25In this study, a Bayesian framework is proposed for investigating uncertainties in input data (i.e. 26 temperature and precipitation) and parameters in a distributed hydrological model as well as their 27 effects on the runoff response in the Kaidu watershed (a snowmelt-precipitation-driven 28 watershed). In the Bayesian framework, Soil and Water Assessment Tool (SWAT) is used for 29 providing the basic hydrologic protocols. The Delayed Rejection Adaptive Metropolis (DRAM) 30 algorithm is employed for the inference of uncertainties in input data and model parameters with 31 global and local adaptive strategies. The advanced Bayesian framework can help facilitate the 32 exploration of variation of model parameters due to input data errors, as well as propagation 33 from uncertainties in data and parameters to model outputs in both snow-melting and 34 non-melting periods. A series of calibration cases corresponding to data errors under different 35 periods are examined. Results show that: (i) input data errors can affect the distributions of 36 model parameters as well as parameters' correlation, implying that data errors could influence 37 the related hydrologic processes as well as their relations; (ii) considering input data errors could 38 improve the hydrologic simulation ability for peak streamflows; (iii) considering errors of 39 temperature and precipitation data as well as uncertainties of model parameters can provide the 40 best modeling simulation performance in the snow-melting period; (iv) accounting for 41 uncertainties in precipitation data and model parameters can provide the best modeling 42 performance during the non-melting period. The findings will help enhance hydrological model's 43 capability for simulating/predicting water resources during different seasons for 44 snowmelt-precipitation-driven watersheds. 45 46 3 Keywords: DRAM, hydrological response, input data errors, Markov Chain Monte Carlo, model 47 157 3. Methodology 158 159 a. Hydrological model 160 161The hydrological processes from precipitation and snow pack to runoff can be simulated using 162SWAT. The foundation behind the hydrologic simulation is soil water balance, in which the 163

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Uncertainties in spatial data associated with basin topography, drainage networks, and land cover characteristics may affect the performance of runoff simulation. Such uncertainties are mainly derived from selection of digital elevation model (DEM) resolution and basin subdivision level. This study focuses on assessing the effects of DEM resolution and basin subdivision level on runoff simulation with a semi-distributed land use-based runoff process model. Twenty-four scenarios based on various DEM resolutions and subdivision levels are analyzed for the Kaidu River Basin. Results can be used for quantifying the uncertainty of input data about spatial information on model simulation, disclosing the interaction between DEM resolution and subdivision level, as well as identifying the optimal system inputs. Results show that the model performance could be enhanced with the increased subdivision level. Results also reveal that the interaction of DEM resolution and subdivision level has slight effects on modeling outputs. Multi-objective fuzzy evaluation is used to further examine the uncertainty in DEM resolution and basin subdivision level on model performance. The results indicate an optimal combination of input parameters is suitable for Kaidu River Basin which could lead to more reliable results of the hydrological simulation.

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In this study, a stepwise-clustered downscaling model (SCDM) is advanced for transferring atmospheric simulation outputs to acquire high-resolution climate projections at a large-scale watershed system. SCDM can operate different temporal resolutions of atmospheric variables with continuous and discrete complexities. SCDM coupling with hydrological model is used for evaluating climate change impacts on hydrology of the Kaidu watershed in northwestern China. The daily and monthly series of large-scale atmospheric simulation outputs for the Kaidu watershed are extracted from the ensemble of GCMs during past, recent and future periods. Results reveal that (1) SCDM is capable of downscaling climate projections for different stations, and can help understand the spatial heterogeneity of climate change, (2) the performance of SCDM is more acceptable for temperature than precipitation, (3) increase trends of T min and T max (minimum and maximum temperatures) from recent to future are projected. Besides, results from multiple downscaled climate change projections are used for driving a daily climate-streamflow hydrological model. Results disclose that the streamflow would increase because temperature change will cause more glacier melt in future.