A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales

Luo, Wuhong; Taylor, Mary E.; Parker, S. R.

doi:10.1002/joc.1583

Cited by 233 publications

(179 citation statements)

References 29 publications

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“…Kriging is a statistical surface-fitting method where the weights to each cell minimize the variance of the interpolation error [11]. Empirical semivarience is computed using input data and a curve is fit to develop a semivariogram.…”

Section: Surface-fitting Map Methodsmentioning

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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Section: Surface-fitting Map Methodsmentioning

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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“…Finer gridded data (e.g., 30 m or 250 m) were resampled to 1 km resolution in this study using the majority principle for categorical values and cubic convolution for numerical values [26]. Wind speed data at 32 km resolution were spatially interpolated to 1 km resolution using an Inversed Distance Weighting (IDW) method [27]. The ASRL model was mostly driven by climatic variables derived from the DAYMET model [28].…”

Section: Input Data For the Asrl Modelmentioning

confidence: 99%

Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA

Shi

Choi

et al. 2013

Remote Sensing

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A methodology to generate spatially continuous fields of tree heights with an optimized Allometric Scaling and Resource Limitations (ASRL) model is reported in this first of a multi-part series of articles. Model optimization is performed with the Geoscience Laser Altimeter System (GLAS) waveform data. This methodology is demonstrated by mapping tree heights over forested lands in the continental USA (CONUS) at 1 km spatial resolution. The study area is divided into 841 eco-climatic zones based on three forest types, annual total precipitation classes (30 mm intervals) and annual average temperature classes (2 °C intervals). Three model parameters (area of single leaf, α, exponent for canopy radius, η, and root absorption efficiency, γ) were selected for optimization, that is, to minimize the difference between actual and potential tree heights in each of the eco-climatic zones over the CONUS. Tree heights predicted by the optimized model were evaluated against GLAS heights using a two-fold cross validation approach (R 2 = 0.59; RMSE = 3.31 m). Comparison at the pixel level between GLAS heights (mean = 30.6 m; standard deviation = 10.7) and model predictions (mean = 30.8 m; std. = 8.4) were also performed. Further, the model predictions were compared to existing satellite-based forest height maps. The optimized ASRL model satisfactorily reproduced the pattern of tree heights over the CONUS. Subsequent articles in this series will document further improvements with the ultimate goal of mapping tree heights and forest biomass globally.

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“…On a national level a few efforts have been made to calculate horizontal wind fields based on station reports (e.g., Luo et al, 2008;Gerth and Christoffer, 1994;Walter et al, 2006). For larger regions like Europe no such data fields are available at present.…”

Section: Introductionmentioning

confidence: 99%

High-resolution daily gridded data sets of air temperature and wind speed for Europe

Brinckmann

Krähenmann

Bissolli

2016

Earth Syst. Sci. Data

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Abstract. New high-resolution data sets for near-surface daily air temperature (minimum, maximum and mean) and daily mean wind speed for Europe (the CORDEX domain) are provided for the period 2001-2010 for the purpose of regional model validation in the framework of DecReg, a sub-project of the German MiKlip project, which aims to develop decadal climate predictions. The main input data sources are SYNOP observations, partly supplemented by station data from the ECA&D data set (http://www.ecad.eu). These data are quality tested to eliminate erroneous data. By spatial interpolation of these station observations, grid data in a resolution of 0.044 • (≈ 5 km) on a rotated grid with virtual North Pole at 39.25 • N, 162 • W are derived. For temperature interpolation a modified version of a regression kriging method developed by Krähenmann et al. (2011) is used. At first, predictor fields of altitude, continentality and zonal mean temperature are used for a regression applied to monthly station data. The residuals of the monthly regression and the deviations of the daily data from the monthly averages are interpolated using simple kriging in a second and third step. For wind speed a new method based on the concept used for temperature was developed, involving predictor fields of exposure, roughness length, coastal distance and ERA-Interim reanalysis wind speed at 850 hPa. Interpolation uncertainty is estimated by means of the kriging variance and regression uncertainties. Furthermore, to assess the quality of the final daily grid data, cross validation is performed. Variance explained by the regression ranges from 70 to 90 % for monthly temperature and from 50 to 60 % for monthly wind speed. The resulting RMSE for the final daily grid data amounts to 1-2 K and 1-1.5 m s −1 (depending on season and parameter) for daily temperature parameters and daily mean wind speed, respectively.

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A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales

Cited by 233 publications

References 29 publications

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

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

Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA

High-resolution daily gridded data sets of air temperature and wind speed for Europe

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