of 191 MJ·mm·hm−2·h−1 in July. The left bank of the mainstream, especially the central and lower sections of the main river and its tributaries, experiences the highest rainfall erosivity. Rainfall factors predominantly influence erosivity, with erosive rainfall showing the strongest correlation (rho = 0.93), while temperature and elevation have relatively minor effects. This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.

Spatiotemporal forecasting of water change trends in Urmia Lake through to 2030, using STC-based models

ABSTRACT The purpose of this paper is to forecast the spatiotemporal water change trends in Urmia Lake through 2030. Three space–time cube-based models were applied. The forest-based forecast model, with a mean of 0.14 forecast root mean square error (RMSE) and 0.39 validation RMSE, had a better performance. According to the model’s results, the peripheral parts of the lake will mainly stay completely arid with a 42.5% extension in the entire period, 21.7% in spring, and a 27% reduction in winter. In the middle parts, the aridity will increase by 121% in summer, 93% in fall, and 38% in spring. Eventually, the centric areas will not be completely arid; however, the water patterns’ extent will decrease by 53.46% in fall, 34.6% in spring, and 28.5% in summer. Additionally, southern, eastern, western, and northern areas will experience drier conditions, respectively. The findings can be used for water resource management and water restoration plans for the lake.

Impact of land use/land cover change on surface water hydrology in Akaki river catchment, Awash basin, Ethiopia

Watershed reactions to environmental changes is critical for water resource use and management planning. The change in land use/land cover (LUCC) alters surface water hydrology and water balance, subsequently it affects the availability of water resources. This study examined Akaki River catchment surface hydrological responses to future land use scenarios. The future LULC simulated using Cellular Automata-Markov Chain (CA-Markov) model in IDRISI software for 2031, 2041, and 2051 under Business as Usual (BAU) and Forest Patch Protection (FPP) scenarios. Water yield (WY), sediment export (SE), and soil loss (SL) were simulated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The findings indicate that under BAU, forest and settlement areas increased 42.64% and 31.47%, whereas water body, grass land, and agriculture dropped 31.99%, 25.72%, and 10.4%, respectively, from 2021 to 2051. The FPP scenario shows a 66.42% increase in forest coverage and a 31.97% decrease in cultivated land between 2021 and 2051. Overall settlement expansion will expand by 31.44%, while water body and grassland will decline by 11.12% and 24.9%. As a result of LULC changes in BAU scenario, the proportions of WY (4.36%), SE (26%), and SL (22.45%) increased. The hydrological components exhibited more gradual variations of 0.55%, 18.8%, and 14.24% for the WY, SE, and SL, respectively, under the FPP scenario. Therefore, the alteration has resulted in a concerning state within the area, necessitating the implementation of comprehensive management techniques to restore the hydrology of the catchment.

A geospatial modelling for glacial lake outburst floods susceptibility analysis in the Central Himalayas

ABSTRACT Glacial lakes have increased throughout a significant portion of the Himalayan region, and hence the present study focuses on a geospatial modelling approach for analysing the susceptibility to glacier lake outburst flood (GLOF) in the central Himalayan region. Multitemporal satellite imageries such as Landsat-TM and Sentinel-2 were taken. The hydrodynamic compatibility tool, i.e. HEC-RAS 5.0, was used for GLOF prediction, and the lake volume and surface area were calculated using established empirical equations. The breach fails and flood flow were approximated, and arising uncertainties were analysed with three outburst scenarios. The number of lakes and their sizes have significantly increased according to the findings of the study. Of these, the Vasundhara Lake (VL), which is located at 4,676 m above the sea level, is assigned to the hazardous category for more examination. It has been found that the lake's area expanded over the past three decades (i.e. 1994–2020), rising from 0.15 to 0.29 km2. The study demonstrates that, in the worst-case situation, infrastructure along riverbanks may be susceptible to harm. The study provides information on the potential effects of GLOF events in the study region that might be used in early warning systems and water resource management plans.

Analysis of Spatial and Temporal Variations in Evapotranspiration and Its Driving Factors Based on Multi-Source Remote Sensing Data: A Case Study of the Heihe River Basin

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions.

Morphometric analysis in the sub basins of the Kali River using Geographic Information System, Karnataka, India

Morphometric analysis serves as a crucial tool for quantitatively characterizing drainage basins and exploring the intricate relationships between hydraulic parameters and geomorphological characteristics. This study presents a comprehensive morphometric analysis of the Kali Subbasin in Karnataka, covering an area of 1175.45 km². Utilizing remote sensing data and GIS tools, we quantified various morphometric parameters, including stream order, stream length, bifurcation ratio, drainage density, stream frequency, form factor, and circularity ratio, to understand the basin's geomorphological and hydrological characteristics. The study area is classified as a seventh-order basin displaying a dendritic drainage pattern with a drainage density of 2.853 km/km². Notably, the bifurcation ratio ranges from 4.396 to 3, indicating low to moderate structural control within the basin. The subbasin's elongated shape, with an elongation ratio of 0.604 and a circularity ratio of 0.298, indicates moderate peak flow and high susceptibility to erosion. Relief parameters showed significant elevation variations, contributing to intense erosion and heightened flood risks in lower-lying areas. The study identifies the basin's transition to a mature geomorphic stage, evidenced by an escalating stream length ratio from lower to higher orders. These findings underscore the importance of tailored watershed management strategies, including floodplain zoning, retention basins, afforestation, and terracing, to mitigate flooding and soil erosion. The study's insights are crucial for developing effective, localized water resource management plans and enhancing the resilience of the Kali Subbasin to future hydrological challenges.