This study explores the impacts of groundwater processes on the simulated land‐surface water balance and hydrometeorology. Observations are compared to multi‐scale Weather Research and Forecasting (WRF) simulations of summer seasons for three years: 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, and supply root‐zone water double that of simulations with 9‐km and 27‐km grid spacing, which 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^oC to 2‐3^oC, 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.

Representing climate‐crop interactions is critical to Earth system modeling. Despite recent progress in modeling dynamic crop growth and irrigation in land surface models (LSMs), transitioning these models from field to regional scales is still challenging. This study applies the Noah‐MP LSM with dynamic crop‐growth and irrigation schemes to jointly simulate the crop yield and irrigation amount for corn and soybean in the central United States. The model performance of crop yield and irrigation amount are evaluated at county‐level against the USDA reports and USGS water withdrawal data, respectively. The bulk simulation (with uniform planting/harvesting management and no irrigation) produces significant biases in crop yield estimates for all planting regions, with root‐mean‐square‐errors (RMSEs) being 28.1% and 28.4% for corn and soybean, respectively. Without an irrigation scheme, the crop yields in the irrigated regions are reduced due to water stress with RMSEs of 48.7% and 20.5%. Applying a dynamic irrigation scheme effectively improves crop yields in irrigated regions and reduces RMSEs to 22.3% and 16.8%. In rainfed regions, the model overestimates crop yields. Applying spatially varied planting and harvesting dates at state‐level reduces crop yields and irrigation amount for both crops, especially in northern states. A “nitrogen‐stressed” simulation is conducted and found that the improvement of irrigation on crop yields is limited when the crops are under nitrogen stress. Several uncertainties in modeling crop growth are identified, including yield‐gap, planting date, rubisco capacity, and discrepancies between available data sets, pointing to future efforts to incorporating spatially varying crop parameters to better constrain crop growing seasons.

The interactions between crops and the atmosphere significantly impact surface energy and hydrology budgets, climate, crop yield, and agricultural management. In this study, a multipass land data assimilation scheme (MLDAS) is proposed based on the Noah‐MP‐Crop model. The ensemble Kalman filter (EnKF) method is used to jointly assimilate the leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) observations to predict sensible (H) and latent (LE) heat fluxes, gross primary productivity (GPP), etc. Such joint assimilation is demonstrated to be effective in constraining the model state variables (i.e., leaf biomass and SM) and optimizing key crop‐model parameters (i.e., specific leaf area [SLA], and maximum rate of carboxylation, Vcmax). The performance of the MLDAS is evaluated against observations at two AmeriFlux cropland sites, revealing good an agreement with the observed H, LE, and GPP. When using optimized model parameters (SLA and Vcmax) and jointly assimilating LAI, SM, and SIF observations, the MLDAS produces 34.28%, 26.90%, and 51.82% lower root mean square deviations for daily H, LE, and GPP estimates compared with the Noah‐MP‐Crop open loop simulation. Our findings also indicate that the H and LE predictions are more sensitive to SM measurements, while the GPP simulations are more affected by LAI and SIF observations. The results indicate that performances of physical models can be greatly improved by assimilating multi‐source observations within MLDAS.

Numerous wetlands in the prairies of Canada provide important ecosystem services, yet are threatened by climate and land‐use changes. Understanding the impacts of climate change on prairie wetlands is critical to effective conservation planning. In this study, we construct a wetland model with surface water balance and ecoregions to project future distribution of wetlands. The climatic conditions downscaled from the Weather Research and Forecasting model were used to drive the Noah‐MP land surface model to obtain surface water balance. The climate change perturbation is derived from an ensemble of general circulation models using the pseudo global warming method, under the RCP8.5 emission scenario by the end of 21st century. The results show that climate change impacts on wetland extent are spatiotemporally heterogenous. Future wetter climate in the western Prairies will favor increased wetland abundance in both spring and summer. In the eastern Prairies, particularly in the mixed grassland and mid‐boreal upland, wetland areas will increase in spring but experience enhanced declines in summer due to strong evapotranspiration. When these effects of climate change are considered in light of historical drainage, they suggest a need for diverse conservation and restoration strategies. For the mixed grassland in the western Canadian Prairies, wetland restoration will be favorable, while the highly drained eastern Prairies will be challenged by the intensified hydrological cycle. The outcomes of this study will be useful to conservation agencies to ensure that current investments will continue to provide good conservation returns in the future.

Precipitation recycling, where evapotranspiration (ET) from the land surface contributes to precipitation within the same region, is a critical component of the water cycle. This process is especially important for the US Corn Belt, where extensive cropland expansions and irrigation activities have significantly transformed the landscape and affected the regional climate. Previous studies investigating precipitation recycling typically relied on analytical models with simplifying assumptions, overlooking the complex interactions between groundwater hydrology and agricultural management. In this study, we use high-resolution climate models coupled with an explicit water vapor tracer algorithm to quantify the impacts of shallow groundwater, dynamic crop growth, and irrigation on regional precipitation recycling in the US Corn Belt. We find that these coupled groundwater–crop–irrigation processes reduce surface temperatures and increase the growing season precipitation. The increase in precipitation is attributed to a significant enhancement of the precipitation recycling ratio from 14 to 18%. This enhanced precipitation recycling is stronger in a dry year than normal and wet years, depending on both large-scale moisture transport and local ET. Our study underscores the critical role of groundwater hydrology and agricultural management in altering the regional water cycle, with important implications for regional climate predictions and food and water security.