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Changes in mean and extreme precipitation characteristics with changing climate may lead to an increase in frequency of hydrological extremes. For studying the impacts of the changing climate on hydrological systems, General Circulation Model (GCM)/Regional Climate Model (RCM) simulated precipitation are used. However, these products should be bias-corrected before used in hydrological simulations to predict hydrological extremes. Most of the existing bias-correction techniques suffer from either of two limitations – (a) they only reduce bias in selected precipitation quantile (either mean or extreme values), and/or (b) they exclude zero values from the analysis, even though their presence is significant in daily precipitation. In this study, a stochastic copula-based bias-correction method (Maity et al., J. Hydrometeorol., 20, 2019, 595), henceforth RMPH method, is used that corrects the bias in any quantile (mean and/or extreme values) of daily precipitation including zero values. The RMPH method is applied across Indian mainland to correct bias in simulated precipitation from the Coordinated Regional Climate Downscaling Experiment (CORDEX). Due to diverse climatic conditions across India, the quality of bias-corrected precipitation is studied separately for different meteorologically homogenous regions of the country. Despite non-uniform distribution of raingauge stations for observed precipitation, the superiority of the bias-corrected precipitation (from RMPH method) in correcting bias and retaining the seasonal variation across the country is evident when compared with tradition bias-correction approach like quantile mapping. The new bias-corrected precipitation dataset developed is particularly suited for hydrological simulations, formulating extreme event mitigation strategies and climate change adaptation strategies. © 2021 The Authors. Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.

Department of Civil & Infrastructure Engineering

World University root node

Geoscience Data Journal

Precipitation of Mainland India: Copula‐based bias‐corrected daily CORDEX climate data for both mean and extreme values

Most of the existing studies on meteorological drought suggest more intense and frequent drought events due to changing climate. However, basin-scale assessment of future agricultural drought is lacking due to many reasons. In this study, the intensity and frequency of future agricultural drought (characterized by the Standardized Soil Moisture Index, SSMI) for 226 sub-basins across India are analyzed, and vulnerable basins are identified. The prediction of the future agricultural drought status is achieved using the wavelet-based drought temporal consequence modeling of meteorological drought with the best performing bias-corrected Coordinated Regional Downscaling Experiment (CORDEX) simulations, selected by Multi-Criteria Decision-Making frameworks. This study reveals a geographically contrasting change in future agricultural drought that indicates more intense agricultural drought in north, north-east, and central India as compared with south India. The area under drought is also expected to increase, and about 20 and 50% of the Indian mainland is expected to suffer from extreme (SSMI < 2) and moderate (SSMI < 1) agricultural drought conditions by the end of this century. Sub-basins lying in north and central India are expected to have a longer time under drought conditions. Thus, the findings of this study will be useful for future planning and preparedness against agricultural productivity. © 2021 The Authors.

Journal of Water and Climate Change

Assessment of basin-wise future agricultural drought status across India under changing climate

EGU General Assembly Conference Abstracts, EGU

Future Changes in extreme precipitation over South Asia and its causes

Analysis and Modeling of Climate Change Impact on Hydrological Extreme Events across India

EGU General Assembly Conference Abstracts

Modeling of Basin Scale Hydro-meteorological association by Hybrid Wavelet-ARX approach

Analysis of observed Indian Summer Monsoon precipitation reveals more increase in extreme precipitation (in terms of its magnitude) over south India compared to north and central India during 1971–2017 (base period: 1930–1970). In the future, analysis of precipitation from the Coordinated Regional Downscaling Experiment indicates a southward shift of precipitation extremes over South Asia. For instance, the Arabian Sea, south India, Myanmar, Thailand, and Malaysia are expected to have the maximum increase (~18.5 mm/day for RCP8.5 scenario) in mean extreme precipitation (average precipitation for the days with more than 99th percentile of daily precipitation). However, north and central India and Tibetan Plateau show relatively less increase (~2.7 mm/day for RCP8.5 scenario). Analysis of air temperature at 850 mb and precipitable water (RCP4.5 and RCP8.5) indicates an intensification of Indian Ocean Dipole in future, which will enhance the monsoon throughout India. Moisture flux and convergence analysis (at 850 mb) show a future change of the direction of south-west monsoon winds towards the east over the Indian Ocean. These changes will intensify the observed contrast in extreme precipitation between south and north India, and cause more extreme precipitation events in the countries like Myanmar, Thailand, Malaysia, etc. © 2020, The Author(s).

Scientific reports

Southward shift of precipitation extremes over south Asia: Evidences from CORDEX data

Hydrological processes are continuously evolving over time under the influence of a number of factors including climatic changes. The effect of these factors leads to the nonstationary nature of hydrologic time series. This chapter focuses on the potential of wavelet transform (WT) to deal with the hydrologic time series prediction considering its time-varying nature. This chapter first presents a brief theoretical background of wavelet transform. Next, a few applications of basin-scale drought predictions are presented to demonstrate the potential of wavelet-based models. When applied to two basins of different sizes, the wavelet-based method is found to satisfactorily capture the interrelationship between different types of droughts and their propagation from one type to another. It should also be noted that the methodology presented is general and can be applied to similar problems dealing with hydrologic prediction. © 2019, Springer Nature Switzerland AG.

Hydrology in a Changing World

Predictability of hydrological systems using the wavelet transformation: Application to drought prediction

Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide. © 2019 American Meteorological Society.

Journal of Hydrometeorology

Bias correction of zero-inflated RCM precipitation fields: a copula-based scheme for both mean and extreme conditions

Interaction between meteorological and hydrologic processes is challenging to model owing to their high spatio-temporal variability. The understanding of their associations can help to ensure future fresh water security with changing climate. In this study, due to continuously evolving nature of these interactions, the hydrological and meteorological variables are studied on wavelet component level. Multi-Resolution Stationary Wavelet Transformation (MRSWT) is used to transform the independent (climatic variable) and dependent (hydrological variable) time series into their components. The components of the dependent time series are modeled using a kernel-based auto-regressive (AR) model for separating their memory part. The residuals are hypothesized to be the effect of interaction of predictor variables and thus, are modeled using the MRSWT components of meteorological variables in an auto-regressive model with exogenous inputs (ARX). Finally, the predicted residuals (effect of climatic variables) are added to the component estimated by kernel-based AR estimator (memory of dependent series components) to obtain the predicted components of the dependent hydrologic variable, which are then inverse-transformed to obtain the predicted dependent hydrologic variable. The developed hybrid Wavelet-ARX is found to capture the information about relationship between synthetically generated data better than a simple ARX model. The model is then applied to predict total monthly rainfall over Upper Mahanadi Basin and is found to effectively extract the information from the poorly associated hydro meteorological variables. While the potential of Wavelet-ARX is found to be impressive for hydro meteorological applications, additionally, discarding some climatic inputs on the basis of their relative importance may lead to better prediction by the developed model. The developed model is suitable for extracting climatic forcings and is desirable in a changing climate. © 2019 Elsevier B.V.

Journal of Hydrology

Hybrid Wavelet-ARX approach for modeling association between rainfall and meteorological forcings at river basin scale

HydroClimatic Conceptual Streamflow (HCCS) model is a conceptual model for prediction and future assessment of daily streamflow using climate inputs and time-varying watershed characteristics. However, without denying its useful salient features in a changing climate, applicability of the HCCS model is limited to the basins without any major man-made river structure(s), such as reservoirs. Considering this, the originally proposed HCCS model is upgraded (hereinafter ‘upgraded HCCS model’) to accommodate the human-intervened release from such structures within the basin, if any, and to include routing component through the river channels without using rigorous information from the river channels. The upgraded HCCS model is expected to be useful to assess (i) the effect on the streamflow at downstream due to upstream dam release, and (ii) the long-term modification required in the reservoir/dam operation under a changing climate for ensuring water-availability in downstream. The upgraded HCCS model is applied to three river basins for assessing the future streamflow characteristics. Two of these basins have one each and the third basin has two major man-made river structures within them. Hadley Centre Coupled Model, version 3 (HadCM3) simulated climate variables till 2035 are used as inputs for demonstration. The model predicts an increase in streamflow in future. In general, the upgraded HCCS model can be applied to any tropical river basin having major man-made river structure(s) for daily streamflow prediction as well as assessment of future streamflow variation considering the changing climate and watershed characteristics. © 2019, Springer Nature B.V.

Water Resources Management

Assessment of Streamflow Variability with Upgraded HydroClimatic Conceptual Streamflow Model

Probabilistic Assessment of Soil Moisture using C-band Quad-polarized Remote Sensing Data from RISAT1

This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by using the synthetic aperture radar data provided by radar imaging satellite1. The novelty of this paper lies in: 1) developing a combined index to understand the role of all the backscattering coefficients with different polarization and soil texture information in influencing the SMC; 2) using normalized incidence angles, which enables the model to be applicable for different incidence angles; and 3) determination of uncertainty range of the estimated SMC. The dimensionality problem, which is frequently observed in the multivariate analysis, is reduced in the development of the combined index by the use of supervised principal component analysis (SPCA). The SPCA also ensures the maximum attainable association between the developed combined index and surface SMC above wilting point (WP). The association between the combined index and the surface SMC above WP is modeled through joint probability distribution by using the Frank copula. The model is developed and validated with the field soil moisture values over 334 monitoring points within the study area. The outcomes obtained by applying the proposed model indicate an encouraging potential of the model to be applied for bareland and vegetated land (<30 cm height). © 1980-2012 IEEE.

IEEE Transactions on Geoscience and Remote Sensing

Satellite-based probabilistic assessment of soil moisture using C-band quad-polarized RISAT1 data

Proceedings of the international conference on hydraulics, water resources {\ldots}

Foreseeing the agricultural and hydrological drought knowing the ongoing meteorological scenarios through wavelet analysis

Droughts are expected to propagate from one type to another - meteorological to agricultural to hydrological to socio-economic. However, they do not possess a universal, straightforward temporal dependence. Rather, assessment of one type of drought (successor) from another (predecessor) is a complex problem depending on the basin's physiographic and climatic characteristics, such as, spatial extent, topography, land use, land cover and climate regime. In this paper, a wavelet decomposition based approach is proposed to model the temporal dependence between different types of droughts. The idea behind is to separate the rapidly and slowly moving components of drought indices. It is shown that the temporal dependence of predecessor (say meteorological drought) on the successor (say hydrological drought) can be better captured at its constituting components level. Such components are obtained through wavelet decomposition retaining its temporal correspondence. Thus, in the proposed approach, predictand drought index is predicted using the decomposed components of predecessor drought. Several alternative models are investigated to arrive at the best possible model structure for predicting different types of drought. The proposed approach is found to be very useful for foreseeing the agricultural or hydrological droughts knowing the meteorological drought status, offering the scope for better management of drought consequences. The mathematical framework of the proposed approach is general in nature and can be applied to different basins. However, the limitation is the requirement of region/catchment specific calibration of some parameters before using the proposed model, which is not very difficult and uncommon though. © 2016 Elsevier B.V.