Land use/land cover (LULC) and climate are two crucial environmental factors that impact watershed hydrology worldwide. The current study seeks to comprehend how the evolving climate and LULC patterns are impacting the hydrology of the Mahanadi Reservoir catchment. A semi-distributed Soil and Water Assessment Tool (SWAT) model was utilized to simulate various water balance elements. Twelve distinct scenarios were developed by combining three different climatic data periods (1985–1996, 1997–2008, and 2009–2020) with four sets of land use maps (1985, 1995, 2005, and 2014). The SWAT model demonstrated strong performance in simulating monthly stream flows throughout the calibration and validation phases. The study reveals that changes in LULC have a distinct effect on the environment. Specifically, the changes in LULC lead to heightened streamflow and reduced evapotranspiration (ET). These changes are mainly attributed to amplified urbanization and the diminished presence of water bodies, forest cover, and barren land within the Mahanadi Reservoir catchment. The combined impact of climate change and LULC shifts reveals complex interactions. Therefore, the present study offers an understanding of how changes in climate and land use over the past few decades have influenced the hydrological behavior of the Mahanadi Reservoir catchment in Chhattisgarh. The findings of this study have the potential to offer advantages to governmental bodies, policymakers, water resource engineers, and planners seeking effective strategies for water resource management. These strategies would be particularly relevant in the context of climate change and land use/land cover changes in ecological regions similar to those of the Mahanadi Reservoir catchment. In addition, a rational regulatory framework for land use patterns is essential for assisting stakeholders in managing water resources and appropriately developing the entire catchment.

The study focuses on the estimation of a technique, a method for developing a phenomenon, to obtain a quantile with minimal or low error (AR and R-RMSE bias). To arrive at such a solution, a case study of the Mahanadi River system (Mahanadi Basin) was conducted along with the integration of various techniques available in past and present literature, to come up with a novel solution. Which could answer practical questions in water resource planning and management for addressing a wide range of problems such as meteorological draught analysis, agricultural planning, precipitation forecasting and downscaling, design of water control and conveyance structures, and land-use planning and management. A gridded rainfall data set of resolution 0.25° x 0.25° (1901 – 2017) obtained from IMD Pune is used to calculate the statistics that will be used for the regionalization of precipitation. Other attributes or variables used for regionalization are seasonality measurements and location parameters (latitude, longitude, and elevation). The L-moment statistics are computed from the time series rainfall data and the ratios of the L-coefficient of variance and the L-coefficient of skewness, i.e., the L-moment ratio, are the main components in computing quantile estimates of selected regions for effective regional frequency analysis. To determine potential scenarios for homogeneous regions, the use of seasonal extreme precipitation will serve as a basis for regionalization.

Rainfall trend analysis is very important in the economic and sociocultural context of the country, like the development of irrigation and water resource management practices. In this paper, Mann-Kendall (M-K), Sen’s slope estimator, and wavelet analysis were used to analyze long-term (1901 ~ 2017) annual and seasonal rainfall trends in India. The sequential Mann-Kendall (SQMK) test is used to estimate the temporal variation of rainfall. The analysis shows monsoon precipitation in “Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Uttar Pradesh, Chhattisgarh, Sikkim, Arunachal Pradesh, Mizoram, Assam, Karnataka, Nagaland, Kerala, Meghalaya, and Goa” have significant trends. The above-mentioned states were subjected to a discrete wavelet transform (DWT) analysis using Daubechies (db5-db10) and Symlet (sym5-sym10) mother wavelet families. The analysis revealed that the trends of subdivisions had a short-term periodicity of less than a decade. Therefore, the present study indicates significant changes over time. By utilizing wavelet techniques, it is possible to gain insight into both short- and long-term tendencies, making it simpler to spot patterns and sudden changes. Therefore, the present study by combining non-parametric tests with wavelet synopsis, offers a thorough comprehension of rainfall trends in India. The collaborative use of these techniques improves the precision of trend identification, offering valuable insights to make well-informed decisions for water resource management and climate adaptation strategies.