R. Mehrotra

Assessing Severe Drought and Wet Events over India in a Future Climate Using a Nested Bias-Correction Approach

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. DOI: 10.1061/(ASCE)HE.1943-5584.0000585.

Comparison of Statistical Downscaling Techniques for Multisite Daily Rainfall Conditioned on Atmospheric Variables for the Sydney Region

Abstract Predictions of rainfall spatial and temporal variability (including climate change effects) on a catchment basis are urgently required by water resource planners within Australia. Large spatial scale predictions of (typically 300 to 500 km grids) global scale climate scenarios output by General Circulation Models (GCMs) are inadequate for such use as they do not capture the large degree of spatial variability over smaller distances, which is inherent in rainfall. Multisite daily rainfall - a common requirement within many hydrological models - is a required input for modelling complex multi-catchment systems, as small scale spatial variability due to factors such as topography has a large bearing on how much rainfall falls in a given area. Statistical downscaling is a technique that can produce such fine spatial scale rainfall pattern predictions conditional on the larger scale climate scenarios output by a GCM. The GLIMCLIM (Generalised Linear Model for daily Climate time series) software package (Chandler, 2002) has been used to analyse and simulate spatial daily rainfall given natural climate variability influences in the UK, and further to predict the influence of various future climate scenarios on regional rainfall by downscaling larger spatial scale GCM simulations. This paper describes the comparison of this method to the non-parametric, non-homogeneous hidden Markov model - kernel probability density estimation (NNHMM-KDE) downscaling technique of Mehrotra & Sharma (2006), a method which has found application in Australia previously.

A software toolkit for correcting systematic biases in climate model simulations

Abstract Simulations from climate models require bias correction prior to use in impact assessments or for statistical or dynamic downscaling to finer scales. There are a number of different approaches to bias correction, although most of these focus on a single variable for a particular location. Another limitation is that often corrections are only applied for one time scale of interest, for example daily or monthly aggregated simulations despite evidence of different bias structures existing at different time scales. Recent works have sought to address each of these limitations and have led to the development of the Multivariate Recursive Nesting Bias Correction (MRNBC) and Multivariate Recursive Quantile-matching Nested Bias Correction (MRQNBC) methods. An open-source software toolkit in the R statistical computing environment has been developed to provide access to these methods. Several applications of the software are demonstrated in this paper along with information about the capabilities of the software.