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

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.     

Hydrological flow paths at the allt A' mharcaidh catchment: An analysis of plot and catchment scale observations

An integrated programme of hydrological monitoring at the 10 km² Allt a' Mharcaidh catchment in north-east Scotland has been based on observations at plot, hillslope and catchment scale. The resonse of the principal soil types has been characterized from a combination of throughflow and three-dimensional tensiometer data at plot scale, and plot sequences have been used to investigate hillslope scale effects. Seep emergence is associated with downslope drainage and local topographic convergence; in parallel preferential pathways generate a highly dynamic throughflow response. Catchment and subcatchment hydrographs mirror the twin dynamic observed at hillslope scale, and a unified hypothesis of response is presented which is consistent with all scales of observations.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.