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Anthropogenic and climatic changes affect the water and energy cycles in High Mountain Asia (HMA), home to over two billion people and the largest reservoirs of freshwater outside the polar zone. Despite their significant importance for water management, consistent and reliable estimates of water storage and fluxes over the region are lacking because of the high uncertainties associated with the estimates of atmospheric conditions and human management. Here, we relied on multivariate data assimilation (MVDA) to provide estimates of energy and water storage and fluxes that reflect the processes occurring in the region such as greening and irrigation-driven groundwater depletion. We developed and employed an ensemble precipitation estimate by blending different precipitation products thereby reducing the uncertainties and inconsistencies associated with precipitation in HMA. Then, we assimilated five variables that capture the changes in hydrology in response to climate change and anthropogenic activities. Overall, our results have shown that MVDA has allowed a better representation of the land surface processes including greening and irrigation-driven groundwater depletion in HMA. [ABSTRACT FROM AUTHOR]

Assimilating remotely sensed surface soil moisture (SSM) into land surface models (LSM) is widely used to improve model representations of soil moisture (SM). However, the efficacy of SSM data assimilation (DA) has been found to be limited, particularly in resolving root‐zone soil moisture (RZSM). This study investigates how the representation of vegetation phenology, modulates the efficacy of SSM DA in enhancing the realism of RZSM simulations. To this end, two sets of climatological leaf area index (LAI) are implemented in Noah‐MP LSM over the state of Texas: (a) Noah‐MP default based on long‐term MODIS observations, (b) an alternative LAI adapted from AVHRR products. The former are found to exhibit conspicuous phase errors whereas the latter are more consistent with observed seasonal cycle. Two sets of DA experiments were performed accordingly, wherein SMAP L3 SSM is assimilated into Noah‐MP equipped with each LAI product from 2015 to 2019, Validation of the resulting products against in‐situ data reveals that (a) using the AVHRR‐based LAI, the Noah‐MP outperforms the baseline in reproducing the dynamics of RZSM, and the outperformance is particularly evident over the warm season and water‐stressed western Texas; (b) using the alternative LAI enhances the ability of DA to improve the accuracy of Noah‐MP RZSM, and to a lesser extent, SSM; and (c) gains in SM attained through improvement of LAI and application of DA is most pronounced over regions featuring tight vertical SM coupling. Additional model mechanistic limitations that need to be overcome to improve efficacy of DA are discussed. Plain Language Summary: Blending remote sensing data into land surface models, known as data assimilation, is a common approach to enhance the accuracy of SM simulation. However, the effectiveness of this approach varies. Vegetation canopy is an important land surface variable that regulates transpiration, soil evaporation, and soil moisture. Errors in its representation can lead to distorted estimates of transpiration and root‐zone soil moisture. This study examines the impact of incorporating improved vegetation growth cycle on the ability of a land surface model to transfer information from the surface to the root‐zone after assimilating remotely sensed soil moisture. Experiments are performed to integrate a satellite‐based surface soil moisture product into a land surface model using both the default and improved vegetation growth cycle. The results show that using the latter improves not only the ability of model to reproduce the dynamics of root‐zone soil moisture, but also enhances the efficacy of data assimilation. The enhancements are the most pronounced in regions with strong vertical coupling of surface and root‐zone soil moisture. Key Points: Vegetation phenology plays a key role in regulating dynamics of SM, and this role is especially prominent in the water‐stressed regionsImproving climatology of phenology as represented by LAI alone leads to more accurate estimates of RZSM by a land surface modelImproved vegetation phenology enhances the efficacy of DA in improving RZSM and SSM; its impact varies with SM vertical coupling tightness [ABSTRACT FROM AUTHOR]

Despite recent advances in land surfacemodeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe- art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to matchmonthly Global Precipitation Climatology Project (GPCP) andGlobal Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets andGravity Recovery and ClimateExperiment (GRACE)TWSestimates in two subcatchments of main tributaries (Madeira and Negro Rivers).At the basin scale, simulated ET ranges from 2.39 to 3.26mmday-1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

This study applies a regional Earth system model (NASA-Unified Weather Research and Forecasting with online chemistry) with updated parameterizations for selected land-air exchange processes and multi-platform observations, to first estimate reactive nitrogen (Nr = oxidized NOy + reduced NHx) emissions from anthropogenic and natural sources, nitrogen dioxide (NO2) column densities and surface concentrations, total and speciated Nr dry or/and wet deposition fluxes during 2018-2023 over the northeastern and Mid-Atlantic US most of which belong to nitrogen oxides-limited or transitional chemical regimes. The estimated multi-year Nr concentrations and deposition fluxes are then compared with and related to ozone (O3), in terms of their spatiotemporal variability and key drivers as well as possible ecosystem impacts. Finally, through three sets of case studies, we identify and discuss about 1) the capability of land data assimilation (DA) to reduce the uncertainty in modeled land surface states at daily-to-interannual timescales, that can propagate into atmospheric chemistry fields; 2) the impacts of irrigation on land surface and atmospheric fields as well as pollutants' ecosystem uptake and impacts; and 3) the impacts of transboundary air pollution during selected extreme events on pollutants' budgets and ecosystem impacts. With the updated model parameterizations and anthropogenic emission inputs, the eastern US surface O3 modeled by this tool persistently agrees better with observations (i.e., with root-mean-square errors staying within 4-7 ppbv for the individual years' May-June-July) than those in literature where model errors often exceed 20 ppbv. Based on model calculations, surface O3 correlates more strongly with early afternoon NO2 columns than formaldehyde columns (r=0.54 and 0.40, respectively). The O3 vegetative uptake overall dropped by ~10% from 2018 to 2023, displaying clearer downward temporal changes than the total Nr deposition due to the declining NOy emission and deposition fluxes competing with the increasing NHx fluxes. It is highlighted that, temporal variability of Nr and O3 concentrations and fluxes on subregional-to-local scales respond to hydrological variability that can be influenced by precipitation and controllable human activities such as irrigation. Deposition processes and biogenic emissions that are highly sensitive to interconnected environmental and plants' physiological conditions, as well as extra-regional sources (e.g., O3-rich stratospheric air and dense wildfire plumes from upwind regions), have been playing increasingly important roles in controlling pollutants' budgets in this area as local emissions go down owing to effective emission regulations and COVID lockdowns. To better inform the design of mitigation and adaptation strategies, it is recommended to continue evaluating and improving the model parameterizations and inputs relevant to these processes in seamlessly coupled multiscale Earth system models using laboratory and field experiments in combination with satellite DA which would in turn benefit remote sensing communities. [ABSTRACT FROM AUTHOR]

This study applies a regional Earth system model (NASA-Unified Weather Research and Forecasting with online chemistry) with updated parameterizations for selected land-air exchange processes and multi-platform observations, to first estimate reactive nitrogen (Nr = oxidized NOy + reduced NHx) emissions from anthropogenic and natural sources, nitrogen dioxide (NO2) column densities and surface concentrations, total and speciated Nr dry or/and wet deposition fluxes during 2018–2023 over the northeastern and Mid-Atlantic US most of which belong to nitrogen oxides-limited or transitional chemical regimes. The estimated multi-year Nr concentrations and deposition fluxes are then compared with and related to ozone (O3), in terms of their spatiotemporal variability and key drivers as well as possible ecosystem impacts. Finally, through three sets of case studies, we identify and discuss about 1) the capability of land data assimilation (DA) to reduce the uncertainty in modeled land surface states at daily-to-interannual timescales, that can propagate into atmospheric chemistry fields; 2) the impacts of irrigation on land surface and atmospheric fields as well as pollutants' ecosystem uptake and impacts; and 3) the impacts of transboundary air pollution during selected extreme events on pollutants' budgets and ecosystem impacts. With the updated model parameterizations and anthropogenic emission inputs, the eastern US surface O3 modeled by this tool persistently agrees better with observations (i.e., with root-mean-square errors staying within 4–7 ppbv for the individual years' May-June-July) than those in literature where model errors often exceed 20 ppbv. Based on model calculations, surface O3 correlates more strongly with early afternoon NO2 columns than formaldehyde columns (r =0.54 and 0.40, respectively). The O3 vegetative uptake overall dropped by ~10 % from 2018 to 2023, displaying clearer downward temporal changes than the total Nr deposition due to the declining NOy emission and deposition fluxes competing with the increasing NHx fluxes. It is highlighted that, temporal variability of Nr and O3 concentrations and fluxes on subregional-to-local scales respond to hydrological variability that can be influenced by precipitation and controllable human activities such as irrigation. Deposition processes and biogenic emissions that are highly sensitive to interconnected environmental and plants' physiological conditions, as well as extra-regional sources (e.g., O3-rich stratospheric air and dense wildfire plumes from upwind regions), have been playing increasingly important roles in controlling pollutants' budgets in this area as local emissions go down owing to effective emission regulations and COVID lockdowns. To better inform the design of mitigation and adaptation strategies, it is recommended to continue evaluating and improving the model parameterizations and inputs relevant to these processes in seamlessly coupled multiscale Earth system models using laboratory and field experiments in combination with satellite DA which would in turn benefit remote sensing communities. [ABSTRACT FROM AUTHOR]

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from -2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability. [ABSTRACT FROM AUTHOR]

An airborne gamma-ray remote-sensing technique provides a strong potential to estimate a reliable snow water equivalent (SWE) in forested environments where typical remote-sensing techniques have large uncertainties. This study explores the utility of assimilating the temporally (up to four measurements during a winter period) and spatially sparse airborne gamma SWE observations into a land surface model (LSM) to improve SWE estimates in forested areas in the northeastern US. Here, we demonstrate that the airborne gamma SWE observations add value to the SWE estimates from the Noah LSM with multiple parameterization options (Noah-MP) via assimilation despite the limited number of measurements. Improvements are witnessed during the snow accumulation period, while reduced skills are seen during the snowmelt period. The efficacy of the gamma data is greater for areas with lower vegetation cover fraction and topographic heterogeneity ranges, and it is still effective at reducing the SWE estimation errors for areas with higher topographic heterogeneity. The gamma SWE data assimilation (DA) also shows a potential to extend the impact of flight-line-based measurements to adjacent areas without observations by employing a localization approach. The localized DA reduces the modeled SWE estimation errors for adjacent grid cells up to 32 km distance from the flight lines. The enhanced performance of the gamma SWE DA is evident when the results are compared to those from assimilating the existing satellite-based SWE retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for the same locations and time periods. Although there is still room for improvement, particularly for the melting period, this study shows that the gamma SWE DA is a promising method to improve the SWE estimates in forested areas. [ABSTRACT FROM AUTHOR]