Interpolation of daily rainfall using spatiotemporal models and clustering

Militino, Ana F.; Ugarte, M. D.; Goicoa, T.; Genton, Marc G.

doi:10.1002/joc.4068

Cited by 19 publications

(17 citation statements)

References 30 publications

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“…The selection of one specific method from the many available gridding interpolation methods that can be applied to precipitation may change the final result (e.g. Creutin and Obled, 1982;Hartkamp et al, 1999;Vicente-Serrano et al, 2003;Dobesch et al, 2007;Hofstra et al, 2008;Hwang et al, 2012;Militino et al, 2015;Contractor et al, 2015;Herrera et al, 2016). For example, Robeson and Ensor (2006) and Ensor and Robeson (2008) argued that the use of geostatistical interpolators for daily precipitation leads to a higher frequency of low-precipitation values while greatly reducing the extreme events.…”

Section: Discussionmentioning

confidence: 99%

SPREAD: a high-resolution daily gridded precipitation dataset for Spain – an extreme events frequency and intensity overview

Serrano‐Notivoli

Beguería

Saz

et al. 2017

Earth Syst. Sci. Data

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Abstract.A high-resolution daily gridded precipitation dataset was built from raw data of 12 858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary islands. The original data were quality-controlled and gaps were filled on each day and location independently. Using the serially complete dataset, a grid with a 5 × 5 km spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterise the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The Mediterranean coast and the Central Range showed the highest frequency and intensity of extreme events, while the number of wet days and dry and wet spells followed a north-west to south-east gradient in peninsular Spain, from high to low values in the number of wet days and wet spells and reverse in dry spells. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that is difficult to achieve when using other methods, pre-selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/7393.

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Section: Discussionmentioning

confidence: 99%

SPREAD: a high-resolution daily gridded precipitation dataset for Spain – an extreme events frequency and intensity overview

Serrano‐Notivoli

Beguería

Saz

et al. 2017

Earth Syst. Sci. Data

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“…HCW has a total drainage area of approximately 230 km 2 . Elevation ranges from 274 m above mean sea level (AMSL) in the headwaters to 177 m AMSL near the watershed outlet.…”

Section: Site Descriptionmentioning

confidence: 99%

“…Accurate mean areal precipitation (MAP) estimates are essential forcing data for hydrologic models [1,2]. However, quantifying MAP can be confounding, particularly when metrological conditions, topography, and/or land use influence the spatiotemporal variability of precipitation [3].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Zeiger

Hubbart

2017

Water

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Accurate mean areal precipitation (MAP) estimates are essential input forcings for hydrologic models. However, the selection of the most accurate method to estimate MAP can be daunting because there are numerous methods to choose from (e.g., proximate gauge, direct weighted average, surface-fitting, and remotely sensed methods). Multiple methods (n = 19) were used to estimate MAP with precipitation data from 11 distributed monitoring sites, and 4 remotely sensed data sets. Each method was validated against the hydrologic model simulated stream flow using the Soil and Water Assessment Tool (SWAT). SWAT was validated using a split-site method and the observed stream flow data from five nested-scale gauging sites in a mixed-land-use watershed of the central USA. Cross-validation results showed the error associated with surface-fitting and remotely sensed methods ranging from −4.5% to −5.1%, and −9.8% to −14.7%, respectively. Split-site validation results showed the percent bias (PBIAS) values that ranged from −4.5% to −160%. Second order polynomial functions especially overestimated precipitation and subsequent stream flow simulations (PBIAS = −160) in the headwaters. The results indicated that using an inverse-distance weighted, linear polynomial interpolation or multiquadric function method to estimate MAP may improve SWAT model simulations. Collectively, the results highlight the importance of spatially distributed observed hydroclimate data for precipitation and subsequent steam flow estimations. The MAP methods demonstrated in the current work can be used to reduce hydrologic model uncertainty caused by watershed physiographic differences.

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“…Similar state-space models were used by [38] to estimate model parameters and missing data of river basin runoff values, and by [39] to interpolate daily rainfall in Navarre (Spain). This package uses the function Stem.Estimation to carry out the iterations of the EM algorithm [40] until convergence.…”

Section: Methodsmentioning

confidence: 99%

Stochastic Spatio-Temporal Models for Analysing NDVI Distribution of GIMMS NDVI3g Images

2017

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Abstract:The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions.

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Interpolation of daily rainfall using spatiotemporal models and clustering

Cited by 19 publications

References 30 publications

SPREAD: a high-resolution daily gridded precipitation dataset for Spain – an extreme events frequency and intensity overview

SPREAD: a high-resolution daily gridded precipitation dataset for Spain – an extreme events frequency and intensity overview

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Stochastic Spatio-Temporal Models for Analysing NDVI Distribution of GIMMS NDVI3g Images

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