Lessons Learned From Modeling Irrigation From Field to Regional Scales

Abstract

Correctly calculating the timing and amount of crop irrigation is crucial for capturing irrigation effects on surface water and energy budgets and land‐atmosphere interactions. This study incorporated a dynamic irrigation scheme into the Noah with multiparameterization land surface model and investigated three methods of determining crop growing season length by agriculture management data. The irrigation scheme was assessed at field scales using observations from two contrasting (irrigated and rainfed) AmeriFlux sites near Mead, Nebraska. Results show that crop‐specific growing‐season length helped capture the first application timing and total irrigation amount, especially for soybeans. With a calibrated soil‐moisture triggering threshold (IRR_CRI), using planting and harvesting dates alone could reasonably predict the first application for maize. For soybeans, additional constraints on growing season were required to correct an early bias in the first modeled application. Realistic leaf area index input was essential for identifying the leaf area index‐based growing season. When transitioning from field to regional scales, the county‐level calibrated IRR_CRI helped mitigate overestimated (underestimated) total irrigation amount in southeastern Nebraska (lower Mississippi River Basin). In these two heavily irrigated regions, irrigation produced a cooling effect of 0.8–1.4 K, a moistening effect of 1.2–2.4 g/kg, a reduction in sensible heat flux by 60–105 W/m2, and an increase in latent heat flux by 75–120 W/m2. Most of irrigation water was used to increase soil moisture and evaporation, rather than runoff. Lacking regional‐scale irrigation timing and crop‐specific parameters makes transferring the evaluation and parameter‐constraint methods from field to regional scales difficult.

Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data

“…The Noah‐MP‐Crop model was implemented in the High‐Resolution Land Data Assimilation System (F. Chen et al., 2007) and Weather Research and Forecasting model (Skamarock et al., 2007). It improves the simulations of vegetation dynamics and surface heat fluxes for corn and soybean at both field and regional scales by using a number of agricultural management data, such as growing degree days (GDDs) and planting/harvesting dates (X. Liu et al., 2016, 2020; Xu, Chen, et al., 2019; Z. Zhang et al., 2020). However, uncertainties in input data, model structures, and model parameters inevitably induce uncertainties in the variables simulated by those process‐based models (X. Li et al., 2007; T. Xu et al., 2011; Zhang, Chen, & Gan, 2016; Zhang, Guanter, et al., 2016; Zhang, Xiao, et al., 2016).…”

“…Data assimilation (DA) methods originated from estimation theory and cybernetics to merge observational information into process‐based models to mitigate uncertainties in model variables and optimize model parameters (X. Li et al., 2020; Liang & Qin, 2008; Xia et al., 2019). This is done within variational‐based or ensemble‐based DA schemes to improve the model performances (He, Xu, et al., 2019; He, Xu, Bateni, et al., 2020; He et al., 2018; Lu et al., 2016, 2017, 2020; Margulis et al., 2002; Xu, Bateni, et al., 2018; Xu, Chen, et al., 2019; Xu, He, et al., 2019; T. Xu et al., 2011, 2015). Studies have assimilated various observational variables such as land surface temperature (LST), leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) into crop models and/or LSMs, which has improved the estimated crop yields (Ines et al., 2013; X. Li et al., 2018; Wang et al., 2014; Xie et al., 2017), vegetation biomass, evapotranspiration (ET), and gross primary production (GPP) within LSMs (Huang et al., 2008; Kumar et al., 2019; Xu, He, et al., 2019; T. Xu et al., 2015).…”

Effects of Irrigation on Water, Carbon, and Nitrogen Budgets in a Semiarid Watershed in the Pacific Northwest: A Modeling Study

“…Such perturbations in surface energy and water fluxes not only lead to surface cooling and decreases in Bowen ratios that modulate boundary layer evolution and convective processes in the atmosphere (DeAngelis et al, 2010; Harding & Snyder, 2012; Pei et al, 2016) but also influence the hydrologic budget. For example, previous studies (Leng et al, 2015; Qiu et al, 2019; Xu et al, 2019) have demonstrated that the hydrologic budget terms such as infiltration, percolation to deep soil layers, recharge to aquifers, and in turn baseflow at field, watershed, and large basin scales can be altered by irrigation.…”

The interaction between urbanization and aerosols during a typical winter haze event in Beijing

“…More detailed research needs to be performed by combining observational data and modeling because the conclusions may vary depending on the scale (Xu et al, 2019). Other il-luminating work with regional models showed that the combined effect of UHIs and aerosols on precipitation depends on synoptic conditions (Zhong et al, 2015).…”