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Estimation of evapotranspiration and recharge flux are fundamental to sustainable water resource management. These fluxes provide valuable insights for decision-makers, enabling them to implement effective strategies that balance water demand with available resources, promote resilience in the face of climate change, and ensure the long-term sustainability of water ecosystems. In-situ observations of evapotranspiration and recharge are scarce and not representative of large areas. An observation driven variational data assimilation system, named LIDA-2 (Land Integrated Data Assimilation framework) is developed to estimate the key parameters (evaporative fraction, bulk heat transfer coefficient, Brooks-Corey parameter) of evapotranspiration and recharge fluxes by assimilating GOES land surface temperature (LST) and SMAP surface soil moisture observations into a coupled water and dual- source energy balance model. Second order information is used to estimate the uncertainty and guide the model toward a well-posed estimation problem. The algorithm is implemented in part of the US southern great plain, and its performance is evaluated through comparison tests, uncertainty analysis and consistency test. Soil moisture and evapotranspiration estimations are validated against in-situ observations. The spatial pattern of estimated annual recharge map is in good agreement with maps from literature. Overall, the VDA based framework demonstrated its efficacy to do largescale mapping of recharge, and evapotranspiration.
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Using Color Coherence Vectors to Evaluate the Performance of Hydrologic Data Assimilation
Abstract An inadequate characterization of hydrogeological properties can significantly decrease the trustworthiness of subsurface flow and transport model predictions. A variety of data assimilation methods have been proposed in order to estimate hydrogeological parameters from spatially scarce data by incorporating them into the governing physical models. In order to quantify the accuracy of the estimations, several metrics have been used such as Rank Histograms, root‐mean‐square error (RMSE), and Ensemble Spread. However, these commonly used metrics do not regard the spatial correlation of the aquifer's properties. This can cause permeability fields with very different spatial structures to have similar histograms or RMSE. In this paper, we propose an approach based on color coherence vectors (CCV) for evaluating the performance of these estimation methods. CCV is a histogram‐based technique for comparing images that incorporate spatial information. We represent estimated fields as digital three‐channel images and use CCV to compare and quantify the accuracy of estimations. The appealing feature of this technique is that it considers the spatial structure embedded in the estimated fields. The sensitivity of CCV to spatial information makes it a suitable metric for assessing the performance of data assimilation techniques. Under various factors, such as numbers of measurements and structural parameters of the log conductivity field, we compare the performance of CCV with the RMSE.
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- Award ID(s):
- 1654009
- PAR ID:
- 10461693
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 55
- Issue:
- 2
- ISSN:
- 0043-1397
- Page Range / eLocation ID:
- p. 1717-1729
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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