Climate change is a major concern in the twentyfirst century and its assessments are associated with multiple uncertainties, exacerbated and confounded in the regions where human interventions are prevalent. The present study explores the challenges for climate change impact assessment on the water resources of India, one of the world's largest human-modified systems. The extensive human interventions in the Energy-Land-Water-Climate (ELWC) nexus significantly impact the water resources of the country. The direct human interventions in the landscape may surpass/amplify/mask the impacts of climate change and in the process also affect climate change itself. Uncertainties in climate and resource assessments add to the challenge. Formulating coherent resource and climate change policies in India would therefore require an integrated approach that would assess the multiple interlinkages in the ELWC nexus and distinguish the impacts of global climate change from that of regional human interventions. Concerted research efforts are also needed to incorporate the prominent linkages in the ELWC nexus in climate/earth system modelling.

Keywords:Statistical downscaling Bayesian multimodel average Uncertainty Signal to Noise Ratio (SNR) s u m m a r y Impacts of climate change are typically assessed with fairly coarse resolution General Circulation Models (GCMs), which are unable to resolve local scale features that are critical to precipitation variability. GCM simulations must be downscaled to finer resolutions, through statistical or dynamic modelling for further use in hydrologic analysis. In this study, we use a linear regression based statistical downscaling method for obtaining monthly Indian Summer Monsoon Rainfall (ISMR) projections at multiple spatial resolutions, viz., 0.05°, 0.25°and 0.50°, and compare them. We use 19 GCMs of Coupled Model Intercomparison Project Phase 5 (CMIP5) suite and combine them with multi model averaging and Bayesian model averaging. We find spatially non-uniform changes in projections at all resolutions for both combinations of projections. Our results show that the changes in the mean for future time periods (2020s, 2050s, and 2080s) at different resolutions, viz., 0.05°, 0.25°and 0.5°, obtained with both Multi-Model Average (MMA) and Bayesian Multi-Model Average (BMA) are comparable. We also find that the model uncertainty decreases with projection times into the future for all resolutions. We compute Signal to Noise Ratio (SNR), which represents the climate change signal in simulations with respect to the noise arising from multi-model uncertainty. This appears to be almost similar at different resolutions. The present study highlight that, a mere increase in resolution by a way of computationally more expensive statistical downscaling does not necessarily contribute towards improving the signal strength. Denser data networks and finer resolution GCMs may be essential for producing usable rainfall and hydrologic information at finer resolutions in the context of statistical downscaling.

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Earth's land cover (LC) has significant influence on land‐atmospheric processes and affects the climate at multiple scales. There are multiple global LC (GLC) data sets which are yet to be evaluated for uncertainties and their propagation into the simulation of land surface fluxes (LSFs) in land surface/climate modeling. The present study assesses the uncertainties in seven GLC products with reference to a regional data set for the simulation of LSFs in India using a macro‐scale land surface model. There is considerable overestimation of the extent of croplands in most of the GLCs. The uncertainties in LCs exert significant bias in the simulation of the LSFs of actual evapotranspiration (ETa), latent heat (LE), and sensible heat (H) fluxes. Uncertainty propagation in LSFs is proportional to the bias in cropping intensity under rainfed condition. The high underrepresentation of cropland area in the UMd data set results in highest bias in LSFs whereas the least cropland bias in Globland30 leads to least bias. Irrigation has higher potential to alter the LSFs than uncertainties related to LC especially in regions with large area under irrigation like India. The changes in LSFs are higher in arid/semiarid regions with medium irrigation intensity than in subhumid regions with high irrigation intensity. This has significant implications for the country's future irrigation expansion plans in the arid/semiarid regions. The study also emphasizes the need for focused efforts to quantify the uncertainties from varying irrigation intensities in the next generation CMIP6 experiments.

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