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Hydrologic Remote Sensing and Land Surface Data Assimilation

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ABSTRACT

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface–atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

No MeSH data available.


Sequential Bayesian scheme for evolution of the conditional probability density of the state variables by assimilating observations from time t-1 to time t.
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f1-sensors-08-02986: Sequential Bayesian scheme for evolution of the conditional probability density of the state variables by assimilating observations from time t-1 to time t.

Mentions: As seen in the schematic of recursive Bayes law (see Figure 1 below), the forecast density of p(xt/Yt-1) can be estimated via Chapman-Kolmogorov equation [33] assuming that xt follows the Markov property, therefore:(5)p(xt/Yt−1)=∫p(xt/xt−1)p(xt−1/Yt−1)dxt−1


Hydrologic Remote Sensing and Land Surface Data Assimilation
Sequential Bayesian scheme for evolution of the conditional probability density of the state variables by assimilating observations from time t-1 to time t.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675527&req=5

f1-sensors-08-02986: Sequential Bayesian scheme for evolution of the conditional probability density of the state variables by assimilating observations from time t-1 to time t.
Mentions: As seen in the schematic of recursive Bayes law (see Figure 1 below), the forecast density of p(xt/Yt-1) can be estimated via Chapman-Kolmogorov equation [33] assuming that xt follows the Markov property, therefore:(5)p(xt/Yt−1)=∫p(xt/xt−1)p(xt−1/Yt−1)dxt−1

View Article: PubMed Central

ABSTRACT

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface–atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

No MeSH data available.