Representation of subsurface hydrology in global land surface models has been advanced but outstanding challenges and opportunities remain, especially in better simulating lateral groundwater flow and aquifer pumping for irrigation. This study improves the representation of groundwater in the latest version of the Community Land Model (version 5) by implementing a prognostic groundwater module that accounts for lateral groundwater flow, aquifer pumping, and conjunctive use of groundwater and surface water for irrigation. In particular, we introduce—for the first time—explicit representation of steady‐state well equation in large‐scale hydrological modeling. Simulations are conducted at ∼5 km resolution over the conterminous US. Groundwater level, terrestrial water storage (TWS), and river discharge are evaluated over the major US aquifers and river basins using in situ observations of groundwater and river discharge and the TWS inferred from the Gravity Recovery and Climate Experiment (GRACE) satellites. The implementation of groundwater pumping results in significant improvements in simulation of spatiotemporal water‐level change over the US High Plains and Central Valley Aquifer (CVA) and that of TWS over the CVA. Improvements in river discharge over the major US river basins are also achieved but are not substantial. Furthermore, the representation of pumping leads to certain overestimation in TWS trend compared to GRACE data in regions with high groundwater withdrawals. The consideration of lateral groundwater flow provides improvements in the subsurface response to pumping and natural wet and dry cycles, particularly at the grid cell level. These results provide a basis for improved groundwater modeling over large scales.

Numerous studies have examined the reliability of various precipitation products over the Mekong River Basin (MRB) and modeled its basin hydrology. However, there is a lack of comprehensive studies on precipitation‐induced uncertainties in hydrological simulations using process‐based land surface models. This study examines the propagation of precipitation uncertainty into hydrological simulations over the entire MRB using the Community Land Model version 5 (CLM5) at a high spatial resolution of 0.05° (∼5 km) and without any parameter calibration. Simulations conducted using different precipitation datasets are compared to investigate the discrepancies in streamflow, terrestrial water storage (TWS), soil moisture, and evapotranspiration (ET) caused by precipitation uncertainty. Results indicate that precipitation is a key determinant of simulated streamflow in the MRB; peak flow and soil moisture are particularly sensitive to precipitation input. Further, precipitation data with a higher spatial resolution did not improve the simulations, contrary to the common perception that using meteorological forcing with higher spatial resolution would improve hydrological simulations. In addition, since high flow indicators are particularly influenced by precipitation data, the choice of precipitation data could directly impact flood pulse simulations in the MRB. Notable differences are also found among TWS, soil moisture, and ET simulated using different precipitation products. Moreover, TWS, soil moisture, and ET exhibit a varying degree of sensitivity to precipitation uncertainty. This study provides crucial insights on precipitation‐induced uncertainties in process‐based hydrological modeling and uncovers these uncertainties in the MRB.

Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. On the one hand, future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human-induced land-use change can affect the climate system through biogeophysical and biogeochemical effects. To investigate these interrelationships, we developed MIROC-INTEG-LAND (MIROC INTEGrated LAND surface model version 1), an integrated model that combines the land surface component of global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land-use models. The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balance, human water management, and crop growth incorporates a land use decision-making model based on economic activities. In MIROC-INTEG-LAND, spatially detailed information regarding water resources and crop yields is reflected in the prediction of future land-use change, which cannot be considered in the conventional integrated assessment models. In this paper, we introduce the details and interconnections of the submodels of MIROC-INTEG-LAND, compare historical simulations with observations, and identify various interactions between the submodels. By evaluating the historical simulation, we have confirmed that the model reproduces the observed states well. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand. The newly developed MIROC-INTEG-LAND could be combined with atmospheric and ocean models to develop an integrated earth system model to simulate the interactions among coupled natural–human earth system components.