Spatial interpolation of precipitation indexes in Sierra Nevada (Spain): comparing the performance of some interpolation methods

Pereira, Paulo; Oliva, Marc; Misiūnė, Ieva

doi:10.1007/s00704-015-1606-8

Cited by 21 publications

(14 citation statements)

References 74 publications

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“…This work provides a representation of the local variability of extremes by using all the available information and applying a local reconstruction method. If the spatial resolution is amplified for a regional study, based on the use of more precipitation data, the results are less smoothed as shown in Pereira et al (2016), which used 36 stations in Sierra Nevada (southern peninsular Spain) to compute NWD, R10mm and R20mm, with similar values to this work specifying a precise spatial distribution. The use of the complete information of the precipitation network in Spain provided a more detailed precipitation distribution over time and space.…”

Section: Discussionmentioning

confidence: 99%

“…Increasing temporal resolution from monthly or annual to daily scale allows analysis of other relevant components of the climate, such as the extreme precipitation events or the length and intensity of dry spells that precede most of the annual hydrological processes. In this respect, daily precipitation grids of different spatial resolutions have been developed at the global (Piper and Stewart, 1996;Menne et al, 2012) or regional scale (Frei and Schär, 1998;Eischeid et al, 2000;Kyriakidis et al, 2001;Rubel and Hantel, 2001;KleinTank et al, 2002;Hewitson and Crane, 2005;Liebmann and Allured, 2005;Haylock et al, 2008;Klok and Klein-Tank, 2009;Mekis and Vincent, 2011;Hwang et al, 2012;Yatagai et al, 2012;Jones et al, 2013;Chaney et al, 2014;Isotta et al, 2014;Hernández et al, 2016).…”

Section: Or Wildfiresmentioning

confidence: 99%

See 1 more Smart Citation

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%

Section: Or Wildfiresmentioning

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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“…Recently, several mixed interpolation techniques have been developed, combining kriging and the secondary information [84,85]. For example, regression kriging (RK) combines the trend fitted by global regression with the kriged residuals, which makes better use of the available data, thereby improving the accuracy of the estimations when compared to ordinary kriging [86].…”

Section: The Downscaling Proceduresmentioning

confidence: 99%

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Zhang

et al. 2018

Remote Sensing

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Abstract:Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial resolution maps of precipitation over a mountainous watershed affected by a monsoon climate. Based on the relationships between precipitation and other environmental variables, such as the Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM), a regression model with a residual correction method was applied to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 product from coarse resolution (25 km) to fine resolution (1 km). Two methods, geographical difference analysis (GDA) and geographical ratio analysis (GRA), were used to calibrate the downscaled TRMM precipitation data. Monthly 1 km precipitation data were obtained by disaggregating 1 km annual downscaled and calibrated precipitation data using monthly fractions derived from original TRMM data. The downscaled precipitation datasets were validated against ground observations measured by rain gauges. According to the comparison of different regression models and residual interpolation methods, a geographically-weighted regression kriging (GWRK) method was accepted to conduct the downscaling of TRMM data. The downscaled TRMM precipitation data obtained using GWRK described the spatial patterns of precipitation reasonably well at a spatial resolution of 1 km with more detailed information when compared with the original TRMM precipitation. The results of validation indicated that the GRA method provided results with higher accuracy than that of the GDA method. The final annual and monthly downscaled precipitation not only had significant improvement in spatial resolution, but also agreed well with data from the validation rain gauge stations (i.e., R 2 = 0.72, RMSE = 161.0 mm, MAE = 127.5 mm, and Bias = 0.050 for annual downscaled precipitation during 2001 to 2015; and R 2 = 0.91, RMSE = 22.2 mm, MAE = 13.5 mm, and Bias = 0.048 for monthly downscaled precipitation during 2001 to 2015). In general, the downscaling-calibration procedure is useful for complex mountainous areas with insufficient ground gauges.

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“…SSVTT also allows the expansion of spatial and seasonal variations by estimating the terms of error and accuracy indices for precipitation [11,12]. The spatial and seasonal variability analysis approach has recently focused not only on investigating the climatic variations [13], but also demonstrating variations in series of rainfall and other climatic elements [14,15]. SSVTT is designed to be affected by two factors, which can be removed from the observed series The current study focused on monthly and seasonal variations.…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal Variability of Seasonal Precipitation in Iran

Javari¹

2016

TOASCJ

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Spatial-seasonal variability and temporal trends has essential importance to climatic prediction and analysis. The aim of this research is the seasonal variations and temporal trends in the Iran were predicted by using rainfall series. The exploratoryconfirmatory method, and seasonal time series procedure (STSP), temporal trend (TT), seasonal least squares (SLS) and spatial (GIS) methods (STSP¬-SLS-GIS) were employed to bring to light rainfall spatial-seasonal variability and temporal trends (SSVTT). To explore the spatial-seasonal variability and temporal trends during the period over 1975 to 2014 at 140 stations. To investigate the spatial-seasonal variability and temporal trends amount of each series was studied using ArcGIS 10.3 on different time scale. New climatic findings for the region: the investigates and predictions revealed that: (a) range of monthly and seasonal changes of rainfall tends to be highest (increasing trend) during winter (Winter Seasonal Index or WUSI=137.83 mm); (b) lowest (decreasing trend) during summer (Summer Seasonal Index or SUSI=20.8l mm) and (c) the coefficient of rainfall seasonal pattern variations in winter to 5.94 mm, in spring to 11.13 mm, in summer to 4.44 mm and in autumn to 8.05 mm with seasonality being the most effective of all. Mean annual rainfall changed from 51.45 mm (at Bafgh) to 1834.9 mm (at Bandar Anzali). Maximum decrease in annual rainfall was obtained at Miandeh Jiroft (-143.83%) and minimum at Abali (-0.013%) station. The most apparent year of variation was 2007 in annual rainfall.

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Spatial interpolation of precipitation indexes in Sierra Nevada (Spain): comparing the performance of some interpolation methods

Cited by 21 publications

References 74 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

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Spatial-temporal Variability of Seasonal Precipitation in Iran

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