Abstract
Seasonal water flow to semi-arid to arid lands worldwide is regularly controlled by precipitation and snowmelt processes in nearby mountain ranges, where few monitoring stations exist. In this paper we present a new approach to assess the seasonal and inter-annual development of precipitation and associated snow-cover area (SCA) and snow–water storage in the Qilian Mountains of NW China. The approach addresses the spatial enhancement and calibration of remote sensing (RS) data acquired from coarse-resolution TRMM (Tropical Rainfall Measuring Mission; ~26-km resolution) and MODIS images in the development of image-surfaces of in-mountain precipitation and surface air temperature at 250-m resolution for input to a new spatially-distributed monthly snow-accumulation and snowmelt model. Spatially-enhanced precipitation and air temperature surfaces were subsequently calibrated using either geographically-weighted regression or simple linear regression and point-data from climate stations. When point-extractions from the calibrated products were compared against a new set of independent climate-station data, their respective values were comparable, giving an overall r2 of 0.87 for precipitation (RMSE=10mmmonth−1) and 0.96 for surface air temperature. With input from calibrated surfaces, monthly SCA and snow–water equivalence (SWE) in the Qilian Mountains were subsequently modeled over a 10-year period (2000–2009). Seasonal values of SCA and SWE were compared with MODIS-based (optical) and AMSR-E passive microwave-based estimates of SCA and SWE. In most cases, comparisons revealed suitable agreement across the various evaluations of SCA. Minor discrepancy between MODIS- and AMSR-E-based estimates of SCA resulted in a mean overlap of 73% when modeled SCA was compared to MODIS-based SCA and 84% overlap, when compared to AMSR-E-based SCA. Modeled and AMSR-E-based estimates of SWE at lower- to upper-mountain elevations (≤3900m above mean sea level; AMSL) compared fairly well. At elevations >3900m AMSL, discrepancies between estimates were largely attributed to an overestimation of local precipitation.
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