Advanced Land Surface Models (LSM) offer a powerful tool for studying hydrological variability. Highly managed systems, however, present a challenge for these models, which typically have simplified or incomplete representations of human water use. Here we examine recent groundwater declines in the US High Plains Aquifer (HPA), a region that is heavily utilized for irrigation and that is also affected by episodic drought. To understand observed decline in groundwater and terrestrial water storage during a recent multiyear drought, we modify the Noah‐MP LSM to include a groundwater irrigation scheme. To account for seasonal and interannual variability in active irrigated area, we apply a monthly time‐varying greenness vegetation fraction (GVF) data set within the model. A set of five experiments were performed to study the impact of groundwater irrigation on the simulated hydrological cycle of the HPA and to assess the importance of time‐varying GVF when simulating drought conditions. The results show that including the groundwater irrigation scheme improves model agreement with ALEXI ET data, mascon‐based GRACE TWS data, and depth‐to‐groundwater measurements in the southern HPA, including Texas and Kansas, and that accounting for time‐varying GVF is important for model realism under drought. Results for the HPA in Nebraska are mixed, likely due to the model's weaknesses in representing subsurface hydrology in this region. This study highlights the value of GRACE data sets for model evaluation and development and the potential to advance the dynamic representations of the interactions between human water use and the hydrological cycle.

Assimilating terrestrial water storage observations from the Gravity Recovery and Climate Experiment (GRACE) mission into land surface models (LSMs) provides an opportunity to disaggregate and downscale GRACE information to finer scales and improve water component estimates in LSMs. However, the performance of GRACE data assimilation (GRACE‐DA) is limited by the lack of representation of human activities in most LSMs. To simultaneously improve GRACE‐DA and reduce the uncertainties in the modeled anthropogenic processes, we assimilate mascon‐based GRACE terrestrial water storage into the Noah‐Multiparameterization LSM that includes groundwater extraction for irrigation. Simulations with and without GRACE‐DA and with and without groundwater pumping for irrigation are performed to study the isolated and combined effects of groundwater irrigation and GRACE‐DA on water and energy fluxes over the High Plains Aquifer (HPA). The results reveal that the DA‐only simulation may erroneously distribute increments across water storage components and affect the related fluxes through biased feedbacks, while the irrigation‐only simulation may overestimate groundwater decline due to shortcomings in the irrigation and groundwater parameterizations. Assimilating GRACE when irrigation is simulated produces the best overall performance for water storage trends over the northern HPA. For the southern HPA, GRACE assimilation with irrigation performs similarly to irrigation‐only simulation for water storage components and evapotranspiration. GRACE assimilation also improves results in nonirrigated regions and can potentially alleviate the overestimation of groundwater trends in regions with greater irrigation uncertainties. This study highlights the potential to advance hydrological data assimilation in the context of anthropogenic water consumption and land‐atmosphere interactions.

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Bushfires are seasonal occurrences in Australia driven by a combination of factors including extreme heat, dryness, natural climate variability and human activities (Pitman et al., 2007). The most recent bushfires during the Australian summer of 2019-2020 have been unprecedented, likely due to the very dry conditions over eastern Australia in the past 2 years resulting from the absence of a La Nina event (King et al., 2020). The Murray-Darling basin in particular, has experienced persistent droughts in the last several years with the summer of 2019-2020 being the driest and hottest period on record. Roughly 25 million acres, including more than 21% of the Australian forests, have burned from the 2019 to 2020 fires (Boer et al., 2020). The fires grew in size during September and October of 2019 and caused major destruction during the Australian summer months until the heavy rain events in mid-January and early February of 2020. Plant physiology and growth are directly affected by soil water deficit conditions and disturbances such as fires. Monitoring vegetation changes, therefore, can help to characterize and diagnose such conditions on the land surface. Remote sensing measurements, particularly from multispectral optical and thermal imagers are typically used to provide spatial characterization of vegetation changes on the land surface from