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The accuracy of snow density in land surface model (LSM) simulations impacts the accuracy of simulated terrestrial water and energy budgets. However, there has been little research that has focused on enhancing snow compaction in operationally used LSMs. A baseline snow simulation with the widely used Noah-MP LSM systematically overestimates snow depth by 55 mm even after removing daily snow water equivalent (SWE) biases. To reduce uncertainties associated with snow compaction, we enhance the most sensitive Noah-MP snow compaction parameter-the empirical parameter for compaction due to overburden (Cbd)-such that Cbd is calculated as a function of surface air temperature as opposed to a fixed value in the baseline simulation. This enhancement improves accuracy in simulated snow compaction across the majority of western U.S. (WUS) SNOTEL test sites (biases reduced at 88% of test sites), with modest bias reductions in cooler accumulation periods (biases reduced at 70% of test sites) and substantial improvements during warmer ablation periods (biases reduced at 99% of test sites). Relatively larger improvements during warm conditions are attributable to the default Cbd value being reasonable for cold temperatures (<=-5 degrees C). Improvements in simulated snow depth and density with observations outside of the training sites and optimization periods support that the snow compaction enhancement is transferable in space and time. Differences between enhanced and baseline gridded simulations across the total WUS support that the enhancement can have important impacts on snowpack evolution, snow albedo feedback, and snow hydrology. Snow density, the ratio of water in the snowpack to the snow depth, is a fundamental property of snow that is important to accurately represent in snow simulations. In this research, we develop and evaluate a model enhancement designed to more accurately simulate the snow densification process in a widely used land surface model, Noah-MP. Evalua

The land surface plays a vital role in the Earth system, with exchanges of energy and water between the land and atmosphere significantly influencing local and regional weather and climate. This chapter presents a review of recent advancements in comprehending and modeling the effects of human activities, particularly urbanization and agriculture management, on land-atmosphere interactions. It summarizes how these processes modify land surface physical properties, impacting the exchange of water, heat, and momentum between the land and atmosphere. Additionally, the chapter explores the influence of urbanization and agriculture on atmosphere boundary layer structures and convection activities. These processes collectively modify cross-scale precipitation patterns, weather phenomena, and climate dynamics. Understanding the complexities of these interactions is crucial for effective land and water resource management, urban planning, and strategies for mitigating climate change impacts. © 2024 American Geophysical Union.

The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) is designed for applications ranging from uncoupled land surface hydrometeorological and ecohydrological process studies to coupled numerical weather prediction and decadal global or regional climate simulations. It has been used in many coupled community weather, climate, and hydrology models. In this study, we modernize and refactor the Noah-MP LSM by adopting modern Fortran code standards and data structures, which substantially enhance 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 as a result of re-organizing model physics into individual process-level Fortran module files, (2) an enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) an enhanced code structure and calling workflow as 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 host weather, climate, and 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, and hydrology models in the future. Overall, the modernized Noah-MP allows a more efficient and convenient process for future model developments and applications.

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 than Calib. CalibTD showed 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. Published 2022. This article is a U.S. Government work and is in the public domain in the USA.

In Table 1, the word in row 1, column3 should be “Lat”, and the word in row 1, column4 should be “Lon” in Table 1 The Figs. 5 and 7 were unclear, and they should be replaced as following © 2021 The Authors