Climate Change Impacts on Water Resources

Information about climate change on the scale of basin is very crucial for development, planning and utilization of water. The Tapi basin is climatically responsive. One of the most crucial parameters of the climate is temperature. Alterations in climate at regional scales influence rudimentary aspects of our life. It is important for policymakers that individual seasonal alterations are illuminated. Therefore, in the current work, trend detection analysis of regional seasonal (four seasons) mean temperature (Tmean) series (1971–2004) appertaining to the Tapi basin is conducted. Evaluation of trend magnitude is performed by using Sen’s slope (SS) test. Identification of dependence of data is performed by using correlogram. Assessment of trend significance for independent and dependent data is performed by using Mann–Kendall (MK) test and block bootstrapping approach with MK test (MKBBS test), respectively. The results showed the presence of statistically significant increasing trend only in regional winter Tmean series of the basin.

Soil and water conservation measures are necessary to know the sequence of dry and wet periods along with the onset and withdrawal of rainy season for successful agricultural management and planning. Daily observed rainfall (1998–2017) are analysed to compare and contrast the large-scale duration characteristics of rainfall over semi-arid region. This paper analyses the trend of rain spell frequency in terms of duration by using standard statistical methods. The analysis has been carried out at four locations, namely Kadegaon, Karad, Vaduj and Vita, in and nearby the Nandani river basin. In the Nandani river basin, the duration of dry spell varies from 69 to 119 days, and wet spell varies from 34 to 87 days. In this study, the rain spells were classified into low, medium, high, very high and extreme rain spells. The important results have been found through analysis of this study. These results are: the spells were examined only for the monsoon season (June–October) because all the above categories of rain spells occur only in this monsoon season. The rain spells help to coordinate various activities, like water effect on crop growth, supplementary irrigation, water release schedule and so on. The maximum dry spell (DS) was 119 days at Vaduj in 2003 and minimum 69 days at Kadegaon in 2006. The maximum wet spell (WS) was 87 days at Karad in 2005 and minimum 34 days at Vaduj in 2003.

India is an agrarian country where more than 70% of population depend largely on agriculture and agro-related business. The projected scenarios of precipitation from climate models predict decrease in some region and increase in others, albeit with large uncertainty in most of the places. The Chhattisgarh state which is called rice bowl of India has number of water resources projects where climate change can change crop water requirement. The present study has been carried out in the command of Tandula reservoir using statistical downscaling of climatic parameters for computation of crop water requirement using RCP2.6, RCP4.5 and RCP8.5 scenarios of coupled model inter-comparison project 5 (CMIP5). For calibration and validation, 26 NCEP rescaled climatic variables from 1971 to 2003 have been used with minimum and maximum temperature and rainfall for concurrent period. The percentage reduction and k-fold cross-validation techniques have been used for selection of best-suited climatic parameters for statistical downscaling and used to generate multiple ensembles of temperature and rainfall for three future assessment periods, namely near century period as FP-1 (2020–2035), mid-century period as FP-2 (2046–2064) and far century period as FP-3 (2081–2099). The projected multiple series of climatic variables were further used to compute evapotranspiration using CLIMWAT and then crop water requirement in the command and compared with corresponding requirement during the base period (BP: 1971–2014). The results of analysis suggested that the mean monthly maximum temperature showed a rising trend in all the months, while significant increase of minimum temperature during winter and rainy season. The average crop water requirement for designed cropping pattern of 82089 ha of kharif paddy during base period under present overall efficiency of 51% during base period may be about 473.7 Mm3 and will increase to 479.0 Mm3 during near century period (2020–2035), 492.7 Mm3 during mid-century period and reduce to 387.9 Mm3 during far century period (2018–2099). The mid-century period may be the most critical among all and it is recommended to develop adaptation measures to combat climate change especially in mid-century period.

The predicted climate change may introduce more stress and need various adaptation strategies to be implemented. Climate change in the Indian context has caused an increase in surface temperature at the rate of 0.2 ℃ per decade from 1971 to 2007. The present work is done to assess the impact of climate change through review of literatures pertaining to it.

The impact of climate change on water resource is a primary concern. The focus of this review was to recognize how climate change affects the water resource of Ethiopia and to identify gaps that can be addressed through research. The result revealed that the peoples living in the country already suffered from the extreme events of climate change, like drought and flood. The nature and magnitude of how global change will affect the Ethiopian water resource is not yet adequately understood. Besides, the studies of climate change impacts on water resources in Ethiopia are concentrated in northern part of the country, based on old climate change emission scenarios and limited number of global climate models (GCM). Making a conclusion based on a single GCM output may not give a clear representation of the future changes, and it is most likely will mislead the decision makers and policy developers. A comprehensive study is highly needed, and the issue has to be addressed at a scale relevant to decision-making based on multiple GCMs under new climate change emission scenarios for better understanding of the potential impacts, informed decision-making, and effectively respond and adapt to projected changes, otherwise the consequences becoming awful.

The study aims to analyze spatio-temporal variations in India Meteorological Department (IMD) gauge-based gridded rainfall data over Godavari river basin, India. A total of 502 rainfall grid stations which covers the entire Godavari basin and 53 years long-term daily rainfall data from 1961 to 2013 is used in the present study. The study focused on trend analysis of individual rainfall grid stations in Godavari basin rather than averaged at sub-basin level to check the internal characteristics of IMD-gridded rainfall. Trend analysis is carried out for three rainfall time scales, namely daily, monthly and annual. As this region receives major contribution of rainfall during South-west monsoon period from June to September, the trend analysis is separately carried out for monsoon time series. To overcome the assumption of normality of input rainfall data, a widely used nonparametric trend analysis test, Mann–Kendall (MK), is adopted in the present study. From the results of trend analysis, a decreasing trend is observed at majority of the stations in all sub-basins of Godavari both for daily and monsoon daily time series data. However, monthly rainfall is observed to be free from trend except for very few stations scattered all over the basin. Further, the annual rainfall, monsoon monthly rainfall and monsoon total rainfall showed an increasing trend of the rainfall stations at sub-basins of Godavari Upper, eastern portion of the Indravati and Godavari Lower. It is worth to mention that all increasing rainfall stations observed at three time scales discussed above are located either at foothills of Western Ghats or foothills of Eastern Ghats along the Coast of Bay of Bengal. Therefore, the present study analysis reveals that except in the hill shadow regions and coastal areas, IMD grid rainfall is free from trend in Godavari river basin during the study period.

There are different methods of forecasting, such as time series analysis, genetic programming regression analysis, wavelet transform, support vector machines, etc. In the present study, forecasting of reference evapotranspiration has been attempted for Nagpur region, Maharashtra State, India so that it can be used to take decisions for reservoir planning. The past meteorological information, such as relative humidity, temperature, wind velocity, solar rays were used for the calculation of reference evapotranspiration (ET0) value by using the Penman-Monteith method, which is recommended as the standard method for determining ET0 by Food and Agricultural Organization (FAO). The data has been trained in artificial neural network (ANN) and the two learning techniques, namely Levenberg–Marquardt algorithm and quasi-Newton algorithm, were compared with the untrained data. Sum square error (SSE), mean square error (MSE) and regression value (R2) have been used as criteria for the performance assessment. Levenberg–Marquardt algorithm is found to be better among the two, for the study area under consideration.

The focus of this chapter is to explore the benefits of time-varying downscaling model (TVDM) to preserve the characteristics of extreme rainfall in the downscaled products. The TVDM was developed based on the hypothesis that the relationship between causal and target variables is non-stationary in the context of climate change. The TVDM utilizes the skill of the Bayesian approach in updating its parameters and thus, incorporates the time-varying relationship between the causal variables and the target variable to be downscaled. In this study, the potential of the TVDM to replicate the extremes in the downscaled precipitation field is explored. The entire Indian landmass is considered as the study area. The output of TVDM is compared with the existing statistical downscaling model (SDSM) and regional climate model (RCM) outputs procured from the coordinated regional climate downscaling experiment (CORDEX). It may be noted that the SDSM assumes the causal–target relationship is time-invariant, whereas the TVDM considers the time-varying relationship since it is based on the non-stationarity assumption. Causal variables from the coupled Hadley centre Global Environmental Model (version-2)—Earth System model (HadGEM2-ES) general circulation model are considered as input to the TVDM and SDSM. Downscaled precipitation field is compared with (i) 0.25° × 0.25° gridded observed precipitation data, procured from the India Meteorological Department (IMD), (ii) output of SDSM at selected locations, and (iii) RCM downscaled precipitation field obtained from CORDEX. Observed precipitation field is found to best correspond to the TVDM downscaled precipitation field as compared to CORDEX and SDSM outputs. This analysis reveals that the TVDM is a promising downscaling technique for the assessment of extreme values. Overall, the TVDM is found to be potential in a changing climate due to its time-varying characteristics considering the non-stationarity issue that exists in the relationship between the causal–target variables.

Globally rapid population, urbanization and industrialization are posing problems such as changes in the land use pattern and climate and contamination of surface waters. Land use changes have potentially large impacts on river water quality, e.g., adaptation of cropland to urban area, crop rotations, establishment of new industries, commercial and residential areas etc. Unplanned developments associated with land use change have notably influenced the river water quality. Further due to climatic changeability such as increased temperature, rainfall change affects surface water quality and aquatic environment. The purpose of this study is to analyze river water quality related to spatial land use in river basin using GIS analysis. To manage river water quality in the most effective and efficient way, the cause and effect relationship of the river basin must be identified. Water quality indices are an effective and easy assessment method to analyze the effect of land use and climate change on stream water quality. The present study focused on development of improved surface water quality indices considering various water quality parameters affected by land use–land cover and climate change scenarios. These indices will help to classify the river and will provide simple interpretation of the monitoring data to aid citizens and managers for decision making to improve river water quality.

Reliable estimation of extreme precipitation is of utmost importance to ensure the structural safety of major civil engineering infrastructures (e.g., urban storm drainage network, spillways of major water control structures, etc.). Conventional practice for design of those infrastructures includes determination of magnitude and frequency of extreme precipitation events. The extreme events usually lie in the tail part of the probability distribution of daily precipitation data. Based on the nature of the tails, different distributions are classified into two categories: heavy-tailed and light-tailed distributions. The tails of heavy-tailed distributions tend to approach zero less rapidly when compared to those of light-tailed distribution (which are having exponential tails). Heavier tails imply more frequency of extreme precipitation events as compared to lighter tails. There is a dearth of attempts to assess the tail behavior of probability distributions of daily precipitation data over India. In this paper, we compare the tails of empirical distributions of daily precipitation data and tails of fitted theoretical distributions (e.g., pareto, lognormal, Weibull and gamma distributions). Gridded precipitation data prepared by the India Meteorological Department (IMD) having a resolution of 0.25° was used for the analysis. The results indicated that heavy-tailed distributions describe the observed precipitation extremes more effectively than the light-tailed distributions. This result shows that the light-tailed distributions, which are widely adopted for the determination of extreme precipitation events, are inadequate to capture the tail behavior of precipitation data accurately. This study reveals the importance of considering heavy-tailed distributions for reliable estimation of extreme precipitation events required for the design of major civil engineering infrastructure. The results from the study can find use in the design of urban storm drainage network, spillways of major water control structures.

Hydroclimatic systems consist of various interacting processes/components. The variables influencing a process and the interaction among the variables is dynamic in nature. Streamflow is one such component of the hydrologic cycle influenced by a large pool of several influencing hydroclimatic variables in different complex ways. There is a plethora of models for streamflow prediction; however, temporal variation in cause–effect relationship may lead to a decaying performance of the developed model. Existing prediction models are mostly stationary in nature or at most update the model parameters retaining the same predictors. This study demonstrates a recently developed concept of time-varying models using graphical modeling (GM) approach to capture the temporal variation in streamflow modeling considering the Upper Mahanadi river basin. This approach provides a detailed conditional independence structure and quantifies the association of the predictors with the predictand that can be utilized for the development of prediction models. The time-varying, GM-based approach shows the need to update the set of input variables for streamflow. The performance of the time-varying model is contrasted with a time-invariant model and support vector regression (SVR)-based models, well-established in the field of hydroclimatology. For the considered study area, the proposed model is found to capture the anomaly in streamflow variation and provides satisfactory prediction performance, at a lead time of one month, for normal as well as extreme flow events. Updating the set of potential predictors and the corresponding model parameters help to improve the predictability of monthly streamflow.

The mechanism of sediment transport is mainly governed by surface water flow within the river basins. Excessive sediment transport plays an important role in reducing the carrying capacity of channel networks and storage capacity of reservoirs/dams. An important task for most of the hydrologists is to determine the reliable stream flow estimate which causes majority of the sediment transport within river basins/stream channels. The transport effectiveness of a stream flow event of particular magnitude in carrying a sediment load is defined as the product of the effect of that event (i.e. sediment transport rate corresponding to the stream flow event) and the frequency with which the event occurs. This approach is famously known as magnitude frequency analysis (MFA). MFA has been widely used to compute “effective discharge” which is considered as the stream flow that is responsible for transportation of majority of the sediments from a river basin or catchment over a long period of time. In MFA, the stream flow at a location is assumed to follow a continuous probability distribution (e.g., normal, lognormal, exponential, gamma, generalized pareto and Poisson) whereas the sediment transport is described by a power law function between stream flow and sediment rate. Subsequently, a transport effectiveness function is constructed by taking product of stream flow distribution with power law function. Finally, the effective discharge can be obtained by maximizing the transport effectiveness function with respect to stream flow. In this paper, effective discharge estimates were determined for 12 stream gauges in Cauvery river basin by fitting appropriate continuous probability distributions (normal, lognormal, exponential, gamma, generalized pareto and Poisson) and assuming power law relationship for sediment transport. Kolmogorov–Smirnov test (KS test) at 1% significance level was tested for fitting probability distributions to daily stream flow data at each of the gauges. Results indicated that all of the above distributions failed to fit stream flow data at all the gauges. However, following the previous literature, the daily stream flow data at every gauge was assumed to follow lognormal distribution and the corresponding effective discharge was determined. Further, recurrence interval was calculated for the effective discharge estimate at the each of the gauge. The results from this study can find use in effective planning and functioning of dams/reservoirs.

Evapotranspiration accounts for a major part of the water budget. Estimation of the evapotranspiration is complex because it involves too many parameters. Over many decades various methodologies have been introduced and applied to the wide range of climatic conditions to predict the water loss from vegetation. Among them FAO-56 Penman-Monteith equation is known to be the widely accepted and it has been validated to the wide range of vegetation and climatic conditions across the globe. Agroforestry is an important and one of the high water-demanding agricultural practices. Due to the presence of multiple crops, the rate of evapotranspiration is generally expected to be high. Therefore, it is important to understand the evaporative demand in the agroforestries to reduce the water consumption in irrigation planning. Due to the lack of measured meteorological data with many parameters, it is important to compare the performances of the methods which are less data-intensive in nature. For this purpose, alternatively Priestley-Taylor, Hargreaves and Turc methods have been used and compared with Penman-Monteith equation. A study carried out in Seegodu watershed consists of coffee plantation, which is one of the major practices in agroforestries to compare the estimates of different methods. The results showed that the Hargreaves and Turc method predicts better results on a temporal scale with only temperature data.

In the present study, the influence of maximum temperature on rainfall events at the daily scale is explored at four locations—Chennai, Kolkata, Mumbai and New Delhi, which represent different climatic regions across India. The widely accepted binning technique is used to pair the daily temperature and rainfall (95th and 50th percentile). Two different data products—global gridded data (1979–2017) and station-based global dataset (1991–2017) are used and the scaling relationships are compared alongside the Clausius-Clapeyron scaling of 6.8% K−1. Results indicate negative scaling, ranging from −0.4 to 22% per degree Celsius (for 95th percentile rainfall) and −3 to −41% per degree Celsius (for 50th percentile rainfall) at all studied locations. It is also noticed that rainy days (> 0.3 mm rainfall) are mostly observed when temperature is above 30 °C in Kolkata and New Delhi, but in Chennai and Mumbai, rainy days are fairly common even when the temperature is below 30 °C. The evolution of the scaling relationship is studied considering four sequential time periods: 1979–1988, 1989–1998, 1999–2008 and 2009–2017. All the locations indicate negative scaling for all decades except New Delhi which indicates a very mild positive scaling during 1979–1988. Moreover, the relationship of daily rainfall with preceding days’ maximum temperature is also studied for Kolkata and Chennai, with the former showing positive scaling at three-day lag and beyond.

Climate change is a global phenomenon which is mainly caused due to anthropogenic activities. Land use-land cover (LULC) changes occur in river basins due to human interventions and change in climate. The change in LULC causes change in evapotranspiration and contributes to further climatic change in the area. Hydrologic extremes occur in a river basin due to the combined effect of climatic extremes and LULC changes. Both climate and LULC change scenarios are usually used for estimating the variation in the hydrological response of a watershed. Hence LULC change detection and estimation is very essential for any watershed prior to the estimation of hydrologic response variation from it. The influence of LULC change on the rainfall variability of a region was studied taking the Bharathapuzha river basin which is spread over Tamil Nadu and Kerala. Bharathapuzha river is the second longest river in Kerala. Drastic changes have taken place in the river basin which caused the degradation of the river. Hence the study of the effect of LULC change in the study region on the rainfall trend is of great significance. The LULC change in the area was estimated both spatially and temporally using remote sensing-based techniques. The LANDSAT1 and LANDSAT8 satellite images for the years 1973 and 2018 were used for determining the land cover change in the study region. Necessary corrections were applied on the mosaicked images and these satellite images were classified using supervised classification. The land use change analysis was done on the classified images and the land use classes used were waterbodies, mixed vegetation, agricultural field, cultivable land, dense trees, barren land/rocky outcrop, built-up area and sandy area. It is seen that there has been severe reduction in dense trees and large increase in barren land and built-up areas over time. The rainfall trend in the study region was estimated using the data obtained from India Meteorological Department. The study indicates that LULC change is associated to the decrease in the rainfall over the area. It denotes the necessity of controlling the LULC changes in the basin for preventing further deterioration of the basin. The rainfall trend analysis also helps to devise proper management plans for the basin.

The rapid development of operational oceanography in recent years has led to improved access to real-time data and products generated from in situ and satellite observations as well as ocean modelling. Operational oceanography is like weather monitoring and forecasting for the ocean. It can provide estimates of essential ocean variables, for example, sea level, temperature and currents. Ocean observations are required in real time and near real time (within a few days or minutes of collection) and sourced from various national and international programs. A number of large open data sets and metadata from observations (in situ and remote sensing) and from model outputs exist which have application in real-time problem solutions in coastal environments. However, these resources have not been used optimally due to limited capacities, and lack of information on their availability and applicability. In this study attempt has been made to discuss available information to improve safety of life at sea, help create wealth and assist in the security and protection of the marine environment through good measuring networks, and systems for making data available swiftly. Timely prediction of storms and other unfavourable weather can be done by having knowledge of meteorological conditions, and how they are developing above the oceans. Outputs can be used to generate data products, applications and services through national authorities, as well as in some cases through other organizations such as metocean service providers and environmental consultants and for these measurements to be of use, good data management, quality control and fast data availability are essential.

Projections of future climate from various climate models indicate that global temperatures are continuously rising. This, in turn, may result in a significant rise in the water levels in the oceans, adversely impacting coastal aquifers. Apart from temperature, some other climatic variables also influence sea level. For better management of coastal aquifers, it is necessary to predict the sea level with a reasonable degree of accuracy. The repercussions of projected climate change on sea level rise can be investigated by projecting future sea level values for different representative concentration pathways (RCPs) using global climate models (GCMs). GCMs are run at a coarser scale; hence for regional-scale studies these projections have to be downscaled before being input into hydrologic models. This paper presents the details and results of a study in which support vector regression (SVR) and regression tree (RT) techniques were applied for statistical downscaling of sea level using climatic variables. The results of both these techniques were compared. It was observed that the performance of the SVR model was better than that of the RT technique.

World population is growing continuously that lead to increase in demand for food. Despite of increase in land area under agriculture production since last decades, one-seventh of people today still do not get sufficient nutrients in their diet. Climate change is well recognized by the scientific community and it further threats the food security for the society. Different parts of the world undergo different level of warming, so it becomes more important to know the regional impact of climate change on the food production. This study aims to find out the impact of climate change on the crop yield of wheat, sorghum, and millet in the upper Godavari river sub-basin using H08 hydrological model. For future simulations, the bias-corrected climate data were taken from five CMIP5 GCMs for RCP4.5 climate scenario from Inter-Sectoral Impact Model Intercomparison Fast Track (ISIMIP-FT) data archive. Our results show that under RCP4.5, wheat yield is projected to decrease by ~37.0% by the end of mid-future (2040–2059) period with respect to present (1980–2001) period. The wheat yield is projected to decrease by ~49.23% by the end of twenty-first century (2080–2099) under RCP4.5. No significant change was observed for the yield of millet and sorghum under RCP4.5. So, sorghum and millet were found to be the crops having no or less impact due to global warming, and wheat production was found to be affected badly due to change in climate. This study demonstrates the impact of climate change on the yield of major crops in the upper Godavari river sub-basin due to temperature stress and water stress in the basin, which is very useful to implement the adaptive means to deal with the possible climate scenarios.

Design of hydraulic structures involves usage of the hydrographs, which are derived from the information of rainfall history at the site. It is well recognized that the shape of the design hydrograph depends on the hyetograph used to generate it. However, for derivation of hyetograph, information regarding temporal distribution of rainfall should be available. It remains a fact that in many places of India and other developing countries, the information on temporal discretization of rainfall is not available. This has led to the adoption of curves from the literature, using which the time distribution of rainfall is derived. Recently, IMD-CWC had published temporal distribution curves for various river basins in India. The curves considered for purposes of comparison in this study include those provided by the NRCS-TR55, US Army Corps and IMD-CWC. The study indicates that the time distribution curve derived for the site from the recorded continuous hourly rainfall data for many years is significantly different from those obtained for the existing curves in the literature. It is also shown that this difference leads to either over-design or under-design of hydraulic elements. The study underlines the importance of obtaining the site-specific curves for important projects so as to carry out economical and safe designs.

The records obtained from the rain gauge stations may contain missing information or other forms of human or machine errors, which might hamper with analysis or lead to incorrect inferences drawn from the data. It is pertinent that the data should be screened and suitable quality checks be applied on the records before using them for hydrological analysis. In this paper, the statistical quality checks suitable for checking the quality of hourly time history of rainfall from a site are discussed along with example applications as a case study.

River carrying sediments is a crucial component of the geochemical cycle and depending on the local factors, the sediments may be beneficial or detrimental for the society. Thus, management of river sediment is a challenging task from economic, social and environmental perspective. In today’s world due to changing climate the policy makers must come out with a more efficient and better management technique of these problems. The present study involves construction of a channel through huge sandbar developed in the Brahmaputra River of Assam in the Guwahati region, over the time leading to serious water extraction problem to an intake supplying water to its locality. The river has shifted north over time and there aroused a water scarcity problem on the south bank. In the present work, MIKE 21C model is used to simulate the water flowing through a channel constructed between the sandbars linking the main river. Initially, based on the field survey, the grid is generated and the bathymetric data are fed into the model. Surface elevation, discharge and water level are introduced as the initial conditions, and boundary conditions with other parameters like the roughness coefficients and eddy viscosity. The simulated velocity was calibrated at the downstream section with the observed one obtained through survey during that period. After the calibration, the vector plots of the velocity and water depth are produced. Combining the knowledge derived from the hydrodynamic model study and geoinformatics study, it has been found that a 10 m wide channel dredged through the sandbars connecting the river with the intake will be able to draw sufficient water from the main river to the intake. Thus, it can be found that MIKE 21C has performed well in handling the complex situation of water scarcity to sustain its availability for the benefit of the society.

Evapotranspiration is a key component of hydrological cycle which together with precipitation determines the water availability within a region. The present study aims to investigate spatiotemporal patterns in evapotranspiration (ET) over the period 1981–2015. The monthly time series of ET obtained from Noah Land Surface Model of GLDAS was aggregated to seasonal and annual time series to carry out the analyses. The response of ET to climate variability is examined by analysing the relationship between trend in ET and key climate parameters (temperature and precipitation) at annual and seasonal timescale. The significance of trend is tested using nonparametric Mann–Kendall test at 5% significance level. Magnitude of trends is defined by slope parameter of linear regression line. The results show an increase in ET over major portion of the basin except for the upper reaches for all the seasons. In the upper reaches (Himalayan region), negative trends prevail in ET at both seasonal and annual temporal scale. However, the trends in precipitation and mean temperature are found to be increasing over the upper Ganga basin. The results reveal the existence of factors other than climate parameters controlling the variations in ET, especially in the upper reaches of Ganga basin located in the Himalayan region.

Sea surface temperature (SST) and oceanic heat content (OHC) play a significant role in tropical cyclogenesis formation as well as in the intensification of tropical cyclones. Prior studies over the recent past indicate the intensification of tropical cyclones as well as increased cyclone size, which has a strong correlation with increased SST over the global ocean basins. In the context of tropical cyclogenesis, the Bay of Bengal basin, a semi-marginal sea in the North Indian Ocean, is quite active. Many tropical cyclones form over this basin and make landfall either as severe or very severe cyclones in the countries surrounding the Bay of Bengal rim. The SST and sub-surface temperature in the Bay of Bengal basin also showed an increasing trend over the recent past. Interestingly, unlike the other ocean basins, the dynamics in the Bay of Bengal region is primarily governed by the reversing monsoon wind system and enormous freshwater influx through primary riverine sources that regulates the transport and distribution of water mass characteristics and the overall circulation characteristics. Differential circulation characteristics can, in turn, lead to spatial and temporal variability of SST and oceanic heat content responsible for tropical cyclone formation and intensification. The present study performed a comprehensive evaluation on the seasonal and inter-annual variability of SST and determines its correlation to maximum sustained wind speed (Vmax) for the past two decades. Strategically, keeping in view the water mass distribution characteristics, the geographical domain in the Bay of Bengal region was sub-divided into four sectors (box of 5° × 5°) and the dependence of SST versus Vmax across these sub-domains was investigated. The study considered 124 cyclonic events that include depressions, cyclonic storms, and severe cyclonic storms from 1996 through 2016. Overall an increasing SST trend was observed in climatological SST obtained from the CMIP5 models ACCESS 1.3 and HadGEM2-ES. The above statement substantiated by the correlation factor and increasing trend in all sub-domains was noticed between Vmax obtained from the Joint Typhoon Warning Centre (JTWC) and SST in ERA-Interim data. Interestingly, the central sector in the Bay of Bengal region showed a different correlation, unlike the other sub-domains. Basin-scale SST anomaly varied between 2 °C (maximum) and 1.7 °C (minimum) during the post-monsoon and between 2.4 °C (maximum) and 1.6 °C (minimum) during the pre-monsoon seasons. The findings from the study indicate a definite increase in Vmax correlated with SST over the recent years. SST variability showed an in-phase correlation with Vmax, and that is consistent over all the four sub-domains in the study area. A closer examination and analysis of both SST and oceanic heat content is warranted, and their mutual trends need to be evaluated with Vmax to better understand the process of tropical cyclone intensification. The study is believed to have importance in the research activity of tropical cyclone modelling.

Realistic estimates of surface wind speed form an essential prerequisite for process-based air-sea studies and also numerical modeling needs. In this context, historical and projected wind speed estimates obtained from global climate and regional models warrant proper assessment and necessary bias corrections before it can be optimally used for rigorous analysis and research needs. Adequate evaluation and bias correction of wind speed estimates are therefore crucial to understand the extremes. For example, they have direct implications on extreme wind waves that can influence the coastal zone. The present study performed a detailed evaluation of wind speed obtained from the Coupled Model Inter-comparison Project Fifth Phase (CMIP5) products to assess their projections for the Bay of Bengal region. A suite of global climate models is employed to generate the CMIP5 products under four Representative Concentration Pathways (RCPs) of 2.6, 4.5, 6.0, and 8.5 and based on differential CO2 emission scenarios. The present study also used the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoys located in the central Bay of Bengal in order to validate and skill assess the CMIP5 wind products under varying RCPs. In addition, an intercomparison exercise that was performed between RAMA buoys data and merged satellite altimeter data from the French Research Institute for Exploitation of the Sea/Laboratory of Oceanography from Space (IFREMER/CERSAT) provided necessary confidence to ascertain the quality of CMIP5 wind speed products. The study signifies that a reasonably good correlation was noticed in the wind speed comparison between CMIP5 GCM products and RAMA buoys (maximum correlation of 0.64), and the correlation factor varied between the suite of models used in CMIP5 experiments. This exercise would provide a detailed know-how on the performance of various GCMs and also provide a basis to select the best-performing GCMs for the Bay of Bengal region. Analysis of the upper 10% (90th percentile) showed a maximum under-estimation/over-estimation of 2.5 m s−1 and 1.5 m s−1, respectively for wind speed comparison between CMIP5 and RAMA buoys data. Although the CMIP5 GCMs are not able to represent the contemporary wind speed climatology satisfactorily, the models such as HadGEM2-ES, HadGEM2-CC, CanESM2, and ACCESS-1.3 showed the best performance concerning near-surface wind speed for the Bay of Bengal region.

Temperature and its analysis play a significant role in planning any irrigation project. However, expeditious climate change and shift have evolved with such footings, which were implausible earlier. On this respect, in the present study, analysis of temperature trend has been carried out over the command area of Eastern Ganga Canal (EGC) project for pre-climate shift period and post-climate shift period. Non-parametric Mann–Kendall and Sen’s methods have been applied to study the trends in annual and seasonal temperature (Kharif, Rabi and Summer). Results showed significant decreasing trends in annual, Kharif, Rabi and Summer season temperature with −0.03 °C/year, −0.02 °C/year, −0.04 °C/year, and −0.02 °C/year respectively, during pre-climate shift period; whereas the significant increasing trend has been observed in all the four parameters during post-climate shift period with 0.02 °C/year. This rise in temperature is directly affecting the water requirement of crops, by rise in reference crop evapotranspiration (ET0). The ET0 showed decreasing trends for above-mentioned period during pre-climate shift whereas significant increasing trends have been observed during post-climate shift. These results will help project managers and farmers in understanding the climate shift and manage them to the development of alternative water management strategies.

The climatic variability for an area is referred to the long-term change in precipitation, temperature, humidity, evaporation, wind speed and other meteorological parameters. In order to identify the change, quantification of environmental change is essential that has occurred already and will be further helpful to make forecast for future. This will result into a better awareness for natural disasters. The objective of the study is to examine the rainfall variability in east as well as in west Rajasthan state in India. This will give an understanding about rainfall trends or changes. In this study, trend analysis has been carried out on monthly, seasonal and annual scale for the 33 districts of the arid as well as semiarid east and west Rajasthan state, India. Mann–Kendall test and Sen’s slope estimator (statistical trend analysis techniques) used to detect trends at the 5% significance level on time series data of the east and west Rajasthan state for the time period, 1871–2016. This test was applied to identify the change in magnitude and direction of existing trend over the time. Trend detection of rainfall using techniques over 146 years shows increasing trend in premonsoon, southwest monsoon and annual precipitation in west Rajasthan while in east Rajasthan, it shows increasing trend in premonsoon, postmonsoon and annual precipitation. For proper water resource management and its planning, the analysis of climatic variables like heavy rainfall, temperature and humidity is helpful in adverse climatic conditions.

The change in climate threatens the abundance of usable water across the globe. Most of the river basins are unable to cope up with the impact of climate change. Hence, assessing the future scenario has become the need of today. General Circulation Model (GCM) provides information at a course grid resolution. Downscaling can help in getting the information at a local scale level from GCM data, which helps the researchers to work on a regional level. Statistical downscaling method is preferred over dynamic downscaling method due to its less complex calculations. Statistical downscaling model (SDSM) is widely used in prediction of future climate scenarios. Here Brahmani–Baitarani river basin is selected as a case study for the downscaling of precipitation in the monthly time scale. SDSM version 4.2 is used as the model and precipitation is taken as the predictand. Predictors are chosen from the NCEP global variables like air temperature, geopotential height, specific humidity, zonal and meridional wind velocities, precipitable water and surface pressure data. The outcome of the study shows an increasing trend in the rainy season of the year. The mean rainfall increases significantly in 2040s than other epoches.

Hydrological parameters are affected by many factors, including long-term effects such as climate change that alter rainfall–runoff relationships, and short-term effects related to human intervention (e.g., dam construction, land-use and land-cover change (LUCC)). The consequential impacts of human-induced climate changes and land-use changes on hydrological parameters have become a big challenge and convinced to give great attention of many researchers. The Kolab river watershed, Odisha is an important watershed supporting drinking water and recreational activities. In this paper, it is assessed the long-term impacts of LULC change and climate changes on hydrological parameters using the Soil and Water Assessment Tool (SWAT) and a detailed LULC record from 1995–2013.

Climate change influences events such as droughts, floods, extreme temperatures and rainfall. The changes in climatic conditions may adversely affect food production, energy generation, and water resources. Rising greenhouse gas (GHG) concentrations in the atmosphere are considered the dominant cause of climate change. General circulation models (GCMs) are used for the projection of climate into future, accounting for the GHG concentrations. However, the coarse spatial resolution of GCM outputs does not permit their direct use in catchment-scale studies. Therefore, either dynamic or statistical downscaling techniques are used for linking GCM outputs to catchment scale hydroclimatic variables. In the present study, beta regression-based statistical downscaling of daily scale climatic variables (precipitation, minimum temperature, and maximum temperature) is performed. The daily frequency climatic variables obtained from Hadley global environment model 2—earth system (HADGEM2-ES) of CMIP 5 project is statistically downscaled for future period (2016–2095) using NCEP potential variables in MATLAB. The methodology is performed for Sai–Gomti interfluve region, Uttar Pradesh, India. The statistical analysis of observed and simulated hydro-climatic variables shows that the average maximum temperature may rise by 4 °C while average minimum temperature may fall by 4.5 °C during 2016–2095.

Rainfall is the main driving variable to impact the hydrologic model results. In the context of unavailability of actual rainfall data, researchers use global data, derived from the satellite for hydrologic assessment of a watershed. In this study, Climate Forecast System Reanalysis (CFSR) precipitation data are tested for its reliability in regard to Assam, India; which is characterized by a wide spatial variation of rainfall. Based on the observed wet days’ records, the CFSR precipitation datasets are compared with two sets of actual gauge rainfall data, viz. Indian Meteorological Department (IMD) data and Tea Garden data. From the results of this study, the CFSR data are found to be 34.94% deficient in annual rainfall volume than the actual gauge measurements.

The change in the meteorological variables is one of the most important factors that affect crop water requirements, and subsequently, water allocation for food production in agriculture-based countries like India. Present study evaluates the application of statistical trend detection tests and examines the magnitude slope of trends in climatic variables viz., rainfall, potential evapotranspiration (PET) and temperature over the west-flowing rivers in the Tapi to Tadri basin, India. In the present study, high-resolution daily gridded rainfall dataset of India Meteorological Department (IMD) at 0.25° × 0.25° resolution, while the PET and temperature data of Climate Research Unit (CRU) at 0.5° × 0.5° resolution have been analyzed for period of 116 years (1901–2016) at annual scale. The trends in aforesaid climate variables have been detected using nonparametric Mann–Kendall (MK) and Modified Mann–Kendall (MMK) tests, and the slope of trend magnitude is computed from Sen’s Slope (SS) test. The results indicated increasing trend in annual rainfall across 61% grids, while decreasing across 37% grids and no trends were observed at remaining grids out of 119 grids. Further, increasing trend in potential evapotranspiration, maximum, mean and minimum temperatures were observed at all the 40 grids. Thus, increase in temperature was greatly responsible for increasing trends in potential evapotranspiration across the study region. The outcomes of the present study provide insight of the climate variability and interaction among the meteorological variables across the Tapi to Tadri basin for the study period.

Global warming has adversely affected the climatic systems on the earth. Long-term alterations in global weather patterns, particularly rise in global temperature have been significantly influencing the hydrological patterns leading to climate change. One of the biggest challenges posed by the global warming-induced climate change is its direct effect on the well-being of humans through the changes in hydrological cycle affecting the sectors such as agriculture, industrial growth, hydropower generation, domestic water supply etc. Subsequently, the researchers have predicted that impacts of climate change such as the melting of glaciers, increased frequency of floods and droughts, changes in evapotranspiration, variations in surface runoff and rising sea level have significant implications to water resources. Developing economies like India are highly vulnerable to these changes as the majority of its population is dependent on agriculture. In addition to the above the increasing water needs for domestic and industrial purposes, depleting groundwater levels and surface water pollution have been stressing the policymakers for sustainable water resource management. The present research exhaustively reviews the influence of climate change on hydrological components, its qualitative and quantitative implications to surface and groundwater resources in Indian scenario through various case studies. The research also proposes the corrective measures for the impact minimization on the available water resources in the present context.

Afghanistan is a semi-arid country and most vulnerable to climate extremes related hazards, including droughts and floods that have caused huge impact on the socioeconomic development of the country. The present study analyzed the observed precipitation and temperature trends for seven agro-climatic zones of Afghanistan over the period 1951–2007 with Asian Precipitation-Highly-Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE). Change in the magnitude of precipitation and temperatures in recent years with reference to distant past was assessed by dividing the historical data into two parts as 1951–1990 and 1991–2006. Further, the trend analysis was performed on daily data to test the increasing or decreasing rainfall and temperature trends using Mann–Kendall trend test for each zone of Afghanistan. The maximum precipitation occurrence months were observed as January–May for all zones of Afghanistan. Whereas, June–December and generally considered as dry months. The maximum temperature was observed in the months of May–August, with hottest month as July for all seven zones of Afghanistan. The annual total precipitation has shown an increasing trend for the zones of South, South–West, East and Central, whereas, a decreasing trend has been observed for the zones of North, North–East and West zones. The trend analysis of the precipitation with gridded data sets reveals for the most part of the Afghanistan region the rainfall has been observed as decreasing. Whereas, for all seven agro-climatic zones of Afghanistan, an increasing trend of temperature in recent years of 2004–2016 was observed. Overall, the North, North–East and West zones of Afghanistan are more vulnerable with decreasing precipitation and increasing temperatures indicating more dry and warm periods indicating increasing drought conditions. Whereas, the South, South–West, East, and Central zones are more vulnerable with increasing trends of both precipitation and temperatures indicating increase of more wet and warm climates.

Climate change has a potential impact on the water resources of a region as well as the occurrences of extreme events. Present study critically investigates the shift, dispersion, and trend of the monsoon season of urban Guwahati, India, using the historical climate data. The climatic variables considered in this study are the rainfall, maximum temperature, and the minimum temperature, collected over a period of 30 years (1980–2009). In this study, instead of considering the crisp boundaries for seasons, the time-series data were partitioned into internally homogeneous segments comprising comparable climatic conditions. The present study utilizes the fuzzy sets to segment the time-series climate data into different seasons. The homogeneity of the climatic variables within each segment/season was confirmed by simultaneous identification of the local probabilistic principal component analysis (PPCA) models. The algorithm facilitates a contiguous clustering in time by detecting changes in the hidden structure of multivariate time series. The present study defines and determines the shift or creep of the seasons as the movement of the center of the segment/season with time, whereas the dispersion as the variation in the length of the segment/season with time. The rainfall trend of the monsoon season was investigated by averaging the climatic variables for each segment of length $$\mu \pm 1.5 \times \sigma$$ μ ± 1.5 × σ , where $$\mu$$ μ is the center of a season and $$\sigma$$ σ is the standard deviation at the timescale. The present study noticed an early occurrence of the premonsoon and monsoon seasons and a late occurrence of the postmonsoon season that indicates the shift of the seasons. This study has observed a spread of the period of premonsoon, monsoon, and postmonsoon seasons after analyzing the dispersion characteristics. Similarly, the rainfall has shown a decreasing trend for all the seasons. Conclusively, this study has evidenced the historical variations in the monsoon season for urban Guwahati regarding shift, dispersion, and the reduction in the rainfall intensity.

This study focuses on the design of intensity–duration–frequency (IDF) curves under climate variability for the Raipur city in Chhattisgarh state, Central India. Design of IDF curve is the process of design of rainfall by the frequency analysis using historical rainfall data. The originality of this study comes with the attention of temporal rainfall variability consideration in design rainfall. To track the inherent rainfall events, shorter duration rainfall data have been used. As the shorter duration rainfall data will give the precise number of rainy events which may be ignored in larger duration rainfall events. The insight of the result gives the asses to check the vulnerability of the hydraulic structure as well as planning, designing and operation under climate uncertainty. The Gumbels extreme value distribution is used to find the design intensity for different durations and different intensities at different return periods. The annual maximum series of precipitation intensity for each duration is obtained and K-S & Chi-Square tests were performed to check the fitness of the rainfall data. To consider the nonstationarity of the rainfall, time series is divided into two parts from the change point. Trend of each series represents the change in intensity of the rainfall with time. These results are explaining the importance of the adaptation of changed climate.

The present study examines the changes in monthly streamflows and their linkages with rainfall variability in the Middle Tapi basin (MTB), India. The Middle Tapi basin (area ≈ 32,920 km2) is part of Tapi basin (area ≈ 65,145 km2) located in the western part of central India. The Girna, Bori and Panjhara rivers are the major gauged tributaries of the Tapi River in the Middle Tapi basin. The streamflow data of eight stream gauging stations on the Tapi River and its tributaries for the period 1973–2013 were collected and analyzed. The non-parametric Modified Mann–Kendall (MMK) and Spearman’s Rho (SR) tests have been used to evaluate the trends in mean monthly streamflows and total monthly rainfall series for all the stream gauging stations. Further, the Sen’s slope estimator test is employed to compute the slope of trend magnitude and percentage changes in the trend. The trends in mean streamflow exhibited spatial homogeneity, wherein, decreasing trends are observed at all stations in the MTB during June (except Savkheda stream gauging station) and August months; while, spatial heterogeneity is observed for July and September months. The total monthly rainfall exhibited dichotomic fluctuations in their trends, wherein, largely increasing trend has been reported for the August month, and decreasing trend is observed for the September month across the MTB. The analysis carried out in the present study would enhance the understanding of the hydro-climatic interactions and influence of climate variability on streamflows within the Middle Tapi basin.

The physically based hydrological models require the estimation of various model parameters through calibration. Several past studies that focused on parameter estimation of hydrological models have found that no single objective performance criterion is adequate for matching different essential characteristics of the observation data. Since physically based hydrological models simulate many of the catchment hydrological processes, it needs to define multiple performance criteria to effectively use the information from various datasets and application of multiobjective optimization for attaining Pareto optimal solutions. In the present study, a Multiobjective Self-adaptive Differential Evolution algorithm (MOSaDE) is applied to perform multiobjective calibration of hydrological models. MOSaDE is an advancement of well-known Differential Evolution (DE) algorithm, using the notion of Pareto dominance, fast nondominated sorting approach, diversity preservation using crowding distance and elitist strategy of joining parent and offspring population. The parameter self-adaptation strategy in the MOSaDE also increases the robustness of the algorithm and alleviate the needs of computationally demanding sensitivity analysis of the algorithm parameters. The methodology is verified for calibration of Variable Infiltration Capacity (VIC) model, which is a popular physically based hydrological model, for a case study in Krishna basin, in India and the results are found to be promising.

Crop yield is affected by climate, prevailing during crop season and inputs applied. As such modeling, the cause and effect relationship between yield and these factors could provide an approach for pre-harvest yield forecast. Prediction of impacts of Climate Change (CC) on crop yields requires a model and its parameters, how crops respond to climate. Predictions from various models often disagree with the climatic variables and its impact. A common method is used to quantify impacts of CC is statistical models trained on historical yields and some simplified measurements of weather parameters, such as growing season average temperature and precipitation. CC is a really big apprehension to the entire world. Its direct impact on crop growth and yield is very important to understand. In the present study, the Fuzzy logic crop yield model was developed by considering different climatic variables. Temperature, rainfall, evaporation, humidity parameters were considered for the crop yield model. The model was developed by considering the 15-year crop yield data and the same period for the climatic variables. The triangular membership function is being adopted in the fuzzy model. In this study, a fuzzy rule-based system (FRBS) using the Takagi Sugeno-Kang approach has been used for developing the crop yield model. Model is validated by the coefficient of correlation and found that there is more than 0.9 coefficient of correlation between observed and evaluated yield.

The increasing rate of global surface temperature is going to have a significant impact on local hydrological regimes and thus on water resources; this leads to the assessment of water resources potential resulting from the climate change impacts. The main parameters that are closely related to climate change are temperature, precipitation and runoff. Therefore, there is a growing need for an integrated analysis that can quantify the impacts of climate change on various aspects of water resources. Quantifying the impacts of land-use change and land cover practices on the hydrological response of a watershed have been an area of interest for hydrologists in recent years as this information could serve as a basis for developing sound watershed management interventions. The degree and type of land cover influence the rate of infiltration, runoff, and consequently the volumes of surface runoff and total sediment loads transported from a watershed. It often results in significant degradation of land resources such as loss of soil by erosion, nutrient leaching and organic matter depletion. However, very few studies in India have used the physically-based hydrological models along with the land use/land cover change conditions. Hence in this current work SWAT model has been used to assess the impact of LU/LC changes on daily and monthly streamflow of Mahanadi River Basin of Sambalpur region. The results of the study indicated that the though land-use patterns have changed, resulting in an increase in agricultural, barren and buildup land and decrease in forest cover leading to an increase in the runoff, but changes have not occurred as significantly as the changes in annual streamflow. However, the number of days of high-intensity rainfall has increased over a decade, which, along with the land-use changes, explains the increase in streamflow.

The present research work is aimed at studying morphological parameters of the Kosi basin, considering its importance in understanding hydrological behavior. For this purpose, radar data, that is, SRTM 1 Arc-Second Global elevation (30 m), is processed using ArcGIS 10.2 software for the analysis. The ArcSWAT Tool is used as an add-in in ArcGIS 10.2 to delineate the watershed and to generate stream networks. For analyzing the basin, several morphological parameters, namely linear aspects, areal aspects and relief aspects, are estimated. In this study, the drainage basin is 7th order with a lower mean bifurcation ratio of 2.21, indicating a structurally less disturbed basin. The form factor, elongation ratio and circulatory ratio show irregular and elongated basin shape. A low drainage density of 0.86 km/km2 indicates coarse drainage texture and highly permeable subsoil materials. Lower stream frequency reveals gentle ground slope, less runoff and more infiltration. Lower relief aspect values justify the basin is in the valley region. This study is of greater importance in the geomorphological study of the basin and in other future investigations like prioritization of various sub-basins in the Kosi River basin.

The work deals with the impact of changing climate on performance of DVC (Damodar Valley Corporation), reservoir system (Fig. 41.1) (comprising Tilaiya, Maithon, Konar and Panchet dams (multipurpose dams), Tenughat dam (single purpose) and Durgapur barrage on river Damodar of India using HEC-5, a simulation model for reservoir operation (developed by HEC, USA). The projected climate has been taken from projected climate output (A1B scenario) of Regional Climate Model PRECIS of Hadley Centre, UK prepared under IPCC (Intergovernmental Panel on Climate Change). Integrated operation and reservoir guide/ rule curve of DVC Authority was used from 1st of June (water year starting date in India) for a future period (2018–2024). Simulations were executed for 1985–1990 (baseline period) and for 2018–2024 (future period) with changed climate data (with current demand and projected demand). The system performance (on a seasonal basis) using “Performance Indices” (viz. volume reliability, time reliability, flood control index) for the future scenario (with current demand) became better than that for baseline condition in four seasons for Panchet reservoir. Reliability of meeting demands (M&I) in November and December in 2020 (with 58% increased projected inflow in comparison to baseline) with projected demand showed improvement (by 14%) over those in October and in months of January to May in 2020 at Maithon reservoir. It was noted that remarkably increased projected inflow (unseasonal) into the Panchet (by 1286.8%) and Maithon (by 1014.4%) reservoirs with reference to a baseline condition in March and May of 2022, respectively, caused flooding (unseasonal and high) downstream Panchet reservoir and bank full flow (unseasonal) downstream Maithon dam in March and May. The results from the study would guide concerned authorities for operating reservoirs for anticipated climate change.

Though land surface models (LSMs) are originally developed for representing water fluxes, carbon fluxes and energy fluxes between land and atmosphere, recently LSMs are being used for hydrological simulation because it has some positive traits in comparison to conventional hydrological models. In this study, the Joint UK Land Environment Simulator (JULES), a land surface scheme of the Met Office Unified Model (UM), is implemented to study the effect of different spatial resolutions on streamflow simulation at the Krishna River basin (catchment area 2,60,000 km2), India. The meteorological datasets used here are WFDEI (WATCH-Forcing-Data-ERA-Interim) global data at the resolution of 1° × 1° and 2° × 2°. The simulation is run for 2001–2008 period with 3 years (2001–2003) of spin-up with 50 spin-up cycle and further simulation of years from 2004–2008. To assess the performance of the stream flow simulation, appropriate statistical parameters such as mean error (ME), root-mean-square-error (RMSE), percentage BIAS (PBIAS) are used to see error statistics. The study results indicate that spatial resolution of routing, driving and ancillary data has a significant effect on model output at the river basin scale. Though we have implemented the simulations in coarser resolutions, such studies on hydrological fluxes with a change of spatial resolution are important to know associated uncertainties with driving data and routing data resolution selection, this, in turn, would also help researchers to make meaningful management decisions to deal with future water security issues.

In the current research, missing value analysis and tests for the presence of homogeneity were applied to the temperature records obtained from seven meteorological observatories spread throughout the state of Kerala. The monthly mean maximum and mean minimum temperature datasets of observatories managed by the Indian Meteorological Department (IMD) for the period 1969–2015 were considered. During analysis, every observatory was studied independently and those observatories which are having missing values for five continuous years, and more were rejected. The missing records were estimated using the Expectation Maximization Algorithm (EMA). The infilled dataset needs to be hydrologically as well as statistically stable for later hydrological and meteorological assessments. The reliability of datasets was tested using eight statistical absolute homogeneity tests. Before applying the homogeneity tests on the datasets, their assumptions must be fulfilled; one predominant assumption is regarding the normal distribution of the dataset. Thereby, the datasets are checked for normality behaviour by four statistical tests, namely Skewness z-ratio, Kurtosis z-ratio, Kolmogorov–Smirnov (KS) and Shapiro–Wilk (SW) test. Out of 14 datasets consisting of seven mean monthly maximum and seven mean monthly minimum temperature, ten datasets were found to be normal at 95% level of confidence (LOC) and the rest four were highly skewed and kurtotic. Following the normality tests, eight statistical absolute homogeneity tests were applied individually on the annual scale by which non-homogeneous stations were detected and eliminated. The datasets which resembled normal behaviour were tested using parametric homogeneity tests such as Linear Regression, Student’s t-test, Cumulative Deviation and Worsley Likelihood Ratio test. Out of normally distributed datasets, only three datasets were statistically homogenous at 99% LOC, and seven datasets failed to clear the homogeneity test. Remaining four non-normally distributed datasets was checked for homogeneity by applying non-parametric tests, such as Distribution-Free CUSUM, Rank-Sum, Median Crossing and Turning Points test. Out of four datasets, three were homogenous at 99% LOC and one failed homogeneity test.

Conservation and preservation of environmental wildlife and fisheries became an important criterion in this era. Therefore, the environmental protection flow standards for major river basins and estuaries have been established in the state of Texas enacted by federal laws. The environmental flow issues can be addressed effectively and efficiently on a daily time step, and thus, a new technique, daily flow pattern hydrograph, is developed and implemented for disaggregating monthly naturalised streamflows to daily while preserving monthly volumes. Constitution of flow pattern hydrograph emphasises numerous issues such as the impact of water resources development at upstream, comparative analyses of available flows, geology, hydrology, the geographical location of gaging stations, etc. The previous parametric and nonparametric studies dealing with streamflow disaggregation methods mostly preserve spatial and temporal statistical characteristics such as variance, skewness and maximum and minimum values. This study has been conducted from the perspective of capturing low flows, and therefore a statistical parameter, duration curve, also known as flow frequency metric, is employed to evaluate and analyse the result. This study is performed for the Trinity River basin located at the state of Texas. However, this technique can be employed in any other river basin irrespective of its shape, and size.

Timely measurement of precipitation across a large area is essential for flood/drought/landslide forecasting. Satellite-based precipitation estimates (SPEs) are one of the promising sources of precipitation data for the above mentioned near real-time (NRT) applications. However, these estimates have inherent errors and uncertainty. Hence, it is crucial to assess and quantify them to support effective NRT applications. Therefore, as a pilot study, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) Real Time (RT) version-7 (V7) product is evaluated during the monsoon of 2002–2013 across a large river basin called Krishna River Basin, located in South-central part of India. It shall be noted that this is the first of its kind study in India. Here, the Total Error in TMPA 3B42RT is decomposed into individual error components such as Hit Error, Miss Precipitation and False Precipitation. The obtained error components are then analyzed spatially and intensity wise. Our results indicate that the leading source of error in TMPA 3B42RT is Hit Error, which shows significant spatial differences between the orographic region and non-orographic regions of the basin. Intensity wise, the TMPART-V7 overestimates very light to moderate precipitation and vice versa for heavy to very heavy precipitation. However, TMPA-RT V7 has excellent detection ability for heavy to very heavy precipitation.

Selection of predictors for statistical downscaling is crucial for establishing the best relationship between predictors (such as Relative humidity, Geopotential height, u-wind, specific humidity, etc.) and predictand (such as precipitation, temperature, etc.). In this paper, the statistical method (factor analysis, correlation coefficient, etc.) are used for selecting the best predictors for downscaling predictand. The selection of potential predictors enhances the performance, computational times and model outputs. The Bagmati river basin, Bihar, India, is a highly flood-prone area. During rains, the Bagmati river carries a discharge much in excess of the channel capacity and brings a huge quantity of debris and sediment from the erodible slopes of the Himalayan. Due to this, the Bagmati river basin is selected for the study to compare different statistical techniques. In this study, 33 years (i.e., from 1981 to 2014) National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis outputs data and 33 years observed rainfall data from Indian Meteorological Department (IMD) Pune are used for different predictors. After comparing different statistical methods, factor analysis gave a better result than other statistical methods.