Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains, north‐west China

Abstract

Snowmelt water is an important freshwater resource in the Altay Mountains in north‐west China; however, warming climate and rapid spring snowmelt can cause floods that endanger both public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature index model based on remote sensing coupled with high‐resolution meteorological data obtained from National Centers for Environmental Prediction (NCEP) reanalysis fields that were downscaled using the Weather Research Forecasting model and then bias corrected using a statistical downscaled model. Validation of the forcing data revealed that the high‐resolution meteorological fields derived from the downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of the temperature index model based on remote sensing were calibrated for spring 2014, and model performance was validated using Moderate Resolution Imaging Spectroradiometer snow cover and snow observations from spring 2012. The results show that the temperature index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash–Sutcliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt run‐off was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt run‐off accounts for 72% of spring run‐off and 21% of annual run‐off. Snowmelt is the main source of run‐off for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt run‐off predictions, so as to prevent snowmelt‐induced floods, and also provide a generalizable approach that can be applied to other remote locations where high‐density, long‐term observational data are lacking. Copyright © 2016 John Wiley & Sons, Ltd.

Snowmelt Water Alters the Regime of Runoff in the Arid Region of Northwest China

“…About 25-48% [18,19] or 18-28% [52] of annual runoff at catchments fed by snow and glacier meltwater in the Tienshan Mountains was contributed to by meltwater. In the Altay Mountains of northern Xinjiang, most catchments were covered by snow; therefore, snowmelt in the spring accounted for 21% of annual runoff [53]. This proportion decreased to 8.9% in the Heihe catchment of the South Qilian Mountains [54].…”

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

“…To overcome this limitation, regional climate models (RCMs) are often used to obtain better representations of surface climatology, because they downscale physically reanalysis products (García-Valdecasas Ojeda et al, 2017;Kryza et al, 2017;WarrachSagi et al, 2013). Previous studies have used RCMs to study SD and SWE dynamics at finer resolutions (grid cell size: 5 to 11 km) when they are driven with reanalyses, and the resolution increases further (grid cell size: 1 km) when using forecasted data (Bellaire et al, 2011;van Pelt et al, 2016;Quéno et al, 2016;Wu et al, 2016).…”

“…Their results support the usefulness of the methodology extracting snowpack trends from these data. Wu et al (2016) used a similar procedure to describe the behavior of snowpack over the Altai Mountains in China. They coupled outputs from the Weather Research and Forecasting (WRF) model (Skamarock et al, 2008) driven by NCEP/NCAR reanalysis with a temperature index model (based on remote sensing), and their results had low error values.…”

High-Resolution Discharge Forecasting for Snowmelt and Rainfall Mixed Events

“…to drive snowmelt simulation in physical [19] and empirical [18] models. Our study shows that driving another model with WRF data is not necessarily crucial to obtaining reliable snowmelt estimates and the WRF-Noah model deriving snowmelt itself can be a valuable estimate.…”

“…WRF with the aforementioned microphysical configurations was used in cold environments: Förster et al [17] conducted catchment-scale simulations, where snow models were integrated into the hydrological modelling system PANTA RHEI forced by WRF meteorological forecasts in the Harz Mountains, Germany, whereas, Wu et al [18] and Zhao et al [19] used WRF to force snowmelt runoff in snow dominated, high mountain ranges of Central Asia. The results indicated good skill of WRF derived snow related variables for hydrological modelling, yet the studies were conducted in snow dominated not urbanized environments, which do not exhibit extensive snowmelt and rainfall mixing.…”