Infrastructure Engineering - Theses

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    Estimating root-zone soil moisture by assimilating remotely sensed biophysical states into modelling
    Hashemian Rahaghi, Mahboobeh Sadat ( 2016)
    In this research, the soil water processes of a simple land surface model are improved to establish a more appropriate soil-biophysical linkage between root-zone moisture content and above-ground states. In the next step, this thesis examines how this modified model coupling affects updating root-zone soil moisture using surface information through assimilation.
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    Improving flood prediction in sparsely gauged catchments by the assimilation of satellite soil moisture into a rainfall-runoff model
    ALVAREZ, CAMILA ( 2015)
    This thesis explores the assimilation of remotely-sensed soil moisture (SM-DA) into a rainfall-runoff model for improving flood prediction within data scarce regions. Satellite soil moisture (SM) observations are used to correct the two main controlling factors of the streamflow generation: the wetness condition of the catchment (state correction scheme) and the magnitude of rainfall events (forcing correction scheme). The core part of the research focuses on the state correction scheme. A simple rainfall runoff model (the probability distributed model, PDM) is used for this. The soil water state of PDM is corrected by assimilating active and passive satellite SM observations using an ensemble Kalman filter. Within this framework, the efficacy of different existing tools for setting up the state correction scheme are evaluated, and new techniques to address some of the key challenges in the assimilation of surface satellite SM observations into hydrological models are introduced. Various options for the state correction scheme were implemented and enhanced through- out the thesis. The proposed schemes consistently led to improved streamflow ensemble predictions for a case study. In the final state correction scheme, the ensemble root mean square error was reduced by 24% at the catchment outlet, the false alarm ratio was reduced by a 9%, and the skill and reliability of the streamflow ensemble were improved after SM-DA. The state correction scheme was also effective at improving the streamflow ensemble prediction within ungauged inner locations, which demonstrates the advantages of incorporating spatially distributed SM information within large and poorly instrumented catchments. I showed that since stochastic SM-DA is formulated to reduce the random component of the SM error (and therefore does not address systematic biases in the model), the efficacy of the state correction schemes was restricted by the model quality before assimilation. This is critical within a data scarce context, where streamflow predictions suffer from large errors coming from the poor quality data used to force and calibrate the model. Additionally, due to the higher control that SM exerts in the catchment runoff mechanisms during minor and moderate floods, the state correction scheme had more skill when the low flows were evaluated. Consequently, SM-DA improved mainly the quality of the streamflow ensemble prediction (skill, reliability and average statistics of the ensemble) but did not significantly reduced the existing biases in the peak flows prediction. These results reveal one key limitation of the proposed approach: improving flood prediction by reducing random (and not systematic) errors in the SM state of a rainfall-runoff model, while SM is probably not the main controlling factor in the runoff generation during major floods within the study catchment. Addressing the above limitation, I set up a forcing correction scheme that aimed at reducing the errors in the rainfall data (the rainfall input, in addition to the infiltration estimates from the model, are probably the main factors controlling the accuracy of flood predictions). I adopted for this the soil moisture analysis rainfall tool (SMART) proposed by Crow et al., (2009). In SMART, active and passive satellite SM were assimilated into the Antecedent Precipitation Index model to correct a near real-time satellite rainfall, which was subsequently used to force PDM (without state correction). The results showed that remotely sensed SM was effective at improving mean-to-high daily satellite rainfall accumulations, which in turn led to a consistent improvement of the streamflow prediction, especially during high flows. The efficacy of the state correction and the forcing correction schemes were compared within 4 catchments. For most cases, the reduction of model SM error by the assimilation of satellite SM led to improved streamflow prediction compared with the correction of the forcing data. This was true for both the low flows and high flows. The outper- formance of the state correction scheme during high flows is counterintuitive with the stronger influence that rainfall probably has during floods, and differs from previous studies. I interpreted these different results by various factors including the methodological configuration (rainfall-runoff model, model error quantification, etc.), the quality of the satellite rainfall data and the quality of the satellite SM retrievals. In agreement with the literature, the combination of the forcing and the state correction schemes further improved flood predictions. The significance of this thesis is in providing novel evidence (based on real data experiments) of the value of satellite soil moisture for improving both an operational satellite rainfall product and the streamflow prediction within data scarce regions. Additionally, I highlighted a number of challenges and limitations within the forcing and state correction schemes. I introduced new techniques to overcome some of these challenges and proposed future strategies to further address them. This contributes to advancing towards a reliable data assimilation framework for improving operational flood prediction within data scarce regions.