Uncertainties in downscaled relative humidity for a semi-arid region in India – A paper in Journal of Earth Systems Science

This paper in Journal of Earth Systems Science deals with uncertainties in downscaled relative humidity for a semi-arid region - Malaprabha basin in India. Researchers and decision-makers who assess the impact of climate change in river basin development, agriculture, hydrology, irrigation management, etc., require future scenarios of relative humidity.

In a river basin, relative humidity is one of the variables used in the estimation of evapotranspiration by Penman–Monteith method which is recommended as a standard method by the Food and Agriculture Organisation. Precipitation and evapotranspiration play key roles in the development of irrigation management programmes which require knowledge of when to irrigate and the amount of water to apply.

Monthly scenarios of relative humidity were obtained for the Malaprabha river basin in India using a statistical downscaling technique. Large-scale atmospheric variables (air temperature and specific humidity at 925 mb, surface air temperature and latent heat flux) were chosen as predictors. The predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis dataset for the period 1978–2000, and (2) simulations of the third generation Canadian Coupled Global Climate Model for the period 1978–2100.

The objective of this study was to investigate the uncertainties in regional scenarios developed for relative humidity due to the choice of emission scenarios (A1B, A2, B1 and COMMIT) and the predictors selected. Multi-linear regression with stepwise screening is the downscaling technique used in this study. To study the uncertainty in the regional scenarios of relative humidity, due to the selected predictors, eight sets of predictors were chosen and a downscaling model was developed for each set. Performance of the downscaling models in the baseline period (1978–2000) was studied using three measures (1) Nash– Sutcliffe error estimate (Ef ), (2) mean absolute error (MAE), and (3) product moment correlation (P).

Results show that the performances vary between 0.59 and 0.68, 0.42 and 0.50 and 0.77 and 0.82 for Ef , MAE and P. Cumulative distribution functions were prepared from the regional scenarios of relative humidity developed for combinations of predictors and emission scenarios. Results show a variation of 1 to 6 per cent relative humidity in the scenarios developed for combination of predictor sets for baseline period. For a future period (2001–2100), a variation of 6 to 15 per cent relative humidity was observed for the combination of emission scenarios and predictors. The variation was highest for A2 scenario and least for COMMIT and B1 scenario.

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