Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

Gully regulates snowmelt runoff, sediment and nutrient loss processes in Mollisols region of Northeast China

Gully is a prominent indicator of land degradation in agroecosystems, functioning as a crucial pathway connecting upslopes to downstream channels. However, little is known about how gully regulates runoff, sediment, and nutrient loss processes in the catchment during snowmelt. In this study, we monitored these processes in situ at both the gully head (the upslope accumulated catchment of the gully head, CGH) and outlet of two representative and typical gully-dominated catchments (F1 and F2) during snowmelt in Mollisols region of Northeast China. Our results showed that runoff discharge of CGH and outlet exhibited a multi-peak trend during snowmelt, driven by the transition from snow melting to soil thawing. This transition resulted in distinct runoff patterns in both CGH and outlet, with significant differences in their response to air temperature. The total runoff yield of CGH accounted for 57.8 % in F1 and 40.6 % in F2 of the total runoff yield of the outlet. Notably, the peak sediment concentration displayed a marked lag compared to the peak runoff discharge, primarily dominated by the increased sensitivity of gully erosion after the thawing of gully slopes. Gully erosion was the main source of sediment yield in the catchment, contributing 98.2 % in F1 and 96.6 % in F2. Furthermore, nutrient concentrations exhibited a decreasing trend during snowmelt. The comparison of high nutrient concentrations in CGH and relatively low nutrient concentrations in outlet highlighted the gully's role in intercepting and diluting runoff nutrients. Hysteresis analysis confirmed the differential contribution of CGH and gully to nutrient sources. CGH accounting for 50.9 % and 93.3 % of runoff TN and runoff TP loss, while contributing only 8.3 % and 5.8 % to sediment TN and sediment TP loss, respectively. These findings offer valuable insights for effective erosion control and nonpoint source pollution management in gully-dominated agroecosystems during snowmelt.

Toward snowpack runoff decision support

Rain-on-snow (ROS) events are commonly linked to large historic floods in the United States. Projected increases in the frequency and magnitude of ROS multiply existing uncertainties and risks in operational decision making. Here, we introduce a framework for quality-controlling hourly snow water content, snow depth, precipitation, and temperature data to guide the development of an empirically based snowpack runoff decision support framework at the Central Sierra Snow Laboratory for water years 2006-2019. This framework considers the potential for terrestrial water input from the snowpack through decision tree classification of rain-on-snow and warm day melt events to aid in pattern recognition of prominent weather and antecedent snowpack conditions capable of producing snowpack runoff. Our work demonstrates how (1) present weather and (2) antecedent snowpack risk can be "learned" from hourly data to support eventual development of basin-specific snowpack runoff decision support systems aimed at providing real-time guidance for water resource management.

A Novel Approach to Validate Satellite Snowfall Retrievals by Ground-Based Point Measurements

A novel method has been proposed for validating satellite radar snowfall retrievals using surface station observations over the western United States mountainous region, where the mean snowfall rate at a station depends on its elevation. First, all station data within a 1° × 1° grid are used to develop a snowfall rate versus elevation relation. This relation is then used to compute snowfall rate in other locations within the 1° × 1° grid, as if surface observations were available everywhere in the grid. Grid mean snowfall rates are then derived, which should be more representative to the mean snowfall rate of the grid than using data at any one station or from a simple mean of all stations in the grid. Comparison of the so-derived grid mean snowfall rates with CloudSat retrievals shows that the CloudSat product underestimates snowfall by about 65% when averaged over all the 768 grids in the western United States mountainous regions. The bias does not seem to have clear dependency on elevation but strongly depends on snowfall rate. As an application of the method, we further estimated the snowfall to precipitation ratio using both ground and satellite measured data. It is found that the rates of increase with elevation of the snowfall to precipitation ratio are quite similar when calculating from ground and satellite data, being about 25% per kilometer elevation up or approximately 4% per every degree Cuisses of temperature drop.