Modeling the hydrologic influence of subsurface tile drainage using the National Water Model

Abstract

Agriculture management practices such as irrigation, fertilizer and pesticide application, and tillage are generally employed to enhance crop productivity and are crucial for global food production and food security. Agriculture subsurface drainage, often known as subsurface tile drainage (TD), is a widely used agriculture water management practice to improve crop growth in regions with shallow water tables or poorly drained soils. According to the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Census of Agriculture 2017, about 22.48 million hectares (Mha) of croplands in the US are tile-drained, and 83.80% of the total tile-drained croplands of the US are concentrated in six Midwestern states (USDA-NASS, 2017; Figure 1a), which is one of the world's most productive areas in terms of food and bioenergy, and it is located in the headwater regions of the Mississippi River (Guanter et al., 2014;Ray et al., 2013).In general, tile drains are buried under the crop root zone to extract saturation water (or free water) from the soil, improve root-zone soil aeration and soil quality, reduce crop root diseases and soil erosion, allow for earlier planting and enhance crop yield (Figure 1b;

Modeling the Hydrologic Influence of Subsurface Tile Drainage Using the National Water Model

“…For each quantile segment of the streamflow series, we estimated the model performance using metrics listed in Table 3. To identify streamflow events, we use a recently developed R package called "RNWMStat" (https://github.com/NCAR/RNWMStat; Valayamkunnath, Liu, et al, 2020). RNWMStat can detect and match streamflow events from the observed and simulated streamflow series by using an approach developed by Kusche et al (2009), Magner et al (2004), Patterson et al (2020), Schneider (2011), andScholkmann et al (2012).…”

“…The NWM source code used in this study is publicly available at: https://github.com/NCAR/wrf_hydro_nwm_public/ (McCreight et al., 2021). The RNWMStat R Package is available at: https://github.com/NCAR/RNWMStat/ (Valayamkunnath, Liu, et al., 2020).…”

Modernizing the open-source community Noah-MP land surface model (version 5.0) with enhanced modularity, interoperability, and applicability

“…Two optional groundwater schemes, one without 2-D lateral flow (Niu et al, 2007) and one with 2-D lateral flow (Fan et al, 2007;Miguez-Macho et al 2007), are available in Noah-MP to simulate groundwater dynamics, including groundwater recharge, water table change, baseflow, seepage, and/or lateral flow. Noah-MP also includes dynamic irrigation and tile drainage processes for agricultural management applications (Valayamkunnath et al, 2021(Valayamkunnath et al, , 2022. Figure 2 summarizes the key water processes and budget components as well as the water balance equation in Noah-MP v5.0.…”

“…Since the release of the original Noah-MP in year 2011 (Niu et al, 2011) 3) tile drainage schemes (Valayamkunnath et al, 2022); (4) dynamic irrigation schemes (sprinkler, micro, and flooding irrigation) (Valayamkunnath et al, 2021); (5) a dynamic crop growth model for corn and soybean (Liu et al, 2016) with enhanced C3 and C4 crop parameters (Zhang et al, 2020); (6) coupling with urban canopy models (Xu et al, 2018;Salamanca et al, 2018) with local climate zone modeling capabilities (Zonato et al, 2021); (7) enhanced snow cover, snow compaction, and wind-canopy absorption parameters (He et al, 2021);…”

Applying double-cropping and interactive irrigation in the North China Plain using WRF4.5

“…When simulating the water forcings that sustain crop growth, some models simply assume no irrigation (Liu et al, 2016), while others incorporate irrigation with fixed amount (Vira et al, 2019) or dynamically based on daily soil conditions (Ozdogan et al, 2010;Qian et al, 2013;Valayamkunnath et al, 2021;Wu et al, 2018b;Yang et al, 2016Yang et al, , 2017Yang et al, , 2019Yang et al, , 2020. With these algorithms to simulate crop phenology and irrigation behaviour, multiple studies have reported significant enhancements in dynamic vegetation predictions and a better understanding of irrigation impact (Xu et al, 2019;Yang et al, 2016;Zhang et al, 2020).…”