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Integration Frameworks for Assimilating Satellite Remote Sensing Observations with Hydrological Model Outputs

Sadat Soltani, Samira | 2023

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  1. Type of Document: Ph.D. Dissertation
  2. Language: English
  3. Document No: 56298 (09)
  4. University: Sharif University of Technology and University od Strasbourg
  5. Department: Civil Engineering
  6. Advisor(s): Ataie Ashtiani, Behzad
  7. Abstract:
  8. Global climate change and anthropogenic impacts lead to alterations in the water cycle, water resource availability and the frequency and intensity of floods and droughts. As a result, developing effective techniques such as hydrological modeling is essential to monitor and predict water storage changes. However, inaccuracies and uncertainties in different aspects of modeling, due to simplification of meteorological physical processes, data limitations and inaccurate climate forcing data limit the reliability of hydrological models. Assimilation of new observations constrains the dynamics of the model based on uncertainties associated with model and data, which can introduce missing water storage signals e.g., anthropogenic and extreme climate change effects. The main objective of this thesis is to provide multi-mission satellite data assimilation into the coupled surface-subsurface hydrological model for the first time to improve predictions of sub-surface water storage, and shed light on the limitations and challenges of assimilating only one source of satellite data. We present a comprehensive data assimilation strategy including (i) GRACE-only (GRACE DA), (ii) SMOS-only (SM DA), and (iii) joint assimilation of GRACE and SMOS (multivariate DA) and how to work with GRACE TWS data errors e.g., the correlated noise of high-frequency mass variations and spatial leakage errors to use the potential of GRACE TWS data as much as possible. We provide benefits and limitations of different data assimilation strategies with emphasis on the capability of multi-mission satellite data for hydrological applications. In this thesis, multi-mission satellite data is assimilated into an integrated two-way coupled subsurface-surface hydrological model which is called ParFlow-CLM using the Ensemble Kalman Filter (EnKF). Interaction between subsurface and surface water is a numerically challenging task and ParFlow-CLM can simulate the physical processes occurring at the interface between the deeper subsurface and the surface. Therefore, investigation of effect of the multivariate data assimilation on ParFlow-CLM model can challenge the capability of this model. To implement this objective, in the first step, an in-depth overview of recent studies on assimilating GRACE TWS data into hydrological models is provided and sheds light on their limitations, challenges and progress. In the second step, the capability of GRACE data in estimation of water budget is investigated and for doing this the central basin of Iran is selected. In the third step, an approach to improve soil moisture and groundwater-level predictions from ParFlow-CLM model using an objective scaling of Manning’s coefficient and saturated hydraulic conductivity is proposed and this approach has been tested over the Upper Rhine basin. The scaling procedure also shows overall improvements in groundwater level estimation (50% on average), particularly where the groundwater level is shallow (less than 5 m from the surface). By using the scaling approach, the average bias in soil moisture for the study domain was decreased from 0.17 mm3/mm3 to 0.1 mm3/mm3. A modification of model parametrization to take into account the impact of scale on hydrodynamic parameters should be done prior to multivariate assimilation approaches. And finally, multivariate data assimilation performance in the three assimilation methodologies is evaluated over a case study in Iran. Furthermore, multiple datasets including in-situ measurements of groundwater and different remotely sensed observations are used to examine the results. As a result, correlation values for top 5 centimeters of soil moisture and change in groundwater storage experienced a 0.17 and 0.12 increase in correlation coefficient with SMAP data and in-situ measurements
  9. Keywords:
  10. Data Aggregation ; Hydrological Modeling ; Extended Kalman Filter ; Remote Sensing ; GRASE Satellite ; Land Water Storage ; Groundwater ; Soil Moisture Monitoring

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