Evaluation of different large‐scale predictor‐based statistical downscaling models in simulating zone‐wise monsoon precipitation over India

Abstract

Selection of suitable predictors for downscaling local-scale precipitation from the wide range of large-scale predictors available in National Center for Atmospheric Research/National Centers for Environmental Prediction (NCAR/NCEP) reanalysis is a challenging task because of the existence of the complex interactions between local-scale predictands and large-scale predictor fields. An attempt was made to assess how well different large-scale predictors were able to reproduce local-scale monsoon precipitation over seven homogeneous zones of India through statistical downscaling. For calibration of downscaling (DS) models, the principal component (PC)-based multiple linear regression approach was adopted where each raw gridpoint predictor field transformed into PCs using empirical orthogonal function (EOF) analysis. The predictors consistently producing better downscaled results across four nonoverlapping calibration and validation periods were identified as "superior predictor" (SP). It was found that some common predictors like precipitable water; specific and relative humidity at different levels have emerged as SP predictors over several zones. In general, SP predictors have not been much sensitive with small changes in the domain size. However, a decline in performances of DS models was noticed for the majority of SP predictors for a large increase in the size of domains. Especially, the largest South Asia domain has been the most inappropriate domain as very few predictors found to be suitable for downscaling. In general, about 40% out of 36 numbers of combined predictors were identified as potential SP predictors over the majority of the zones. Several numbers of combined SP predictors have also produced slightly superior skills compared to single SP predictors. In many cases, predictors showing poor performance as single predictors have produced improved performances when combined with other predictors.

Modeling the impact of climate change on the hydrology of Andasa watershed

“…They reported that the reasons for differing results related to climate change are related to the inconsistent use of both climate and hydrologic models, emission scenarios, and GCM output downscaling techniques. Akhter et al (2018) also reported that differences in rainfall projections may occur due to the variations of atmospheric domain sizes which determine the hydroclimatology of a watershed by affecting the selection and number of sensible predictors.…”

High-Resolution Precipitation Gridded Dataset on the South-Central Zone (34° S–41° S) of Chile

“…Spatial fitting, selection of monthly fields from the WRF model, and spatial selection of the best monthly products of global grid precipitation are analyzed for each regional domain (Figure 1A): The results of these processes are displayed for eight hydrological basins (Figure 1B). Our strategy uses a range of statistics following Cardoso et al (2013), Ji et al (2015), and Akhter et al (2019): standardized standard deviation (σ e , Eq. 6), standardized mean square error (nrmse, Eq.…”

How well are the downscaled CMIP5 models able to reproduce the monsoon precipitation over seven homogeneous zones of India?

“…The categories of predictors are (a) circulation variables (Geo‐potential height, mean sea level pressure, u and v wind components), (b) moisture variables (specific and relative humidity, precipitable water) and (c) thermal variable (temperature) at different pressure levels that is, 500, 850 and 1,000 hPa. All the considered predictors for the present study, have been previously used for downscaling over Indian domain (Pervez and Henebry, ; Akhter et al, ). Although large‐scale atmospheric circulation may explain local precipitation behaviour, other variables such as humidity and temperature in various atmospheric levels that are not directly related to pressure field could also play a significant role in controlling precipitation on smaller scales (Abaurrea and Asín, ).…”

“…K E Y W O R D S CMIP5 models, perfect prognosis, performance metrics, principal component regression, quantile mapping, statistical downscaling 1 | INTRODUCTIONThe recent two generations of the global climate models (GCMs) from the fourth and fifth assessment reports of the IPCC, providing a range of horizontal resolutions 103 to 455 km (average of 254 km) in case of coupled model inter comparison project phase 3 (CMIP3) and 68 to 342 km (average of 193 km) in case of coupled model inter comparison project, phase 5 (CMIP5), are still considered as coarse grid models because they are usually unable to incorporate local/regional scale features like topography, clouds, land-use etc. adequately (Sachindra et al, 2014;Akhter et al, 2018). The scale mismatch between the GCM outputs and the hydroclimatic information needed at the catchment level is a major obstacle in climate impact assessment studies of hydrology and water resources (Willems and Vrac, 2011).…”