Spatiotemporal models in the estimation of area precipitation

Severino, Eduardo; Alpuim, Teresa

doi:10.1002/env.733

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…For regions with sparse meteorological stations, stochastic interpolation methods can be used to estimate the spatial distribution of climatic variables (Li et al , 2005). The COK interpolation used in this study has previously yielded good results for temperature, rainfall, solar radiation, relative humidity, and wind speed (Ishida and Kawashima, 1993; Phillips et al , 1997; Apaydin et al , 2004; Severino and Alpuim, 2005). As demonstrated in this study, climate model predictions can provide very useful secondary information for temperature COK.…”

Section: Discussionmentioning

confidence: 84%

Interpolation of monthly mean temperatures using cokriging in spherical coordinates

Aznar

Gloaguen

Tapsoba

et al. 2012

Intl Journal of Climatology

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Paleoclimate reconstructions are generally validated on recent periods. To obtain a set of instrumental records at the regional scale, a time series of monthly mean temperatures in Northeastern Canada were interpolated for the 1961-2000 period. Records were provided by 202 meteorological stations. Temperatures derived from a Canadian regional climate model (Climate Model CRCM 4.2.3 from the AMNO domain produced at ∼50 km resolution) were added as secondary information to take into account local heterogeneity and temporal dependencies. Geostatistical interpolation of the measured temperature was calculated using CRCM modelled data as a covariable and then compared to an ordinary kriging performed on a time series of mean temperature anomalies. Spherical distances between locations were calculated taking into account the curvature of the Earth with monthly semivariances being modelled using Cauchy variograms. Mean absolute error values (1.5 ± 1.2°C) were calculated for the whole period using cross-validation procedures. Errors were found to have the same order of magnitude in the central part of the study area where few recorded temperatures were available. Monthly mean temperature grids are publicly available through the Institut National de la Recherche Scientifique

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Section: Discussionmentioning

confidence: 84%

Interpolation of monthly mean temperatures using cokriging in spherical coordinates

Aznar

Gloaguen

Tapsoba

et al. 2012

Intl Journal of Climatology

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“…There are precedents to this approach; an analogy would be the composite datasets formed by the merging of raingauge observations and radar data (e.g. Severino and Alpuim, 2005), although clearly there are fundamental differences between continuous rainfall fields and trend statistics based on catchment streamflow. New methods would have to be developed to support the derivation of such composite datasets in the future.…”

Section: Regional Trend Patterns and Uncertaintymentioning

confidence: 99%

Filling the white space on maps of European runoff trends: estimates from a multi-model ensemble

Stahl

Tallaksen

Hannaford

et al. 2012

Hydrol. Earth Syst. Sci.

145

122

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Abstract. An overall appraisal of runoff changes at the European scale has been hindered by "white space" on maps of observed trends due to a paucity of readily-available streamflow data. This study tested whether this white space can be filled using estimates of trends derived from model simulations of European runoff. The simulations stem from an ensemble of eight global hydrological models that were forced with the same climate input for the period 1963-2000. The derived trends were validated for 293 grid cells across the European domain with observation-based trend estimates. The ensemble mean overall provided the best representation of trends in the observations. Maps of trends in annual runoff based on the ensemble mean demonstrated a pronounced continental dipole pattern of positive trends in western and northern Europe and negative trends in southern and parts of eastern Europe, which has not previously been demonstrated and discussed in comparable detail. Overall, positive trends in annual streamflow appear to reflect the marked wetting trends of the winter months, whereas negative annual trends result primarily from a widespread decrease in streamflow in spring and summer months, consistent with a decrease in summer low flow in large parts of Europe. High flow appears to have increased in rain-dominated hydrological regimes, whereas an inconsistent or decreasing signal was found in snow-dominated regimes. The different models agreed on the predominant continental-scale pattern of trends, but in some areas disagreed on the magnitude and even the direction of trends, particularly in transition zones between regions with increasing and decreasing runoff trends, in complex terrain with a high spatial variability, and in snow-dominated regimes. Model estimates appeared most reliable in reproducing observed trends in annual runoff, winter runoff, and 7-day high flow. Modelled trends in runoff during the summer months, spring (for snow influenced regions) and autumn, and trends in summer low flow were more variableboth among models and in the spatial patterns of agreement between models and the observations. The use of models to display changes in these hydrological characteristics should therefore be viewed with caution due to higher uncertainty.

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“…In addition, for calculation of confidence levels, estimation variance is used in many other cases, such as designing an optimal measurement network, calculating the error reduction resulted from increasing the number of sampling, and assessing different sampling methods. Estimation variance can be defined with the following equation [20]:…”

Section: Meteorological Criteriamentioning

confidence: 99%

An Integrated Approach for Site Selection of Snow Measurement Stations

Saghafian

Davtalab

Rafieeinasab

et al. 2016

Water

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Snowmelt provides a reliable water resource for meeting domestic, agricultural, industrial and hydropower demands. Consequently, estimating the available snow water equivalent is essential for water resource management of snowy regions. Due to the spatiotemporal variability of the snowfall pattern in mountainous areas and difficult access to high altitudes areas, snow measurement is one of the most challenging hydro-meteorological data collection efforts. Development of an optimum snow measurement network is a complex task that requires integration of meteorological, hydrological, physiographical and economic studies. In this study, site selection of snow measurement stations is carried out through an integrated process using observed snow course data and analysis of historical snow cover images from National Oceanic Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) at both regional and local scales. Several important meteorological and hydrological factors, such as monthly and annual rainfall distribution, spatial distribution of average frequency of snow observation (FSO) for two periods of snow falling and melting season, as well as priority contribution of sub-basins to annual snowmelt runoff are considered for selecting optimum station network. The FSO maps representing accumulation of snowfall during falling months and snowpack persistence during melting months are prepared in the GIS based on NOAA-AVHRR historical snow cover images. Basins are partitioned into 250 m elevation intervals such that within each interval, establishment of new stations or relocation/removing of the existing stations were proposed. The decision is made on the basis of the combination of meteorological, hydrological and satellite information. Economic aspects and road access constraints are also considered in determining the station type. Eventually, for the study area encompassing a number of large basins in southwest of Iran, several new stations and relocation of some existing stations are proposed.

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Spatiotemporal models in the estimation of area precipitation

Cited by 12 publications

References 8 publications

Interpolation of monthly mean temperatures using cokriging in spherical coordinates

Interpolation of monthly mean temperatures using cokriging in spherical coordinates

Filling the white space on maps of European runoff trends: estimates from a multi-model ensemble

An Integrated Approach for Site Selection of Snow Measurement Stations

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