Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.

The Effect of Aspect and Elevation on Critical Zone Architecture in the Reynolds Creek Critical Zone Observatory: A Seismic Refraction Study

In the northern hemisphere within snow-dominated mountainous watersheds north-facing slopes are commonly more deeply weathered than south-facing slopes. This has been attributed to a more persistent snowpack on the north facing aspects. A persistent snowpack releases its water into the subsurface in a single large pulse, which propagates the water deeper into the subsurface than the series of small pulses characteristic of the intermittent snowpack on south-facing slopes. Johnston Draw is an east-draining catchment in the Reynolds Creek Critical Zone Observatory, Idaho that spans a 300 m elevation gradient. The north-facing slope hosts a persistent snowpack that increases in volume up drainage, while the south-facing slope has intermittent snowpack throughout the drainage. We hypothesize that the largest difference in weathering depth between the two aspects will occur where the difference in snow accumulation between the aspects is also greatest. To test this hypothesis, we conducted four seismic refraction tomography surveys within Johnston Draw from inlet to outlet and perpendicular to drainage direction. From these measurements, we calculate the weathering zone thickness from the P-wave velocity profiles. We conclude that the maximum difference in weathering between aspects occurs ¾ of the way up the drainage from the outlet, where the difference in snow accumulation is highest. Above and below this point, the subsurface is more equally weathered and the snow accumulations are more similar. We also observed that the thickness of the weathering zone increased with decreasing elevation and interpret this to be related to the observed increase soil moisture at lower elevations. Our observations support the hypothesis that deeper snow accumulation leads to deeper weathering when all other variables are held equal. One caveat is the possibility that the denser vegetation contributes to deeper weathering on north-facing slopes via soil retention or higher rates of biological weathering.

Assessment of ICESat-2 Level 3A Products for Snow Depth Estimation in Remote, Mountainous Watersheds

Seasonal snowpack accounts for ~70% of the water supply in the western United States, and measuring snow accumulation and ablation remotely has long been a stated goal of NASA. The 2018 launch of ICESat-2, a spaceborne Lidar system, has offered unparalleled spatial and temporal coverage of mountainous terrain with the potential for unprecedented vertical accuracy. Data from ICESat-2 are used to measure seasonal snow depths using the level-3A ATL08 (land and canopy elevation) product for the Reynolds Creek Experimental Watershed in southwest Idaho and the ATL06 (land ice elevation) product for Wolverine Creek in the Kenai Mountains of Alaska. The methodology for coregistering ICESat-2 transects to reference digital terrain models then estimating snow depths as the difference between the ICESat-2 and reference elevations is described. Median and MAD snow depths for transects from 2019 and 2020 are 3.1 +/- 6.7m at Reynolds Creek EW and are 5.5 +/- 2.1m at Wolverine glacier. Here we find that measuring snow depths using ICESat-2 is crude in variable, vegetated terrain covered by the ATL08 data product, and that there is not a strong relationship between the residual values reported at Reynolds Creek EW and terrain parameters such as slope, aspect, vegetative coverage, and elevation. We do find that the ATL06 analysis results in reasonable first-order estimates of snow depth but that the evolution of the glacier surface elevations must be more accurately constrained in order to ensure the snow depth estimates are unbiased.

Stone Content Influence on Land Surface Model Simulation of Soil Moisture and Evapotranspiration at Reynolds Creek Watershed

Considerable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah land surface model with multiphysics option (Noah-MP) in simulating evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in situ soil moisture data during the growing season of year 2015, obtained from the Lower Sheep subcatchment within the Reynolds Creek Experimental Watershed in southwestern Idaho. We evaluated the performance of Noah-MP considering four different scenarios with 1) a one-layer soil profile with Noah-MP default soil hydraulic parameters and three more five-layer soil profiles using 2) Noah-MP default soil hydraulic parameters; 3) soil hydraulic parameters derived from a pedotransfer function using field observations; and 4) hydraulic parameters from scenario 3, which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input, and vegetation parameters. Our results indicate that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration.

Dynamic process connectivity explains ecohydrologic responses to rainfall pulses and drought

Ecohydrologic fluxes within atmosphere, vegetation, and soil systems exhibit a joint variability that arises from forcing and feedback interactions. These interactions cause fluctuations to propagate between variables at many time scales. In an ecosystem, this connectivity dictates responses to climate change, land-cover change, and weather events and must be characterized to understand resilience and sensitivity. We use an information theory-based approach to quantify connectivity in the form of information flow associated with the propagation of fluctuations between variables. We apply this approach to study ecosystems that experience changes in dry-season moisture availability due to rainfall and drought conditions. We use data from two transects with flux towers located along elevation gradients and quantify redundant, synergistic, and unique flow of information between lagged sources and targets to characterize joint asynchronous time dependencies. At the Reynolds Creek Critical Zone Observatory in Idaho, a dry-season rainfall pulse leads to increased connectivity from soil and atmospheric variables to heat and carbon fluxes. At the Southern Sierra Critical Zone Observatory in California, separate sets of dominant drivers characterize two sites at which fluxes exhibit different drought responses. For both cases, our information flow-based connectivity characterizes dominant drivers and joint variability before, during, and after disturbances. This approach to gauge the responsiveness of ecosystem fluxes under multiple sources of variability furthers our understanding of complex ecohydrologic systems.