Location: Watershed Management Research2018 Annual Report
1a. Objectives (from AD-416):
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.
1b. Approach (from AD-416):
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.
3. Progress Report:
In regards to Objective 1, the Northwest Watershed Research Center (NWRC) continues to host the National Science Foundation funded Reynolds Creek Critical Zone Observatory (RC-CZO). In support of this collaboration (2052-13610-012-01R, “Reynolds Creek Carbon Critical Zone Observatory”), and in support of ARS’ Long-Term Agroecosystem Research (LTAR) network, ARS scientists in Boise, Idaho compiled and published 31-years (1984-2014 water years) of meteorological data on a 10 meter grid across Reynolds Creek Experimental Watershed (RCEW). This meteorological data includes hourly precipitation, temperature, humidity, solar and thermal radiation, and wind. This dataset is critical for modeling hydrological processes in RCEW and is especially useful in partitioning precipitation into rain and snow, thus enabling the evaluation of impacts of a changing rain-on-snow transition elevation across a variety of processes related to streamflow generation, water supply, and vegetation productivity. A detailed evaluation was conducted and published based on a RCEW sub-watershed (Johnston Draw). This 31-year dataset was used by ARS scientists in Boise, Idaho to overcome the challenge of how to compile the necessary input data for large mountainous basins needed to implement process-based models, such as iSnobal (ARS snow model developed at NWRC), for operational purposes. The Spatial Modeling Resource Framework (SMRF) utility was further developed, finalized and published this past year based on the experience of developing the 31-year dataset. Analysis of the rain-on-snow transition zone in RCEW was also conducted and the resulting data and a manuscript has been published. This model package is now being used in an operational context over basins across the western U.S. This includes the Boise River basin (7,000 square kilometers (km2)), the extended Tuolumne River basin (1681 km2), the San Joaquin and Lakes basins (4,800 km2), and the RCEW basin (240 km2). Though RCEW is the smallest basin, the fact that the others are based on a 50 meter raster, and RCEW is based on a 10 meter raster, makes RCEW computationally the largest basin for the model application. In collaboration with the University of California (UC), the University has applied the model package to the American River basin, and the Merced-Tuolumne region. A challenge to implementing snow modeling in large basins is the difficulty of obtaining accurate precipitation data. ARS scientists in Boise, Idaho have done extensive work on integration of Light Detection and Ranging (LiDAR) snow depth fields into the iSnobal model. The data, including LiDAR snow depth time-series, has been published and has significantly alleviated the challenge. In addition, a refined version of the snow density algorithm built into SMRF and iSnobal was developed and tested. This work has resulted in a drafted manuscript. The modeling package is based upon the Image Processing Workbench (IPW) software code, which has been updated and is now published. New instrumentation has been tested for evaluating the snow distribution on the landscape. An in-depth field experiment was conducted in March, 2018 in the Sierra Nevada Mountains (boundary between San Joaquin and Lakes basins) to evaluate and compare various methods for estimating snow volume and conditions over a controlled area. Measurements were collected with terrestrial LiDAR, a drone mounted digital photography for Structure from Motion (SFM), active radar, and aircraft spectrometer. Ground measurements were taken from a series of snow pits for validation of snow depth, density, temperature, and liquid water content. Snow-off data will be collected this fall to conclude this experiment. A PostDoc from the University of California, Merced is now a part of NWRC’s water supply forecasting team and brings an expertise in LiDAR processing and hydrologic modeling (2052-13610-012-25S, “Sierra Nevada Water Supply Forecasting”). This added expertise will assist in making significant progress towards inclusion of sub-surface and streamflow model components into Boise’s modeling system. In regards to Objective 2, NWRC has processed, analyzed, and submitted to the AmeriFlux Network the first three years of data collected by eddy covariance systems at the three core sites for the RC-CZO. The data collected from the three sites indicate that carbon sequestration is the norm for these sagebrush sites. Based on the data collected, a manuscript has been drafted that evaluates the controlling factors for wintertime plant and soil respiration. A manuscript was published describing the rapid recovery of carbon fluxes after the Upper Sheep Creek 2007 prescribed fire in RCEW. Intensive data collection is now ongoing to better understand the processes involved by measuring individual components such as reparation, plant growth, plant phenology and overall water balance. ARS researchers in Boise, Idaho are placing special emphasis on how carbon dioxide generation by respiration varies with soil depth. In regards to Objective 3, a large dataset describing soil conditions on opposing north and south facing slopes has been assembled and published in Ag Data Commons, National Agricultural Library. This dataset addresses the major differences in soil temperature and plant available water that affect vegetation composition, phenology and response to management. In support of the dataset, the Simultaneous Heat and Water (SHAW) model was run and shown to accurately capture the major differences between slope aspects. This has important implications for large-scale modeling approaches and management. This work continues to progress with a redesigned monitoring network that was deployed this past year. The Soil Ecohydrology Model (SEM) modeling project, which is intended to estimate rangeland productivity, has been expanded by NWRC scientists to produce two-dimensional outputs (maps) of soil water and productivity, and to ingest data from different sources for input. Current versions of SEM rely on field data collection or estimation for vegetation parameters thus making the expanded SEM a huge improvement. In addition to SEM, other remote sensing approaches are currently being tested. In regards to Objective 4, Boise’s LTAR has developed an expanded vegetation monitoring network, with replication. The network has now been implemented and data is being collected. In addition to traditional on-the-ground monitoring of vegetation parameters such as density, species, leaf area, litter, production, new drone technology and terrestrial LiDAR techniques are under development and evaluation by NWRC. The RCEW is also participating in a cross-LTAR site dissolved organic matter experiment. In support of LTAR, components of an aspirational system have been identified with respect to grazing regime and a large project is currently underway to evaluate the impacts of grazing on invasive annual weed infestation.
1. Automated data preparation for water supply forecasting. The amount of effort required to manually check and prepare data to run water supply forecasting models severely limits application of models based on the physical processes of snowmelt and hydrology over large areas of the western U.S. ARS scientists in Boise, Idaho developed and implemented software called, Spatial Modeling for Resources Framework (SMRF) that allows automation of model input data processing over large areas making it possible to apply in real-time physics-based hydrologic modeling to generate better estimates of the amount and timing of streamflow. Improved streamflow estimates are critical to water resource management for agriculture, ecosystem services, flood control, drought management, power generation, and domestic water supply. SMRF now makes it feasible to transfer complex modeling approaches into a functional operational application that has the potential to scale sophisticated hydrologic models to the entire western U.S. SMRF is being tested for implementation by operational forecasters at the California Department of Water Resources, the Natural Resources Conservation Service National Water and Climate Center, and the U.S. Bureau of Reclamation.
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