Recipient Organization
UNIV OF MINNESOTA
(N/A)
ST PAUL,MN 55108
Performing Department
Soil, Water, and Climate
Non Technical Summary
With over 10,000 lakes, Minnesota is rich in water resources, but growing and diversifying demands on water mean that we, too, face water stress, declining lake levels, and threats to water quality. Along with the major US crops of field corn and soybean, Minnesota is a top producer of sweet corn, peas, and potatoes--which are all increasingly irrigated with groundwater. Compared to other states, Minnesota retains a comparative advantage in water resources to support healthy communities, and current increasing trends in precipitation are projected to continue through this century; however, threats to water resources include the timing and magnitude of precipitation, and the management of the landscape. Irrigation removes drought risk but will deplete coldwater trout streams, lakes, and wetlands and increase groundwater nitrates without adaptive management.To secure a long-term sustainable water future and meet growing demand in crop productivity, knowledge gaps about the availability and consumption of water and emerging threats to water security must be filled. To identify locations of current and future water stress, I will use a state of the art land surface model (Agro-IBIS) to quantify current and projected water availability across the state. The model calculates a water balance in individual grid cells (~10km) based on inputs from precipitation and losses through evapotranspiration, drainage, and runoff for any time period of interest. Outputs will identify regions of water abundance and water scarcity and crop yield, and be used to evaluate past, present, and future (to the end of the 21st century) scenarios of climate, land-use, and development.
Animal Health Component
25%
Research Effort Categories
Basic
75%
Applied
25%
Developmental
(N/A)
Goals / Objectives
With over 10,000 lakes, Minnesota is rich in water resources, but growing and diversifying demands on water mean that we, too, face water stress and threats to water quality. Along with the major US crops of field corn and soybean, Minnesota is a top producer of sweet corn, peas, and potatoes--which are all increasingly irrigated with groundwater. Compared to other states, Minnesota retains a comparative advantage in water resources to support healthy communities, and current increasing trends in precipitation are projected to continue through this century; however, threats to water resources include the timing and magnitude of precipitation, and the management of the landscape. Irrigation removes drought risk but will deplete coldwater trout streams, lakes, and wetlands and increase groundwater nitrates without adaptive management.Human activities have altered landscapes in ways that affect the fluxes of energy, water, and carbon between the atmosphere and the land surface. Understanding the relationships among these factors and how they are likely to change as a result of changes in land cover, land management, and climate is critical for responsive and sustainable management of water and land resources. For example, removing vegetation or converting from one land-use type to another (e.g. conversion of grassland or forest to agriculture) has been shown to significantly increase runoff and streamflow (Twine et al. 2004). Changes in land use can also affect the delivery of nutrients and sediment to surface waters and groundwater (Donner and Kucharik 2008). The processes that dominate water fluxes between the land surface and atmosphere and fluxes of nutrients and sediments are complex and vary over time and space. Addressing questions about how changes in land use, water use, and climate will affect the amount and quality of water seasonally and spatially requires sophisticated modeling approaches.I propose to use an adaptation of a dynamic global vegetation model (DGVM) that includes modules for vegetation canopy physics, soil physics and hydrology, phenology, and ecosystem biogeochemistry (Kucharik et al. 2000). The model, called Agro-IBIS, was developed specifically for the continental US and can represent common cropping systems represented in Minnesota such as corn, soybean, and wheat, along with natural ecosystems of grasslands, forests, and shrublands. Agro-IBIS is currently being adapted and tested for use in sweet corn, peas, and potatoes. Agro-IBIS allows for variable fertilizer inputs as well as irrigation and farmer management decisions. The model has been evaluated over natural and managed ecosystems, including its simulation of crop yields (Kucharik, 2003), leaf area index and gross primary productivity (Twine and Kucharik, 2008) and surface energy balance (Webler et al., 2012), and has been used to evaluate impacts of nitrogen leaching on nitrate export in the Mississippi River Basin (Donner et al., 2002), trends in productivity in the 20th Century (Twine and Kucharik, 2009), climate-regulation services of natural and agricultural ecoregions (Anderson-Teixeira et al., 2012), and effects of trends in planting date and cultivar on yields and surface energy balance (Sacks and Kucharik, 2011).Another key advantage of using a DGVM is the ability to use the model to understand the consequences for water quality and quantity due to specific interventions in different parts of the state. Climate, as well as the coverage of natural and managed ecosystem types varies across Minnesota. Whereas many other models do not directly simulate the growth of vegetation in their water balance calculations, the Agro-IBIS model allows predictions of changes in water fluxes (to evapotranspiration, irrigation, surface runoff and groundwater recharge) and nutrient losses based on local climate, vegetation, and management.My work will also take advantage of the latest advancements in future climate projections and incorporate these data into water balance modeling. Agro-IBIS uses as input high-resolution climate data down-scaled from the most recent CMIP5 global climate model output (used in the 2013 IPCC AR5 report). These updated climate models have improved estimates for how water availability will change in the future, including variability in the seasonality and intensity of precipitation out to the year 2100. I have recently downscaled six global climate models (currently at 1-3 degree resolution - about 100-300 km) to a 10 km resolution for input into the Agro-IBIS model. This downscaled climate data product will be useful for the water balance modeling in Minnesota, as well as for other analyses and models that rely on downscaled climate information.The outputs of the water balance model can be interpreted to identify regions of water scarcity or water stress. Quantifying and mapping water scarcity is crucial to managing shortages and finding solutions, such as identifying regions where it is important to re-use water or to anticipate tradeoffs among competing water uses. Periodic and localized scarcity of water is common, even in water-rich regions like Minnesota.I propose to evaluate present and future vulnerabilities of Minnesota's major cropping systems to water scarcity to inform irrigation and nitrogen management practices, with the goal of sustaining Minnesota's crop productivity and water resources.OBJECTIVES1. Simulate water balance across Minnesota for major biomes: forest, grasslands, croplands of field corn, soybean, and wheat, and newly developed modules for sweet corn, pea, and potato under current climate conditions and projected climate through 2100.2. Simulate water balance across Minnesota's major biomes under various land use scenarios that maximize yield and minimize consumption of water resources. Produce maps of Groundwater recharge, yield response, and nitrate leaching under different irrigation development scenarios (irrigated vs. rainfed, agriculture vs. forest land use, historical/future time periods).Anderson-Teixeira, K.J. et al., 2012. Climate-regulation services of natural and agricultural ecoregions of the Americas. Nature Climate Change, 2(3): 177-181.Donner, S.D., M.T. Coe, J.D. Lenters, T.E. Twine, J.A Foley. 2002. Modeling the impact of hydrological changes on nitrate transport in the Mississippi River Basin from 1955-1994. Global Biogeochem. Cycles, 16: 3, doi:10.1029/2001GB001396.Kucharik, C.J., 2003. Evaluation of a process-based agro-ecosystem model (Agro-IBIS) across the U.S. Cornbelt: Simulations of the interannual variability in maize yield. Earth Interactions, 7: 1-33.Kucharik, C.J. et al., 2000. Testing the performance of a Dynamic Global Ecosystem Model: water balance, carbon balance, and vegetation structure. Global Biogeochemical Cycles, 14(3): 795-825.Sacks, W.J. and C.J. Kucharik. 2011. Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance. Agricultural and Forest Meteorology, 151: 882-894.Twine, T. E., C. J. Kucharik, and J. A. Foley. 2004. Effects of land cover change on the energy and water balance of the Mississippi River basin. Journal of Hydrometeorology 5.4: 640-655.Twine, T.E. and C. J. Kucharik. 2008. Evaluating a terrestrial ecosystem model with satellite information of greenness. Journal of Geophysical Research-Biogeosciences, 113(G3): G03027, doi:10.1029/2007JG000599.Twine, T.E. and C.J. Kucharik. 2009. Climate impacts on net primary productivity trends in natural and managed ecosystems of the central and eastern United States. Agricultural and Forest Meteorology: doi:10.1016/j.agrformet.2009.05.012.Webler, G., Roberti, D.R., Cuadra, S.V., Moreira, V.S. and Costa, M.H., 2012. Evaluation of a Dynamic Agroecosystem Model (Agro-IBIS) for Soybean in Southern Brazil. Earth Interactions, 16.
Project Methods
Agro-IBIS is a DGVM adapted from the Integrated Biosphere Simulator (IBIS; Foley et al., 1996; Kucharik et al., 2000) to simulate the growth and management of food crops including field corn, soybean, wheat (Kucharik and Brye, 2003) and the newly parameterized sweet corn, peas, and potatoes, bioenergy crops (VanLoocke et al., 2010), and the growth of natural vegetation. The model's hierarchical structure simulates fast response processes that vary hourly such as energy, water, carbon, and momentum balance of canopy and soil, processes that vary daily such as leaf growth, and slow response processes like soil carbon storage and turnover. Time-varying parameters determine carbon allocation to specific carbon pools (i.e., leaf, stem, root, and grain). Crop growth stages of planting, emergence, grain fill, senescence, and harvest are explicitly simulated based on accumulated growing degree-days. The simulation of agricultural crops in the vegetation model, in particular, is a state-of-the art tool not yet represented in most other models of this type, but one that has been tested extensively for > 10 years in the Agro-IBIS model. The model is responsive to management options (e.g., irrigation, fertilizer application, planting date) and environmental stresses (e.g., temperature, moisture, radiation, humidity). Agro-IBIS is capable of responding to trends in climate and atmospheric carbon dioxide through semi-mechanistic processes that scale to the canopy and model grid cell. Input requirements include soil texture class at each of 11 soil layers with variable depths, solar radiation or cloud cover, air temperature, precipitation, humidity, and wind speed.I will use Agro-IBIS to simulate the growth and water use of vegetation at every 10-km grid cell statewide. Outputs of the model include water loss through evapotranspiration, drainage, and runoff for any time period of interest. I will also use the model to simulate changes in nutrient fluxes and irrigation as a function of changing agricultural or land-use practices.In order to run the model in Minnesota, I need to process soils, land use, and climate data to parameterize the model. As noted above, this activity requires downscaling global climate data from the most recent global climate models for use at finer spatial resolutions in Minnesota. Six global climate models have already been downscaled and I propose to downscale four more as part of this project. Using a number of models allows for a range of climate futures in Minnesota, which allows for a range of possible future water resource scenarios, providing the best possible information to stakeholders. Outputs of the model will include gridded maps of water balance, including quantification of streamflow and groundwater recharge and changes in water quality by sub-watershed. Evaluating model performance and quantifying uncertainty is a necessary component of our research plan. Agro-IBIS has been shown to predict reasonable historic maize yields in Iowa when driven with downscaled CMIP5 data (Ummenhoffer et al. 2015). I will evaluate the model's simulation of productivity, energy exchange, and water use with historic datasets in Minnesota before performing future climate scenarios. In addition, using datasets from 6-10 different CMIP5 models will allow me to quantify uncertainty among climate variables that drive Agro-IBIS predictions. Though Minnesota records how much water we use for agricultural irrigation (~80-120 billion gallons per year), we do not know if these resources are remaining in place or being exported. Evapotranspiration transforms 90% of the water used by plants into atmospheric vapor that can either be exported downwind to another region or remain in place to form precipitation. I will use a regional weather model (WRF) that uses Agro-IBIS modules to quantify connections between groundwater and rainfall impacts. I will conduct rainfed and irrigated simulations over the Upper Midwest to understand how precipitation patterns change during dry, average, and wet years. Agro-IBIS keeps track of groundwater applied as irrigation, which I will use to estimate the quantity of groundwater imported or exported from Minnesota as precipitation.Foley, J.A. et al., 1996. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochem. Cycles, 10(4): 603-628.Harding, K., T. Twine, and Y. Lu. 2015. Effects of dynamice crop growth on the simulated precipitation response to irrigation. Earth Interactions, 19(14). doi:10.1175/EI-D-15-0030.1.Kucharik, C.J. et al., 2000. Testing the performance of a Dynamic Global Ecosystem Model: water balance, carbon balance, and vegetation structure. Global Biogeochemical Cycles, 14(3): 795-825.Kucharik, C.J. and K.R. Brye. 2003. Integrated BIosphere Simulator (IBIS) yield and nitrate loss predictions for Wisconsin maize receiving varied amounts of Nitrogen fertilizer. Journal of Environmental Quality, 32: 247-268.Ummenhofer, C., H. Xu, T. Twine, E. Girvetz, H. McCarthy, N. Chhetri, and K. Nicholas. 2015. How climate change affects extremes in maize and wheat yield in two cropping regions. Journal of Climate, 28(12), 4653-4687.VanLoocke, A., C.J. Bernacchi, and T.E. Twine. 2010. The impacts of Miscanthus x giganteus production on the Midwest US hydrologic cycle. Global Change Biology Bioenergy, 2(4): 180-191.