The Murredu watershed in Telangana State was chosen for the morphometric and land use/land cover (LULC) analysis in this current study. Geographical information system (GIS) and remote sensing (RS) techniques can estimate the morphometric features and LULC analysis of a catchment. A total of fourteen sub-watersheds (SWs) were created from the watershed (SW 1 to SW 14), and sub-watersheds were prioritized based on morphometric and LULC features. Evaluation of various morphometric characteristics such as linear aspects, relief aspects, and aerial aspects has been carried out for every sub-watershed to prefer ranking. Four parameters were utilized for the LULC analysis to rank and prioritize sub-watersheds. The sub-watersheds were categorized into three groups as low, medium, and high, for soil and water conservation priority based on morphometric and LULC analysis. Using morphometric analysis, higher priorities have been assigned to SW 12 and SW 1, while using LULC analysis, higher priorities have been assigned to SW 9 and SW 11. SW 10 and SW 13 are the most common sub-watersheds that fall within the same priority while using morphometric and LULC analysis. The coefficient of regression results reveals that stream length and stream order, and also stream number and stream order, have a strong association. The deployment of soil and water conservation measures may be conducted in the high-priority sub-watersheds.
A hydrological model helps in understanding of the hydrological processes and useful to measure water resources for effective water resources management. Hydrological cycle describes evaporation, condensation, precipitation and collection of earth water and on again. Hydrological models have been used in different watersheds across the world. The runoff estimation process is the most complex in nature that depends on the meteorological data and also on the various watershed physical parameters. To generate runoff data for a particular watershed it is needed to find out various parameters related to precipitation models. The HEC HMS (a Centre for Hydrological Engineering and Hydrological Modelling Systems introduced by the US Army Corps of Engineers) is a popularly used watershed model to simulate rainfall runoff process. The input variables used by hydrological models are rainfall data, runoff data, wind speed, relative humidity, soil type, catchment properties, hydrogeology and other properties. The Hydrological Modeling can also be an event based or may be continuous. This model is used to predict future impacts of the climate changes on the runoff of River basin and it is used to simulate runoff in ungauged watershed. This literature review represents that application of rainfall runoff modelling using HEC HMS is helpful in prediction of flood, water management and socio-economic development as well as food security. Keywords: HEC-HMS, hydrological modeling, rainfall-runoff simulation, soil type.
Rainfall–runoff (R–R) analysis is essential for sustainable water resource management. In the present study focusing on the Peddavagu River Basin, various modelling approaches were explored, including the widely used Soil and Water Assessment Tool (SWAT) model, as well as seven artificial intelligence (AI) models. The AI models consisted of six data-driven models, namely support vector regression, artificial neural network, multiple linear regression, Extreme Gradient Boosting (XGBoost) regression, k-nearest neighbour regression, and random forest regression, along with one deep learning model called long short-term memory (LSTM). To evaluate the performance of these models, a calibration period from 1990 to 2005 and a validation period from 2006 to 2010 were considered. The evaluation metrics used were R2 (coefficient of determination) and NSE (Nash–Sutcliffe Efficiency). The study's findings revealed that all eight models yielded generally acceptable results for modelling the R–R process in the Peddavagu River Basin. Specifically, the LSTM demonstrated very good performance in simulating R–R during both the calibration period (R2 is 0.88 and NSE is 0.88) and the validation period (R2 is 0.88 and NSE is 0.85). In conclusion, the study highlighted the growing trend of adopting AI techniques, particularly the LSTM model, for R–R analysis.
The sustainable management of groundwater resources is crucial for ecological diversity, human health, and economic growth. This study employs scientific concepts and advanced techniques, including the analytic hierarchy process (AHP) and Fuzzy-AHP, to identify groundwater potential zones (GWPZs). Thematic maps representing drainage density, elevation, soil, geomorphology, slope, land use and land cover, and rainfall are used to delineate the GWPZs. Both techniques are employed to assign weights to these thematic maps based on their characteristics and water potential. The study revealed that in the investigated area, 17.76 and 18.27% of the final GWPZs (AHP and Fuzzy-AHP) can be classified as having poor potential, while 72.79 and 71.07% are categorized as having moderate potential. Moreover, 9.45 and 10.69% of the final GWPZs are identified as having high potential using the AHP and Fuzzy-AHP models, respectively. Receiver operating characteristics (ROCs) analysis is employed to validate these findings, demonstrating that the Fuzzy-AHP technique achieves an accuracy of 74% in identifying GWPZs in the region. This study utilises the best method derived from both models to identify 26 suitable locations for artificial recharge sites. The reliable findings of this research offer valuable insights into decision-makers and water users in the Kinnerasani Watershed.
Water resource management is critical in the face of climate change to reduce water scarcity and meet the demands of an expanding population. Prioritization of watersheds has gained significance in natural resource management, particularly in the context of watershed management. This study prioritizes sub-watersheds for the Peddavagu basin using five methods. The four methods mentioned above can be estimated utilizing remote sensing (RS) and geographic information system (GIS) approaches, while linear discriminant analysis (LDA) is estimated using machine learning techniques. The catchment resulted in the formation of 13 sub-watersheds. The quantitative measurements of morphometric analysis, including linear, relief, and areal, were considered, and 18 morphometric characteristics were chosen to rank and prioritize sub-watersheds. Principal component analysis (PCA) was used to rank and prioritize sub-watersheds based on four highly correlated morphometric parameters. The land use/land cover (LULC) analysis used four features to prioritize sub-watersheds. The LDA analysis used two features to prioritize sub-watersheds. Using hypsometric integral (HI) values, prioritization has been done. Sub-watersheds were prioritized. Based on five methods, the sub-watersheds were classified as low, medium, and high. Among the sub-watersheds identified as high priority, immediate priority is assigned to SW10. Decision-makers in the research region can use the findings to plan and implement watershed management techniques.
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