Search for: All records

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. 

Abstract 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. 

Abstract 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. 

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. 

Abstract 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. 

Abstract Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.