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Groundwater has been the largest accessible freshwater source globally. The overall dimensions of every basin's water budgeting are closely linked to the quantity quality of available surface and subsurface water. In this paper, the groundwater levels trend in six districts within the Baitarani River Basin, Odisha State, India has been analyzed. Data on the depth to groundwater level for pre and post-monsoon season from 32 wells spread over six different regions from 1997 to 2020 were analyzed. Sen's slope techniques and creative trend analysis were used to investigate the behavior of groundwater level changes in pre- and post-monsoon periods to identify both monotonic and non-monotonic behaviors. The findings showed that, throughout the pre-monsoon and post-monsoon periods, the depth to groundwater level for four Tubewell, five Borewell, and seven Dugwell locations increased. However, only three tubewell stations Champua, Bhadrak, and Karanjia exhibited declining trends in the pre- and post-monsoon periods. In the case of borewell, only one station (Anadapur) has a negative trend in post-monsoon. For Dugwell, during pre-monsoon Banspal represents the –ve trend, and Bhandaripokhari, Banspal, Anandapur, and Joda show negative trends in post-monsoon seasons. A significant decline in groundwater level was observed in three stations for tubewell, one station for borewell, and four stations for dugwell observed in the current study. Accordingly, spatial maps of increasing and decreasing trends of groundwater levels were prepared, which can be used by water resource managers to identify the causes of declining groundwater levels for managing the resources effectively. [ABSTRACT FROM AUTHOR]

Analysis of rainfall distribution and its changing pattern plays a vital role in managing water resources in a region. This work examined the spatio-temporal variability and trend of rainfall on yearly and season-wise scales during 1971–2013 in the Wainganga river basin situated in middle India. The Mann–Kendall (MK) test was implemented for identifying the temporal variation in rainfall trends. The magnitude of this changing trend was estimated by applying Sen's slope (SS) method. A paired sample t-test was also employed to appraise the statistical significance of changes in rainfall data. The results of MK and SS tests show both upward and downward trends. The results show positive and negative trends in the south-eastern and northwestern parts of the study area for annual rainfall. The monsoon rainfall also shows very close proximity with annual rainfall data trends. The t-test states that the observed changes in precipitation are statistically significant. After trend and pattern analysis, state-space models (SSMs) were used to predict rainfall for future scenarios. Four SSMs (single, double, and triple exponential, autoregressive integrated moving average (ARIMA)) were employed for rainfall prediction. The analysis shows that the ARIMA is best for rainfall prediction with architecture of (0,0,0) (2,1,0) and can be used in Wainganga River basin. [ABSTRACT FROM AUTHOR]

Characterization of nonlinear trends in time series of hydroclimatic variables exhibiting nonstationarity is necessary for more realistic projections of climate change and for optimal design of hydraulic structures. The present study was conducted to demonstrate the applicability of a novel Monte-Carlo-based singular spectrum analysis (SSA) to characterize nonlinear trends in historical time series of rainfall characteristics. Long-term (1960–2015) rainfall records for 17 gauges located in the Malaprabha River Basin, India, were used to analyze spatiotemporal variabilities of trends in rainfall totals and number of rainy days for annual and seasonal time periods. While the traditional Sen's Slope and Mann–Kendall (MK) trend tests indicated statistically nonsignificant decreasing monotonic trends at most gauge stations, SSA revealed the existence of steep nonlinear trends and distinct change points in the direction of the trend over the period of record for both rainfall and rainy days. Results of this study demonstrate the potential for SSA to extract crucial information on the trajectories of nonlinear trends and change points in time series of hydroclimatic variables that exhibit nonstationarity. [ABSTRACT FROM AUTHOR]

Trend analysis of streamflow and its linkages to rainfall can facilitate the development of water resource management plans for watersheds. The present study focuses on trends in observed streamflow of the lower Tamiraparani river sub-basin in Tamilnadu, India using Mann - Kendall test and Sen's slope estimator for a period of 30 years. From this analysis, both the annual streamflow, annual rainfall increased very marginally at the rate of 0.1799 m3/s/year and 4.3741 mm/year, respectively. Similarly, in the northeast monsoon period, both the rainfall and streamflow were increased very minimally during the analysis period. However, the observations made during the southwest monsoon showed declining trends for most of the rainfall stations and no significant trend was observed for streamflow. The calculated Spearman's Rho correlation factor (p) between rainfall and streamflow showed weak correlation during summer (0.26) and very strong correlation during northeast monsoon (0.83), strong correlation during southwest monsoon (0.53) and moderate correlation during winter (0.40). From this analysis, using Mann - Kendal test and Spearman's correlation it implies that other than precipitation and temperature, urbanization, watershed and ecological development of the catchment area could have also influenced the trends of streamflow. [ABSTRACT FROM AUTHOR]