Different hydrological models provide diverse perspectives of the system being modeled, and inevitably, are imperfect representations of reality. Irrespective of the choice of models, the major source of error in any hydrological modeling is the uncertainty in the determination of model parameters, owing to the mismatch between model complexity and available data. Sensitivity analysis (SA) methods help to identify the parameters that have a strong impact on the model outputs and hence influence the model response. In addition, SA assists in analyzing the interaction between parameters, its preferable range and its spatial variability, which in turn influence the model outcomes. Various methods are available to perform SA and the perturbation technique varies widely. This study attempts to categorize the SA methods depending on the assumptions and methodologies involved in various methods. The pros and cons associated with each SA method are discussed. The sensitivity pertaining to the impact of space and time resolutions on model results is highlighted. The applicability of different SA approaches for various purposes is understood. This study further elaborates the objectives behind selection and application of SA approaches in hydrological modeling, hence providing valuable insights on the limitations, knowledge gaps, and future research directions.

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Abstract. The negative impacts of climate change are expected to be felt over wide range of spatial scales, ranging from small basins to large watersheds, which can possibly be detrimental to the services that natural water systems provide to the society. The impact assessment of future climate change on hydrologic response is essential for the 10 decision makers while carrying out management and various adaptation strategies in a changing climate. While, the availability of finer scale projections from regional climate models (RCM) has been a boon to study changing climate conditions, these climate models are subjected to large number of uncertainties, which demands a careful selection of an appropriate climate model, however. In an effort to account for these uncertainties and select suitable climate models, a multi-criteria ranking approach is deployed in this study. Ranking of CORDEX RCMs 15 is done based on its ability to generate hydrologic components of the basin, i.e., runoff simulations using Soil Water Assessment Tool (SWAT) model, by deploying Entropy and PROMETHEE-2 methods. The spatial extent of changes in the different components of hydrologic cycle is examined over the Ganga river basin, using the top three ranked RCMs, for a period from January 2021 to December 2100. It is observed that for monsoon months (June, July, August and September), future annual mean surface runoff will decrease substantially (-50 % to -20 10%), while the flows for post-monsoon months (October, November and December) are projected to increase (10-20 %). While, extremes are seen to be increasing during the non-monsoon months, a substantial decrease in medium events is also highlighted. The increase in wet extremes is majorly supplemented by the increased snowmelt runoff during those months. Snowmelt is projected to increase during the months of November to March, with the month of December witnessing 3-4 times increase in the flow. Base flow and recharge are 25 alarmingly decreasing over the basin. Major loss of recharge is expected to occur in central part of the basin. The present study offers a more reliable regional hydrologic impact assessment with quantifications of future dramatic changes in different hydrological sub-system and its mass-transfer, which will help in quantifying the changes in hydrological components in response to climate change changes in the major basin Ganga, and shall provide the water managers with substantive information, required to develop ameliorative strategies. 30

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