Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China

Rain-on-snow(ROS)events involve rainfall on snow surfaces,and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China(ARNC).In this study,using daily snow depth data and daily meteorological data from 68 meteorological stations provided by the China Meteorological Administration National Meteorological Information Centre,we investigated the spatiotemporal variability of ROS events in the ARNC from 1978 to 2015 and examined the factors affecting these events and possible changes of future ROS events in the ARNC.The results showed that ROS events in the ARNC mainly occurred from October to May of the following year and were largely distributed in the Qilian Mountains,Tianshan Mountains,Ili River Valley,Tacheng Prefecture,and Altay Prefecture,with the Ili River Valley,Tacheng City,and Altay Mountains exhibiting the most occurrences.Based on the intensity of ROS events,the areas with the highest risk of flooding resulting from ROS events in the ARNC were the Tianshan Mountains,Ili River Valley,Tacheng City,and Altay Mountains.The number and intensity of ROS events in the ARNC largely increased from 1978 to 2015,mainly influenced by air temperature and the number of rainfall days.However,due to the snowpack abundance in areas experiencing frequent ROS events in the ARNC,snowpack changes exerted slight impact on ROS events,which is a temporary phenomenon.Furthermore,elevation imposed lesser impact on ROS events in the ARNC than other factors.In the ARNC,the start time of rainfall and the end time of snowpack gradually advanced from the spring of the current year to the winter of the previous year,while the end time of rainfall and the start time of snowpack gradually delayed from autumn to winter.This may lead to more ROS events in winter in the future.These results could provide a sound basis for managing water resources and mitigating related disasters caused by ROS events in the ARNC.

Test of newly developed conceptual hydrological model for simulation of rain-on-snow events in forested watershed

A conceptual hydrological model that links the Xin＇anjiang hydrological model and a physically based snow energy and mass balance model, described as the XINSNOBAL model, was developed in this study for simulating rain-on-snow events that commonly occur in the Pacific Northwest of the United States. The resultant model was applied to the Lookout Creek Watershed in the H. J. Andrews Experimental Forest in the western Cascade Mountains of Oregon, and its ability to simulate streamflow was evaluated. The simulation was conducted at 24-hour and one-hour time scales for the period of 1996 to 2005. The results indicated that runoffand peak discharge could be underestimated if snowpack accumulation and snowmelt under rain-on-snow conditions were not taken into account. The average deterministic coefficient of the hourly model in streamflow simulation in the calibration stage was 0.837, which was significantly improved over the value of 0.762 when the Xin＇anjiang model was used alone. Good simulation performance of the XINSNOBAL model in the WS 10 catchment, using the calibrated parameter of the Lookout Creek Watershed for proxy-basin testing, demonstrates that transplanting model parameters between similar watersheds can orovide a useful tool for discharge forecastin~, in un^au^ed basins.

Performance of climate reanalyses in the determination of pan-Arctic terrestrial rain-on-snow events

Rain-on-snow(ROS)events can cause rapid snowmelt,leading to flooding and avalanches in the pan-Arctic and can also lead to starvation and the death of massive ungulates.Reanalysis products(e.g.,ERA-I,ERA5-land,JRA55,MERRA2)are the primary source data for the research about ROS events in the large-scale region.However,the accuracy and reliability of reanalyses have never been evaluated with respect to the determination of terrestrial ROS events.The present study aims to statistically evaluate the performance of reanalysis datasets in identifying ROS events with different criteria based on in-situ rainfall data and MODIS snow cover product.The results show that all reanalysis datasets exhibit poor performance(Recall≤0.16,Kappa coefficient≤0.26,F-score≤0.42,MCC≤0.33)in all criteria in the pan-Arctic,mainly due to the low accuracy of rainfall data(r≤0.56).Nevertheless,the spatial distribution pattern and hot spots of ROS from all reanalysis datasets are essentially close.The hot spots of ROS are mainly located on the coast of Alaska,Norway,and Greenland.All reanalyses demonstrate an increase in rainy days,but there is little overall change in ROS events due to the reduction in snow cover days.This work suggests that none of the current reanalyses are reliable in the determination of ROS events due to the poor representation of the rainfall parameterization scheme.The development of alternative strategies that can investigate ROS events at large-scale is urgently needed in a changing Arctic under rapid warming.

A rain-on-snow mixed flood forecast model and its application

Based on summarizing the rule of rainstorm and snowmelt mixed flood,the structure of rain-on-snow runoff-generation is discussed;and critical temperature is used to determine the form of precipitation and snowmelt factor,taking into account rainfall volume of snowmelt.A rain-on-snow flood forecast model is developed by combining LL-Ⅰdistributed hydrology model.The Kalangguer River,an internal river in Xinjiang Autonomous Region,is taken for example.It is indicated that the model has a higher precision of forecasting;its determinacy coefficient is greater than 0.80.