Research Project: Understanding Ecological, Hydrological, and Erosion Processes in the Semiarid Southwest to Improve Watershed Management

Location: Southwest Watershed Research Center

2024 Annual Report

Objectives
Objective 1. Quantify the magnitude and variability of the water balance components in semiarid landscapes and identify their controlling processes. 1.A: As an LTAR observatory, continue to collect and curate WGEW datasets including precipitation, runoff, sediment, pond runoff and sediment, meteorology, soil moisture, fluxes, vegetation, spatial datasets, and make datasets available under FAIR principles. 1.B: Quantify intra-storm variation in stable isotope values of precipitation over WGEW and identify relative influence of moisture source, season, local weather and sub-cloud processes. 1.C: Track daily watershed water balance components for rangeland ecosystems in the WGEW and SRER for improved assessment of water status and associated productivity. 1.D: Incorporate a variety of enhancements into watershed and erosion models maintained by the SWRC to add additional sub-processes, reduce predictive uncertainty, make them easier to use, enhance integration with land management agency workflows, and expand their use geographically. Objective 2: As part of the Long-Term Agroecosystem Research (LTAR) network, characterize and quantify impacts of water and agriculture/water management on semiarid watershed and agroecosystem processes. 2.A: Assess how novel remote sensing tools and low-cost, automated optical imagery can be used to quantify evapotranspiration and vegetation carbon uptake in water-limited regions. 2.B: Improve large-scale mapping of rangeland vegetation cover, lifeform, and biomass to classify rangeland ecological sites and states. 2.C: Quantify the long-term variability of riparian woodland evapotranspiration and CO2 exchange and their controls. 2.D: Assess impacts of altered temporal rainfall regime on semiarid grassland water and carbon cycling processes. Objective 3: Quantify and predict effects of climatic change, plant community transitions, and conservation practices on ecological, hydrological, and erosion processes. 3.A: Develop new conceptual and quantitative frameworks to assess the impacts of brush management on ecosystem structure and function and enhanced delivery of ecosystem services. 3.B: Assess impacts of climate change, wildfire, and vegetation management on hydrology and erosion processes across spatial scales within the rangeland-dry forest continuum. Two Goals are included for this Sub-objective. 3.C: Conduct field-based experiments on southwestern U.S. rangelands to assess the impact of woodland encroachment/infilling and tree removal conservation practices on vegetation, surface soils, and hydrology and erosion processes. 3.D: Evaluate the hydrologic, geomorphic, and ecologic impacts of failed soil and water conservation structures in Southwest rangelands. 3.E: Quantify how weather variability and potential changes in climate impact ecosystem net and gross carbon uptake in the water-limited Southwest. 3.F: Quantify how snowmelt amount and timing are impacted by vegetation structure under changing climate, wildfire, and vegetation management in the semiarid interior western U.S. 3.G: Estimate runoff and erosion risks over western U.S. rangelands.

Approach
Objective 1. A. Collect and make available Walnut Gulch Experimental Watershed (WGEW) datasets including precipitation, runoff, sediment, pond runoff and sediment, meteorology, soil moisture, fluxes, vegetation, spatial datasets. B. Quality-control and collate precipitation samples during summer rainfall events using a custom autosampler. C. Make measurements of precipitation, soil water content, runoff and evapotranspiration in the headwater watersheds of the WGEW and Santa Rita Experimental Range from the SECA network to track daily water balance components. D. Add functionality to existing runoff and erosion models to improve the applicability and ease of use for watershed management and assessments. Objective 2. A. Evaluate novel remote sensing spectral tools across the gradients of spatial and temporal dryland measurements. B. Use field measurements of cover, biomass and lifeform along with remotely sensed data to classify states on ecological sites. Structure from Motion will be used to estimate the distribution of cover and biomass by lifeform using machine learning (ML) and estimate erosion and runoff model parameters within the common site/state combinations. C. Use eddy covariance flux data from a riparian woodland site to better understand what controls annual ET and productivity. D. Utilize the Rainfall Manipulation facility in the SRER to fully control precipitation (using rainout shelters and irrigations) over hydrologically isolated plots with equal mixtures of multiple semiarid grassland plants and initiate hydroclimate disturbance treatments. Objective 3. A. Test for impacts on measured runoff after brush management treatments and demonstrate Rangeland Hydrology and Erosion Model (RHEM) capability to accurately simulate runoff and erosion processes for tree canopy and intercanopy areas on untreated and treated sites. B. Conduct a series of field studies quantifying impacts of fire on vegetation, ground cover, soil water repellency, infiltration, and runoff and erosion processes, and evaluate climatic and vegetation controls on surface water supplies using daily streamflow records in watersheds of the Colorado River Basin. C. Use artificial rainfall simulation and overland flow experiments to quantify infiltration, runoff, rainsplash, and erosion on tree-encroached sagebrush with tree-removal practices. D. Quantify the impacts of failed conservation structures using LiDAR data, aerial photographs, and satellite imagery. E. Use water and carbon flux data to better understand ecosystem responses to short and long term climate variability and improve models. F. Combine various datasets to quantify how snowmelt amount and timing are impacted by vegetation structure. G. Employ ML methods complemented by auxiliary data to develop relationships to field-collected variables from monitoring locations across the West and determine if ML techniques can predict RHEM parameters and runoff and erosion predictions directly.

Progress Report
This report documents progress for project 2022-13610-013-000D, titled, “Understanding Ecological, Hydrological, and Erosion Processes in the Semiarid Southwest to Improve Watershed Management”. In support of Objective 1, ARS researchers in Tucson, Arizona, continued to make progress on four sub-objectives. Under Sub-objective 1A, soil moisture data from four sites, on the Walnut Gulch Experimental Watershed and on the Santa Rita Experimental Range were made available through AmeriFlux. The meteorological data from three sites on Walnut Gulch were also updated and available via the Unit’s website, and the fourth meteorological site on the Audubon Research Ranch still needs additional processing and quality control. For Sub-objective 1B, individual storms were tracked from their source region to the sampling locations. This will aid in the identification of storm characteristics that could influence the isotopic signature of the samples. In support of Sub-objective 1C, computer scripts to calculate and plot site water balance variables were refined and figures on the webpages were automatically updated. Estimates of evapotranspiration using annual watershed water balance measurements at the Semiarid Ecohydrological Array (SECA) savanna and shrubland sites were compared with evapotranspiration measurements from eddy covariance. In support of Sub-objective 1D, a set of overland flow simulations was run to understand how vegetation pattern impacts water balance partitioning and source-sink behavior. Under Sub-objective 1D, the Rangeland Hydrology and Erosion Model (RHEM)-Snow was fully coupled with KINEROS2 (K2), and further testing of RHEM-Snow across United States has also been completed. The Natural Resources Conservation Service (NRCS) National Resources Inventory (NRI) field sample programs has sampled tens of thousands of field locations. These data were used with a machine learning algorithm to develop a RHEM emulator that is much faster than the desktop version. An additional 30,000 field sample points were acquired from the Department of Interior (DOI) Assessment, Inventory, Monitoring (AIM). The combined set of field points were supplemented with co-registered climate, remote sensing, and soils data. An Artificial Neural Network (ANN) was developed with the combined set of data with the goal of predicting RHEM inputs of vegetation life form, foliar cover and ground cover). Using the ANN predicted RHEM inputs, predictions of runoff, erosion, and sediment yield, were able to capably reproduce those outputs using observed data. The Automated Geospatial Watershed Assessment (AGWA) tool was largely converted from ArcGIS to ArcPro. This is a necessary step in making AGWA internet accessible. Substantial progress using machine learning and remote sensing was made in developing parameters for the CLImate GENerator to complete global parameter coverage. In support of Objective 2, research continued under four sub-objectives. In suppor of Sub-objective 2A, Red-Green-Blue (RGB) measures of greenness have proven to be more reliable, affordable and informative than sun-induced fluorescence (SiF). Daily greenness was analyzed from remote automated cameras over 60 experimental rainfall plots at RainMan to determine how temporal repackaging of rainfall impacts the start, end, and duration of the grassland growing season. Also, under Sub-objective 2A, thermal imagery was proven to be more informative than SiF. Time series of thermal images were processed for 25 experimental rainfall plots at RainMan and compared to other plot level measurements. In support of Sub-objective 2B, canopy cover estimates for three ecological states have been developed based on unmanned aerial systems (UAS) imagery on the Santa Rita Experimental Range, which should also be applicable, after testing, on the Walnut Gulch Experimental Watershed. For Sub-objective 2C, the 22-year Charleston Mesquite Woodland eddy covariance record was used to understand what controls the interannual variability of land-atmosphere carbon dioxide and water fluxes in a riparian woodland. Under Sub-objective 2D, the dataset for Southwest ecosystem flux sites was updated and used to examine the 2020 Southwest drought. Also, in support of Sub-objective 2D, 45 years of daily weather station data were used to run a 1-dimensional soil moisture model over 335 locations in the western U.S. and evaluate how changes in daily-scale rainfall variability affects the depth, duration, and intensity of root zone soil moisture drought. Also, analyses were made to evaluate the seasonal legacy effects of experimentally imposed winter drought on grassland productivity in the subsequent summer growing season. In support of Objective 3, research continued under seven sub-objectives. Under Sub-objective 3Ai, analysis of the factors affecting the increase or decrease of connected bare areas on the Santa Rita Experimental Range from 1986 to 2024 was completed. As well, a manuscript describing the algorithm and interface to the Rangeland Brush Estimation Tool (RaBET) to assess woody plant cover trends was developed. Under Sub-objective 3Aii, ARS researchers applied regional and national datasets on rangeland vegetation and conditions to evaluate the current distribution and hydrologic functioning of pinyon and juniper woodland types throughout the Western United States. This work was conducted in lieu of planned modeling efforts solely for the Great Basin Region and thereby expands the geographic domain of the original study. Under Sub-objective 3Bi, soil water repellency and infiltration data from novel experiments on burned dry forests in the Santa Catalina Mountains of southern Arizona were compiled and analyzed to assess the spatial patterns in soil water repellency and its effect on infiltration into burned forest soils. Results contributed to a multi-site study on the topic. In support of Sub-objective 3C, data collected from rainfall simulation experiments on intact and woodland-encroached rangelands at Grand Staircase Escalante National Monument were compiled, analyzed, and used to examine the impacts of woodland encroachment on vegetation, soils, and hydrology and erosion processes. Additional rainfall simulation experiments were conducted on the monuments woodland-encroached sagebrush rangeland subjected to tree removal by whole tree mastication. Data from this study will help understand the effects of pinyon and juniper control practices on vegetation, ground cover, and runoff and erosion processes on sagebrush rangelands. For Sub-objective 3D, research continued to test, train, and implement a machine learning model to identify earthen water control berms at sites in New Mexico where high resolution lidar data are available. A geodatabase of earthen berm information was expanded to include berms in New Mexico HUC8 watersheds. A simulation model based on the Saint-Venant shallow water equations was developed to model the hydrologic impact of berms. Under Sub-objective 3E, micrometeorological data collection for the SECA sites continued. Site data was quality-checked twice yearly and submitted to the AmeriFlux network database where the dataset was published and made publicly available. Also, an improved subsurface structure and hydrology routine for a widely used dynamic global vegetation model was developed. The update includes spatially variable root-zone water storage capacity including plant access to water stored in both soils and weathered bedrock. Finally, a machine learning method to predict total plant-accessible storage based on climate data inputs. Deviations in actual storage from modeled storage were then associated with geologic substrate to demonstrate ways in which the substrate can limit plant water access. In support of Sub-objective 3F, field data from two forested sites including forest canopy, snowpack, and soil moisture were used to train a 1-dimensional soil moisture model and analyze the impacts of forest management on root zone water stress and deep percolation. Also, under Sub-objective 3F, airborne laser maps of terrain, forest structure, and snow depth were combined with snow-photography (Snowtography) to train a 3-D computer simulation model and evaluate the effects of tree arrangement on snowpack and snowmelt. Moreover, five new stations were installed to evaluate the impacts of vegetation mastication treatments in a recovering wildfire burn area in Arizona. The Snowtography network operated by ARS scientist coordination, reached 21 stations comprising daily snow measurement at ~500 points arrayed across gradients of elevation, forest type, and vegetation management in Arizona and Colorado. For Sub-objective 3G, additional analysis was conducted to improve estimation of the effective hydraulic conductivity in the RHEM model for land that has been disturbed. Finally, a new project scientist worked with the streamflow depletion Powell Center working group to identify research priorities for improving detection and management of streamflow depletion due to groundwater use. With members of this group, the impact of groundwater pumping on water temperature in streams was evaluated using two different modeling approaches.

Accomplishments
1. Enhancing water conservation in national forests. Across Western U.S. mountain watersheds managed by the United States Forest Service (USFS), vegetation management is being widely deployed in efforts to reduce wildfire risk, but this management can alter snowpack exposure to sun and wind, affecting the amount and timing of snowmelt water resources in unknown ways. ARS scientists in Tucson, Arizona, collaborated with USFS, Natural Resources Conservation Service (NRCS), and scientists and partners from universities, water utilities, and conservations groups to model the effects of vegetation management on snow water resources in forests surrounding NRCS Snow Telemetry (SNOTEL) Network stations. Depending upon the spatial arrangement of remaining trees, snowmelt amounts were changed by up to 20%, while snowmelt timing was changed by up to one month, compared to NRCS SNOTEL. This research provides a practical framework to inform USFS vegetation management strategies that enhance water conservation for both ecosystems and people.

2. Quantifying woody plant cover on rangelands: introducing the Rangeland Brush Estimation Tool (RaBET). In a collaborative effort, ARS scientists from Tucson, Arizona, partnered with the Natural Resources Conservation Service and the University of Arizona to address a critical challenge: quantifying woody plant cover (commonly known as encroachment) across diverse rangelands in the western United States. Leveraging high-resolution aerial photography and Landsat satellite data, they developed sophisticated algorithms that classify and extrapolate woody cover across 15 Major Land Resource Areas. Now, rangeland managers, conservation groups, and individual ranchers can accurately assess woody plant encroachment on specific land parcels of interest. This tool empowers informed land management decisions, benefiting both ecosystems and stakeholders.

3. Artificial neural network model for RHEM model inputs. The Natural Resources Conservation Service (NRCS), National Resources Inventory (NRI) field sampling program has sampled tens of thousands of field locations. These data were used with a machine learning algorithm to develop a Rangeland Hydrology and Erosion Model (RHEM) emulator that is 13 billion times faster than the desktop version. An additional 30,000 field sample points were acquired from the Department of Interior (DOI) Assessment, Inventory, Monitoring (AIM). ARS scientists working with NRCS and colleagues from the University of California-Davis combined the set of field points and supplemented with co-registered climate, remote sensing, and soils data. An Artificial Neural Network (ANN) was developed with the combined set of data with the goal of predicting RHEM input (vegetation life form, foliar cover and ground cover (percent litter, basal area, rock, and biocrust)). Using the ANN predicted RHEM inputs, predictions of runoff, erosion, and sediment yield, and reproduced those outputs. using observed data with a coefficient of determination of roughly 90%, 40%, and 40%, respectfully.

Review Publications
Li, L., Hao, Y., Wang, W., Biederman, J.A., Zheng, Z., Wang, Y., Tudi, M., Qian, R., Zhang, B., Che, R., Song, X., Cui, X., Xu, Z. 2023. Effects of extra-extreme precipitation variability on multi-year cumulative nitrous oxide emission in a semiarid grassland. Agricultural and Forest Meteorology. 343. Article 109761. https://doi.org/10.1016/j.agrformet.2023.109761.
Zheng, Z., Wen, F., Biederman, J.A., Tudi, M., Lv, M., Xu, S., Cui, X., Wang, Y., Hao, Y., Li, L. 2024. Methane uptake responses to extreme droughts regulated by seasonal timing and plant composition. Catena. 237. Article 107822. https://doi.org/10.1016/j.catena.2024.107822.
Steiner, B., Scott, R.L., Hu, J., MacBean, N., Richardson, A., Moore, D. 2024. Using phenology to unravel differential soil water use and productivity in a semiarid savanna. Ecosphere. 15(2). Article e4762. https://doi.org/10.1002/ecs2.4762.
Al-hamdan, O., Williams, C.J., Pierson Jr., F.B., Hernandez, M., Nouwakpo, S.K. 2024. Estimating effective hydraulic conductivity (Ke) for the Rangeland Hydrology and Erosion Model (RHEM). Journal of the ASABE. 67(1):141-149. https://doi.org/10.13031/ja.15652.
Biederman, J.A., Zhang, F., Dannenberg, M., Yan, D., Reed, S., Smith, W. 2024. Reply to comment on "Five decades of observed daily precipitation reveal longer and more variable drought events across much of the western United States". Geophysical Research Letters. 51(1). Article e2023GL105124. https://doi.org/10.1029/2023GL105124.
Saeedimoghaddam, M., Nearing, G., Goodrich, D.C., Hernandez, M., Guertin, D., Metz, L., Wei, H., Ponce-Campos, G., Burns, I., McCord, S.E., Nearing, M., Williams, C.J., Houdeshell, C., Rahman, M., Meles, M.B., Barker, S. 2024. An artificial neural network to estimate the foliar and ground cover input variables of the Rangeland Hydrology and Erosion Model. Journal of Hydrology. 631. Article 130835. https://doi.org/10.1016/j.jhydrol.2024.130835.
Holifield Collins, C.D., Skirvin, S., Kautz, M.A., Winston, Z., Curley, D., Corrales, A., Bishop, A., Bishop, N., Norton, C., Ponce-Campos, G., Armendariz, G.A., Metz, L., Heilman, P., van Leeuwen, W. 2023. Rangeland Brush Estimation Tool (RaBET): An operational remote sensing-based application for quantifying woody cover on western rangelands. Remote Sensing. 15(21). Article 5102. https://doi.org/10.3390/rs15215102.
Thomas, A., Kolb, T., Biederman, J.A., Venturas, M., Ma, Q., Yang, D., Dore, S., Tai, X. 2024. Mitigating drought mortality by incorporating topography into variable forest thinning strategies. Environmental Research Letters. 19(3). Article 034035. https://doi.org/10.1088/1748-9326/ad29aa.
Spaeth, K., Rutherford, W.A., Houdeshell, C., Williams, C.J., Simpson, B., Green, S., Toledo, D.N., Suffridge, E., McCord, S.E. 2024. Insights from the USDA Grazing Land National Resources Inventory and field studies. Journal of Soil and Water Conservation. 79(3):37A-42A. https://doi.org/10.2489/jswc.2024.0107A.
Naito, A.T., Archer, S., Heilman, P. 2024. Comparing the predictive capacity of allometric models in estimating grass biomass in a desert grassland. Rangeland Ecology and Management. 93:72-76. https://doi.org/10.1016/j.rama.2024.01.004.
Zhu, Q., Chen, J., Charles P-A, B., Sonnentag, O., Montagnani, L., O’Halloran, T., Scott, R.L., Forsythe, J., Song, B., Zou, H., Duan, M., Li, X. 2024. Albedo-induced global warming potential following disturbances in global temperate and boreal forests. Journal of Geophysical Research-Biogeosciences. 129(3). Article e2023JG007848. https://doi.org/10.1029/2023JG007848.
Cubello, F., Polyakov, V.O., Meding, S., Kadoya, W., Beal, S., Dontsova, K. 2024. Movement of TNT and RDX from composition B detonation residues in solution and sediment during runoff. Chemosphere. 350. Article 141023. https://doi.org/10.1016/j.chemosphere.2023.141023.
Wen, F., Biederman, J.A., Hao, Y., Qian, R., Zheng, Z., Cui, X., Zhao, T., Xue, K., Wang, Y. 2024. Extreme drought alters methane uptake but not methane sink in semi-arid steppes of Inner Mongolia. Science of the Total Environment. 915. Article 169834. https://doi.org/10.1016/j.scitotenv.2023.169834.
Zhang, Y., Fang, J., Smith, W., Wang, X., Gentine, P., Scott, R.L., Migliavacca, M., Jeong, S., Litvak, M., Zhou, S. 2023. Satellite solar-induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought. Global Change Biology. 29(12):3395-3408. https://doi.org/10.1111/gcb.16683.
Javadian, M., Scott, R.L., Biederman, J.A., Zhang, F., Fisher, J., Reed, S., Potts, D., Villarreal, M., Feldman, A., Smith, W. 2023. Thermography captures the differential sensitivity of dryland functional types to changes in rainfall event timing and magnitude. New Phytologist. 240(1):114-126. https://doi.org/10.1111/nph.19127.
Ponce-Campos, G., McClaran, M., Heilman, P., Gillan, J. 2023. UAV and satellite-based sensing to map ecological sites at the landscape scale. Open Journal of Ecology. 13(8):560-596. https://doi.org/10.4236/oje.2023.138035.
Dwivedi, R., Biederman, J.A., Broxton, P., Pearl, J., Lee, K., Svoma, B., van Leeuwen, W., Robles, M. 2024. How three-dimensional forest structure regulates the amount and timing of snowmelt across a climatic gradient of snow persistence. Frontiers in Water. 6. Article 1374961. https://doi.org/10.3389/frwa.2024.1374961.
Broxton, P., van Leeuwen, W., Svoma, B., Walter, J., Biederman, J.A. 2023. Subseasonal to seasonal streamflow forecasting in a semiarid watershed. Journal of the American Water Resources Association. 59(6):1493-1510. https://doi.org/10.1111/1752-1688.13147.
Feldman, A., Feng, X., Felton, A., Konings, A., Knapp, A., Biederman, J.A., Poulter, B. 2024. Plant responses to changing rainfall frequency and intensity. Nature Reviews Earth & Environment. 5:276-294. https://doi.org/10.1038/s43017-024-00534-0.
Scott, R.L., Johnston, M., Knowles, J., MacBean, N., Mahmud, K., Roby, M.C., Dannenberg, M. 2023. Interannual variability of spring and summer monsoon growing season carbon exchange at a semiarid savanna over nearly two decades. Agricultural and Forest Meteorology. 339. Article 109584. https://doi.org/10.1016/j.agrformet.2023.109584.
Zobell, R., Spaeth Jr., K., Williams, C.J., Goodrich, S., Jacobson, B., Camp, C., Cameron, A. 2023. Gradient analysis and classification of tall forb communities in the Bridger-Teton National Forest, United States. Rangeland Ecology and Management. 90:294-307. https://doi.org/10.1016/j.rama.2023.04.002.
Lapides, D.A., Hahm, W., Forrest, M., Rempe, D., Hickler, T., Dralle, D. 2024. Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and functioning. Biogeosciences. 21(7):1801-1826. https://doi.org/10.5194/bg-21-1801-2024.
Zipper, S., Brookfield, A., Ajami, H., Ayers, J., Beightel, C., Fienen, M., Gleeson, T., Hammond, J., Hill, M., Kendall, A., Kerr, B., Lapides, D.A., Porter, M., Parimalarenganayaki, S., Rohde, M., Wardropper, C. 2024. Streamflow depletion caused by groundwater pumping: Fundamental research priorities for management-relevant science. Water Resources Research. 60(5). Article e2023WR035727. https://doi.org/10.1029/2023WR035727.
Polyakov, V.O., Nichols, M.H., Cavanaugh, M.L. 2024. Determining sediment deposition dynamics influenced by check dams in a semi-arid mountainous watershed. Earth Surface Processes and Landforms. 49(6):1849-1857. https://doi.org/10.1002/esp.5802.
Lapides, D., Grindstaff, G., Nichols, M.H. 2024. Automated earthwork detection using topological persistence. Water Resources Research. 650(2). Article e2023WR035990. https://doi.org/10.1029/2023WR035990.
Hahm, W., Dralle, D., Lapides, D.A., Ehlert, R., Rempe, D. 2024. Geologic controls on apparent root-zone storage capacity. Water Resources Research. 60(3). Article e2023WR035362. https://doi.org/10.1029/2023WR035362.