Stereo bathymetry to monitor small seasonal agriculture water ponds in ungauged areas

Snow density plays an important role in natural and human systems but current methods for estimating snow density are limited, especially in the Arctic. This work presents a new method using satellite data to estimate snow density in remote areas. An experiment was conducted in the Canadian Arctic to evaluate this method and it appears to replicate density estimates from manual sampling well. With more work this method could be applied to estimate snow density across large areas of the Arctic.

We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO₂ release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.

Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.

Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.

This paper presents a new validation method for the downscaling of GRACE (Gravity Recovery and Climate Experiment) data. It measures the improvement of the downscaled data against the low-resolution data in both temporal and, for the first time, spatial domains. This validation method offers a standardized and comprehensive framework to interpret spatially and temporally the quality of the downscaled products, supporting future efforts in GRACE downscaling methods.