Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

2016 ◽  
Vol 9 (6) ◽  
pp. 2628-2637 ◽  
Author(s):  
Abedeh Abdolghafoorian ◽  
Leila Farhadi
Keyword(s):  
Data Assimilation ◽  
Uncertainty Quantification ◽  
Land Surface ◽  
Hydrologic Modeling ◽  
Variational Data Assimilation

scholarly journals A Comparison of the Triangle Retrieval and Variational Data Assimilation Methods for Surface Turbulent Flux Estimation

2005 ◽  
Vol 6 (6) ◽  
pp. 1063-1072 ◽  
Author(s):  
Steven A. Margulis ◽  
Jongyoun Kim ◽  
Terri Hogue
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Land Surface ◽  
Measurement Errors ◽  
Structural Model ◽  
Turbulent Fluxes ◽  
Surface Fluxes ◽  
Variational Data Assimilation ◽  
Model Errors

Abstract Future operational frameworks for estimating surface turbulent fluxes over the necessary spatial and temporal scales will undoubtedly require the use of remote sensing products. Techniques used to estimate surface fluxes from radiometric surface temperature generally fall into two categories: retrieval-based and data assimilation approaches. Up to this point, there has been little comparison between retrieval- and assimilation-based techniques. In this note, the triangle retrieval method is compared to a variational data assimilation approach for estimating surface turbulent fluxes from radiometric surface temperature observations. Results from a set of synthetic experiments and an application using real data from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site indicate that the assimilation approach performs slightly better than the triangle method because of the robustness of the estimation to measurement errors and parsimony of the system model, which leads to fewer sources of structural model errors. Future comparison work using retrieval and data assimilation algorithms will provide more insight into the optimal approach for diagnosis of land surface fluxes using remote sensing observations.

scholarly journals Partitioning Evapotranspiration into Soil Evaporation and Canopy Transpiration via a Two-Source Variational Data Assimilation System

2016 ◽  
Vol 17 (9) ◽  
pp. 2353-2370 ◽  
Author(s):  
Tongren Xu ◽  
Sayed M. Bateni ◽  
Steven A. Margulis ◽  
Lisheng Song ◽  
Shaomin Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Soil Evaporation ◽  
Variational Data Assimilation ◽  
Heat Diffusion ◽  
Heihe River ◽  
Source Surface ◽  
Heat Diffusion Equation ◽  
Canopy Transpiration ◽  
Mean Square Errors

Abstract The primary objective of this study is to assess the accuracy of the two-source variational data assimilation (TVDA) system for partitioning evapotranspiration (ET) into soil evaporation (ETS) and canopy transpiration (ETC). Its secondary aim is to compare performance of the TVDA system with the commonly used two-source surface energy balance (TSEB) method. A combination of eddy-covariance-based ET observations and stable-isotope-based measurements of the ratio of evaporation and transpiration to total evapotranspiration (ETS/ET and ETC/ET) over an irrigated cropland site (the so-called Daman site) in the middle reach of the Heihe River basin (northwestern China) was used to investigate these objectives. The results indicate that the TVDA method predicts ETS and ETC more accurately than TSEB. Root-mean-square errors (RMSEs) of midday (1300–1500 LT) averaged soil and canopy latent heat flux (LES and LEC) estimates from TVDA are 23.1 and 133.0 W m−2, respectively. Corresponding RMSE values from TSEB are 41.9 and 156.0 W m−2. Compared to TSEB, the TVDA method takes advantage of all of the information in land surface temperature observations in the estimation period by leveraging a dynamic model (the heat diffusion equation) and thus can generate more accurate LES and LEC estimates.

Progress in integrating remote sensing data and hydrologic modeling

2014 ◽  
Vol 38 (4) ◽  
pp. 464-498 ◽  
Author(s):  
Xiaoyong Xu ◽  
Jonathan Li ◽  
Bryan A. Tolson
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Land Surface ◽  
Hydrologic Modeling ◽  
Snow Water Equivalent ◽  
Research Effort ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data ◽  
The Past

Remote sensing and hydrologic modeling are two key approaches to evaluate and predict hydrology and water resources. Remote sensing technologies, due to their ability to offer large-scale spatially distributed observations, have opened up new opportunities for the development of fully distributed hydrologic and land-surface models. In general, remote sensing data can be applied to land-surface and hydrologic modeling through three strategies: model inputs (basin information, boundary conditions, etc.), parameter estimation (model calibration), and state estimation (data assimilation). There has been an intensive global research effort to integrate remote sensing and land/hydrologic modeling over the past few decades. In particular, in recent years significant progress has been made in land/hydrologic remote sensing data assimilation. Hence there is a demand for an up-to-date review on these efforts. This paper presents an overview of research efforts to combine hydrologic/land models and remote sensing products (mainly including precipitation, surface soil moisture, snow cover, snow water equivalent, leaf area index, and evapotranspiration) over the past decade. This paper also discusses the major challenges remaining in this field, and recommends the directions for further research efforts.

scholarly journals The Land Variational Ensemble Data Assimilation fRamework: LaVEnDAR

2019 ◽  
Author(s):  
Ewan Pinnington ◽  
Tristan Quaife ◽  
Amos Lawless ◽  
Karina Williams ◽  
Tim Arkebauer ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Time Window ◽  
Variational Data Assimilation ◽  
Surface Model ◽  
State Variables ◽  
Maize Crop ◽  
Area Index ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Abstract. The Land Variational Ensemble Data Assimilation fRamework (LaVEnDAR) implements the method of Four-Dimensional Ensemble Variational data assimilation for land surface models. Four-Dimensional Ensemble Variational data assimilation negates the often costly calculation of a model adjoint required by traditional variational techniques (such as 4DVar) for optimising parameters/state variables over a time window of observations. In this paper we implement LaVEnDAR with the JULES land surface model. We show the system can recover seven parameters controlling crop behaviour in a set of twin experiments. We run the same experiments at the Mead continuous maize FLUXNET site in Nebraska, USA to show the technique working with real data. We find that the system accurately captures observations of leaf area index, canopy height and gross primary productivity after assimilation and improves posterior estimates of the amount of harvestable material from the maize crop by 74 %. LaVEnDAR requires no modification to the model that it is being used with and is hence able to keep up to date with model releases more easily than other data assimilation methods.

scholarly journals Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States

2015 ◽  
Vol 16 (6) ◽  
pp. 2481-2500 ◽  
Author(s):  
Andrew J. Newman ◽  
Martyn P. Clark ◽  
Jason Craig ◽  
Bart Nijssen ◽  
Andrew Wood ◽  
...  
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Land Surface ◽  
Measurement Errors ◽  
Hydrologic Modeling ◽  
Lapse Rate ◽  
High Threshold ◽  
Good Reliability ◽  
Uncertainty Estimates ◽  
Precipitation And Temperature

Abstract Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980–2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices.

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