FY 2017 Performance Metrics

Extend the capabilities of the DOE’s Advanced Climate Model for Energy (ACME) to simulate and evaluate human-natural interdependencies for the carbon and water cycles.



Implement and evaluate the impacts of irrigation on the hydrologic cycle in the ACME model

Product Definition

Globally, around 70% and 90% of freshwater withdrawals and consumptions are used for irrigation purposes, respectively [Döll, 2009]. The large-scale water withdrawals from rivers, lakes, reservoirs, and aquifers have directly and substantially altered the terrestrial water and energy cycles [Haddeland et al., 2006; Kustu et al., 2010; Wada et al., 2010; Famiglietti et al., 2011; Leng et al., 2014; Pohkrel et al., 2015]. Irrigation impacts are expected to increase with growing world population and food demand, while production of bioenergy crops may further exacerbate irrigation water demands. Hence understanding the role of irrigation in human-Earth system interactions is important for adaptive planning of water, land, and energy use. During the last two decades, noteworthy progress has been made to represent irrigation in global hydrological models for assessing water resource availability and use, but there have been limited efforts in modeling irrigation in global land surface models and Earth system models (ESMs). From a modeling perspective, estimation of irrigation amount and choices of irrigation water sources and methods are key aspects in parameterizing irrigation water use and modeling its impacts. The most under-represented aspect of irrigation modeling is the irrigation method that determines how the extracted water is applied to the irrigated areas. Given the various approaches used to model irrigation, considerable discrepancy exists in simulating irrigation effects, especially at local/regional scales. To reduce such uncertainty, an interactive irrigation scheme has been developed and incorporated into the Accelerated Climate Modeling for Energy (ACME) Land Model (ALM) with consideration of the irrigation water sources (i.e., surface water and groundwater) and methods (i.e., drip, sprinkler and flood irrigation). The model has been used to investigate how the simulated irrigation effects are influenced by the water sources and methods for irrigation. By designing different irrigation scenarios, the pathways through which irrigation affects land surface water balance and how differences in irrigation water sources and methods affect irrigation water use efficiency are studied using numerical experiments. A set of numerical experiments with the new irrigation scheme in ALM showed that both different water sources (e.g., groundwater versus surface water) and methods of irrigation (e.g., sprinkler, flood, or drip) could lead to large differences in simulating the hydrological impacts of irrigation, suggesting that modeling of irrigation must consider those two factors to reduce uncertainty.

Product Documentation

The Accelerated Climate Modeling for Energy (ACME) is an ESM developed by the U.S. Department of Energy (DOE). To represent irrigation in the ACME Land Model (ALM), a groundwater pumping module has been added following Leng et al., 2014 so that the estimated irrigation water demand can be met by withdrawing water from surface water storage (i.e., accumulated total runoff) and/or groundwater. The fraction of surface water withdrawal to the total water withdrawal is constrained by a global data set. The water table depth is updated after water is extracted for irrigation. In addition to the direct impacts on groundwater resources, changes in water table depth lead to subsequent changes in the subsurface drainage and recharge from the bottom soil layer to the aquifer, thus indirectly influencing soil water content through soil-aquifer interactions.
After the irrigation water is extracted, it can be applied to the irrigated areas using three distinct irrigation methods, i.e., drip, sprinkler, and flood irrigation. In practice, drip irrigation applies water directly to the root zone to reduce the wetted area, so it features the highest water use efficiency. Sprinkler irrigation is less efficient because a large fraction of the water is sprayed into the air, which can result in wind drift and evaporation losses. In flood irrigation, less water is lost to evaporation than in sprinkler irrigation, but more water can be lost as runoff in the fields. The irrigation methods are parameterized mainly following the guidance of reflecting the distinct water use efficiencies among the three methods. For sprinkler irrigation, water is added directly to the canopy as precipitation. In drip irrigation, water is applied directly and slowly to the soil layers in the root zone to facilitate plant water use. In flood irrigation, water is poured directly and quickly to the ground surface in a period of 30 min. as precipitation falling on the ground surface, bypassing the canopy. The approach implemented in ALM can reflect the distinct irrigation water use efficiencies among the three irrigation methods, which is important for evaluating water use and hydrologic impacts driven by irrigation.

A series of sensitivity experiments were performed with ALM at 1-degree resolution driven by observed atmospheric forcing [Qian et al., 2006]. After cycling the atmospheric forcing for model spin-up, results for 1971-2005 were compared to contrast simulations with different irrigation water sources (surface water only, surface water and groundwater) and different irrigation methods (sprinkler with irrigation water applied to the canopy versus directly to the ground, flood, and drip) with water sources from both surface and groundwater. The impacts of water sources and irrigation methods were evaluated by comparing the effects of irrigation on water fluxes (e.g., evapotranspiration and runoff), soil moisture, groundwater table depth, and irrigation water use efficiency.


Develop and Implement Global Crop-Planting Date Triggers for the ACME Model

Product Definition

Climate and weather conditions are among the key drivers of crop suitability and productivity in a region. The influence of climate and weather on the growing season determine the amount of time crops spend in each growth phase, which in turn impacts productivity and, more importantly, yields. Precipitation and temperature are responsible for 30% of the variability in crop yields (Lobell and Field, 2007). Planting date can also have a strong influence on yields (Deryng et al., 2011; Drewniak et al., 2013; Waongo et al., 2015), with earlier planting generally resulting in higher yields (Choi et al., 2017). Crop models are sensitive to planting date: for example, early planting results in higher yield for soybean crops because cooler spring temperatures slow crop development and allow an extended growing season (Drewniak et al., 2013). Furthermore, planting date is changing. Maize planting date in the Midwest U.S. is now two weeks earlier than just a few decades ago (Kucharik, 2006), which has resulted in a 19-53% increase in yields across many U.S. states (Kucharik, 2008). Although this trend is the result of improved genotypes (Kucharik, 2006) rather than early spring warming, longer growing seasons caused by future climate change could also drive early planting decisions. The Accelerated Climate Model for Energy (ACME) Land Model (ALM) does include temperature thresholds to define crop planting dates, but the application is limited to mid-latitudes. A more robust metric that includes other climate factors (i.e., precipitation) is added to improve the predicted timing of crop planting and will also allow global crop production.

Product Documentation

Crop models need an accurate method to predict plant date to allow these models to: 1) capture changes in crop management to adapt to climate change, 2) accurately model the timing of crop phenology, and 3) improve crop simulated influences on carbon, nutrient, energy, and water cycles. Climate plays a strong role governing planting date (Choi et al., 2017). Previous studies have used climate as a predictor for planting date (Deryng et al., 2011; Waha et al., 2012; Waongo et al., 2014), although other factors besides climate influence a farmer’s decision to plant (e.g. workability, economics, culture) (Iizumi and Ramankutty, 2014). Climate as a plant date predictor has more advantages than the use of fixed plant dates. For example, crop expansion and other changes in land use due to changing temperature conditions, new drought or cold tolerant crop hybrids and cultivars, and increased food demands can be accommodated without additional inputs. As such, a new methodology to implement predictive planting date based on climate inputs is added to the Accelerated Climate Model for Energy (ACME) Land Model (ALM). The model considers two main sources of climate data important for planting: precipitation and temperature. The new ALM model is compared with observations to test its ability to capture planting dates globally.