Seasonal extreme rainfall over Indian monsoon region: a moisture budget analysis to distinguish the role of ENSO and non-ENSO forcing

Theoretical and Applied Climatology - Indian summer monsoon rainfall (ISMR) variability of ± 10% of its long-term mean leads to flood and drought, affecting the life and economic...     

Quantifying groundwater sensitivity and resilience over peninsular India

Groundwater in India plays an important role to support livelihoods and maintain ecosystems and the present rate of depletion of groundwater resources poses a serious threat to water security. Yet, the sensitivity of the hydrological processes governing groundwater recharge to climate variability remains unclear in the region. Here we assess the groundwater sensitivity (precipitation–recharge relationship) and its potential resilience towards climatic variability over peninsular India using a conceptual water balance model and a convex model, respectively in 54 catchments over peninsular India. Based on the model performance using a comprehensive approach (Nash Sutcliffe Efficiency [NSE], bias and variability), 24 out of 54 catchments are selected for assessment of groundwater sensitivity and its resilience. Further, a systematic approach is used to understand the changes in resilience on a temporal scale based upon the convex model and principle of critical slowing down theory. The results of the study indicate that the catchments with higher mean groundwater sensitivity (GWS) encompass high variability in GWS over the period (1988–2011), thus indicating the associated vulnerability towards hydroclimatic disturbances. Moreover, it was found that the catchments pertaining to a lower magnitude of mean resilience index incorporates a high variability in resilience index over the period (1993–2007), clearly illustrating the inherent vulnerability of these catchments. The resilience of groundwater towards climatic variability and hydroclimatic disturbances that is revealed by groundwater sensitivity is essential to understand the future impacts of changing climate on groundwater and can further facilitate effective adaptation strategies.     

Application of semi‐distributed hydrological model for basin level water balance of the Ken basin of Central India

In the present study, a semi‐distributed hydrological model soil and water assessment tool (SWAT) has been employed for the Ken basin of Central India to predict the water balance. The entire basin was divided into ten sub basins comprising 107 hydrological response units on the basis of unique slope, soil and land cover classes using SWAT model. Sensitivity analysis of SWAT model was performed to examine the critical input variables of the study area. For Ken basin, curve number, available water capacity, soil depth, soil evaporation compensation factor and threshold depth of water in the shallow aquifer (GWQ_MN) were found to be the most sensitive parameters. Yearly and monthly calibration (1985–1996) and validation (1997–2009) were performed using the observed discharge data of the Banda site in the Ken basin. Performance evaluation of the model was carried out using coefficient of determination, Nash–Sutcliffe efficiency, root mean square error‐observations standard deviation ratio, percent bias and index of agreement criterion. It was found that SWAT model can be successfully applied for hydrological evaluation of the Ken basin, India. The water balance analysis was carried out to evaluate water balance of the Ken basin for 25 years (1985–2009). The water balance exhibited that the average annual rainfall in the Ken basin is about 1132 mm. In this, about 23% flows out as surface run‐off, 4% as groundwater flow and about 73% as evapotranspiration. Copyright © 2013 John Wiley & Sons, Ltd.     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Simulating rainfall time-series: how to account for statistical variability at multiple scales?

Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is the modified Markov model, belonging to the family of Markov-chain based techniques, which introduces non-stationarity in the chain parameters to preserve the long-term behavior of rainfall. The second technique is direct sampling, based on multiple-point statistics, which aims at simulating a complex statistical structure by reproducing the same data patterns found in a training data set. The two techniques are compared by first simulating a synthetic daily rainfall time-series showing a highly irregular alternation of two regimes and then a real rainfall data set. This comparison allows analyzing the efficiency of different elements characterizing the two techniques, such as the application of a variable time dependence, the adaptive kernel smoothing or the use of low-frequency rainfall covariates. The results suggest, under different data availability scenarios, which of these elements are more appropriate to represent the rainfall amount probability distribution at different scales, the annual seasonality, the dry-wet temporal pattern, and the persistence of the rainfall events.     

Geochemical evolution of groundwater in hard-rock aquifers of South India using statistical and modelling techniques

ABSTRACT

Multivariate statistical analysis and inverse geochemical modelling techniques were employed to deduce the mechanism of groundwater evolution in the hard-rock terrain of Telangana, South India. Q-mode hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to extract the hydrogeochemical characteristics and classify the groundwater samples into three principal groups. Use of thermodynamic stability diagrams and inverse geochemical modelling in PHREEQC identified the chemical reactions controlling hydrogeochemistry of each of the groups obtained from statistical analysis. The model output showed that a few phases are governing the water chemistry in this area and the geochemical reactions responsible for evolution of groundwater chemistry along the flow path are (i) dissolution of evaporite minerals (dolomite, halite); (ii) dissolution of primary silicate minerals (albite, anorthite, K-feldspar, biotite); (iii) precipitation of secondary silicate minerals (kaolinite, quartz, gibbsite, Ca-montmorillonite) along with anhydrite and calcite; and (iv) reverse ion exchange processes.     

A multi-isotope approach (O,H, C,S, B and Sr) to understand the source of water and solutes in some the thermal springs from West Coast geothermal area,India

The West Coast belt, consisting of nearly 60 thermal springs, is one of the most diversified geothermal fields in India. The present work describes the multi-isotopic (O, H, C, S, B and Sr) characterization of thermal waters carried out in the Tural-Rajwadi geothermal field, situated in southern sector of the west coast geothermal area. The aim of this study is to delineate the origin of thermal water as well as to ascertain the sources of carbon, sulphur, boron and strontium dissolved in those thermal springs. The stable isotopes (δ²H and δ¹⁸O) and tritium data indicate that these thermal springs are not recently recharged rain water rather, it contains very old component of water. Oxygen-18 shift is observed due to rock-water interaction over a long period of time. Carbon isotopic composition of DIC points out to the silicate weathering with soil CO₂ coming from C₃ type of plants whereas δ³⁴S of dissolved sulphate confirms the marine origin of sulphate. This marine signature is basically derived from paleo-seawater possibly entrapped within the flows. Boron isotopic data reveals that both the seawater and rock dissolution are the sources of boron in the thermal waters whereas high ⁸⁷Sr/⁸⁶Sr ratios (0.7220–0.7512) of the thermal waters conclusively establishes that archean granitic basement is the predominant rock source of strontium, not the Deccan flood basalts. In addition, like strontium, concentrations of lithium, rubidium and caesium are also governed by the rock-water interaction. Thus, the combined use of this multi-isotope technique coupled with trace element concentrations proves to be an effective tool to establish the sources of solutes in the thermal water.     

Dynamical downscaling approach for wintertime seasonal-scale simulation over the Western Himalayas

The performance of RegCM4 for seasonal-scale simulation of winter circulation and associated precipitation over the Western Himalayas (WH) is examined. The model simulates the circulation features and precipitation in three distinct precipitation years reasonably well. It is found that the RMSE decreases and correlation coefficient increases in the precipitation simulations with the increase of model horizontal resolutions. The ETS and POD for the simulated precipitation also indicate that the performance of model is better at 30 km resolution than at 60 and 90 km resolutions. This improvement comes due to better representation of orography in the high-resolution model in which sharp orography gradient in the domain plays an important role in wintertime precipitation processes. A comparison of model-simulated precipitation with observed precipitation at 17 station locations has been carried out. Overall, the results suggest that 30 km model produced better skill in simulating the precipitation over the WH and this model is a useful tool for further regional downscaling studies.     

Estimation of wave period statistics using numerical coastal wave model

We used field and model wave data to investigate that zero crossing and average wave period distribution follow Gamma distribution. Since Gamma CDF is an infinite power series, further mathematical treatment is difficult. Hence its shape parameter is approximated to the nearest integer to arrive at Erlang distribution. An expression is derived from Erlang distribution to estimate various mean wave periods and significant wave period and validated by relative root mean square (RRMS) error criteria. It is shown by mathematical logic that the significant wave period distribution follows Erlang (or Gamma) distribution and is validated. The average of one-third and one-tenth highest wave periods (T _s ) estimated from Erlang distribution are in accordance with the values computed from recorded buoy and numerical coastal wave model wave period data. The predicted T _s values from coastal wave model wave period data underestimates the values from buoy wave period data.