Location: Water Management and Systems Research2022 Annual Report
Objective 1: Improve biophysical and ecohydrologic components of crop and ecosystem services models ranging in spatial scale from sub-field areas to watersheds by linking process-based modeling, data assimilation, and artificial intelligence (AI). Sub-objective 1.A: Develop ecophysiological model components for croplands. Sub-objective 1.B: Enhance modeling of crop phenology, yield, and ET in semi-arid conditions at daily to seasonal and plot to watershed scales. Objective 2: Inform precision agriculture (water and nutrient management in crop systems) and precision conservation within fields, across farms, and at regional watershed scales, using high-resolution process modeling and machine learning. Sub-objective 2.A: Improve the Agricultural Ecosystems Services (AgES) process model using components from Objective 1; develop and test a subdaily version of AgES. Sub-objective 2.B: Apply AgES to simulate on-farm precision conservation; train surrogate models for users; publish long-term data and results. Objective 3: Quantify current and future impacts of climate variability, land-use change, land disturbance (e.g., wildfire, insect infestation), and rehabilitation on water resources from source-water catchments in snow-dominated agricultural watersheds. Sub-objective 3.A: Develop and implement snow-process model components and ecosystem × hydrometeorology interactions. Sub-objective 3.B: Develop geospatial methods to analyze and model hydrologic function and response to change, from “fire to farm”. Sub-objective 3.C: Predict the ecohydrological impact of precision-conservation treatments in source-area catchments.
Agricultural productivity and ecosystem services are inextricably linked to water resources that are facing dual pressures of decreasing supply and increasing demand. In the western US, water resources are predominantly derived from the melting of seasonal high-elevation snowpack where disturbance (e.g., wildfire, insect infestation) and hydrologic and ecosystem functioning can directly impact water availability for agricultural production, i.e., “fire to farm”. Additionally, shifting precipitation patterns and increasing air temperatures are resulting in smaller and earlier peak snowpack water equivalents and advancing the timing of snowmelt and peak streamflow. Subsequent impacts of these changes to moisture availability affect natural ecosystem functioning, plant ecophysiological responses, and vegetation contributions to water cycling, in turn affecting ecosystem services and downstream water availability and quality. This project aims to improve the understanding of ecohydrological processes in semi-arid western US agricultural watersheds by considering the continuum of water resources from streamflow generation in the mountains through ecosystem controls of water cycling to impacts of farm-level water limitations on crop growth and productivity. To address these needs, we will use a variety of data-assimilation tools, process-based models, and artificial intelligence (AI) to better characterize the soil-plant-atmosphere pathway across landscape types. Model components and improvements resulting from this research will inform precision agriculture and conservation across spatiotemporal scales and improve quantification of water supply responses to climate, disturbance, and management (Fig. 1). By focusing on agricultural watersheds, this project will develop holistic tools and build broad and spatially resolved datasets that will improve crop production under limited water, operational forecasting of water supplies and ecosystem services, precision conservation of water quality, and broader earth-systems research to inform land surface models.
Objective 1. ARS researchers in Fort Collins, Colorado, are measuring and modeling carbon uptake and solar induced fluorescence in corn and winter wheat. These measurements and modeling efforts will be used to improve predictions of crop productivity in global climate models and aid the interpretation of satellite data for quantifying spatiotemporal variability in crop productivity and relationship to satellite observed spectral indices. Objective 2. In collaboration with Colorado State University, we have developed new methods of cloud computing with the model-as-a service for the ARS Agricultural Ecosystems Services (Ages) watershed model. This is demonstrated in a Jupyter notebook (https://colab.research.google.com/drive/1oTB3so4gcVvwYdr8tuquXSL_mWNjFUQI?usp=sharing), which guides users through the workflow of setting up the cloud services integration platform (CSIP), loading Ages project data, running the model once, and graphing examples of the output. Each application of Ages to a new site or watershed requires calibration of a set of model parameters to fit measured variables, such as streamflow, where the model runs thousands of times using different parameters. Previous methods in the object modeling system (OMS) ran Ages sequentially (one run at a time). A new method can run many instances in parallel on a local compute server or cloud service. A prototype of the new parallel calibration service is illustrated at https://colab.research.google.com/drive/1aHmgj0wD4PaND75rMi7Lm_zHv30t2E9F?usp=sharing. Objective 3. In collaboration with Colorado State University, we have installed weather stations and sensor networks at six sites throughout the Cameron Peak and East Troublesome burn areas at elevations ranging from 2500-3500 m. The weather stations and sensor networks will monitor spatial variability in weather variables key to predicting snowmelt rate. These data will also be used to downscale gridded climate data and develop new high-resolution maps of temperature and relative humidity in mountainous regions for the southern Rocky Mountains. We also continued data collection and analysis of a dense network of temperature and relative humidity sensor networks at a lower elevation prairie site that has shown substantial differences in air temperate (up to 20° C) across a small (~10 ha) field with limited elevation relief (< 20 m). Additionally, researchers are collaborating with local resource management agencies, non-profits, and university faculty to implement and monitor pre- and post-fire conservation treatments in the Cameron Peak and East Troublesome burn areas – the two largest wildfires in Colorado history. These conservation treatments include pre-fire fuels reduction and post-fire revegetation treatments, and aerial application of wood strand mulching to burned catchments to reduce erosion, increase soil moisture, improve vegetation recovery, and reduce flood and debris flow risk. Researchers are monitoring streamflow, water quality, erosion, vegetation recovery, and soil moisture, the results of this project will improve best management practices and guide resource managers towards the most effective and efficient use of resources for both pre-fire risk reduction and post-fire remediation.
1. Model shows how fire changes forested mountain source-water hydrology. Forest fire occurrence and severity have dramatically increased in the western U.S. and are causing major changes to agricultural source-water hydrology, which could dramatically affect food production, municipal water supplies, and flood occurrence and severity. ARS scientists in Fort Collins, Colorado, developed a new model, building on collaboration with the U.S. Geological Survey, Forest Service, Colorado State University, and others, that directly addresses the needs of New Mexico Department of Homeland Security and Emergency Management for sustainable preservation, management, and response in fire-impacted source-water areas. Model results of sub-alpine forest conditions after a fire in south-central New Mexico accurately demonstrated a 170% increase in streamflow and predicted drier forest soils, which may hinder forest restoration. The modeling tool improves understanding of hydrologic response to fire and will be used by municipalities and forest managers to guide both fire mitigation and post-fire restoration in the western U.S.
Cattau, M.E., Mahood, A.L., Balch, J.K., Wessman, C.A. 2022. Modern pyromes: Biogeographical patterns of fire characteristics across the contiguous United States. Fire. 5(4). Article e95. https://doi.org/10.3390/fire5040095.
Mahood, A.L., Lindrooth, E.J., Cook, M.C., Balch, J.K. 2022. Country-level fire perimeter datasets (2001-2021). Scientific Data - Nature. 9. Article e458. https://doi.org/10.1038/s41597-022-01572-3.
Mankin, K.R., Wells, R.M., Kipka, H., Green, T.R., Barnard, D.M. 2022. Hydrologic effects of fire in a sub-alpine watershed: AgES outperforms previous PRMS simulations. Transactions of the ASABE. 65(4):751-762. https://doi.org/10.13031/ja.14881.