On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation
| Autor: | Loizu, Massari, Álvarez-Mozos, Tarpanelli, Brocca, Casalí |
|---|---|
| Přispěvatelé: | Universidad Pública de Navarra. Departamento de Proyectos e Ingeniería Rural, Nafarroako Unibertsitate Publikoa. Landa Ingeniaritza eta Proiektuak Saila |
| Rok vydání: | 2018 |
| Předmět: |
Matching (statistics)
010504 meteorology & atmospheric sciences Hydrological catchment models 0208 environmental biotechnology Soil science hydrology 02 engineering and technology 01 natural sciences remote sensing Data assimilation Streamflow Stream flow simulation Water content data assimilation 0105 earth and related environmental sciences Water Science and Technology Cumulative distribution function Variance (accounting) Scatterometer flood Surface soil moisture 020801 environmental engineering Ensemble Kalman Filter ASCAT Environmental science Ensemble Kalman filter soil moisture |
| Zdroj: | Academica-e: Repositorio Institucional de la Universidad Pública de Navarra Universidad Pública de Navarra Academica-e. Repositorio Institucional de la Universidad Pública de Navarra Instituto de Salud Carlos III (ISCIII) Advances in water resources (2017). doi:10.1016/j.advwatres.2017.10.034 info:cnr-pdr/source/autori:Loizu, J., Massari, C., Álvarez-Mozos, J., Tarpanelli, A., Brocca, L., Casalí, J./titolo:On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation/doi:10.1016%2Fj.advwatres.2017.10.034/rivista:Advances in water resources/anno:2017/pagina_da:/pagina_a:/intervallo_pagine:/volume |
| DOI: | 10.1016/j.advwatres.2017.10.034 |
| Popis: | Assimilation of remotely sensed surface soil moisture (SSM) data into hydrological catchment models has been identified as a means to improve stream flow simulations, but reported results vary markedly depending on the particular model, catchment and assimilation procedure used. In this study, the in fluence of key aspects, such as the type of model, re-scaling technique and SSM observation error considered, were evaluated. For this aim, Advanced SCATterometer ASCAT-SSM observations were assimilated through the ensemble Kalman filter into two hydrological models of different complexity namely MISDc and TOPLATS) run on two Mediterranean catchments of similar size (750 km2). Three different re-scaling techniques were evaluated (linear re-scaling, variance matching and cumulative distribution function matching), and SSM observation error values ranging from 0.01% to 20% were considered. Four different efficiency measures were used for evaluating the results. Increases in Nash-Sutcliffe efficiency (0.03–0.15) and efficiency indices (10–45%) were obtained, especially when linear re-scaling and observation errors within 4-6% were considered. This study found out that there is a potential to improve stream flow prediction through data assimilation of remotely sensed SSM in catchments of different characteristics and with hydrological models of different conceptualizations schemes, but for that, a careful evaluation of the observation error and re-scaling technique set-up utilized is required. This study was partially funded by the Spanish Ministry of Science and Innovation (Project CGL2011-24336), the Spanish Ministry of Innovation and Competitiveness (Project CGL2015-64284-C2-1-R MINECO/FEDER) and by the Public University of Navarre through a pre-doctorate research scholarship to the first author. |
| Databáze: | OpenAIRE |
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