Using the Climate Forecast System Reanalysis as weather input data for watershed models

Fuka, Daniel R.; Walter, M. Todd; MacAlister, Charlotte; DeGaetano, Arthur T.; Steenhuis, Tammo S.; Easton, Zachary M.

doi:10.1002/hyp.10073

Cited by 381 publications

(278 citation statements)

References 28 publications

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“…In our approach, we exploit this correlation to build and parameterize a Bayesian network model for inferring missing values for the relative humidity values from other climate variables. As a data source, we use the surface reanalysis dataset Climate Forecast System Reanalysis (CFSR) [45,46]. We downloaded a set of daily data of all variables, covering Central America and Southern Mexico (a total of 855 pixels) for the years 1979 to 2000.…”

Section: Datamentioning

confidence: 99%

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Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

Lara-Estrada

Rasche

Sucar

et al. 2018

Land

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Abstract:Climate data availability plays a key role in development processes of policies, services, and planning in the agricultural sector. However, data at the spatial or temporal resolution required is often lacking, or certain values are missing. In this work, we propose to use a Bayesian network approach to generate data for missing variables. As a case study, we use relative humidity, which is an important indicator of land suitability for coffee production. For the model, we first extracted climate data for the variables precipitation, maximum and minimum air temperature, wind speed, solar radiation and relative humidity from the surface reanalysis dataset Climate Forecast System Reanalysis. We then used machine learning algorithms to define the model structure and parameters from the relationships of the variables found in the dataset. Precipitation, maximum and minimum air temperature, wind speed, and solar radiation are then used as proxy variables to infer missing values for monthly relative humidity and relative humidity for the driest month. For this, we used both complete and incomplete initial data. In both scenarios of data availability, the comparison of estimated and measured values of relative humidity shows a high level of agreement. We conclude that using Bayesian Networks is a practical solution to estimate relative humidity for coffee agricultural planning.

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Section: Datamentioning

confidence: 99%

“…MRH: monthly relative humidity, and RHDM: relative humidity of the driest month. Data source: surface reanalysis dataset Climate Forecast System Reanalysis (CFSR) [45,46]. (SICSS), Kompetenzzentrum Nachhaltige Universität (KNU), and the Research Unit Sustainability and Global Change at the Universität Hamburg.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

Lara-Estrada

Rasche

Sucar

et al. 2018

Land

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“…The climate data were obtained from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) site: globalweather.tamu.edu (see also Fuka et al, 2013) and rainfall measurements from field gauges obtained from the Agência Nacional de Águas (ANA, National Water Agency: hidroweb.ana.gov.br). We interpolated multiple spatial points to produce daily weather variables for each HRU; see details in the results.…”

Section: Data Input and Processingmentioning

confidence: 99%

Hydrological modeling of the Ribeirão das Posses – An assessment based on the Agricultural Ecosystem Services (AgES) watershed model

et al. 2017

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Southeastern Brazil has recently experienced drought conditions that have impacted watershed conservation and the management of water quality and quantity for agricultural and urban demands. The Ribeirão das Posses watershed is being monitored as a headwater of the Jaguarí River, which is one of the contributing rivers of the Cantareira Reservoir Complex in the state of São Paulo. The landscape has changed over the last century from native forests to more homogeneous vegetation for pastures, crops and some forest plantations of eucalyptus, which have cumulative impacts on water yield and quality. Currently, the Projeto Conservador das Águas (Water Conservationist Project) has planted small areas with native species vegetation in order to recover degraded areas. The objective of this study was to evaluate the quantity of water in the Ribeirão das Posses Basin by both measurements and by simulating hydrological responses. The Agricultural Ecosystem Services (AgES) watershed model was applied to simulate water movement and storage among land areas. The simulation period was from 2009 to 2014, because the daily streamflow and meteorological data were available for model calibration and testing. We discuss data input requirements, model calibration to fit measured streamflow, and sensitivity to spatially variable rainfall inputs. The calibrated model may be used to estimate streamflow during periods of missing data, and in the future to estimate impacts of land use changes on stream water quantity and quality. Such information can be used in programs of payments for ecosystem services.Keywords: climate, ecosystem services, hydrology, water conservation, watershed model. Modelagem hidrológica do Ribeirão das Posses -Uma avaliação baseada no modelo Serviços Agrícolas Ecossistêmicos (AgES) da bacia RESUMOA recente estiagem no Sudeste do Brasil tem impactado a conservação de bacias hidrográficas e a gestão da qualidade e quantidade da água para as demandas agrícolas e urbanas. A Bacia do Ribeirão das Posses está sendo monitorada por ser uma bacia de cabeceira da Bacia do Rio Jaguari, importante contribuinte do Sistema Cantareira no estado de São Paulo. A paisagem foi alterada no século passado, a partir de florestas nativas para uma vegetação mais homogênea como pastos, campos agrícolas e florestas de eucalipto, que geram impactos cumulativos sobre a quantidade e qualidade da água. Atualmente, o Projeto Conservador das Águas tem restaurado pequenas áreas com vegetação nativa para a recuperação de áreas degradadas. O objetivo dessa pesquisa foi avaliar a quantidade da água na Bacia do Ribeirão das Posses usando dados observados e dados da simulação de respostas hidrológicas. O modelo de bacia "Serviço Agroecossistêmico" (AgES) é aplicado para simular a variação no armazenamento e movimento da água nas áreas terrestres. O período de simulação (2009)(2010)(2011)(2012)(2013)(2014) foi definido em função da disponibilidade dos dados de vazão e meteorológicos para calibrar e testar o modelo. Dados de entrada requisitad...

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“…CFSR provides the required 199 weather data such as precipitation, maximum and minimum temperatures, relative humidity, solar radiation, and wind speed 200 that used in SWAT for runoff simulation (Fuka et al, 2013 andTomy et al, 2016). SWAT provides two options to input the 201 weather data, the simulated and gauged weather.…”

Section: Weather Data 198mentioning

confidence: 99%

Integrated Impact of Digital Elevation Model and Land Cover Resolutions on Simulated Runoff by SWAT Model

Al-Khafaji

Al-Sweiti

2017

Preprint

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Abstract. Complemental interactive effects of the Digital Elevation Model (DEM) and Land Cover (LC) resolutions on the 7estimated runoff by using Soil and Water Assessment Tool (SWAT), which is of critical importance for water resource 8 management, was investigated in this paper. Also, to specify the optimal DEM and LC resolutions for maximizing accuracy 9 of the estimated runoff for Dokan, Adhaim, and Duhok watersheds located in Iraq. Twenty daily time step based SWAT 10 models of each watershed were implemented using five DEMs in conjunction with five LCs. Assessment of models results 11shows that the watershed delineation significantly affected by DEM resolution especially in flat regions. However, there is 12 no clearly discernible trend of this effect on the determination of watershed boundary, stream network, number of sub-basins 13 and total area. Furthermore, the number of Hydrologic Response Units (HRUs) and the maximum altitudes are directly 14 related to the DEM whereas the minimum altitudes have an inverse relationship with the DEM. Also, the number of HRUs 15 increases with the increase in LC resolution until it reaches a maximum value and then starts to gradually decrease. While 16 there is no significant trend between the accuracy of the estimated runoff and the increase in the DEM and LC resolutions.

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Using the Climate Forecast System Reanalysis as weather input data for watershed models

Cited by 381 publications

References 28 publications

Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

Hydrological modeling of the Ribeirão das Posses – An assessment based on the Agricultural Ecosystem Services (AgES) watershed model

Integrated Impact of Digital Elevation Model and Land Cover Resolutions on Simulated Runoff by SWAT Model

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