Tile drainage is one of the dominant agricultural management practices in the United States and has greatly expanded since the late 1990s. It has proven effects on land surface water balance and quantity and quality of streamflow at the local scale. The effect of tile drainage on crop production, hydrology, and the environment on a regional scale is elusive due to lack of high-resolution, spatially-explicit tile drainage area information for the Contiguous United States (CONUS). We developed a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions.
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;
Despite the advances in climate change modeling, extreme events pose a challenge to develop approaches that are relevant for urban stormwater infrastructure designs and best management practices. The study first investigates the statistical methods applied to the land‐based daily precipitation series acquired from the Global Historical Climatology Network‐Daily (GHCN‐D). Additional analysis was carried out on the simulated Multivariate Adaptive Constructed Analogs (MACA)‐based downscaled daily extreme precipitation of 15 General Circulation Models and Weather Research and Forecasting‐based hourly extreme precipitation of North American Regional Reanalysis to discern the return period of 24‐hr and 48‐hr events. We infer that the GHCN‐D and MACA‐based precipitation reveals increasing trends in annual and seasonal extreme daily precipitation. Both BCC‐CSM1‐1‐m and GFDL‐ESM2M models revealed that the magnitude and frequency of extreme precipitation events are projected to increase between 2016 and 2099. We conclude that the future scenarios show an increase in magnitudes of extreme precipitation up to three times across southeastern Virginia resulting in increased discharge rates at selected gauge locations. The depth‐duration‐frequency curve predicted an increase of 2–3 times in 24‐ and 48‐h precipitation intensity, higher peaks, and indicated an increase of up to 50% in flood magnitude in future scenarios.
Abstract. The US Northern Great Plains and the Canadian Prairies are known as the world’s breadbaskets for its large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model, for a long time period (13-year) and fine spatial scale (4-km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at point-scale, (2) applying a dynamic planting/harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the US Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting/harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications for estimating crop production, simulating the land-atmosphere interactions in croplands, and crop growth’s responses to the raising temperatures amid climate change.
Abstract. The widely-used open-source community Noah-MP land surface model (LSM) is designed for applications ranging from uncoupled land-surface and ecohydrological process studies to coupled numerical weather prediction and decadal global/regional climate simulations. It has been used in many coupled community weather/climate/hydrology models. In this study, we modernize/refactor the Noah-MP LSM by adopting modern Fortran code and data structures and standards, which substantially enhances the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization and interoperability by re-organizing model physics into individual process-level Fortran module files, (2) enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) enhanced code structure and calling workflow by leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standard, and (5) enhanced driver and interface structures to couple with host weather/climate/hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather/climate/hydrology models in the future. Overall, the modernized Noah-MP will allow a more efficient and convenient process for future model developments and applications.
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;
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