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DOE OSTI2026

BSEC ecohydrological and water quality fluxes from RHESSys Simulations in USGS gauged watersheds

Baltimore Environmental Social Collaborative (BSEC) Water and Water Quality Simulations from RHESSys Model The repository contains RHESSys (Tague & Band, 2004; source code) simulated ecohydrological and nutrient (nitrogen only) fluxes at daily, basin-average (RHESSys_basin_output) and monthly, grid (RHESSys_patch_output) levels. We currently simulated the following 8 watersheds in Baltimore: Dead Run Baisman Run Scotts Level Branch Moores Run Powder Mill Run Maidens Choice Run Stony Run The watershed boundaries of all studied watersheds are stored in Watershed_Boundary folder. Variables and their units are listed in the metadata. Spatial projection, NAD83 / UTM zone 18N (EPSG:26918) is used for patch-level, netCDF-format files. For more information, please contact Ruoyu Zhang (rz3jr@virginia.edu).

Baltimore MD↗

NASA NTRS2018

Recent Updates in the SMAP Level-4 Soil Moisture Algorithm

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level-4 Soil Moisture (L4_SM) product. The L4_SM data are published with a mean latency of ~2.5 days from the time of observation and provide global, three-hourly, 9-km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. The L4_SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF). In 2018, the L4_SM algorithm was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high-quality, space-borne remote sensing observations. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of surface soil moisture from below under non-equilibrium conditions, which brings the model's surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture. Moreover, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4, and the "catchment deficit" model variable was removed from the EnKF state vector to avoid degrading the model's groundwater estimates.Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation-minus-forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4_SM product and the lessons learned from the effort.

Reichle, Rolf H.↗

DOE OSTI2023

AmeriFlux FLUXNET-1F US-xDC NEON Dakota Coteau Field School (DCFS)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xDC NEON Dakota Coteau Field School (DCFS). This is the FLUXNET version of the carbon flux data for the site US-xDC NEON Dakota Coteau Field School (DCFS) produced by applying the standard ONEFlux (1F) software. Site Description - The Dakota Coteau Field School (DCFS) and Prairie Lake (PRLA) field sites are co-located in an agricultural area used primarily for cattle grazing, just a few miles east of the Woodworth and Prairie Pothole sites at the Chase Lake National Wildlife Refuge. DCFS covers 7.8 km2 (3 square miles) of grazing land in Stutsman County, ND, between the tiny communities of Pingree and Woodworth. The population here is sparse, but the land has been transformed by agricultural activities over the last 150 years. The field site has been used only for grazing, but other land in the surrounding area has been converted to corn and soybean production. DCFS is located in an area known as the "Prairie Pothole Region," a band of tall and mixed prairie that stretches across parts of North and South Dakota, Minnesota and the Canadian provinces of Alberta, Saskatchewan and Manitoba. Historically, this area supported tall to mid-height prairie grasses, including blue gamma and green needlegrass. The land here is pocked by thousands of depressions left behind by glaciers 10,000 years ago, resulting in a series of small lakes and wetland areas known as prairie potholes. These potholes receive most of their water from spring snowmelt and are a primary source of groundwater recharge for the region. NEON data will help researchers monitor the effects of climate change on the Northern Plains ecosystem. Over the last 30 years, the hydrological cycle in the plains has changed dramatically, trending wetter overall and diverging from the historical ten-year cycles. Temperatures are also rising, leading to changes in plant phenology cycles and species distribution that could negatively impact migratory bird populations and other animal species.

Network), NEON (National Ecological Observatory↗

DOE OSTI2025

Advancing the Representation of Human Actions in Large‐Scale Hydrological Models: Challenges and Future Research Directions

Characterizing the impact of human actions on terrestrial water fluxes and storages at multi-basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human-water interactions in large-scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (~10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper-resolution” hydrological models (~0.1–1 km), multi-sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human-water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper-resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort.

catchment hydrology↗