کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5771306 | 1629908 | 2017 | 43 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States
ترجمه فارسی عنوان
پیش بینی رطوبت خاک ریشه با ویژگی های خاک و داده های رطوبت ماهواره ای در نزدیکی سطح در ایالات متحده
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کلمات کلیدی
North American Carbon ProgramPTFRMSENACPAMSR-EsmARMCMCEnKFModerate Resolution Imaging Spectroradiometer - Spectroradiometer تصویربرداری با وضوح تصویر متوسطSNOTEL - اسنولتScan - اسکن کردنMODIS - تابشسنج طیفی تصویربرداری با وضوح متوسط یا MODIS Soil - خاکRoot - ریشهRoot zone soil moisture - ریشه خاک ریشه خاک استRoot mean square error - ریشه میانگین خطای مربعMoisture - مرطوبMonte Carlo Markov Chain - مونت کارلو مارکوف زنجیره ایKalman - کالمنEnsemble Kalman Filter - گروه کالمن فیلتر
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
فرآیندهای سطح زمین
چکیده انگلیسی
Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cmâ3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cmâ3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = â0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 546, March 2017, Pages 393-404
Journal: Journal of Hydrology - Volume 546, March 2017, Pages 393-404
نویسندگان
D. Baldwin, S. Manfreda, K. Keller, E.A.H. Smithwick,