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Abstract. The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) isdesigned for applications ranging from uncoupled land surfacehydrometeorological and ecohydrological process studies to coupled numericalweather prediction and decadal global or regional climate simulations. It hasbeen used in many coupled community weather, climate, and hydrology models. Inthis study, we modernize and refactor the Noah-MP LSM by adopting modern Fortrancode standards and data structures, which substantially enhance the modelmodularity, interoperability, and applicability. The modernized Noah-MP isreleased as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization as a result of re-organizing model physics into individualprocess-level Fortran module files, (2) an enhanced data structure with newhierarchical data types and optimized variable declaration andinitialization structures, (3) an enhanced code structure and calling workflowas a result of leveraging the new data structure and modularization, (4) enhanced(descriptive and self-explanatory) model variable naming standards, and (5) enhanced driver and interface structures to be coupled with the hostweather, climate, and hydrology models. In addition, we create a comprehensivetechnical documentation of the Noah-MP v5.0 and a set of model benchmark andreference datasets. The Noah-MP v5.0 will be coupled to variousweather, climate, and hydrology models in the future. Overall, the modernizedNoah-MP allows a more efficient and convenient process for future modeldevelopments and applications.

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.

Subsurface tile drainage (TD) is a dominant agriculture water management practice in the United States (US) to enhance crop production in poorly drained soils. Assessments of field‐level or watershed‐level (<50 km²) hydrologic impacts of TD are becoming common; however, a major gap exists in our understanding of regional (>105 km²) impacts of TD on hydrology. The National Water Model (NWM) is a distributed 1‐km resolution hydrological model designed to provide accurate streamflow forecasts at 2.7 million reaches across the US. The current NWM lacks TD representation which adds considerable uncertainty to streamflow forecasts in heavily tile‐drained areas. In this study, we quantify the performance of the NWM with a newly incorporated tile‐drainage scheme over the heavily tile‐drained Midwestern US. Employing a TD scheme enhanced the uncalibrated NWM performance by about 20–50% of the fully calibrated NWM (Calib). The calibrated NWM with tile drainage (CalibTD) showed enhanced accuracy with higher event hit rates and lower false alarm rates thanCalib.CalibTDshowed better performance in high‐flow estimations as TD increased streamflow peaks (14%), volume (2.3%), and baseflow (11%). Regional water balance analysis indicated that TD significantly reduced surface runoff (−7% to −29%), groundwater recharge (−43% to −50%), evapotranspiration (−7% to −13%), and soil moisture content (−2% to −3%). However, TD significantly increased soil profile lateral flow (27.7%) along with infiltration and soil water storage potential. Overall, our findings highlight the importance of incorporating the TD process into the operational configuration of the NWM.

This study explores the impacts of groundwater processes on the simulated land‐surface water balance and hydrometeorology. Observations are compared to multiscale Weather Research and Forecasting (WRF) simulations of three summer seasons: 2012, 2013, and 2014. Results show that a grid spacing of 3 km or smaller is necessary to capture small‐scale river and stream networks and associated shallow water tables, which supplies additional root‐zone water double that of simulations with 9‐km and 27‐km grid spacing and is critical to replenishing the depleted vegetation root zones and leads to 150 mm more evapotranspiration. Including groundwater processes in convection‐permitting models is effective to reduce: (1) 2‐m temperature warm biases from 5–6 to 2–3 °C and (2) the low precipitation bias by half. The additional groundwater supply to active soil flux in convection‐permitting simulations with groundwater for June‐August is nearly translated into the same amount of increased precipitation in the domain investigated.

The study postulates that crop rooting depth representation plays a vital role in simulating soil‐crop‐atmospheric interactions. Rooting depth determines the water access for plants and alters the surface energy participation and soil moisture profile. The aboveground crop growth representation in land surface models continues to evolve and improve, but the root processes are still poorly represented. This limitation likely contributes to the bias in simulating soil‐crop‐related variables such as soil moisture and associated water and energy exchanges between the surface and the atmosphere. In Noah‐MP‐Crop, the rooting depth of crops is assumed as 1 m regardless of crop types and the length of growing seasons. In this study, a simple dynamic rooting depth formulation was integrated into Noah‐MP‐Crop. On comparing with soil moisture observations from the in situ Ameriflux, USDA Soil Climate Analysis Network, and the remote‐sensed Soil Moisture Active Passive data set, the results highlight the improved performance of Noah‐MP‐Crop due to modified rooting depth. The improvements were noted in terms of soil moisture and more prominently in terms of the energy flux simulations at both field scale and regional scale. The enhancements in soil moisture profiles reduce the biases in surface heat flux simulations. The impact of rooting depth representation appears to be particularly significant for improving model performance under drought‐like situations. Although it was not possible to validate the simulated rooting depth due to lack of observations, the overall performance of the model helps emphasize the importance of enhancing the representation of crop rooting depth in Noah‐MP‐Crop.

We extend a stochastic aerosol‐snow albedo model to explicitly simulate dust internally/externally mixed with snow grains of different shapes and for the first time quantify the combined effects of dust‐snow internal mixing and snow nonsphericity on snow optical properties and albedo. Dust‐snow internal/external mixing significantly enhances snow single‐scattering coalbedo and absorption at wavelengths of <1.0 μm, with stronger enhancements for internal mixing (relative to external mixing) and higher dust concentrations but very weak dependence on snow size and shape variabilities. Compared with pure snow, dust‐snow internal mixing reduces snow albedo substantially at wavelengths of <1.0 μm, with stronger reductions for higher dust concentrations, larger snow sizes, and spherical (relative to nonspherical) snow shapes. Compared to internal mixing, dust‐snow external mixing generally shows similar spectral patterns of albedo reductions and effects of snow size and shape. However, relative to external mixing, dust‐snow internal mixing enhances the magnitude of albedo reductions by 10%–30% (10%–230%) at the visible (near‐infrared) band. This relative enhancement is stronger as snow grains become larger or nonspherical, with comparable influences from snow size and shape. Moreover, for dust‐snow external and internal mixing, nonspherical snow grains have up to ~45% weaker albedo reductions than spherical grains, depending on snow size, dust concentration, and wavelength. The interactive effect of dust‐snow mixing state and snow shape highlights the importance of accounting for these two factors concurrently in snow modeling. For application to land/climate models, we develop parameterizations for dust effects on snow optical properties and albedo with high accuracy.