Climate spatial variability and data resolution in a semi-arid watershed, south-eastern Arizona

Skirvin, S. M.; Marsh, Stuart E.; McClaran, Mitchel P.; Meko, David M.

doi:10.1006/jare.2002.1086

Cited by 22 publications

(15 citation statements)

References 36 publications

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“…Rather than depending solely on geostatistics for interpolation in such regions (Hevesi et al 1992;Philips et al 1992;Martinez-Cob 1995, 1996Papamichail and Metaxa 1996;Tang et al 1998;Prudhomme and Reed 1999;Goovaerts 2000;Naoum and Tsanis 2003;Skirvin et al 2003;Subyani 2004;Diodato 2005;Lloyd 2005), researchers have employed regression models with multiple topographic parameters (Daly et al 1994(Daly et al , 2002Prudhomme and Reed 1998;Brunsdon et al 2001;Johansson and Chen 2003;Vicente-Serrano et al 2003;Naoum and Tsanis 2004) and even Principal Components of Digital Elevation Model (DEM) (Gyalistras 2003;Thomas and Herzfeld 2004).…”

Section: Study Area and The Data Usedmentioning

confidence: 99%

Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region

Basistha

Arya

Goel

2007

Water Resour Manage

140

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Continuous rainfall data in grid format are required to run models for hydrological and agricultural research as well as water resources planning and management. The present work attempts to prepare a normal annual rainfall map in Himalayan region of India lying in Uttarakhand state at 1 km spatial resolution which currently is not available. In the region, India Meteorological Department maintains observatories/raingauge stations and data from 44 stations were used in this study. A comparative analysis of interpolation techniques like Inverse Distance Weighted, Polynomial, Splines, Ordinary Kriging and Universal Kriging shows that Universal Kriging with hole-effect model and natural logarithmic transformation with constant trend having Root Mean Square Error (RMSE) of 328.7 is the best choice. This is followed by Ordinary Kriging (RMSE 329.1), Splines (RMSE 392.4), Inverse Distance Weighted (RMSE 409.8) and Polynomial Interpolation (RMSE 418.5).Cross validation of the results shows the largest over prediction at Tehri rainfall station (62.5%) and largest under prediction at Nainital station (−36.5%). Physiographic zone wise, the least errors occur in the plains and the largest in the Great Himalayas. The spatial average rainfalls are 1,472 mm for Terai/Bhabar, 1,782 mm for the Shivalik ranges, 1,591 mm for the Lesser Himalayas and 1,635 mm for the Great Himalayan region. The mean areal rainfall in the region is 1,608 mm.

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Section: Study Area and The Data Usedmentioning

confidence: 99%

Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region

Basistha

Arya

Goel

2007

Water Resour Manage

140

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“…In GS methods, a semivariogram plays the central role in the analysis. Skirvin et al (2003) showed the relation between the sill, threshold and the semivariogram model and topography and rainfall pattern. Holawe and Dutter (1999) evaluated spatial and temporal rainfall variation using GS methods.…”

Section: Introductionmentioning

confidence: 99%

“…They illustrated the ability of the semivariogram to present the temporal variability of rainfall. More details about similar studies can be obtained in (Tabios and Salas, 1985;Sun et al, 2000;Skirvin et al, 2003;Touazi et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices

Akhtari

Morid

Mahdian

et al. 2008

Intl Journal of Climatology

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Drought monitoring is an essential component of drought risk management. Drought indices -functions of precipitation showing the severity of dryness during a particular time period -are often used for monitoring purposes. These indices may only be calculated originally at a limited number of sites where observations on climate variables are available. However, what is required for monitoring is to estimate the spatial distribution of drought severity over larger areas in the form of maps. In this article, several geostatistical methods including kriging, co-kriging and thin plate smoothing splines with and without secondary variables, as well as Thiessen polygons and weighted moving average were assessed for the derivation of maps of drought indices. The techniques are evaluated using 1-month Standardized Precipitation Index (SPI) and Effective Drought Index (EDI) at 43 climatic stations in the Tehran province of Iran. The results indicate that although kriging is the most accurate method, weighted moving average provides a reasonable accuracy combined with the simplicity and speed of the procedure, and thus, can be recommended for operational drought monitoring system in the region.

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“…(For example, Arizona Department of Water Resources, 2002;Bolm et al, 2002;Bredehoeft et al, 1999a,b;Carter et al, 2000;Chehbouni et al, 2000;Correll et al, 1996;Kepner et al, 2000Kepner et al, , 2002aKepner et al, , 2002bLacher, 1994;Liverman et al, 1997a,b;Liverman and Meredith, 2002;MacGuire, 1997;Maingi et al, 2002;Moote and Gutiérrez, 2001;Orr and Colby, 2002;Pinker et al, 2000;Pool and Coes, 1999;Skirvin et al, 2002;Snyder and Williams, 2000). Presently government agencies, academic institutions and non-governmental organizations on both sides of the border gather and disseminate information about the basin.…”

Section: Lesson 1: Standardize and Disseminate Datamentioning

confidence: 96%

From knowledge to action: lessons and planning strategies from studies of the upper San Pedro basin

McSherry

Steiner

Ozkeresteci

et al. 2006

Landscape and Urban Planning

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Climate spatial variability and data resolution in a semi-arid watershed, south-eastern Arizona

Cited by 22 publications

References 36 publications

Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region

Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region

Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices

From knowledge to action: lessons and planning strategies from studies of the upper San Pedro basin

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