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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]

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

The Coordinated Enhanced Observing Period (CEOP) is an international project that was first proposed by the Global Energy and water Cycle Experiment (GEWEX) in 1997 and was formally launched in 2001. Since that time it has been adopted by the World Climate Research Programme (WCRP), which views it as an essential parts of its strategy for developing global datasets to evaluate global climate models, and by the Integrated Global Observing Strategy Partnership (IGOS-P), which views it as the first element of its global water cycle theme. The United States has been an active partner in all phases of CEOP. In particular, the United States has taken the lead in contributinh data from a number of reference sites, providing data processing, and archiving capabilities and related research activities through the GEWEX Americas Prediction Project (GAPP). Other U.S. programs and agencies are providing components including model and data assimilation output, satelite data, and other services. The U.S. science community has also been using the CEOP database in model evaluation and phenomenological studies. This article summarizes the U.S. contributions during the first phase of CEOP and outlines opportunities for readers to become involved in the data analysis phase of the project.

Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. © 2004 Elsevier Ltd. All rights reserved.

The feasibility of using spatially distributed information to improve the predictive ability of a spatially distributed land surface water and energy balance model (LSM) was explored at the U.S. Department of Agriculture Agricultural Research Service (USDA-ARS) Walnut Gulch Experimental Watershed in southeastern Arizona. The inclusion of spatially variable soil and vegetation information produced unrealistic simulations that were inconsistent with observations, which was likely an artifact of both discretely assigning a single set of parameters to a given area and inadequate knowledge of spatially varying parameter values. Because some of the model parameters were not measured or are abstract quantities a multiobjective least squares strategy was used to find catchment averaged parameter values that minimize the prediction error of latent heat flux, soil heat flux, and surface soil moisture. This resulted in a substantial improvement in the model's spatially distributed performance and yielded valuable insights into the interaction and optimal selection of model parameters.

The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surface- subsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time.