Rainfall is a principal element of the hydrological cycle and its variability is important from both the scientific as well as socio-economic point of view. This study presents an analysis based on ...the precipitation variation in Assam, India over 102 years from 1901 to 2002. Precipitation data from 21 stations have been collected. These data have been analyzed for both annual and seasonal variation. For trend analysis, Mann-Kendell and Sen’s slope estimator test were used. To compare seasonal variations, three seasons of winter, summer and monsoon have been considered. Mean annual precipitation varied from 2,074 mm (at Tinsukia) to 3,538 mm (at North Chahar Hills). The most probable year of change was 1959 in annual precipitation. Time series of the Standardized Precipitation Index (SPI) depict that near normal occurs in about 68 years out 102 years, and in 2.48 years out of 102 years there was an extreme wet. All these findings can help provide rational regulatory and policy in relation to water resources to maintain the health of the various ecosystems that make up Assam, India.
•A non-stationary meteorological drought index is developed using large-scale climate indices.•A copula-based bivariate analysis of drought properties is carried ...out.•Reliability-Resilience-Vulnerability of the non-stationary drought index is evaluated.
The most of the available drought indices do not incorporate the environmental changes in the present scenario of climate change. In an attempt to encompass the climate variability in the computation of meteorological drought, a non-stationary gamma distribution with climate indices in its location parameter as a covariate is proposed. The performance of the non-stationary drought is evaluated based on the statistical performance as compared to the stationary drought. Focusing on two Himalayan states in India, the meteorological drought events are described and assessed based on the stationary and non-stationary drought index. Moreover, the bivariate analysis of different drought properties is carried out and compared with the univariate analysis. The management indices such as reliability, resilience, and vulnerability are also computed based on the developed drought index. The results in the study indicate that in most of the cases the non-stationary drought index is capable of capturing the drought characteristics over the study areas. The variability in the probability density of different drought properties is observed under 12-month drought scale in most of the cases. During bivariate analysis, a compare difference is noticed between secondary and primary return periods. Moreover, higher reliability and resilience is noticed during 12-month scale drought period. The newly developed drought index and the copula-based analysis of drought properties provide a new concept for robust and effective management practices in the changing environment.
The forecast of the sediment yield generated within a watershed is an important input in the water resources planning and management. The methods for the estimation of sediment yield based on the ...properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment yield. The focus of present study is to deal with the development of decision tree based M5 Model Tree and wavelet regression models for modeling sediment yield in Nagwa watershed in India. A comparison is also performed with the artificial neural network (ANN) model for streamflow forecasting. The root mean square errors (RMSE), Nash-Sutcliff efficiency index (N-S Index), and correlation coefficient (R) statistics are used for the statistical criteria. A comparative evaluation of the performance of M5 Model Tree and wavelet regression versus ANN clearly shows that M5 Model Tree and wavelet regression can prove more useful than ANN models in estimation of sediment yield. Further, M5 model tree offers explicit expressions for use by design engineers.
This paper draws attention to highlight the spatial and temporal variability in precipitation lapse rate (PLR) and precipitation extreme indices (PEIs) through the mesoscale characterization of ...Teesta river catchment, which corresponds to north Sikkim eastern Himalayas. A PLR rate is an important variable for the snowmelt runoff models. In a mountainous region, the PLR could be varied from lower elevation parts to high elevation parts. In this study, a PLR was computed by accounting elevation differences, which varies from around 1500m to 7000m. A precipitation variability and extremity were analysed using multiple mathematical functions viz. quantile regression, spatial mean, spatial standard deviation, Mann–Kendall test and Sen's estimation. For this reason, a daily precipitation, in the historical (years 1980–2005) as measured/observed gridded points and projected experiments for the 21st century (years 2006–2100) simulated by CMIP5 ESM-2M model (Coupled Model Intercomparison Project Phase 5 Earth System Model 2) employing three different radiative forcing scenarios (Representative Concentration Pathways), utilized for the research work. The outcomes of this study suggest that a PLR is significantly varied from lower elevation to high elevation parts. The PEI based analysis showed that the extreme high intensity events have been increased significantly, especially after 2040s. The PEI based observations also showed that the numbers of wet days are increased for all the RCPs. The quantile regression plots showed significant increments in the upper and lower quantiles of the various extreme indices. The Mann–Kendall test and Sen's estimation tests clearly indicated significant changing patterns in the frequency and intensity of the precipitation indices across all the sub-basins and RCP scenario in an intra-decadal time series domain. The RCP8.5 showed extremity of the projected outcomes.
•Downscaling of the precipitation using CMIP5/ESM2 RCP scenarios•Calculation of precipitation lapse rate at sub-catchment scale•Trends of precipitation lapse rate at Sikkim Himalayas using mathematical models•Precipitation variability and heterogeneity assessment in an intra-annual time series domain
Abstract
Indian cities have frequently observed intense and severe heat waves for the last few years. It will be primarily due to a significant increase in the variation in heat wave characteristics ...like duration, frequency, and intensity across the urban regions of India. This study will determine the impact of future climate scenarios like SSP 245 and 585 over the heat wave characteristics. It will present the comparison between heat waves characteristics in the historical time (1981 to 2020) with future projections, i.e., D
1
(2021–2046), D
2
(2047–2072), and D
3
(2073–2098) for different climate scenarios across Indian smart cities. It is observed that the Coastal, Interior Peninsular, and North-Central regions will observe intense and frequent heat waves in the future under SSP 245 and 585 scenarios. A nearly two-fold increase in heat wave' mean duration will be observed in the smart cities of the Interior Peninsular, Coastal, and North Central zones. Thiruvananthapuram city on the west coast has the maximum hazard associated with heat waves among all the smart cities of India under both SSPs. This study assists smart city policymakers in improving the planning and implementation of heat wave adaptation and mitigation plans based on the proposed framework for heat action plans and heat wave characteristics for improving urban health well-being under hot weather extremes in different homogeneous temperature zones.
•This study computes the urban drought risk for Indian smart cities before the beginning of the Indian smart cities mission.•This study considers three decadal variability (1982–2013) in ...meteorological, hydrological, vegetation, and soil moisture parameters for inducing water scarcity and drought conditions in urban regions.•The research investigations revealed that urban drought risk is maximum for Bangalore, Chennai, and Surat cities. Northwest, West Central, and South Peninsular urban regions have higher risk among all the urban regions of India.
In 2015 the beginning of the Indian Smart Cities’ mission was one of the significant steps taken by the Indian government to make the urban environment resilient to climate change impact and extreme weather events like drought, floods, heatwaves, etc. This study computes the urban drought risk for Indian smart cities before the beginning of the Indian smart cities mission. This study considers three decadal variability (1982–2013) in meteorological, hydrological, vegetation, and soil moisture parameters for inducing water scarcity and drought conditions in urban regions. Hazards associated with urban drought-inducing parameters variability, vulnerability, and exposure of Indian smart cities were used to compute the Urban drought risk. The research investigations revealed the maximum urban drought risk for Bangalore, Chennai, and Surat cities. Northwest, West Central, and South Peninsular urban regions have higher risk among all the urban regions of India. Indian smart cities mission can be used to make Indian cities resilient to urban drought risk and increase their sustainability. The present research aligned with several national and international agreements by providing an urban drought risk rank for Indian cities, making them less vulnerable to extreme weather events and improving their resilience to climate change.
Rainfall is a principal element of the hydrological cycle and its variability is important from both the scientific as well as practical point of view. Wavelet regression (WR) technique is proposed ...and developed to analyze and predict the rainfall forecast in this study. The WR model is improved combining two methods, discrete wavelet transform and linear regression model. This study uses rainfall data from 21 stations in Assam, India over 102 years from 1901 to 2002. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The root mean square errors (RMSE), N-S index, and correlation coefficient (R) statistics were used for evaluating the accuracy of the WR models. The accuracy of the WR models was then compared with those of the artificial neural networks (ANN) models. The results of monthly rainfall series modeling indicate that the performances of wavelet regression models are found to be more accurate than the ANN models.
Regionalization methods are often used in hydrology for frequency analysis of floods. The hydrologically homogeneous regions should be determined using cluster analysis instead of the geographically ...close stations. In view of the ongoing environmental and climate changes in the Northeastern of India, regionalization of homogeneous rainfall region is essential to lay out an effective flood frequency analysis of this region. The choice of appropriate cluster approach used according to the data of the basin is also significant. In the context of this study, total precipitation data of stations operated by Indian Meteorological Department (IMD) in Northeastern of India basins for cluster analysis are used. Further, five cluster validity indices, namely Partition Coefficient, Partition Entropy, Extended Xie-Beni index, Fukuyama-Sugeno index and Kwon index have been tested to determine the effectiveness in identifying optimal partition provided by the fuzzy c mean clustering algorithm (FCM). A comparison is also performed using K- Mean clustering algorithm. Additionally, regional homogeneity tests based on L-moments approach are used to check homogeneity of regions identified by both cluster analysis approaches. It was concluded that regional homogeneity test results show that regions defined by FCM method are sufficiently homogeneous for regional frequency analysis.
•Nonstationary COVID 19 risk analysis combining climatic and socioeconomic factors.•Strong climate influence on COVID 19 cases was observed in 474 (76.08%) out of 623 districts.•The total population ...in 50% of districts in 19 out of 35 states were lying under high risk.
This study investigates the influence of climate variables (pressure, relative humidity, temperature and wind speed) in inducing risk due to COVID 19 at rural, urban and total (rural and urban) population scale in 623 pandemic affected districts of India incorporating the socioeconomic vulnerability factors. We employed nonstationary extreme value analysis to model the different quantiles of cumulative COVID 19 cases in the districts by using climatic factors as covariates. Wind speed was the most dominating climatic factor followed by relative humidity, pressure, and temperature in the evolution of the cases. The results reveal that stationarity, i.e., the COVID 19 cases which are independent of pressure, relative humidity, temperature and wind speed, existed only in 148 (23.7%) out of 623 districts. Whereas, strong nonstationarity, i.e., climate dependence, was detected in the cases of 474 (76.08%) districts. 334 (53.6%), 200 (32.1%) and 336 (53.9%) districts out of 623 districts were at high risk (or above) at rural, urban and total population scales respectively. 19 out of 35 states were observed to be under high (or above) Kerala, Maharashtra, Goa and Delhi being the most risked ones. The study provides high-risk maps of COVID 19 pandemic at the district level and is aimed at supporting the decision-makers to identify climatic and socioeconomic factors in augmenting the risks.
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