Project : USDA ARS

ARS Home » Pacific West Area » Boise, Idaho » Northwest Watershed Research Center » Research » Research Project #441534

Research Project: Ecohydrology of Sustainable Mountainous Rangeland Ecosystems

Location: Northwest Watershed Research Center

2025 Annual Report

Objectives
Objective 1) Develop improved snowmelt and streamflow forecasting tools. Sub-objective 1A) Improve spatial representation of precipitation and solar radiation as snow model forcing data. Sub-objective 1B) Develop and improve model linkages between spatially distributed snowmelt and streamflow generation. Objective 2) Quantify and predict terrestrial ecosystem carbon dynamics, including rangeland productivity, soil respiration, carbon flux, and carbon sequestration in response to water availability and climate variability. Sub-objective 2A) Identify and model linkages between climate variability, water availability, and primary productivity. Sub-objective 2B) Improve understanding of soil carbon dynamics related to soil carbon sequestration. Objective 3) Develop long-term observational data sets for climate, hydrology, vegetation, soils, geophysics, and water quality to make inferences about function, long-term productivity and sustainability of rangeland ecosystems that can be widely used in local, regional, and national models and in collaboration with the LTAR network. Sub-objective 3A) Maintain and enhance long-term observational infrastructure for climate, hydrology, vegetation, soils, geophysics, and water quality in support of network wide LTAR collaborations and research community at large. Sub-objective 3B) Quantify climate change effects on hydrology and the past, present, and future sustainability of rangeland ecosystems using the long-term dataset from RCEW.

Approach
Objective 1 builds on the snowmelt and streamflow forecasting advancements made with the iSnobal model during the last five-year project cycle that enabled near real-time snowmelt forecasting in support of operational water supply forecasting and water management. We will take a four-pronged approach to further improve operational streamflow forecasting: 1) We will take advantage of recent advances in estimating precipitation patterns and snow depth over mountainous areas from airplane overflights; 2) We will use satellite observations of solar reflectance, snow cover, and cloud cover to better estimate the solar energy absorbed by the snow; 3) We will develop approaches to estimate streamflow from simulated snowmelt using historical relationships between measured streamflow and simulated snow melt; and 4) We will couple the iSnobal model with an existing model that routes snowmelt water to the stream. In Objective 2, we will combine field observations and modeling tools to better understand and predict water and carbon dynamics in semi-arid rangeland ecosystems. Tools for quantifying and modeling vegetation productivity and carbon storage of sagebrush ecosystems will be developed, providing a better understanding of vegetation productivity and soil carbon sequestration in water-limited ecosystems. Research will capitalize on the network of research sites along an elevation/climate gradient within the Reynolds Creek Experimental Watershed (RCEW). Measurements include CO2 uptake and emission from plants and soil, weather observations, soil temperature/water/CO2 profiles, chambers that measure soil CO2 emission, etc. Annual vegetation surveys and cameras that track plant growth/phenology are available at three of the sites. Using the natural gradient in climate and productivity across the research sites presents a unique opportunity to study factors regulating carbon fluxes and productivity and observe changes in ecosystem function as climate and ecohydrological properties shift. Data will be used to test and improve existing models that simulate management and climate on vegetation productivity and carbon storage within the soil. In Objective 3, we will expand the scientific infrastructure of the RCEW to: 1) quantify offsite transfer of water and carbon in streams and groundwater; 2) measure changes in productivity and carbon cycling as sagebrush ecosystems transition to invasive annual grasses; and 3) support collaborations both within USDA-ARS, especially with the Long-Term Agroecosystem Research (LTAR) network, and with our University collaborators. We also take advantage of our long-term record to document ecohydrological change that has occurred in the past 60 years on the RCEW. Approaches that will be pursued if initial methods are unsuccessful include: 1) using alternative satellite products if the data from the aging MODIS satellite proves problematic, 2) using existing inhouse computational infrastructure if the coupled snowmelt-streamflow model does not lend itself to a High-Performance Cluster, 3) using a different model (UNSATCHEM) if soil inorganic process are significant and cannot be easily implemented into the SHAW model.

Progress Report
This report documents FY 2025 progress for project 2052-13610-015-000D, “Ecohydrology of Sustainable Mountainous Rangeland Ecosystems”, which began in January 2022. In support of Sub-objective 1A, research continued on implementing a precipitation rescaling module into the iSnobal physics-based snow model. This module uses periodic airborne snow depth measurements to guide where precipitation accumulates as snow across the Mores Creek study watershed in Idaho. Once the modeled snow is more accurately distributed across the basin, the accuracy of the energy and mass balance calculations within iSnobal is greatly improved, leading to better water supply forecasts for agricultural producers. Further testing remains for how this approach will perform in the ARS Reynolds Creek Experimental Watershed (RCEW). Additionally in support of Sub-objective 1A, a scientific manuscript was published describing a framework for incorporating snow reflectance data from the MODerate resolution Imaging Spectrometer (MODIS) satellite into the iSnobal model. ARS researchers in Boise, Idaho, showed that satellite surface reflectance improves estimates of how much energy the snowpack absorbs each hour throughout the year and leads to better predictions of spring snowmelt magnitudes and timing. For Sub-objective 1B, progress was made in modeling streamflow timing in the Tollgate sub-watershed of the RCEW using long-term meteorological data collected from 1984 to the present. The Cold Regions Hydrologic Model (CRHM) was chosen for this project due to its relatively good performance in other watersheds and because it has been studied in previous investigations at RCEW. Results of the comparison of CRHM modeled streamflow with measured streamflow data will lead to further implementation of a streamflow module in the Automated Water Supply Model (AWSM). Progress was made in support of Sub-objective 2B, to improve understanding of soil carbon dynamics related to soil carbon sequestration. Collaborations with ARS scientists in Boise, Idaho, and scientists from Idaho State University applied hydrochemical and isotopic signatures from springs and wells to trace carbon sources in ground water and assessed carbon sequestration rates via silicate weathering within the RCEW. The researchers demonstrated that carbon systems can change in conjunction with soil and meteorological variability and highlighted challenges in interpreting carbon evolution over time. The research findings document progressive water-rock interactions in deep groundwater and advance understanding of carbon, nutrient, and water cycling in semi-arid watersheds. The respective research contributed to two peer-reviewed journal articles. Under Sub-objective 3B, significant progress was made in developing a long-term 40-year dataset (1984-2023) of gridded meteorological conditions derived from the dense weather station network in the RCEW. This hourly, 10-meter gridded dataset includes air temperature, relative humidity, solar and thermal radiation, wind speed and direction, and precipitation; all the variables required as forcing data for testing and comparing distributed land surface models. This dataset, when published, will be a vital scientific community resource for improving current and developing new hydrologic and atmospheric models.

Accomplishments
1. Improved water supply forecasting for ranchers, farmers, and producers. Timing of snowmelt for agricultural water supply in the mountainous western United States is highly controlled by the amount of solar radiation that is either absorbed or reflected by snow. Because these are not commonly measured and snowmelt models often use simplified modeling approaches, this can be a large source of error in forecasting snowmelt and streamflow. ARS researchers in Boise, Idaho, and colleagues from the University of Utah addressed this limitation by combining numerical weather predictions with satellite observations to improve estimates of snow’s absorbance and reflectance of solar radiation, thereby reducing error rates of forecasted snow depletion by 80% from an error of up to 33 days to errors up to 6 days in the Colorado River Basin. These methods will be incorporated into real time forecasting methods to produce more reliable predictions of snowmelt runoff and streamflow for water supply forecasters and reservoir managers, yielding more accurate knowledge of water availability and timing for farmers, ranchers, and other producers.

2. New tool for predicting plant productivity. Ecological models are important tools used by land managers and scientists for predicting ecosystem responses to weather trends and disturbances, such as wildfire, but they often struggle to reproduce seasonal patterns of plant productivity, particularly in rangeland ecosystems. Inaccurate predictions of plant productivity (growth and vegetation changes) cause further errors in these models over time. ARS scientists in Boise, Idaho, enhanced the Simultaneous Heat and Water (SHAW) model to enable prediction of plant productivity. New methods were developed to apply SHAW to a suite of new plant communities and ecosystem types. This is a critical step forward in assessing trajectories of rangeland ecosystems undergoing vegetation transitions. The enhanced model provides a new tool to aid land managers and producers in effectively forecasting agroecosystem productivity.

3. A Rangeland Hydrology and Erosion Model for snow-dominated uplands. Runoff and erosion modeling for high elevation rangelands must account for water input, in the form of rainfall and snowmelt, to effectively forecast hydrologic and erosion risks to resources, property, and human life. Scientists from ARS (Tucson, Arizona; Boise, Idaho), the Natural Resource Conservation Service (Davis, California), and the University of Arizona (Tucson, Arizona) developed a new approach to effectively represent rainfall and snowmelt contributions to hillslope runoff and erosion predictions by the Rangeland Hydrology and Erosion Model (RHEM). Assessment of the new approach found that more effective partitioning of water inputs from rain and snow can reduce predicted runoff and sediment responses at the annual time scale by more than 20% relative to treating all water input as rainfall. For rain-on-snow events, the new approach found runoff and sediment responses can either be enhanced or muted depending on variability in storm and snowpack characteristics. Overall, the new approach more appropriately partitions water input and provides more realistic predictions of magnitude of cold-season runoff and erosion events from snow-dominated uplands relative to that of previous RHEM versions. The advances in this study provide resource managers, local planners, and other users an enhanced RHEM tool for predicting runoff and erosion and associated risks for snow-dominated rangelands.

4. USDA rangeland inventory protocols provide framework to conserve and sustain rangelands. Rangelands in the United States and around the world commonly undergo substantive transitions in vegetation structure and ecological function due to intensive land use, invasive species, and natural disturbances. Land managers, ranchers, and other producers require predictive knowledge of plant community responses to land use, natural disturbances, and management practices to enhance, manage, and sustain rangeland ecosystems and delivery of agroecosystem services. In this study, USDA-developed protocols for assessing rangeland ecological conditions and functions were applied by a collaborative team of ARS (Reno, Nevada; Mandan, North Dakota; Tucson, Arizona; Boise, Idaho), Natural Resource Conservation Service (NRCS), and university scientists to the Aqmola Region, Kazakhstan, to develop a framework for rangeland plant community characterization and respective community responses to land use, disturbances, and management. A rangeland resource inventory (partially modelled after the NRCS’s National Resources Inventory) sampled 51 locations across the Aqmola region. Field collected data were used to identify unique plant community assemblages and dynamics and to develop Ecological Site Descriptions for guiding management. The collaboration provided a basis for testing USDA developed protocols in informing Ecological Site Descriptions and respective land management strategies for novel agroecosystems. Successful application of the prevailing framework demonstrates utility of the quantitative and qualitative assessment protocols in evaluating ecological condition and health of diverse rangeland conditions and highlights its potential for application to addressing agroecosystem productivity on rangelands in the United States.

5. Effectiveness of gap intercept methods to measure rangeland connectivity. Measures of gaps between plants are commonly used by land managers and practitioners to assess vegetation structure in context with ecological functions on water-limited lands. Numerous studies have demonstrated the utility of gap measures as indicators of ecosystem structural and functional (pattern and process) relationships, but results have varied across the many methodologies, applications, and studied domains. Despite these mixed results, assessment of gap distances between plant canopies and bases remains a common practice in rangeland monitoring. Thus, a clearer understanding of the utility of the gap intercept method to inform ecosystem processes and services is needed. A collaborative group of ARS scientists (Las Cruces, New Mexico; Burns, Oregon; Reno, Nevada; Tucson, Arizona; Boise, Idaho) evaluated available datasets and the utility of various gap measures to assess a suite of ecosystem functions. They provided key recommendations on the application of gap intercept methods for assessing rangelands and targeting management actions. The greater accuracy and consistency provided by this guidance should improve delivery of rangeland ecosystem services to the people of the United States.

U.S. DEPARTMENT OF AGRICULTURE

Northwest Watershed Research Center: Boise, ID