Assimilating Soil Moisture Data Research Articles

Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface‐Subsurface Model for Southwestern Germany

AbstractIntegrated terrestrial system models predict the coupled water, energy and biogeochemical cycles. Simulations with these models are affected by uncertainties of model parameters, initial and boundary conditions, atmospheric forcings and the biophysical processes. Data assimilation (DA) can quantify and reduce the uncertainty. This has been tested intensively for single compartment models, but far less for integrated models with multiple compartments. We constructed a virtual reality (VR) with a coupled land surface‐subsurface model under the Terrestrial Systems Modeling Platform, which mimics the Neckar catchment in southern Germany. Soil moisture and groundwater level (GWL) data extracted from the simulated VR are used as measurements to be assimilated with state‐only/state‐hydraulic parameter estimation. Soil moisture DA improves soil moisture characterization in the vertical profile and the neighboring grid cells, with a 40 ∼ 60% reduction of root mean square error (RMSE) over the observation points. In spite of a small ensemble size of 64 members, assimilating soil moisture data improved saturated hydraulic conductivity estimation around the measurement locations. The characterization of evapotranspiration and river discharge only show limited improvements (1% at observation points and less than 0.1% in RMSE at 3 selected gauge locations respectively). GWL DA not only improves the GWL characterization (76 ∼ 88% RMSE reduction at observation locations) but also soil moisture for some cases. In addition, a clear improvement in GWL characterization is observed up to 8 km from the observations, and updating the model states of the saturated zone only instead of the complete domain gives better performance.

Impact of Soil Moisture Data Resolution on Soil Moisture and Surface Heat Flux Estimates through Data Assimilation: A Case Study in the Southern Great Plains

Abstract The spatial heterogeneity and temporal variation of soil moisture and surface heat fluxes are key to many geophysical and environmental studies. It has been demonstrated that they can be mapped by assimilating soil thermal and wetness information into surface energy balance models. The aim of this work is to determine whether enhancing the spatial resolution or temporal sampling frequency of soil moisture data could improve soil moisture or surface heat flux estimates. Two experiments are conducted in an area mainly covered by grassland, and land surface temperature (LST) observations from the Geostationary Operational Environmental Satellite (GOES) mission are assimilated together with either an enhanced L-band passive soil moisture product (9 km, 2–3 days) from the Soil Moisture Active Passive (SMAP) mission or a merged product (36 km, quasi-daily) from the SMAP and the Soil Moisture Ocean Salinity (SMOS) mission. The results suggest that the availability of soil moisture observations is increased by 41% after merging data from the SMAP and the SMOS missions. A comparison with results from a previous study that assimilated a coarser SMAP soil moisture product (36 km, 2–3 days) suggests that enhancing the temporal sampling frequency of soil moisture observations leads to improved soil moisture estimates at both the surface and root zone, and the largest improvement is seen in the bias metric (0.008 and 0.007 m3 m−3 on average at the surface and root zone, respectively). Enhancing the spatial resolution, however, does not significantly improve soil moisture estimates, particularly at the surface. Surface heat flux estimates from assimilating soil moisture data of different spatial or temporal resolutions are very similar.

Modeling regional pollution transport events during KORUS-AQ: Progress and challenges in improving representation of land-atmosphere feedbacks.

This study evaluates the impact of assimilating soil moisture data from NASA's Soil Moisture Active Passive (SMAP) on short-term regional weather and air quality modeling in East Asia during the Korea-US 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 semi-coupled 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.

Applying SMOS soil moisture data into the National Weather Service (NWS)’s Research Distributed Hydrologic Model (HL-RDHM) for flash flood guidance application

After rainfall, soil moisture is the most important factor dictating flash flooding, since rainfall infiltration and runoff are based on the saturation of the soil. However, continuous and regional soil moisture data acquisition is difficult by ground-based measurement. As such, soil moisture is often derived from land surface models and used by agencies such as the National Oceanic and Atmospheric Administration's National Weather Service (NOAA/NWS) as a proxy for estimates of soil moisture at the surface in order to support operational flood forecasting. The current Flash Flood Guidance (FFG) system at the Arkansas Red Basin River Forecast Center (ABRFC) provides gridded flash flood guidance (GFFG) by using the soil moisture from the NWS Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) to scale Natural Resources Conservation Service curve numbers. This study evaluates the contribution of remote sensing technology to quantifiable improvements in HL-RDHM soil moisture as well as adding a satellite-based soil moisture component to the NWS FFG Algorithm. The Soil Moisture and Ocean Salinity (SMOS) satellite of European Space Agency operates at an L-band (1.4GHz) wavelength which offers relatively deeper penetration and has lower sensitivity to vegetation impacts than other microwave satellite platforms. It has been shown to be well-suited for observing surface soil moisture. The purpose of this paper is to determine, execute, and assess a method of SMOS data assimilation applicable for use with the HL-RDHM modeling system. The value of remote sensing data in constraining modeled soil moisture states is evaluated. Results from the technique developed in this study imply a potential for SMOS-based improvement of the GFFG product. The technique is also expected to be useful for assimilating soil moisture data from the Soil Moisture Active Passive (SMAP).

Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling

summary Hierarchical Bayesian (HB) modeling allows for multiple sources of uncertainty by factoring complex relationships into conditional distributions that can be used to draw inference and make predictions. We applied an HB model to estimate the parameters and state variables of a parsimonious hydrological model – GR4J – by coherently assimilating the uncertainties from the model, observations, and parameters at Coweeta Basin in western North Carolina. A state-space model was within the Bayesian hierarchical framework to estimate the daily soil moisture levels and their uncertainties. Results show that the posteriors of the parameters were updated from and relatively insensitive to priors, an indication that they were dominated by the data. The uncertainties of the simulated streamflow increased with streamflow increase. By assimilating soil moisture data, the model could estimate the maximum capacity of soil moisture accounting storage and predict storm events with higher precision compared to not assimilating soil moisture data. This study has shown that hierarchical Bayesian model is a useful tool in water resource planning and management by acknowledging stochasticity.

Tikhonov regularization as a tool for assimilating soil moisture data in distributed hydrological models

AbstractDischarge, water table depth, and soil moisture content have been observed at a high spatial and temporal resolution in a 44 ha catchment in Costa Rica over a period of 5 months. On the basis of the observations in the first 3 months (period A), two distinct soil moisture models are identified and calibrated: a linear stochastic time‐varying state‐space model, and a geo‐statistical model. Both models are defined at various spatial and temporal resolutions. For the subsequent period of 2 months (period B), four different ways to predict the soil moisture dynamics in the catchment are compared: (1) the application of the dynamic models in open‐loop form; (2) a re‐calibration of the dynamic models with soil moisture data in period B, and subsequent prediction in open‐loop form; (3) prediction with the geo‐statistical models, using the soil moisture data in period B; (4) prediction by combining the outcomes of (1) and (3) via generalized cross‐validation. The last method, which is a form of data assimilation, compares favourably with the three alternatives. Over a range of resolutions, the predictions by data assimilation have overall uncertainties that are approximately half that of the other prediction methods and have a favourable error structure (i.e. close to Gaussian) over space as well as time. In addition, data assimilation gives optimal predictions at finer resolutions compared with the other methods. Compared with prediction with the models in open‐loop form, both re‐calibration with soil moisture observations and data assimilation result in enhanced discharge predictions, whereas the prediction of ground water depths is not improved. Copyright © 2002 John Wiley &amp; Sons, Ltd.