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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]

Earth's vegetation has been increasing over the past decades, altering water and energy cycles by changing evapotranspiration (ET). Greening, caused by climatic and anthropogenic factors, has high rates in High Mountain Asia (HMA). Here we focus on two HMA basins (the Yangtze and the Ganges‐Brahmaputra) to contrast the impacts of climate‐ and human‐induced greening on ET. Though the rate of greening is similar in both basins, anthropogenic influences lead to dissimilar responses in ET. In the Yangtze, climate‐induced greening increases ET, with the increase in moisture being high enough to meet the ET demand. In the Ganges‐Brahmaputra, irrigation‐induced greening does not alter annual ET, only pre‐monsoon ET increases. The dry season declines in water storage due to pumping decrease ET, while laboriously meeting the demand. This study provides a representative example of the contrasting influences of climate induced and anthropogenic driven processes on the seasonality of ET. Plain Language Summary: The significant increases in vegetation occurring on Earth are susceptible to altering the climate by affecting evapotranspiration. However, the responses in evapotranspiration to the changes in vegetation depend on the drivers of greening. Here we use High Mountain Asia as a testbed to contrast the impacts of greening on evapotranspiration. Irrigation‐induced greening leads to an increase in transpiration and evapotranspiration in the pre‐monsoon. However, because irrigation decreases water storage it decreases evapotranspiration in the post‐monsoon. Climate‐induced greening, on the contrary, increases both evapotranspiration and water storage. Key Points: Climate and human induced greening have different effects on evapotranspiration and therefore the land‐atmosphere interactionsThough irrigation‐induced greening rises pre‐monsoon evapotranspiration, the latter decreases in post‐monsoon due to groundwater depletionClimate‐induced greening increases evapotranspiration because the energy demand is easily met [ABSTRACT FROM AUTHOR]

Irrigation is the largest human intervention in the water cycle that can modulate climate extremes, yet irrigation water use (IWU) remains largely unknown in most regions. Microwave remote sensing offers a practical way to quantify IWU by monitoring changes in soil moisture caused by irrigation. However, high‐resolution satellite soil moisture data is typically infrequent (e.g., 6–12 days) and thus may miss irrigation events. This study evaluates the ability to quantify IWU by assimilating high‐resolution (1 km) SMAP‐Sentinel 1 remotely sensed soil moisture with a physically based land surface model (LSM) using a particle batch smoother (PBS). A suite of synthetic experiments is devised to evaluate different error sources. Results from the synthetic experiments show that unbiased simulations with known irrigation timing can produce an accurate irrigation estimate with a mean annual bias of 0.45% and a mean R2 of 0.97, relative to observed IWU. Unknown irrigation timing can significantly deteriorate the model performance, resulting in an increased mean annual bias to 23% and decreased mean R2 to 0.36. Adding random noise to synthetic observations does not significantly decrease model performance except for the experiments with low observation frequency (>12 days). In real‐world experiments, the PBS data assimilation approach underestimates observed IWU by 18.6% when the timing of IWU is known. IWU estimates are consistently significantly higher over irrigated pixels compared to the non‐irrigated pixels, indicating data assimilation skillfully conveys irrigation signals to the LSM. Plain Language Summary: Determining and modeling the amount of water applied as irrigation for agriculture is a very difficult task. Traditionally, it is measured in situ, and in most parts of the world such observations are not available or the local agencies do not share the data due to many socio‐economic/political reasons. Studies have shown that signal of irrigation over an agricultural field can be observed through a high‐resolution microwave remote sensing technique. However, the determination of the quantity of irrigation applied to the agricultural field without in situ measurement is very challenging. This study demonstrates a possible way to quantify irrigation through modeling and assimilating microwave remote sensing‐based soil moisture data. The results of this study show significant skills to quantify the amount of water used for irrigation. Such study is essential for water resource management and budgeting. Key Points: Complex human irrigation decisions can be modeled using the particle batch smoother (PBS) approachIrrigation can be estimated accurately by assimilating a weekly high‐resolution soil moisture data containing the irrigation signalInstead of using the irrigation module in the land surface model, PBS can be used to account for irrigation [ABSTRACT FROM AUTHOR]

Rain‐on‐snow (ROS) over snow‐dominated regions such as High Mountain Asia (HMA) modulates snowmelt and runoff and is key contributor in influencing water availability and hazards (e.g., floods and landslides). We studied the trends in ROS in HMA over the past two decades from 2001 to 2018 using the land surface model Noah‐MP driven by an ensemble precipitation data set. Our results show that changes in precipitation phase and rainfall are altering ROS. Because of the strong physical heterogeneity and atmospheric dynamics of HMA, ROS characteristics and trends are region‐dependent and ROS occurs predominantly over the Indus, Ganges‐Brahmaputra, and northwestern basins. In the Indus, ROS representing ∼5% of the annual precipitation and ∼20% of the annual snowmelt, has an increasing trend. This is contrary to the Ganges‐Brahmaputra characterized by decreasing ROS trends, where it represents ∼11% of the annual precipitation and ∼60% of the annual snowmelt. In the northwestern basins, ROS has bidirectional trends due to elevation patterns and trends in rainfall, and it constitutes ∼5 to ∼10% of the annual precipitation. Increasing trends in ROS over Indus contribute to reducing the snowpack in late summer, with concerns of reduced water availability and increased groundwater exploitation. Similarly, because of its high amount and contribution to snowmelt, the decreasing ROS trends in the Ganges‐Brahmaputra will have consequences of decreased recharge from the headwaters and exacerbated use of groundwater unless increasing trends in rainfall compensate for the decreasing snowmelt. These results provide new insights on ROS‐driven changes in the hydrological cycle over HMA. Key Points: In the Indus, rain‐on‐snow (ROS) represents more than 5% of the annual precipitation and has an increasing trendIn the Ganges‐Brahmaputra, ROS which represents more than 10% of the annual precipitation is decreasingIn the Amu Darya, Syr Darya, and Ili, ROS constitutes 5%–10% of the annual precipitation and has a bidirectional trend [ABSTRACT FROM AUTHOR]

Flash droughts evolve and intensify rapidly under the influence of anomalous atmospheric conditions. In this study, we investigate the role of assimilating remotely sensed soil moisture (SM) and vegetation properties in capturing the evolution and impacts of two flash droughts in the Northern Great Plains. We find that during 2016 drought triggered by anomalously high temperatures and excessive evaporative demands, multivariate data assimilation (DA) of MODIS‐derived leaf area index (LAI) and Soil Moisture Active Passive SM within Noah‐Multiparameterization model helps capture elevated transpiration at onset. Assimilation of LAI particularly helped model the resulting rapid decline in SM during onset with as high as 10.0% steeper rate of decline compared to the simulation without any assimilation. Modeled‐SM anomalies exhibit a 7.5% and 11.7% increase in similarity with Evaporative Stress Index (ESI) data and U.S. Drought Monitor (USDM) maps, respectively. In contrast, during 2017 flash drought driven by record‐low precipitation during summers, SM assimilation resulted in largest rates of decline in rootzone SM, as large as 48.4% compared to results from no assimilation. Multivariate DA of SM and LAI results in 6.7% and 14.3% higher spatial similarity with ESI and USDM, respectively, and is necessary to model rapid intensification caused by anomalous precipitation deficits. This study elucidates the need to incorporate multiple observational constraints from remote sensing to effectively capture rapid onset rates, intensification, and severity of flash drought following different propagation mechanisms. This is fundamental for drought early detection to provide a wider window of response and implement efficient mitigation strategies. Plain Language Summary: A class of droughts called flash droughts develop rapidly under unusual weather conditions, often characterized by either warm temperatures or low precipitation or both. In this study, we employ the soil moisture (SM) and leaf area index (LAI) retrievals from the NASA Soil Moisture Active Passive mission and MODIS product, respectively, for characterizing the flash droughts of 2016 and 2017 in the Northern Great Plains. The results demonstrate that LAI observations, when assimilated within a land surface model, are effective in capturing high transpiration at the onset of 2016 drought driven by intense heat waves. The 2017 flash drought, however, was initiated by a precipitation deficit where information on SM is necessary to capture the rapid drying of soils. The modeled outputs not only capture the rapid drying of soil at the onset of droughts but are also spatially and temporally consistent with Evaporative Stress Index data and U.S. Drought Monitor maps. The study highlights the role of multivariate assimilation of remotely sensed vegetation and SM information to capture the rapid rates of onset and contrasting pathways of flash drought development. Key Points: Multivariate assimilation of remotely sensed vegetation and soil moisture helps characterize recent flash droughts in Northern Great PlainsHeatwave‐driven warm flash drought requires assimilating vegetation conditions to capture impact on transpiration during rapid developmentUse of soil moisture data is necessary to represent rapid drying of soils during the dry flash drought intensified by moisture deficit [ABSTRACT FROM AUTHOR]

Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on‐ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor‐based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1–1.1 and 0.3–0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14–35 mm mo−1 and 10–82 g m−2 mo−1 are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation‐informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states. Plain Language Summary: Agricultural irrigation accounts for more than 70% of freshwater use over the globe and can impact local and regional water resources, crop productivities, and climate and weather systems. Investigating impact of irrigation heavily relies on models, which vary in terms of modeling structure, input data sources, assumptions, etc. Given these variations, models are often subject to large uncertainties in estimating irrigation water use. The goal of this study is to explore the potential to integrate satellite observations of vegetation conditions with models to improve estimates of irrigation and its impact across the Contiguous United States. We find that integrating satellite observations is helpful in correcting simulation of vegetation growth, leading to a better estimation of irrigation water use and its impact on surface soil moisture, evapotranspiration, and agricultural productivities. These results underscore the effectiveness of using satellite vegetation observations to improve irrigation modeling. Key Points: Moderate Resolution Imaging Spectroradiometer leaf area index (LAI) data are assimilated into the Noah‐MP land surface model to inform simulation of irrigation schedules and volumesIrrigation simulations without LAI assimilation overestimate irrigation amount and increase the BIAS for evapotranspiration (ET) and GPPAssimilating LAI to constrain irrigation shows the overall best performance for surface soil moisture, ET, and GPP [ABSTRACT FROM AUTHOR]

This second paper of the two‐part series focuses on demonstrating the impact of assimilating satellite‐based snow cover and freeze/thaw observations into the hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point‐scale, basin‐scale, and domain‐scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point‐scale assessment is mainly conducted via evaluating against ground‐based measurements. In general, the assimilation enabled estimates are better than no‐assimilation counterparts. Second, the basin‐scale runoff assessment demonstrates that across three snow‐dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness‐of‐fit statistics through mitigating the negative effects brought by the fixed long‐term precipitation correction factors. For example, when forced by the bias‐corrected precipitation, the assimilation‐enabled experiment improves the bias by 69%, the root‐mean‐squared error by 30%, and the unbiased root‐mean‐squared error by 18% (relative to the no‐assimilation counterpart). Finally, the domain‐scale assessment is conducted via evaluating against satellite‐based SWE and skin temperature products. Both sets of domain‐scale analysis further corroborate the findings in the point‐scale evaluations. Overall, this study suggests the benefits of the proposed multi‐variate assimilation system in improving the cryospheric‐hydrological process within a land surface model for use in HMA. Key Points: The skills of the High Mountain Asia‐Land Data Assimilation System (version 1) with and without multi‐variate assimilation are presentedThe assimilation enabled experiments generally perform better than the no‐assimilation counterpartsThe assimilation enabled experiments mitigate the negative effects arising from the fixed long‐term precipitation correction factors [ABSTRACT FROM AUTHOR]

The NASA LIS/WRF‐Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi‐scale surface hydrological modeling capabilities of the WRF‐Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub‐surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF‐Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open‐loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF‐Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications. Plain Language Summary: Land surface models are useful because of their ability to resolve surface‐atmosphere feedbacks, including those with vegetation. Land surface models also have the capability to assimilate surface observations, usually measured through remote sensing techniques, into the model. Hydrologic models have the strength of resolving horizontal movements of water both on the surface and through the sub‐surface. In the present study, we combine the data‐assimilation capabilities of the NASA‐LIS land surface model with the WRF‐Hydro hydrologic model to combine the utility of both systems. We use this new system to demonstrate the impact of assimilating snow water equivalent, measured from an aircraft, on both the land surface and streamflow from the model in the Tuolumne River basin in Central California. Results show that assimilation of snow water equivalent into the coupled model corrects snow errors and improves the streamflow in both wet and dry years. We find that hydrologic processes that are now added to the land surface model impact simulated soil moisture and evapotranspiration. These findings are important because the ability for a model to better resolve streamflow, from snow assimilation, could be beneficial for water management. Key Points: Snow water equivalent data‐assimilation improves hydrologic response in the coupled LIS/WRF‐Hydro model for a case study in the Tuolumne River basinHorizontal surface routing increases soil moisture and evapotranspiration downstream near channels in LIS/WRF‐Hydro, compared to an LSM‐only simulation [ABSTRACT FROM AUTHOR]

The Western Land Data Assimilation System (WLDAS) is a custom instance of the NASA Land Information System that combines land surface parameters, meteorological forcing data, and satellite products within a land surface model to produce daily estimates of the water and energy budget variables for the western United States. WLDAS was configured through discussions with partners, with the goal of groundwater sustainability planning for the state of California in mind. The publicly available output dataset has a 1-km grid resolution and spans 1979--present, which makes it suitable for water resources assessments. The data are also able to contextualize the recent drought events in California. Assimilation of Leaf Area Index, which is demonstrated herein to improve simulation over agricultural areas in California, specifically in terms of evapotranspiration in irrigation regions, will be included along with other data assimilation in subsequent releases of WLDAS. [ABSTRACT FROM AUTHOR]

It is known that representing wetland dynamics in land surface modeling improves models' capacity to reproduce fluxes and land surface boundary conditions for atmospheric modeling in general circulation models. This study presents the development of the full coupling between the Noah‐MP land surface model (LSM) and the Hydrological Modeling and Analysis Platform (HyMAP) flood model in the NASA Land Information System and its application over the Inner Niger Delta (IND), a well‐known hot‐spot of strong land surface‐atmosphere interactions in West Africa. Here, we define two experiments at 0.02° spatial resolution over 2002–2018 to quantify the impacts of the proposed developments on simulating IND dynamics. One represents the one‐way approach for simulating land surface and flooding processes (1‐WAY), that is, Noah‐MP neglects surface water availability, and the proposed two‐way coupling (2‐WAY), where Noah‐MP takes surface water availability into account in the vertical water and energy balance. Results show that accounting for two‐way interactions between Noah‐MP and HyMAP over IND improves simulations of all selected hydrological variables. Compared to 1‐WAY, evapotranspiration derived from 2‐WAY over flooding zones doubles, increased by 0.8 mm/day, resulting in an additional water loss rate of ∼18,900 km3/year, ∼40% drop of wetland extent during wet seasons, and major improvement in simulated water level variability at multiple locations. Significant soil moisture increase and surface temperature drop were also observed. Wetland outflows decreased by 35%, resulting in a substantial Nash‐Sutcliffe coefficient improvement, from −0.73 to 0.79. It is anticipated that future developments in water monitoring and water‐related disaster warning systems will considerably benefit from these findings. Plain Language Summary: Many hydrological modeling systems are composed of land surface models (LSMs) capable of representing vertical land surface‐atmosphere interactions, and flood models capable of simulating the lateral redistribution of surface waters across a prescribed river network and floodplains. These models are oftentimes one‐way coupled, meaning that surface waters are neglected in the vertical water and energy balance. This study presents a generalized approach for the two‐way coupling (i.e., surface waters are taken into account in the vertical water and energy balance) between the Noah‐MP LSM and the HyMAP flood model. The proposed approach is tested over the Inner Niger Delta (IND), a well‐known hot‐spot of strong land surface‐atmosphere interactions in West Africa. To quantify the impact of the proposed development on the spatiotemporal variability of simulated hydrological variables, we performed two model experiments, one representing the one‐way approach, i.e., Noah‐MP neglects surface waters, and the proposed two‐way coupling, where surface water availability is taken into account. Results show that accounting for two‐way interactions between Noah‐MP and HyMAP over IND improves simulations of all selected hydrological variables. The generalized approach proposed here could be an asset to existing water monitoring systems and easily replicated over different domains or at the global scale. Key Points: The full coupling of land surface and flood models in NASA's Land Information System is described and evaluated over the Inner Niger DeltaIncreased evapotranspiration resulted in 18,900 km3/year water loss to the atmosphere, decreasing wetland outflows by 35% and extent by 40%Compared to an uncoupled system, the proposed implementation resulted in substantial improvements of all selected hydrological variables [ABSTRACT FROM AUTHOR]

This first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (∼1‐km) and 0.25° (∼25‐km). The assessment is conducted via comparisons against ground‐based observations and satellite‐derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground‐based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper‐resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse‐resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse‐resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper‐resolution precipitation products to drive model simulations. Key Points: The skill of a hyper‐resolution, off‐line terrestrial modeling system used for the High Mountain Asia region is presentedThe study emphasizes the importance of using hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrainThe study emphasizes the importance of an accurate hyper‐resolution precipitation product used to drive model simulations [ABSTRACT FROM AUTHOR]

Climate variability is an important driver of irrigation water use in many regions. Efforts to anticipate climate change impacts on future water availability can benefit from understanding how irrigation water demand has responded to these drivers to date. Here we apply satellite‐derived data, meteorological reanalysis, an advanced land surface model, and available state‐level reports to quantify irrigation demand sensitivities to temperature and precipitation across the Contiguous United States, for the period of 2002–2017. As expected, strong negative correlations are found between precipitation and irrigation withdrawals, both simulated and reported. Temperature sensitivities, however, vary by region and season, as do the interactive effects of temperature and precipitation on irrigation. Climate‐induced irrigation variability is largest in transitional climate zones. These transitional zones are generally separate from the regions where rates of irrigation withdrawals are greatest, such that climate‐induced variability in irrigation demand represents a water resource consideration that is distinct from chronic overpumping. Key Points: Widespread sensitivity of irrigation water use to precipitation is found in both model and available reportsTemperature and interactions of temperature—precipitation sensitivity vary by region and season and might be underestimated by the modelGeographical disconnects exist between aquifers that are stressed by pumping and the basins that exhibit strong climate sensitivities [ABSTRACT FROM AUTHOR]

Though coarse in spatial resolution, the nearly all weather measurements from passive microwave sensors can help in improving the spatio‐temporal coverage of optical and thermal infrared sensors for monitoring vegetation changes on the land surface. This study demonstrates the use of vegetation optical depth (VOD) retrievals from the Soil Moisture Active Passive mission for capturing the vegetation alterations from the recent 2019 to 2020 Australian bushfires and drought. The impact of vegetation disturbances on terrestrial water budget is examined by assimilating the VOD retrievals into a dynamic phenology model. The results demonstrate that assimilating VOD observations lead to improved simulation of evapotranspiration, runoff, and soil moisture states. The study also demonstrates that the vegetation changes from the 2019 to 2020 Australian drought and fires led to significant modifications in the partitioning of evaporative and runoff fluxes, resulting in increased bare soil evaporation, reduced transpiration, and higher runoff. Plain Language Summary: Satellite remote sensing, provides the ability to obtain integrated assessments of vegetation changes on the land surface. In this study, we employ the vegetation optical depth (VOD) retrievals from the NASA SMAP mission for characterizing the recent 2019‐2020 Australian drought and bushfires. The results demonstrate that SMAP VOD retrievals are effective in capturing the vegetation alterations from the unprecedented fires and drought. The study also examined the associated impact of these changes on the hydrological cycle. The removal of vegetation impacted the components of evaporation, by reducing the transpiration and increasing the bare soil evaporation. The results show that the reduction in transpiration likely amplified the amount of precipitation that is converted to runoff. Key Points: Soil Moisture Active Passive vegetation optical depth (VOD) detects features of the 2019–2020 Australian bushfires and droughtVOD assimilation improves land surface states and fluxesThe vegetation changes from the 2019 to 2020 Australian fires and drought causes significant changes in the water budget partitioning [ABSTRACT FROM AUTHOR]

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. Key Points: GRACE TWS data are assimilated into the Noah‐MP land surface model that includes groundwater extraction for irrigationAssimilating GRACE without irrigation produces biased increments, while simulating irrigation without GRACE may exacerbate model errorCombining GRACE‐DA and irrigation shows the overall best performance for water storage components and evapotranspiration [ABSTRACT FROM AUTHOR]

Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled land-atmosphere system. One way to improve irrigation representation in models is to assimilate soil moisture observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new enhanced soil moisture product from the Soil Moisture Active Passive satellite is able to capture irrigation signals over three semiarid regions in the western United States. This marks an advancement in Earth-observing satellite skill and the ability to monitor human impacts on the water cycle. [ABSTRACT FROM AUTHOR]

Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade-offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble. [ABSTRACT FROM AUTHOR]

In snowmelt-driven river systems, it is critical to enable reliable predictions of the spatiotemporal variability in seasonal snowpack to support local and regional water management. Previous studies have shown that assimilating satellite-station blended snow depth data sets can lead to improved snow predictions, which however do not always translate into improved streamflow predictions, especially in complex mountain regions. In this study, we explore how an existing optimal interpolation-based blending strategy can be enhanced to reduce biases in satellite snow depth products for improving streamflow predictions. Two major new considerations are explored, including: (1) incorporating terrain aspect and (2) incorporating areal snow coverage information. The methodology is applied to the bias reduction of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth estimates, which are then assimilated into the Noah land surface model via the ensemble Kalman Filtering (EnKF) for streamflow predictions in the Upper Colorado River Basin. Our results indicate that using only observations from low-elevation stations such as the Global Historical Climatology Network (GHCN) in the bias correction can lead to underestimation in streamflow, while using observations from high-elevation stations (e.g., the Snow Telemetry (SNOTEL) network) along with terrain aspect is critically important for achieving reliable streamflow predictions. Additionally incorporating areal snow coverage information from the Moderate Resolution Imaging Spectroradiometer (MODIS) can slightly improve the streamflow results further. [ABSTRACT FROM AUTHOR]

[i] Operational land surface models (LSMs) compute hydrologie states such as soil moisture that are needed for a range of important applications (e.g., drought, flood, and weather prediction). The uncertainty in LSM parameters is sufficiently great that several researchers have proposed conducting parameter estimation using globally available remote sensing data to identify best fit local parameter sets. However, even with in situ data at fine modeling scales, there can be significant remaining uncertainty in LSM parameters and outputs. Here, using a new uncertainty estimation subsystem of the NASA Land Information System (LIS) (described herein), a Markov chain Monte Carlo (MCMC) technique is applied to conduct Bayesian analysis for the accounting of parameter uncertainties. The Differential Evolution Markov Chain (DE-MC) MCMC algorithm was applied, for which a new parallel implementation was developed. A case study is examined that builds on previous work in which the Noah LSM was calibrated to passive (L-band) microwave remote sensing estimates of soil moisture for the Walnut Gulch Experimental Watershed. In keeping with prior related studies, the parameters subjected to the analysis were restricted to the soil hydraulic properties (SHPs). The main goal is to estimate SHPs and soil moisture simulation uncertainty before and after consideration of the remote sensing data. The prior SHP uncertainty is based on the original source of the standard SHP lookup tables for the Noah LSM. Conclusions are drawn regarding the value and viability of Bayesian analysis over alternative approaches (e.g., parameter estimation, lookup tables) and further research needs are identified. [ABSTRACT FROM AUTHOR]

Snow cover area affects snowmelt, soil moisture, évapotranspiration, and ultimately streamflow. For the Distributed Model Intercomparison Project - Phase 2 Western basins, we assimilate satellite-based fractional snow cover area (fSCA) from the Moderate Resolution Imaging Spectroradiometer, or MODIS, into the National Weather Service (NWS) SNOW-17 model. This model is coupled with the NWS Sacramento Heat Transfer (SAC-HT) model inside the National Aeronautics and Space Administration's (NASA) Land Information System. SNOW-17 computes fSCA from snow water equivalent (SWE) values using an areal depletion curve. Using a direct insertion, we assimilate fSCAs in two fully distributed ways: (1) we update the curve by attempting SWE preservation, and (2) we reconstruct SWEs using the curve. The preceding are refinements of an existing simple, conceptually guided NWS algorithm. Satellite fSCA over dense forests inadequately accounts for below-canopy snow, degrading simulated streamflow upon assimilation during snowmelt. Accordingly, we implement a below-canopy allowance during assimilation. This simplistic allowance and direct insertion are found to be inadequate for improving calibrated results, still degrading them as mentioned above. However, for streamflow volume for the uncalibrated runs, we obtain: (1) substantial to major improvements (64-81%) as a percentage of the control run residuals (or distance from observations), and (2) minor improvements (16-22%) as a percentage of observed values. We highlight the need for detailed representations of canopy-snow optical radiative transfer processes in mountainous, dense forest regions if assimilation-based improvements are to be seen in calibrated runs over these areas. [ABSTRACT FROM AUTHOR]

ABSTRACT Advancements in both land surface models (LSMs) and land surface data assimilation, especially over the last decade, have substantially advanced the ability of land data assimilation systems (LDASs) to estimate evapotranspiration (ET). This article provides a historical perspective on international LSM intercomparison efforts and the development of LDASs, both of which have improved LSM ET skill. In addition, an assessment of ET estimates for current LDASs is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model soil moisture products into the Noah LSM Version 3.2 with the North American LDAS Phase 2 (NLDAS-2), forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS-2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study quantifies the immediate influences of aerosols from urban anthropogenic and shipping emission sources on carbon and water fluxes in East Asia on a cloudy day in spring 2016 when strong regional pollution transport occurred and intensive field campaign measurements are available. Within National Aeronautics and Space Administration (NASA)'s Land Information System (LIS), a long‐term offline Noah‐multiparameterization (MP) simulation with dynamic vegetation is performed. Modeled soil moisture and leaf area index are evaluated with satellite observations to ensure that land surface conditions are moderately well reproduced. The LIS output is then used to initialize several coupled NASA‐Unified Weather Research and Forecasting model simulations with online chemistry in which urban anthropogenic and shipping emissions are (1) largely based on the Hemispheric Transport of Air Pollution Phase 2 inventory for 2010, (2) reduced by 20% and 50% for all chemical species, and (3) adjusted only for nitrogen oxides (NOx) using satellite observations. Overall, modeled gross primary productivity and evapotranspiration almost linearly increase with the all‐species emission reductions, but their responses to emission‐induced aerosol optical depth (AOD) changes show strong spatial variability resulting from combined radiation and temperature impacts. Using satellite‐observation‐constrained NOx emissions, modeled nitrogen species and AOD better match various measurements at some locations/times. All‐species and NOx‐only emission adjustments lead to different gross primary productivity and evapotranspiration changes with AOD, especially over South Korea. This study demonstrates the importance of accurately quantifying emission impacts on atmosphere‐biosphere interactions. Improving more species' emission inputs for Earth system models, including applying effective chemical data assimilation methods, is strongly encouraged. Key Points: Carbon and water fluxes are calculated by Noah‐MP (with dynamic vegetation) coupled with atmospheric weather and chemistryOverall, GPP and ET almost linearly increase with the all‐species urban/shipping emission reductions on a cloudy day in East AsiaGPP and ET responses to emission‐induced AOD changes differ by region and for all‐species and NOx‐only emission adjustments [ABSTRACT FROM AUTHOR]

This study evaluates the impact of assimilating soil moisture data from National Aeronautics and Space Administration (NASA)'s Soil Moisture Active Passive (SMAP) on short‐term regional weather and air quality modeling in East Asia during the Korea‐U.S. Air Quality Study (KORUS‐AQ) airborne campaign. SMAP data are assimilated into the Noah land surface model using an ensemble Kalman filter approach in the Land Information System framework, which is semicoupled with the NASA‐Unified Weather Research and Forecasting model with online chemistry (NUWRF‐Chem). With SMAP assimilation included, water vapor and carbon monoxide (CO) transport from northern central China transitional climate zones to South Korea is better represented in NUWRF‐Chem during two studied pollution events. Influenced by different synoptic conditions and emission patterns, impact of SMAP assimilation on modeled CO in South Korea is intense (>30 ppbv) during one event and less significant (<8 ppbv) during the other. SMAP assimilation impact on air quality modeling skill is complicated by other error sources such as the chemical initial and boundary conditions (IC/LBC) and emission inputs of NUWRF‐Chem. Using a satellite‐observation‐constrained chemical IC/LBC instead of a free‐running, coarser‐resolution chemical IC/LBC reduces modeled CO by up to 80 ppbv over South Korea. Consequently, CO performance is improved in the middle‐upper troposphere whereas degraded in the lower troposphere. Remaining negative CO biases result largely from the emissions inputs. The advancements in land surface modeling and chemical IC/LBC presented here are expected to benefit future investigations on constraining emissions using observations, which can in turn enable more accurate assessments of SMAP assimilation and chemical IC/LBC impacts. Key Points: SMAP assimilation most strongly modifies short‐term regional atmospheric modeling skill in East Asia transitional climate zonesSMAP assimilation improves water vapor and carbon monoxide transport from northern central China to South KoreaSMAP assimilation impact on air quality modeling skill in part relies on the quality of model chemical IC/LBC and various emission inputs [ABSTRACT FROM AUTHOR]