Location: Northwest Watershed Research Center2021 Annual Report
1)Quantify and predict the form and spatial distribution of precipitation and snow ablation at different scales and their effects on streamflow forecasting in mountainous terrain. 1A)Quantify changes in the rain/snow transition elevation and analyze the impact these changes will have on water supply for ecosystems and agriculture. 1B)Develop, validate and apply physics-based snow models that integrate the methods from 1A and are capable of real-time operation over large mountain basins. 2)Quantify linkages between water availability, energy balance, and terrestrial carbon dynamics in Great Basin rangeland ecosystems. 2A)Determine water and carbon fluxes along an elevation gradient across the rain/snow transition. 2B)Determine post-fire net ecosystem exchange in the rain/snow transition zone. 3)Determine how spatially variable topography and soil properties affect the spatial and temporal distribution of ET and plant productivity in mountainous terrain in a warming climate. 3A)Quantify the effects of variable slope/aspect and vegetation on soil climate in snow-affected areas. 3B)Measure and simulate the effects of early snow melt on plant water stress and recharge in complex terrain. 4)As part of the LTAR network, and in concert with similar long-term, land-based research infrastructure in the Great Basin region, use the Great Basin LTAR site to improve the observational capabilities and data accessibility of the LTAR network and support research to sustain or enhance agricultural production and environmental quality in agroecosystems characteristic of the region. Research and data collection are planned and implemented based on the LTAR site application and in accordance with the responsibilities outlined in the LTAR Shared Research Strategy, a living document that serves as a roadmap for LTAR implementation. Participation in the LTAR network includes research and data management in support of the ARS GRACEnet and/or Livestock GRACEnet projects. 4A)Enhance observational capabilities and research infrastructure in support of long-term research of Great Basin ecosystem productivity. 4B)Process, clean and publish descriptions of, and have the USDA National Agricultural Library host long-term snow, hydrologic and ecosystem data from the RCEW LTAR. 4C)Create “business as usual” and “aspirational’ production and ecosystem service system scenarios as outlined by the LTAR common experiment. Assess the sustainability of both systems and develop new strategies to enable greater sustainability.
The goal of Obj. 1 is to provide water management agencies improved streamflow forecasts by modifying the research snow model, iSnobal, for real-time operational application over large river basins. A topographically based data distribution utility will be developed using the long data record and distributed measurement network in the Reynolds Creek Experimental Watershed (RCEW) to evaluate the location and stability of the rain/snow transition zone. The ARS snow model iSnobal will be improved and applied over large basins for long periods of time, or in real-time for forecasting purposes, to evaluate its potential as a tool for water resource managers and forecasting. If iSnobal is incompatible with existing water supply models, then modifications to iSnobal will be considered. Obj. 2 will investigate how rangeland water use and productivity are affected across the rain/snow transition by measuring water and carbon fluxes along an elevational gradient that spans the transition elevation. Data from previous studies on energy and water fluxes processed for carbon fluxes will be used to understand fluxes of carbon that are influenced by water availability, climate and soils along a precipitation/elevation gradient subject to climate change. Water, energy and carbon flux data from the Upper Sheep Creek prescribed fire in RCEW will be used to identify relationships between carbon fluxes and vegetation observations before and after prescribed fire, and to assess the effect of fire on CO2 fluxes. Several approaches for assessing the influence of vegetation disturbance have been identified in anticipation that some will not prove useful. After exploring all approaches, a combination of the most fruitful will be pursued. In Obj. 3, measured soil climate data and model simulation will be used to evaluate how local variations in snow melt will affect plant water stress and recharge. Using existing measured data from two past RCEW studies in the rain/snow transition zone, the Simultaneous Heat and Water (SHAW) model will be used to simulate soil climate, snowmelt dynamics, deep percolation and evapotranspiration for varying slope, aspect and vegetative cover conditions. The impact of transitioning from snow to rain on ecohydrologic processes will be evaluated using existing RCEW data and field instrumentation to determine the correlation between melt out and dry down dates and the effect of melt out date on recharge and plant water stress. If existing data and simulation models used are found inadequate, new data will be collected and/or different models will be tested and applied. Obj. 4 will continue detailed environmental monitoring and data sharing in support of the Long-Term Agroecosystem Research (LTAR) network in order to determine productivity of critical Great Basin shrub-steppe ecosystems. The ability to study long-term effects of management practices on ecosystem productivity will be improved by enhancing observational capabilities and publishing research data sets for use by the larger scientific community in and outside ARS. If data sets cannot be published by the National Agricultural Library, other data outlets will be considered.
In support of Objective 1, research continued to quantify mountain precipitation during the snow accumulation season more accurately at varying scales and the resultant effects of changes in the seasonal precipitation phase on streamflow generation. ARS researchers in Boise, Idaho, continued to develop a precipitation rescaling algorithm that relies on high-resolution spatially distributed snow depths from multiple Light Detection and Ranging (LiDAR) surveys. This rescaling has led to more realistic precipitation distributions in complex mountain terrain when coarse atmospheric model input is used as iSnobal (snow model) forcing data. ARS scientists made significant progress in furthering the development of the snow hydrology program including emphasis on linkages of snow melt and streamflow. ARS researchers in Boise, Idaho, also focused on the development of the water quality monitoring program through multiple locations within the Reynolds Creek Experimental Watershed (RCEW) located in Murphy, Idaho. This is critical because it constrains possible sources and timing of snowmelt delivery to streams. In addition, working with Idaho State University (2052-13610-012-27R, “Reynolds Creek Carbon Critical Zone Observatory”), researchers in Boise, Idaho, have initiated groundwater monitoring and spring water aging. This undertaking included drilling a groundwater well with two depth sampling “ports” near a RCEW weir in Johnston Draw (a core research site). This study will help define subsurface flow paths and provide ground truth for geophysical data. It is also critical for understanding the impacts of drought in the context of multi-year conditions. ARS scientists in Boise, Idaho, established and submitted for publication new geophysical transect lines intended to track snowmelt from hillslope to stream in RCEW’s Johnston Draw and Upper Sheep Creek. Lastly, ARS researchers have worked collaboratively with Kings River Association, (2052-13610-012-32R, “Operation and Development of iSnobal model for Kings River”) and Natural Resources Conversation Service, (2052-13610-012-31I, “Applications of iSnobal, a Physically-Based Distributed Snowmelt Model, in Support of NRCS Water Supply Forecasting”) by continuing to produce biweekly reports of snowpack results across five large river basins. In support of Objective 2, an informal collaborative effort was initiated with University of Texas, El Paso, to include sites within RCEW as part of the National Science Foundation Critical Zone Collaborative Network (CZNet) to study dryland soil carbon processes. A study site was set up on an irrigated site at ARS in Kimberly, Idaho, to contrast soil carbon process between dryland and irrigated sites. Additionally, an invited chapter, “Modeling Soil-Plant-Climate-Management Processes and Their Interactions in Cropping Systems: Challenges for the 21st Century,” has been accepted for publication. ARS scientists in Boise, Idaho, are also near publication of a paper describing and modeling soil carbon dioxide respiration in response to climate-driven variables. In support of Objective 3, a manuscript documenting measured and simulating slope and aspect effects on soil climate is currently under review. ARS researchers in Boise, Idaho, were able to both document and simulate conditions that strongly control the productivity of rangelands in complex terrain. Researchers also consolidated a more sustainable monitoring network in RCEW’s Johnston Draw. ARS scientists at Boise, Idaho, working in conjunction with the Reynolds Creek Critical Zone Observatory (2052-13610-012-27R, “Reynolds Creek Carbon Critical Zone Observatory”), analyzed geophysical transect data designed to enhance the understanding of aspect effects. In support of Objective 4, the water quality program has progressed from the testing phase to the implementation phase by incorporating procedures necessary for success of a long-term program. This includes established procedures, instrumentation, and personnel to perform the required work. This supports local research as well as being part of two national programs, one a part of the Long-Term Agroecosystem Research (LTAR) program. ARS scientists in Boise, Idaho, have also recently submitted work describing three-dimensional monitoring of soil water at RCEW’s LTAR business-as-usual site. This has provided a unique means of documenting in a non-destructive way, the bypassing of incoming water through the root zone, which has important implications for rangeland productivity. ARS researchers in Boise, Idaho, have also made substantial progress in preparing long-term data sets. Boise has nearly completed the task of cleaning and linking long-term (40-year) neutron probe soil water data to ongoing electronic monitoring. These linkages are critical because the neutron probe data collection program has been discontinued. During this process, ARS researchers have developed a new method for correcting temperature effects on the electronic soil water instrumentation.
1. Automated water supply model for snow water supply forecasting. Accurate real-time information on the amount of water stored in mountain snowpacks is essential for optimal management of downstream reservoirs that control essential water for farm irrigation, power generation, recreational use, and flood control across the western United States. Current statistical water management tools for snow water supply forecasting are simple to use but cannot accurately characterize the impacts of extreme climatic variability that leads to great uncertainty in the snow water supply forecasts and ultimately the amount of water available to water managers. ARS scientists in Boise, Idaho, have developed the Automated Water Supply Model that uses a physically-based snow model (iSnobal) to accurately estimate climate impacts on snow water supply over large river basins. The Automated Water Supply Model was designed to streamline and standardize iSnobal simulations and has been tested in both research and operational applications with overwhelming success. This tool has allowed water supply forecasters with the California Department of Water Resources and the National Weather Service, Colorado Basin River Forecast Center, to readily incorporate complex physically-based modeling into operational water supply forecasts.
Boehm, A.R., Hardegree, S.P., Glenn, N., Reeves, P.A., Moffet, C., Flerchinger, G.N. 2021. Slope and aspect effects on seedbed microclimate and germination timing of fall-planted seeds. Rangeland Ecology and Management. 75:58-67. https://doi.org/10.1016/j.rama.2020.12.003.
Dashti, H., Pandit, K., Glenn, N., Shinneman, D., Flerchinger, G.N., Hudak, A., De Graaf, M., Flores, A., Ustin, S., Ilangakoon, N., Fellows, A.W. 2020. Performance of the Ecosystem Demography model (EDv2.2) in simulating gross primary production capacity and activity in a dryland study area. Agricultural and Forest Meteorology. 297. Article 108270. https://doi.org/10.1016/j.agrformet.2020.108270.
Goodrich, D.C., Heilman, P., Anderson, M.C., Baffaut, C., Bonta, J.V., Bosch, D.D., Bryant, R.B., Cosh, M.H., Endale, D.M., Veith, T.L., Havens, S.C., Hedrick, A., Kleinman, P.J., Langendoen, E.J., Mccarty, G.W., Moorman, T.B., Marks, D.G., Pierson Jr, F.B., Rigby Jr, J.R., Schomberg, H.H., Starks, P.J., Steiner, J., Strickland, T.C., Tsegaye, T.D. 2020. The USDA-ARS experimental watershed network – Evolution, lessons learned, societal benefits, and moving forward. Water Resources Research. 57(2). Article e2019WR026473. https://doi.org/10.1029/2019WR026473.
Havens, S.C., Marks, D.G., Sandusky, M.L., Hedrick, A., Johnson, M.J., Robertson, M.E., Trujillo, E. 2020. Automated Water Supply Model (AWSM): Streamlining and standardizing application of a physically based snow model for water resources and reproducible science. Computers and Geosciences. 144. Article 104571. https://doi.org/10.1016/J.CAGEO.2020.104571.
He, H., Flerchinger, G.N., Kojima, Y., Dyck, M., Lv, J. 2020. A review and evaluation of 39 thermal conductivity models for frozen soils. Geoderma. 382. Article 114694. https://doi.org/10.1016/j.geoderma.2020.114694.
He, H., Flerchinger, G.N., Kojima, Y., He, D., Hardegree, S.P., Dyck, M., Horton, R., Wu, Q., Si, B., Lv, J. 2021. Evaluation of 14 frozen soil thermal conductivity models with observations and SHAW model simulations. Geoderma. 403. Article 115207. https://doi.org/10.1016/j.geoderma.2021.115207.
Kim, H., Wigneron, J., Kumar, S., Dong, J., Wagner, W., Cosh, M.H., Bosch, D.D., Holifield Collins, C.D., Starks, P.J., Seyfried, M.S., Lakshmi, V. 2020. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions. Remote Sensing of Environment. 251:112052. https://doi.org/10.1016/j.rse.2020.112052.
Liu, P., Bindlish, R., Fang, B., Lakshmi, V., O'Neill, P., Yang, Z., Cosh, M.H., Bongiovqnni, T., Bosch, D.D., Holifield Collins, C.D., Starks, P.J., Prueger, J.H., Seyfried, M.S., Livingston, S.J. 2021. Assessing disaggregated SMAP soil moisture products in the United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14:2577-2592. https://doi.org/10.1109/JSTARS.2021.3056001.
Marshall, A., Link, T., Flerchinger, G.N., Nicolsky, D., Lucash, M. 2021. Ecohydrologic modelling in a deciduous boreal forest: Model evaluation for application in non-stationary climates. Hydrological Processes. 35(6). Article e14251. https://doi.org/10.1002/hyp.14251.
Parajuli, K., Zhao, L., Jones, S., Tarboton, D., Hipps, L., Torres-Rua, A., Sadeghi, M., Flerchinger, G.N., Rockhold, M.L. 2020. Stone content influence on land surface model simulation of soil moisture and evapotranspiration at Reynolds Creek Watershed. Journal of Hydrometeorology. 21(8):1889-1904. https://doi.org/10.1175/JHM-D-19-0075.1.
Wlostowski, A., Molotch, N., Anderson, S., Brantley, S., Chorover, J., Dralle, D., Kumar, P., Li, L., Lohse, K., Mallard, J.M., McIntosh, J., Murphy, S.F., Parrish, E., Safeeq, M., Seyfried, M.S., Shi, Y., Harman, C. 2020. Signatures of hydrologic function across the critical zone observatory network. Water Resources Research. 57(3). Article e2019WR026635. https://doi.org/10.1029/2019WR026635.