Multivariate Interpolation of Precipitation Using Regularized Spline with Tension

Hofierka, Jaroslav; Parajka, J.; Mitášová, Helena; Mitáš, Luboš

doi:10.1111/1467-9671.00101

Cited by 110 publications

(79 citation statements)

References 17 publications

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“…To illustrate this, imputed and DWD estimates of seasonal rainfall are mapped in Figs 7 and 8. Both these maps were derived by spatially interpolating the data using Regularized Spline with Tension interpolation (Mitasova & Mitas, 1993), which is considered suitable for interpolation of precipitation in regions with significant variation in topography (Hofierka, 2002). Spatial variation and seasonal rainfall patterns of GLM simulations are considered to fit reasonably the historical patterns estimated by DWD at the Kyoga basin scale, for each of the seasons DJF, MAM, JJA and SON.…”

Section: Glm Resultsmentioning

confidence: 99%

Multi-site stochastic modelling of daily rainfall in Uganda

Kigobe

McIntyre

Wheater

et al. 2011

Hydrological Sciences Journal

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The availability of precipitation records in Uganda has significantly decreased since the 1970s, severely limiting the data available for hydrological modelling. This problem has been addressed in this study by using the generalized linear modelling (GLM) framework to develop stochastic daily rainfall models that have the capability for extending and infilling historic data sets. We have used a relatively sparse raingauge network in the Kyoga basin (in the Upper Nile) to reproduce spatial and temporal patterns of precipitation by fitting occurrence (logistic) and amounts (gamma) models as functions of dominant seasonal, climate and geographical controls. The GLMs were able to reproduce rainfall properties when predefined climatic zones are modelled independently. We were not able to represent the spatial variability of the equatorial climate with one integrated basin scale model, however this is not thought to preclude application to basin scale hydrological modelling.Key words generalized linear models; rainfall; stochastic models; climate change; Uganda; Kyoga; ENSO Modélisation stochastique multi-sites de la pluie journalière en Ouganda Résumé La disponibilité des données de précipitations en Ouganda a considérablement diminué depuis les années 1970, limitant fortement les données disponibles pour la modélisation hydrologique. Ce problème a été abordé dans cette étude dans le cadre de la modélisation linéaire généralisée (MLG), afin de développer des modèles stochastiques de pluie journalière qui puissent étendre et compléter les données historiques. Nous avons utilisé un réseau de pluviomètres de relativement faible densité dans le bassin du Kyoga (dans le Nil Supérieur), afin de reproduire les patrons spatiaux et temporels des précipitations en modélisant l'occurence (logistique) et les volumes (gamma) comme fonctions des forçages dominants saisonniers, climatiques et géographiques. Les MLG sont en mesure de reproduire les propriétés des pluies lorsque des zones climatiques pré-définies sont modélisées de façon indépendante. Nous n'avons pas pu représenter la variabilité spatiale du climat équatorial avec un modèle à l'échelle intégrée du bassin, cependant ceci ne semble pas devoir empêcher la mise en oeuvre d'une modélisation hydrologique à l'échelle du bassin.

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Section: Glm Resultsmentioning

confidence: 99%

Multi-site stochastic modelling of daily rainfall in Uganda

Kigobe

McIntyre

Wheater

et al. 2011

Hydrological Sciences Journal

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“…Firstly a DEM was generated from the contour map using the regularized spline with tension (RST) algorithm. The RST interpolation is considered as one of the effective interpolation methods available for elevation data (Hofierka et al, 2002). The RST method is based on the assumption that the approximation function should pass as closely as possible to the given data and should be as smooth as possible (Mitasova et al, 1995).…”

Section: Dem Data Setsmentioning

confidence: 99%

A geomorphology-based approach for digital elevation model fusion – case study in Danang city, Vietnam

et al. 2014

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Abstract.Global digital elevation models (DEM) are considered a source of vital spatial information and find wide use in several applications. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM) and Shuttle Radar Topographic Mission (SRTM) DEM offer almost global coverage and provide elevation data for geospatial analysis. However, GDEM and SRTM still contain some height errors that affect the quality of elevation data significantly. This study aims to examine methods to improve the resolution as well as accuracy of available free DEMs by data fusion techniques and evaluating the results with a high-quality reference DEM. The DEM fusion method is based on the accuracy assessment of each global DEM and geomorphological characteristics of the study area. Land cover units were also considered to correct the elevation of GDEM and SRTM with respect to the bare-earth surface. The weighted averaging method was used to fuse the input DEMs based on a landform classification map. According to the landform types, the different weights were used for GDEM and SRTM. Finally, a denoising algorithm (Sun et al., 2007) was applied to filter the output-fused DEM. This fused DEM shows excellent correlation to the reference DEM, having a correlation coefficient R 2 = 0.9986, and the accuracy was also improved from a root mean square error (RMSE) of 14.9 m in GDEM and 14.8 m in SRTM to 11.6 m in the fused DEM. The results of terrain-related parameters extracted from this fused DEM such as slope, curvature, terrain roughness index and normal vector of topographic surface are also very comparable to reference data.

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“…The Data Preparation Block (DPB) implemented the tools to evaluate the reference climatology from the observations at weather stations or gridded datasets, and the projected climatology from climate model outputs using the delta correction method (Hamlet et al 2010). To map the reference/projected climatology, the regularized spline with tension interpolation technique by Hofierka et al (2002) was implemented. The DBP also included the procedures to calculate the SFDR using the observed daily river discharges (Shevnina 2013).…”

Section: Resultsmentioning

confidence: 99%

Hydrological Probabilistic Model MARCS and Its Application to Simulate the Probability Density Functions of Multi-year Maximal Runoff: The Russian Arctic as a Case of Study

Shevnina

Gaidukova

2017

The Interconnected Arctic — UArctic Congress 2016

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Climate warming has been and will continue to be faster in the Arctic compared to the other domains of the world, which generates major challenges for human adaptation. Among others, the development of socio-economic infrastructure and strategic planning requires long-term projections of water availability and extreme hydrological events. In this context, it is preferable that the projections of river runoff should be performed statistically, allowing the evaluation of economical risks and costs for hydraulic structures, which are connected to changes in hydrological extremes. In this study, the hydrological model MARCS (MARcov Chan System) is suggested as a tool to simulate the parameters of probability density functions (PDFs) of maximal runoff or peak flow, based on climate projections of the Representative Concentration Pathways. Following that, the PDFs of the maximal runoff were constructed within the Pearson Type III distributions to estimate the runoff values of a small exceedance probability. To evaluate the risks and costs of a long-term investment based on the future projections of river maximal discharge of 1 % probability, simple calculations were performed for the new bridge over the Nadym River as an example.

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Multivariate Interpolation of Precipitation Using Regularized Spline with Tension

Cited by 110 publications

References 17 publications

Multi-site stochastic modelling of daily rainfall in Uganda

Multi-site stochastic modelling of daily rainfall in Uganda

A geomorphology-based approach for digital elevation model fusion – case study in Danang city, Vietnam

Hydrological Probabilistic Model MARCS and Its Application to Simulate the Probability Density Functions of Multi-year Maximal Runoff: The Russian Arctic as a Case of Study

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