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.

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Precise characterization of surface air temperature at different spatial scales remains a challenge as it often requires a dense network of temperature sensors. The most common approach for estimating surface air temperature at a desired location in hydrological and terrestrial models is to interpolate surface air temperature observations obtained from a sparse set‐up of temperature sensors using the global standard uniform lapse rate. This interpolation technique, though easy to use, can lead to unreliable results, as the global standard uniform lapse rate may not be representative of seasonal and local variations in lapse rate. In this study, linear and nonlinear regression relationships are derived to estimate mean minimum, mean and mean maximum near surface air temperatures (at monthly and annual time scales) across India. The normal daily air temperature data from 439 stations and their corresponding latitude, longitude and elevation are used in the analysis. The computed temperature gradients exhibit seasonal variation. The effects of elevation and longitude are more pronounced in pre‐monsoon (March–May) and post‐monsoon (October–November) months, whereas the effects of latitude are more in winter months (December–February). Results indicate that the global standard uniform lapse rate (− 0.65 ° C 100 m− 1) is only applicable for monthly mean maximum temperature for April and May. The highest interpolation reliability is obtained for monthly mean minimum temperature. The regression equations though simple, hold promise for describing surface air temperature across India.

Spatial heterogeneity in soil properties has been a challenge for providing field-scale estimates of infiltration rates and surface soil moisture content over natural fields. In this study, we develop analytical expressions for effective saturated hydraulic conductivity for use with the Green-Ampt model to describe field-scale infiltration rates and evolution of surface soil moisture over unsaturated fields subjected to a rainfall event. The heterogeneity in soil properties is described by a log-normal distribution for surface saturated hydraulic conductivity. Comparisons between field-scale numerical and analytical simulation results for water movement in heterogeneous unsaturated soils show that the proposed expressions reproduce the evolution of surface soil moisture and infiltration rate with time. The analytical expressions hold promise for describing mean field infiltration rates and surface soil moisture evolution at field-scale over sandy loam and loamy sand soils.

Scaling relationships are needed as measurements and desired predictions are often not available at concurrent spatial support volumes or temporal discretizations. Surface soil moisture values of interest to hydrologic studies are estimated using ground based measurement techniques or utilizing remote sensing platforms. Remote sensing based techniques estimate field-scale surface soil moisture values, but are unable to provide the local-scale soil moisture information that is obtained from local measurements. Further, obtaining field-scale surface moisture values using ground-based measurements is exhaustive and time consuming. To bridge this scale mismatch, we develop analytical expressions for surface soil moisture based on sharp-front approximation of the Richards equation and assumed log-normal distribution of the spatial surface saturated hydraulic conductivity field. Analytical expressions for field-scale evolution of surface soil moisture to rainfall events are utilized to obtain aggregated and disaggregated response of surface soil moisture evolution with knowledge of the saturated hydraulic conductivity. The utility of the analytical model is demonstrated through numerical experiments involving 3-D simulations of soil moisture and Monte-Carlo simulations for 1-D renderings-with soil moisture dynamics being represented by the Richards equation in each instance. Results show that the analytical expressions developed here show promise for a principled way of scaling surface soil moisture.