Climate Change Impact on Water Resources

Interactions between different components of the hydrologic cycle show a time-varying characteristic due to the impact of climate change that lead to the non-stationarity in many hydroclimatic variables. In fact, a lack of stationarity in most of the hydroclimatic processes is realized in many cases. In such situation, alternative methodologies that can effectively learn (adapt) from the changing climate will help in development of effective and efficient hydroclimatic models. This study presents the potential of a recently developed approach, namely temporal networks. These time-varying network structures help in hydroclimatic modelling by (i) identifying the complex association (dependence structure) among the large pool of influencing variables and (ii) identifying the temporal variability of the dependence structure to capture the time-varying characteristics in the association among the hydroclimatic variables. The approach helps to improve the accuracy of the model performance under a changing climate. As a demonstration, we picked out the slowly changing soil moisture regime at a location and attempted to capture its time-varying characteristics through temporal networks based time-varying modelling framework. Our target is to predict the monthly soil moisture with one-month to one-season (three months) in advance. The performance of the temporal networks based model is contrasted with the time-invariant modelling philosophy. Towards this, (i) time-invariant network model, as the closest counterpart, and (ii) Support Vector Regression (SVR) based models, Machine Learning (ML) technique commonly implemented in the field of hydroclimatology, are used. We established that the temporal networks satisfactorily capture the soil moisture variability over time.

General circulation models (GCMs) provide the data to study climate change under different scenarios, but they operate on a coarse scale. Therefore, to assess the hydrological impacts of global climate change on a regional scale, the output from a GCM must be downscaled to finer resolution. Moreover, regional precipitation simulations can be improved by using physically relevant variables from GCMs at different pressure levels as predictors. In this study, we have explored different deep learning techniques for multi-site downscaling of daily precipitation over the Mahanadi basin using large-scale hydroclimate variables as predictors. Hydroclimatic variables from NCEP reanalysis data (available at 2.5° × 2.5° resolution) are used to downscale the daily precipitation product at observational grid-scale (i.e., 1° × 1° spatial resolution). Different deep learning architectures, viz., deep neural network (DNN), 2D- and 3D-convolutional neural network (CNN), and hybrid-DNN are trained on these spatio-temporal variables. The results show that deep learning models have the ability to use the spatial information from predictor variables over the Indian subcontinent to capture monsoon patterns and downscale daily precipitation. The 2D-CNN model is able to learn the spatial features from high-dimensional predictor variables over continental sized domains. 3D-CNN further reduces the number of parameters and is able to learn from the stacked high-dimensional spatio-temporal datasets at different vertical pressure levels with comparative ease. The hybrid-DNN is employed to make use of spatial structure of the predictor datasets as well as the information from the GCM precipitation outputs of neighboring grid points of the observation grids. The 2D-CNN, 3D-CNN, and hybrid-DNN perform better than the DNN showing the usefulness of exploiting the spatial gridded structure of the predictors. This study highlights the potential of deep learning techniques in learning precipitation patterns from coarse-resolution climate model outputs and in downscaling daily precipitation.

The benefit of the time-varying downscaling model (TVDM) in downscaling the historical precipitation (1951–2015) data is explored in this study. The Godavari River Basin (GRB) is considered for demonstrating the results. The General Circulation Model (GCM) outputs from Canadian Earth System Model, version-2 (CanESM2) are considered for the analysis. The observed precipitation is obtained from India Meteorological Department (IMD), Pune. Firstly, the TVDM is calibrated using 40 years (1951–1990) of historical data and then validated for the remaining 15 years (1991–2005) in the historical period. Secondly, the downscaled data are analyzed with the observed precipitation data using different statistical measures. The results have shown a good association between observed and downscaled precipitation during both calibration and validation periods across the GRB. For instance, the correlation coefficient (R) ranges between 0.71 and 0.89 at various locations in the GRB. Further, the extreme events are also assessed using 90th and 95th percentiles and found a better match between the observed and TVDM downscaled data. Overall, the TVDM shows a promising result in modeling the precipitation. Hence, the TVDM can be used to downscale the future precipitation of GRB under the changing climate scenario, and the outputs can be used for many local-scale impact assessment studies.

The downscaling of coarser scale general circulation model (GCM) variables, preferably the rainfall and temperature, to finer resolution followed by estimation of uncertainty/bias in the downscaled outcomes are essentially required prior to their application for hydrological assessments and decision-making. The kernel regression-based statistical downscaled (KRSD) rainfall data of five GCM models, from Coupled Model Intercomparison Project Phase-5 (CMIP-5), have been assessed to ascertain their ability to simulate the magnitude, variability, and extremes of Indian summer monsoon rainfall (ISMR) over the Tapi River basin (TRB) in India. The KRSD rainfall of GCMs is compared with gridded rainfall data obtained from India Meteorological Department-Pune (IMD) for the period 1951–2005 on annual and monsoon months (JJAS). The GCMs underestimate annual rainfall (PRCPTOT) on average by 21.7–28.4% over the TRB. Further, GCMs overestimate the number of rainy days (RD) and longest spell of consecutive rainy days (CWD) at an annual scale vis-à-vis IMD gridded rainfall dataset. The four-to seven-fold overestimation of CWD and RD is observed during September month compared to June, July, and August months. Also, one-day (Rx1D), five-day (Rx5D), and rainfall extremes above 95th percentile value (R95) from GCMs observed underestimation of the parameters ranging from 46.4 to 52.7%, 41.4 to 44.1%, and 45.0 to 48.8%, respectively. The present investigation concludes that the GCM fails to account for the seasonality of ISMR over TRB. Overall, KRSD rainfall underestimates the PRCPTOT and rainfall extremes while overestimating the RD and CWD over the TRB. Thus, rainfall intensities are significantly underestimated for the historical period over TRB.

The evaluation of climate change impacts on hydrology using Global Climate Models (GCM) and emission scenarios is incomplete, without quantifying the uncertainty associated with it. As the uncertainties play a significant role in such analysis, it is important to quantify them in order to develop productive management and decision-making capabilities. The objective of the present study is to model the GCM and scenario uncertainty in the Western Ghats (WG) region of South India using Reliability Ensemble Average (REA) for the estimation of stream flows. The analysis is carried out grid-wise, for monsoon (JJAS) rainfall in near future (2011–2040). The statistically downscaled (kernel regression) rainfall data at 0.25° resolution for 5 CMIP-5 GCMs CNRM, CCCMA, MPIMR, MPILR, and BNU for RCP 4.5 and 8.5 are used in the present study. The upper-middle and lower regions along with the elevation profile (lowland, midland, and ghats) of WG are chosen as a criterion for quantifying the uncertainty associated with GCM models and emission scenarios. Irrespective of the topography criteria, the uncertainty associated with GCM is found to be more significant than the scenario uncertainty. The GCM model shows a good correlation with the latitude profile in WG. The GCM MPILR and CCCMA have higher weightage in lower and middle regions as compared to the others while the GCM CNRM is less pronounced in the high elevation zones along the basin.

Climate change can cause various negative impacts on water resources system, ecosystem, etc. To deal with these effects, it is necessary to study the climate change. There are various ways to study climate change in which one of the way is the study of downscaling. Downscaling is the procedure in which prediction of information is done for local scale area from the available information of a large scale area. In the downscaling of climatic variables, General Circulation Model (GCM) plays an important role. GCM gives larger scale climatic variables. With the help of this downscaling, we can predict different climatic variables such as temperature, precipitation for future time period over the selected area. To perform this downscaling there are different ways, we can classify it as statistical downscaling and dynamical downscaling. In statistical downscaling, we can find relation between predictant and predictors and this statistical relation we use for the future prediction of the selected climatic variable. In dynamical downscaling, we use Regional Climatic Model (RCM), and with the help of this, we carry out downscaling procedure. In this study, statistical downscaling has studied for temperature parameter (Tmax and Tmin) by considering the basic equation given by Wilby in (Inter-research 10:163–178 [1]). The study area selected for this study is lower Godavari Sub-basin, Maharashtra State, India (Latitude: 19° 11′, Longitude: 76° 33′). In this study, in the first step, statistical downscaling has been done with the help of statistical downscaling model (SDSM) software by using HadCM3 GCM with A2a and B2a scenarios for temperature parameter for the future time period up to 2099. In second step, the statistical downscaling again performed by using basic equation given by Wilby (Inter-research 10:163–178 [1]) in excel which is named as “Excel Model.” Temperature values predicted up to 2099. These results are considered with three different series such as 2020s, 2050s, and 2080s. Downscaled results of temperature parameter by “SDSM” model and “Excel Model” were compared for future series. After study of these results, it is concluded that SDSM gives higher value of change in mean monthly daily value of Tmax and Tmin than that of “Excel Model.”

The daily rainfall projections derived using global climate model (GCMs) plays important role in assessment of the future climate over the study area. The current study represents the future daily rainfall states over Tapi basin using k-means clustering and Classification and Regression Tree (CART) modelling. The Tapi basin spreaded over 65,145 km2 and represented by 351 grids of 0.25º resolution. The rainfall data collected from India Meteorological Department (IMD) and General Circulation Model (GCM) outputs (i.e., CanESM2, CNRM-CM5, CanESM5, CNRM-CM6) of Coupled Model Intercomparison Project phase 6 CMIP6 (SSP245), and 5 CMIP5 (RCP4.5) and used in the projection of future daily rainfall states over study area. The projected daily rainfall states from CMIP5 and CMIP6 were compared for both aforementioned GCMs. The results based on the CMIP-5 model indicated that the almost dry rainfall state is increasing over the Tapi basin while results based on CMIP-6 model revealed increase in the medium rainfall state. Overall, the almost dry days are decreasing and high and medium state daily rainfall is increasing over the study area under CMIP6.

The northeastern region of India receives very high rainfall during the pre-monsoon and summer monsoon season, causing flood events, landslides, damage of crops, etc. The magnitude of these extreme events is increasing day by day. Therefore, the study of extreme precipitation has become very critical in predicting its consequences. Impacts of the changes in extreme events in the near as well as far future may be assessed by utilizing Intergovernmental Panel on Climate Change’s (IPCC) global climate models (GCMs). However, the applicability of these models varies from region to region and is highly dependent on the characteristics of the region. Hence, correlation amongst the datasets should be studied before utilizing these climate models in planning and management-related works. In the present work, an attempt is made to assess the suitability of three global climate model (GCM) data from the coupled model intercomparison project phase 5 (CMIP5) under the extreme carbon concentration scenario (RCP8.5) in capturing the extreme precipitations occurring in the northeastern region of India. For this purpose, 30-year observed precipitation data (1971–2000) of 2 different stations have been used. For assessing the extremes, 2 extreme precipitation indices (EPIs) have been utilized, viz. mean precipitation (MP), cumulative wet days (CWD), cumulative dry days (CDD), annual maximum 1-day precipitation (AMP1), annual maximum 5-day precipitation (AMP5), precipitation less than 1 mm (P1), precipitation less than 3 mm (P3), and precipitation more than 40 mm (P40). The results indicate fair correlations between the observed and climate model datasets. However, coming to a definite conclusion still needs further research by including more GCMs, which may give better results. Studies on annual rainfall over the region have shown no significant trends.

The increase in greenhouse gases has triggered substantial changes in the precipitation patterns and extremes both at local as well as global scale. Hence, a great interest has emerged in society for assessing the impacts of climate change under various plausible future scenarios. The north-eastern region of India receives a high amount of rainfall during the monsoon season which brings heavy floods to the region every year. The devastation caused by these annual flood events is of very high magnitude. Therefore, assessment of occurrences of these events in the coming years has become very crucial for proper water resources planning and management. In this study, an attempt is made for assessing the impact of climate change on precipitation extremes in the north-eastern region of India in future from 2006 to 2100. For this purpose, 30-year observed precipitation data (1971–2000) and three Global Climate Models (GCMs)–GFDL-CM3, GFDL-ESM2G and GFDL-ESM2M data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the extreme carbon concentration scenario (RCP8.5) has been used. For assessing the extremes, 8 extreme precipitation indices (EPIs) have been utilized, viz. mean precipitation (MP), cumulative wet days (CWD), cumulative dry days (CDD), annual maximum 1-day precipitation (AMP1), annual maximum 5-day precipitation (AMP5), precipitation less than 1 mm (P1), precipitation less than 3 mm (P3) and precipitation more than 40 mm (P40). The results have shown significant increase in the case of indices like MP, CWD and AMP1. These changes indicate the possibility of increase in extreme flood events and subsequently points towards future risks associated with them.

Water been a spatio-temporal variable resource have various uncertainties affecting the hydrological cycle that should be properly understood for management of water resources. Hydro-meteorological events like floods, droughts, cyclones, glacier melting, etc. are occurring frequently nowadays and are of great concern to hydrologist and climate scientists. These events are ignited due to combined effect of climate change, land use and land cover (LULC) changes and anthropogenic activities in the watershed. Various researchers, scientists and organizations are now working on climate change impact along with its mitigation and adaptation strategies to be adopted in regional and global level. This review paper aims in providing basic, essential and integrated information for hydrologist and beginners working in the field of climate change impact assessment on water resources. The terms such as emission scenarios, representative concentration pathways (RCPs), general circulation model (GCM), regional climate model (RCM), data downscaling, bias correction and uncertainties in climate modelling are discussed. The paper summarizes past literature and concludes with appropriate and most frequently used models and methods adopted in each step of climate change impact assessment. This will help researchers to perform acceptable assessment and give rational results that can help policy makers to take appropriate decisions regarding adaptation and mitigation strategies.

This study assesses the spatio-temporal changes in trends in extreme streamflow indices for nineteen stream gauging stations in the Godavari River basin (area ≈ 312,812 km2), India. The daily streamflow data were quality checked and thereafter adopted to derive the magnitude (total annual runoff, annual maximum 1-day and 5-day streamflows) and threshold (total streamflow exceeding the threshold corresponding to 95th and 99th percentile discharge and mean annual flood discharge) based extreme streamflow indices for each station. The non-parametric Pettit's test is adopted for change point detection, while the Spearman's Rho and Modified Mann–Kendall tests are executed to detect the significance of trends in the extreme streamflow indices. Further, the changes in the distributional characteristics of mean and extreme flows are analyzed using a non-parametric kernel density estimate and Mann–Whitney test by dividing the entire duration into three sub-periods (i.e., before 1980, during 1981–1995, and after 1995). The results indicated declining trends in total annual runoff and extreme streamflows at most stations across the basin. The significant changes in the distributional characteristics of streamflows are observed in the sub-period after 1995 compared to the other sub-periods. The reported decrease in total runoff would put additional stress on the freshwater ecosystem services, which are already stressed due to human interventions.

The current study analyses the changes in streamflow regimes using daily observed streamflow data at sixteen stream gauging stations in the Mahanadi River basin. In this study, non-parametric Pettit's test is used for detection of abrupt change, while Spearman's Rho and modified Mann–Kendall tests are used for trend assessment in annual total (QTOT) and maximum (Qx1day) streamflow series at a 5% significance level. Further, flow duration curves (FDCs) are derived at decadal time scales from daily streamflow data at each station to analyze the changes in streamflow regimes. The percentage change in the FDC quantiles representing low, moderate, high and peak flow conditions for the current period with respect to the baseline period are evaluated. The results indicate the presence of a significant change point in QTOT and Qx1day for the Bamnidhi and Kesinga stations. A contrasting pattern in the streamflow trends is evident between the northern (decreasing trend) and southern (increasing trend) parts of the Mahanadi basin, particularly upstream of the Hirakud reservoir. The changes in the decadal FDCs are classified into two broad categories based on the changes in magnitude and timing of streamflows; wherein seven stations have shown a notable transition from perennial to intermittent behavior. In contrast, no changes in the perennial nature of the stream were noticed at eight stations. The changes in the streamflow quantiles indicate an increase (decrease) in peak and high flow (dry and low flow) conditions in the region upstream of the Hirakud dam. Such contrasting changes in the streamflow characteristics would need careful consideration by the local authorities to resolve water management issues in the basin.

Changes in freshwater water availability have been noticed across several river basins over the years due to climatic and anthropogenic influences. The current study investigates the changes in streamflow characteristics over a semi-arid river basin in South India, viz., Cauvery River basin (area ≈ 81,155 km2), using daily streamflow data. The data has been thoroughly checked for consistency and completeness. Several streamflow indices representing the magnitude, frequency, and duration of peak and low flow conditions are derived from the daily data at fourteen stream gauging stations in the Cauvery basin. The change point and trend in the streamflow indices are analyzed by non-parametric Pettitt's and Modified Mann–Kendall and Spearman's rho tests, respectively. A significant change point in total annual runoff was reported for three stations, while the extreme flows did not exhibit any change point. The results indicated a decrease in the water availability, peak as well as low flows in the basin across most stations. Such a decrease in water availability and low flows would aggravate the water stress conditions in the basin and have adverse impacts on the stream ecology.

Based on daily streamflow and suspended sediment load data from five gauging stations located on Wainganga basin, spatial and temporal variations in seasonal streamflow and suspended sediment load from 1969 to 2018 are investigated. The Mann–Kendall (MK) test, Spearman’s rank correlation and Innovative trend technique (ITA) are applied to detect the trend in seasonal streamflow and suspended sediment load. The Sen’s slope test is used to find the trend magnitude. MK test indicates increasing trend in streamflow at Kumhari and Rajegaon stations, whereas Spearman’s rank correlation test and Innovative trend techniques report decreasing trend for both the stations. Similarly, MK test reported increasing trend in suspended sediment load at Rajegaon and Satrapur stations. But, Spearman’s rank correlation test and Innovative trend techniques report decreasing trend for both the stations. Compared to MK trend test, ITA technique showed good agreement with Spearman’s rank correlation.

Groundwater exploitation has been increasing day by day due to the population increase, and overexploitation advances to severe groundwater depletion. This paper examines the trend analysis and spatiotemporal disparity for groundwater levels (GWL) in three Mandals, i.e., Bheemunipatnam, Visakhapatnam rural, and Visakhapatnam urban areas in Visakhapatnam district for the period from 2008 to 2020. The data of GWL in piezometric wells and open wells are used to predict the GWL trend present in the study area. For spatial analysis, the interpolation technique of inverse distance weighted is applied to develop the GWL maps. Mann–Kendall test and Sen’s slope estimator were considered to predict the trend present in the GWL. From the exploration of the analysis, it is reflected that there is an increasing trend in GWL in some locations in the study area. Bheemunipatnam, Venkatapuram, Sivajipalem, Madhuravada, Chukkavanipalem, and YSR park are the significant places where groundwater is declining. Increasing depth trends to GWL, especially near the coast, can cause a decline in water quality and may lead to seawater intrusion.

The purpose of the current study is to identify the temporal trends in rainfall and temperature trends in Peninsular India's Pennar Basin. Rainfall data were taken for a period of 1971–2020 (50 years) at 0.25º × 0.25º and temperature data were taken for a period of 1996–2020 (25 years) at 1º × 1º resolution, respectively, from IMD, Pune, and are analyzed at annual, monsoon seasonal and daily peak time scales. Trends in the rainfall and temperature are detected by non-parametric modified Mann–Kendall (MMK) and Spearman’s correlation ratio (SCR) tests, and their change in magnitude with time is reported using Sen’s slope estimator. A graphical method ‘Innovative Trend analysis’ is also used to obtain the nature of the pattern for the respective time series data at different time scales. The annual rainfall data have shown a significantly positive trend across most of the grids. The monsoon seasonal rainfall data have shown a mix of both positive and negative trends, while the daily peak rainfall data have shown more of a negative trend. The maximum and minimum temperature data in all the time scales show either negative trend or no trend. These trends have been confirmed by non-parametric tests and a graphical method used in the study.

Trend detection of hydroclimatic variables has a significant impact in the context of climate change. Rainfall and temperature are the major factors that affect the entire hydrological cycle. This study has been carried out to find the trend of rainfall and temperature in the Damoh District of Madhya Pradesh. Trend analysis is carried out in this study from 1984 to 2012 for monthly, seasonal, and yearly time series. Mann–Kendall and Sen's slope tests were carried out to find the trend of both rainfall and temperature over the study area. There is falling trend in Pre-Monsoon rainfall (Z = − 0.26 and S = − 0.104) and Monsoon rainfall (Z = − 0.61 and S = − 5.404) and slightly rising trend in Post-Monsoon rainfall (Z = 0.04 and S = 0.000). There is a significant falling trend in the annual rainfall (Z = − 1.36 and S = − 12.124). There is a steep fall in the trend of annual rainfall after 1994. All three seasons show the falling trend for seasonal temperature: Pre-Monsoon temperature (Z = − 0.18 and S = − 0.007), Monsoon temperature (Z = − 1.44 and S = − 0.028), and Post-Monsoon temperature (Z = − 0.81 and S = − 0.018). The results of this study indicate that there is a significant impact of temperature on rainfall.

Rapid urbanization is a factor in climate change, which has negative effects on the environment. Change must occur as much as feasible in order to lessen the effects. Understanding the climatic conditions over years, if not decades, is necessary to analyze the shift. The temperature and rainfall patterns for a certain site are the most frequently researched climate change factors; however, it differs from place to place. As a result, it is essential to understand the spatiotemporal dynamics of meteorological variables in the context of a changing climate in order to identify effective adaptation strategies, particularly in nations where agriculture dominates the economy. As a result, this study looks at both long-term trends and short-term fluctuations in rainfall and temperature in the Gujarat city of Bharuch. Researchers examined data on precipitation and temperature from 1981 to 2020. The difficulties were looked at and analyzed using statistical trend analysis techniques like the Mann–Kendall test and Sen's slope estimator. The annual maximum and minimum temperatures have showed a growing trend, whereas the monsoon's maximum temperature has shown a falling trend, according to a thorough analysis of the statistics over the past 39 years. Throughout the monsoon season, rainfall is gradually increasing (Sen's slope = 0.76). The lowest temperature trend was modestly warming or growing over the study period, while the maximum temperature trend was declining (Sen's slope = − 0.13). The lowest temperature trend analysis result, however, is statistically significant at the 95 percent level of confidence, but the highest temperature trend analysis result is not.

Study on extremes of climate is necessary as they impact distressingly on life and economy. A necessity exists to know effect of change in climate on temperature for a proposed city. Thus, in the present study, the climate change impacts on temperature are assessed for the proposed Amaravati city, Andhra Pradesh state, India. Trends of temperature variations during the time period, 1981–2021, are found by considering temperature time series as daily data from POWER LaRC NASA portal. Mann–Kendall [M–K] test and Sen’s slope estimator are applied to perform trend analysis of temperature because of climate change. Various revised versions of Mann–Kendall (M–K) test and R are applied to analyze trend as the temperature time series is significantly autocorrelated. The trend analysis shows that the result of climate change on minimum temperature in the considered study area is about 1.12e − 02 ℃/year with increasing trend and is about 8.38e − 03 ℃/year with increasing trend for maximum temperature. The findings from the present study may aid to find temperature impacts because of climate change for any existing and/or proposed city for transforming as a smart city.

Both climatology and hydrology are involved in trend analysis to investigate climate change scenarios and improve the efficiency of climate impact studies. The long-term variation in precipitation, temperature, humidity, evaporation, wind speed, and other meteorological factors is referred to as climatic variability for an area. The purpose of this study was to investigate and estimate the relevance of the possible trend of variables such as rainfall in the Mindhola River Basin in the Bardoli Taluka of Gujarat's Surat District. The study's objective is to look at rainfall variability in the Mindhola River Basin for the next 30 years, from 1990 to 2020. Innovative trend analysis (ITA) for rainfall variability in the Mindhola River Basin was used to conduct a rainfall trend analysis on a monthly, seasonal, and annual basis in this study. The ITA approach could discover some trends that the MK test would miss. This test was used to determine the magnitude and direction of a current trend over time. This will give an understanding about rainfall trends or changes. This study also includes the drought analysis of rainfall using the Standardized Precipitation Index (SPI). In this study, SPI values and SPI plots are prepared in the RStudio software. The monthly, seasonal, and annual trends of precipitation for Bardoli region are in monotonic increasing trends or it is best fitted for the region. The drought study on the basis of rainfall suggests that at present, Bardoli region may not affected by the severe drought because it lies in near normal condition or moderately wet condition. This study helps policymakers, managers, and local authorities in taking protective measures for drought.

The present study aims to assess the drought trends, seasonal and annual rainfall patterns at multiple time scales using Mann–Kendall and Sen’s slope estimator over the Sirohi district of western Rajasthan, India, as this district is experiencing severe drought conditions due to a lack of annual rainfall and high variability. The Standardized Precipitation Index (SPI) is used to assess the drought pattern in the Sirohi district on monthly, seasonal, and annual time scales. The monthly time scales of SPI-3, SPI-6, SPI-9, and SPI-12, as well as the seasonal time scales of winter, pre-monsoon, southwest monsoon, and post-monsoon, are used to estimate drought using SPI for 102 years (1901–2002). The long-term series of monthly rainfall data from 33 stations from 1901 to 2021 (120 years) is used for this purpose. The spatial variation of positive and negative trends, as well as the findings of the trend analysis at different time scales, has been worked out. The drought pattern will be shown by analyzing SPI time series trends at each station in the study region. Drought trend analysis based on the SPI is proven to be more sensitive to different time scales. The findings show that rainfall in the study region is decreasing insignificantly throughout the winter, pre-monsoon, and s-w monsoon seasons. In addition, the result shows that all the time scales are capable of detecting rainfall regimes in the study region. This sort of drought regional trend analysis might aid the Sirohi district administration’s decision-makers in planning and managing existing water resources to fulfill the demands of agricultural and drinking water for the people in the study area.

Variation in precipitation amounts and distribution patterns leads to changes in general atmospheric circulation, cloud cover, surface albedo, and concentrations of air pollutants in the context of climatic variability. Industrial, residential, and agricultural water demands largely depend on rainfall. Even rainfall variability significantly affects people's livelihood. This study evaluates the temporal variation in rainfall for the Kota district of Rajasthan state in India. Eight rainfall monitoring stations were utilised to collect precipitation data for 40 years (1981–2020). Trend analysis has been performed for monthly, seasonal, and annual rainfall series with the help of Mann–Kendall (non-parametric) and linear regression (parametric) trend tests. Standardised rainfall anomaly and wetness index were estimated to determine the excess in total annual rainfall. The monthly distribution of precipitation is contrasted with the help of the precipitation concentration index. Both non-parametric and parametric trend tests estimate an increasing trend in precipitation for February, March, June, July, August, and September months, reflecting an increase in the total annual precipitation for the research area. The analysis of precipitation data shows a very high inter and intra variability in annual rainfall (C.V. = 169.45). A very high non-uniformity of rain is observed from the analysis of PCI. The maximum concentration of precipitation (~84.50) took place in monsoon months. Annual rainfall has significantly increased over the last four decades, indicating the need for proper rainwater management and utilisation plans to take maximum benefits shortly.

This paper presents some interesting results of trend analysis of long-term rainfall data series. The long-term precipitation data series for India the period 1871–2016, published in 2017 by Kothawale and Rajeevan, were used. Mann–Kendall, Sen’s Innovative Trend Analysis Method and Wavelet Decomposition were used to analyze the data. While MK test showed no trend in any of the series used, (Sen in J Hydrol Eng, 2012) method revealed trends in different ranges of the data. Further, the wavelet decomposed series helped identify the periods of high variabilities in the various components of data series. Additional insights provided by these methods could be of immense value in identifying the likely future behavior of the data and the implications for water management.

The objective of the research is to use spatio-temporal variability and trend analysis to examine how the rainfall pattern is changing in the Upper Bhima Sub-basin of Maharashtra, India. To achieve this objective, rainfall data was analyzed for monsoon and annual timescale using CHRS precipitation data. The study considered five agro-climatic zones viz. Western ghat zone, Transition zone I, Transition zone II, Water scarcity zone and Assured rainfall zone within the watershed. This work analyses the pattern, distribution and trend behavior of rainfall for the period from 1983 to 2018. Since more than 85% of precipitation occurs during monsoon, the analysis has been performed for monsoon season and annual average precipitation data. Statistical summary like the mean, standard deviation and coefficient of variation and inferential statistics like linear regression and standardized anomaly were utilized for the analysis. The Mann–Kendall non-parametric test is used to analyze the trend of precipitation in different agro-climatic zones and the Sen’s slope estimator is used to analyze magnitude of the trend. Spatial variation of the rainfall is analyzed and studied in geographical information system (GIS) environment. The effect of changing climate and regional environment on the spatial and temporal variation of rainfall is clearly noticeable in this study. Climate change strongly affects the agriculture activities where irrigation mainly depends on monsoon precipitation. Hence, policymakers and stakeholders should give importance to proper design and adopting area specific approaches to reduce the adverse effects on crop production at regional level. Rainwater harvesting and advancement in present irrigation facilities could be taken as best possible options in the areas having scarce and more inconsistent rainfall.

The Majuli Island, due to its geographical location and topography, is under constant risk of rainfall-driven flash floods in the Brahmaputra River, which cause colossal damages to life and properties almost yearly. In this view, a study on the temporal variations in precipitation at the local scale is necessary for mitigating such flood hazards. The annual and seasonal rainfall trends in the region of the upper Brahmaputra River basin up to Majuli River Island, India, are investigated in this study using a 0.25° × 0.25° resolution gridded precipitation dataset for the period 1979–2014 obtained from Climate Forecast System Reanalysis (CFSR) database. This study aims to find out the precipitation trend’s direction and magnitude and the precipitation time series trend using the non-parametric Mann–Kendall trend test and Sen’s slope estimator. The annual precipitation trend shows no negative or positive statistically significant trend in the study area. In contrast, the winter season has shown an increasing trend (which is not statistically significant at a 0.05 level of significance. Interestingly the monsoon season revealed a significant trend with Sen’s slope values of 1.78 mm/year from 1998 to 2014. This study’s findings may benefit other researchers and play a significant role in flood control and flood management in this area.

The drastic change in precipitation as a result of climate change is altering the pattern of stream flows and demands, as well as the spatial and temporal distribution of runoff, soil moisture, and groundwater reserves leading to hazardous events like landslides, floods, drought, etc. Trend analysis has proved to be a potential tool by providing useful information on the possibility of changes of rainfall trends in future. The current study uses the Statistical Downscaling Model-Decision Centric (SDSM-DC) and GIS to evaluate the imminent regional and temporal variations in rainfall caused by various climatic conditions. The study area considered for this study is central zone of Himachal Pradesh. To evaluate rainfall patterns and their reactions to climate variability, seven rainfall stations in the study area were selected. The study concludes that the changing climate has resulted in a significant change in precipitation patterns when compared to previous climatic conditions. Numerous natural hazards, including floods, landslides, cloudbursts, etc., are occurring in the area as a result of the rate of change in seasonal and yearly precipitation over the years as indicated by precipitation trends. Changes in the pattern and an increase in pre-monsoon precipitation are additional significant impacts of climate change.

Adequate knowledge of the temporal variations of extreme rainfall events is key to modeling and forecasting extreme hydrologic events (e.g., floods) and for undertaking water-related emergency measures and management strategies. In this study, the temporal variations of extreme hourly rainfall events are examined using trend analysis on rainfall indices. The extreme indices are developed based on hourly monsoon (July to September) rainfall data (from 2000 to 2010) collected from five rain gauges in the Savitri River basin, Maharashtra, India. Commonly used Mann-Kendall and Sen’s slope tests are employed in this study to identify the trends. The indices are representing the total rainy hours, the maximum value of 1-h rainfall, and the hourly distribution of rainfall in a day. To obtain the hourly distribution in a day, a day is divided into six time periods, each with a length of four hours (early morning from 02:00 a.m. to 06:00 a.m., morning from 06:00 a.m. to 10:00 a.m., afternoon from 10:00 a.m. to 02:00 p.m., evening from 02:00 p.m. to 06:00 p.m., late evening from 06:00 p.m. to 10:00 p.m., and night from 10:00 p.m. to 02:00 a.m.). The hourly rainfall trend analysis shows that the total rainy hours and the maximum hourly rainfall show an increasing trend during July and September. The trend of rainfall occurring from early morning to night increases during September. However, during July, rainfall occurring from morning to night shows an increasing trend for most of the stations. But during August, the hourly rainfall occurring in a day is showing no clear pattern in the basin.

Analysing the spatiotemporal distribution of precipitation patterns and their effects on flash floods in moist, humid environments is crucial for determining the situation and offering suitable adaptation strategies. In addition to trend analysis and homogeneity tests, the climate variability under expected flash floods was analysed using the coefficient of variation, number of wet days, precipitation concentration index, and predicted maximum precipitation, while the flash flood magnitude index and flood magnitude ratio were used to determine streamflow episodes. A case study basin for humid climatic conditions has been proposed as the Swarna River basin in the western region of Udupi, which suffers high levels of climate fluctuation due to flash floods. Precipitation patterns were increasing from the headwaters of the Swarna River basin to the downstream. According to the findings of the homogeneity tests carried out by Pettitt, SNHT, Buishand, and Von Neumann, there is no appreciable variation between the pre- and post-alteration points in the mean of the precipitation.

Climate change is influencing and will continue to affect essential natural resources, such as water. Its effect on agriculture is usually considered as one of the most serious challenges in water resource management. In this study, the bias-corrected future climate data from the global climate model (GCM), ACCESS-ESM1.5, has been used to estimate the monthly crop water requirement for paddy in the Seonath sub-basin, Chhattisgarh State, India. The bias-corrected outputs of the ACCESS-ESM1.5 GCM model and projection of the future temperature and rainfall were done for two Shared Socioeconomic Pathway (SSP) scenarios, namely the SSP370 and SSP 585. Further, the future crop water requirement was calculated for the SSP370 and SSP585 scenarios using the CROPWAT model for the period of 2015–2099 with three future periods (FP) 2015–2045, 2046–2075, and 2076–2099. The reference evapotranspiration ETo was calculated using ETo calculator given by FAO. The results indicate rising in temperature and rainfall over future periods when compared to the base period (1981–2014). The annual average temperature has been projected to increase by 2.07 °C and 2.61 °C from 2015 to 2099, when compared to the base period for the SSP 370 and SSP 585 scenarios, respectively. The annual average rainfall has been projected to increase from 1207.7 mm in the base period to 1441.1 mm and 1400.1 mm for SSP 370 and SSP 585 scenarios. The average reference evapotranspiration (ETo) values showed an increase from 4.54 mm/day to 4.61 mm/day and 4.72 mm/day for SSP 370 and SSP 585 scenarios, respectively. The average annual crop water requirements (CWRs) showed an increase of 17.01% and 18.45% for the SSP 370 and SSP 585 scenarios. For optimal irrigation planning, projected deviation in required values can be used in the culturable command area of the Seonath sub-basin.

The FAO CROPWAT tool was utilised to estimate the future reference evapotranspiration and maize crop evapotranspiration for years 2030, 2060 and 2090 under RCP scenarios 2.6 and 8.5 for Sehore district of Madhya Pradesh, India. The statistically downscaled GCM CanESM2 climate model projections were used as input to the CROPWAT for prediction of future reference and crop evapotranspiration data. The values of constants viz. Kcinitial, Kcmid and Kcend were fixed to 0.5, 1.15 and 0.6, respectively, as per the FAO-56 for maize crop. In the Sehore region, the ET0 and ETc values for RCP 2.6 were calculated to be in range of (400.5–512) mm and (430.5–448.4) mm, respectively, during years 2030, 2060 and 2090, while the ET0 and ETc values for RCP 8.5 were found out to be (466–740.5) mm and (440.5–492.5) mm, respectively, during years 2030, 2060 and 2090, respectively. The RCP scenario 8.5 is the worst case scenario in which the reference evapotranspiration as well as crop water requirement for maize crop has been showing high demands of water. The results of this work can be utilised for proper irrigation scheduling for the maize crop and thereby reducing the agricultural risks due to climate change.

Climate can be demarcated as the weather condition, which has been measured over a long period of time. Climate change studies reveal that it will affect the water requirement of different crops. Consider this as a major effect on agriculture, a study was taken to know the climate change impact on the water requirement of crops cultivated in the area of Amreli district, Gujarat. For this study, meteorological data (maximum temperature, minimum temperature, precipitation, relative humidity, wind velocity, and solar radiation) of periods 2001–2020 are used. Future meteorological parameters were predicted for the period of 2031–2090 using a ClimGen weather generator. Results of generated future climatological data are showing increasing and decreasing trend, but overall climatic data shows increasing trend for the future years till 2090. These results show that climatological parameters are changing in upcoming years. Crop evapotranspiration (ETc) was determined with the help of CROPWAT 8.0 using daily climatological parameters for generated weather data, and then, the water requirement of different crops was determined. The clear effect of climatological parameters on the water requirement crops of Rabi and Kharif was identified in results. To meet the increasing demand of water, water resources should be increased by increase in water level and doing water conservation efficiently. Farmers should also be motivated to use different methods for irrigation as sprinkler and drip irrigation systems according to the requirement of crops instead of using flooding methods.

One of the most important hydrologic tools used in design of hydraulic and water resource projects and flood control structures in urban areas by hydraulic engineers is the intensity–duration–frequency (IDF). IDF curves are the representation of relationship between duration, intensity and return period (frequency) of rainfall, which are obtained from a series of analysis of observed rainfall data. In most part of India, short duration rainfall is scarce and only daily rainfall data are available. In such case, it is required to convert the daily rainfall data into hourly using India Metrological Department (IMD) formulas. Assessing the adverse effects of climate change and adapting to them is one way to reduce vulnerability caused, specifically confronting city floods. Since, the rainfall IDF curves are used in the design of water resources projects, in order to have safe and economically stable hydraulic structures. In the present study, the rainfall data of 119 years (1901–2020) were collected from IMD. The aim of this study is to obtain IDF curves having durations of 15 min, 30 min, 45 min, 1 h, 2 h and 3 h for the Surat city. The maximum rainfall intensity curves of different durations like 15 min, 30 min, 45 min, 1 h, 2 h and 3 h are derived from daily rainfall data using IMD formula. It was found that rainfall intensity of 60 mm/h can be used in the design of water resources project and an equation is obtained which can be used to compute daily maximum intensity at any given return period. The developed curves are useful for planning and design of urban storm water and water conservation measures for the Surat city.

Calculating rainfall intensity–duration–frequency curve (IDF curve) is a pre-requisite in water resource engineering for the development, management and planning of hydraulic infrastructures such as barrages, spillways and for various engineering projects against design floods. The objective of this study is to develop an IDF curve relationship for Tezpur, Assam for a short duration of 15 years which will be helpful for the design of drainage work like storm sewers, culverts, etc. In this study, the rainfall data of 15 years, i.e., from 2007 to 2021 has been collected from the Water Resource Department of Tezpur. Firstly, the peak annual daily rainfall was found out and then, Gumbel, log-normal and normal distributions have been used to calculate probable maximum rainfall intensity for a return period of 2, 10, 25, 50, 75 and 100 years from the maximum annual rainfall. The other objective of this study is to compare the IDF curves derive from these three distributions and is to find the best IDF curve for Tezpur. For Gumbel’s analysis, the value of reduced mean and reduced standard deviation has been taken as 0.5128 and 1.0206, respectively, for 15 numbers of sample sizes. The data trend illustrates that as duration increases, the intensity value declines and that rainfall increases with length for a particular duration as return period increases, intensity tends to increase. It is also observed that log-normal distribution gives the lowest variation for different return periods. The derived IDF curve model can be further used for flood forecasting.

Assessment of extreme rainfall is needed to be carried out to prevent floods and droughts and applied to the studies on water resources projects. This can be done by extreme value analysis (EVA) that consists of fitting probability distributions to the annual 1-day maximum rainfall (AMR) series. This paper presents a study on EVA of rainfall for Pune and Vadgaon Maval sites using method of moments, maximum likelihood method and L-moments (LMO) estimators of log normal, extreme value type-1 (EV1), generalized extreme value (GEV) and generalized Pareto distributions. The evaluation of probability distributions adopted in EVA is made by goodness-of-fit (viz., Chi-square and Kolmogorov–Smirnov) tests, D-index and fitted curves of the estimated rainfall. On the basis of the results obtained from the study, it is found that EV1 (LMO) is better suited for rainfall estimation for Pune whereas GEV (LMO) for Vadgaon Maval.

Most of the existing studies on the long-term correlation between fog and climate variables have considered fog visibility and fog duration data for analysis. The use of fog duration in terms of number of fog days has suffered from the lack of a universally accepted definition of a fog day. Additionally, fog duration fails to quantify fog intensity. While fog visibility data can be used to quantify fog intensity for short-term analysis, their non-additive nature and high variability over a short period of time render them unsuitable for long-term analysis. In this study, a fog index based on the extinction of light intensity governed by Beer’s Law is developed. The index is defined as the ratio of the energy attenuated by a fog event with a given visibility to the energy attenuated by the same fog event with the visibility assumed to be zero. The additive nature of the index allows quantification of the fog intensity over any time window. The index was found to be bounded and independent of the choice of unit systems. Furthermore, the index can be employed for studying the relationship between long-term fog conditions and climate variables such as soil moisture, sea-level and surface pressure, near surface and sea-surface temperature. The developed index is applied to understand fog phenomenon in north India and explore its linkages with local meteorological parameters at seasonal scale.

Data on fine-resolution precipitation are necessary for the design of hydraulic structures, the estimation of rainfall erosivity, and the assessment of urban hydrological climate change. In Ethiopia, precipitation data are typically available on a daily or monthly basis. The number of weather stations equipped with automatic gauges for measuring precipitation rates is small. Therefore, a scientific approach is required to disaggregate existing coarser resolution rainfall data to finer resolution. It is also necessary to regionalize the developed disaggregation models to simulate sub-hourly rainfall data across a wide region. The multiplicative cascade models appear to be interesting instruments for simulating fine-resolution rainfall because of its connection to the multifractal theory. Thus, the study aims to regionalize random cascade model (RCM) parameters and analyze their potential and limitations. The newly developed regional models are tested on rainfall data of Awash River Basin in Ethiopia. The models demonstrated acceptable accuracy in simulating the sub-hourly rainfall, demonstrating the potential of parameter regionalization.