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Accurate estimation of snow accumulation and melt is a critical part of decision‐making in snow‐dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah‐MP and Noah. The original LSMs and the coupled models (Noah‐MP‐Crocus and Noah‐Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference data sets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah‐Crocus configuration reducing the RMSE and bias of snow depth from 2% to 12% and 57% to 75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah‐MP. The Coupled Noah‐MP‐Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments. Plain Language Summary: We introduce a new approach that combines Crocus, a detailed snow model, with land surface models (LSMs) in the NASA Land Information System. As a demonstration, we coupled Crocus with Noah‐MP and Noah. In each time step, Crocus uses atmospheric forcing data, snow‐ground interface temperature, and soil water content from the LSMs to run snow physics. It then returns total profile snow water equivalent (SWE) and snow depth (SD) states to the LSMs. We compare the SD, SWE, and other water and energy states and fluxes from the original LSMs and the coupled models (Noah‐MP‐Crocus and Noah‐Crocus) against various independent reference data sets. The coupled Noah‐Crocus configuration improves the RMSE and bias of SD and SWE. However, the coupled Noah‐MP‐Crocus configuration improves the bias but slightly degrades the RMSE of SD and SWE. Despite the improvements in SD and SWE, a corresponding enhancement in evapotranspiration and soil moisture is not observed. We attribute these discrepancies to uncertainties in LSM parameterizations and associated biases in these variables. The coupling framework introduced here is modular and can be extended to enable the interaction between a given specialized physics model and the general LSM. Key Points: This work presents a new framework that couples the Crocus snow model, with Land Surface Models within the NASA Land Information SystemThe coupled systems improve the estimation of snow depth and snow water equivalent relative to the gridded observationsThe corresponding impacts in other water budget terms, such as evapotranspiration, soil moisture, and streamflow, are mixed [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]

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]

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]

The focus of this study is on the role of precipitation uncertainty in the estimation of soil texture and soil hydraulic properties for application to land-atmosphere modeling systems. This work extends a recent study by Santanello et al. (2007) in which it was shown that soil texture and related physical parameters may be estimated using a combination of multitemporal microwave remote sensing, land surface modeling, and parameter estimation methods. As in the previous study, the NASA-GSFC Land Information System modeling framework, including the community Noah land surface model constrained with pedotransfer functions (PTF) for use with the Parameter Estimation Tool, is applied to several sites in the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona during the Monsoon '90 experiment period. It is demonstrated that the application of PTF constraints in the estimation process for hydraulic parameters provides accuracy similar to direct hydrologic parameter estimation, with the additional benefit of simultaneously estimated soil texture. Precipitation uncertainty is then represented with systematically varying sources, from the high-density precipitation gauge network in WGEW to lower quality sources, including spatially averaged precipitation, single gauges in and near the watershed, and results from the continental-scale North American Regional Reanalysis data set. It is demonstrated that the quality of the input precipitation data set, and particularly the accuracy of the data set, in both detection of convective (heavy) rainfall events and reproduction of the observed rainfall rate probabilities, is a critical determinant in the use of successive remote sensing results in order to establish and refine estimates of soil texture and hydraulic properties. [ABSTRACT FROM AUTHOR]