Comparison of two kriging interpolation methods applied to spatiotemporal rainfall

Bargaoui, Zoubeïda; Chebbi, A.

doi:10.1016/j.jhydrol.2008.11.025

Cited by 128 publications

(28 citation statements)

References 22 publications

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“…In order to explore the temporal variation of ten large river basins, Kriging interpolation was chosen to generate grid data due to its good performance with geographic data [41]. The spherical semi-variogram model was used as the weighting function in interpolation [11].…”

Section: Spatial Interpolationmentioning

confidence: 99%

Daily Precipitation Changes over Large River Basins in China, 1960–2013

Jia

Zhu

2016

Water

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Based on a high-quality dataset of 713 daily precipitation series, changes in daily precipitation events during 1960-2013 were observed in China's ten largest river basins. Specifically, the amount of precipitation in four categories defined by fixed thresholds and their proportion on total precipitation were analyzed on annual and seasonal time scales. Results showed annual precipitation increased by 1.10 mm/10yr in China, but with obvious spatial differences. Regionally, annual precipitation increased significantly in northwestern rivers, upstream areas of the Yangtze River, the Yellow River, southwestern rivers (due to increase in light and moderate precipitation); and in southeastern rivers, downstream areas of the Yangtze River, and the Pearl River (due to increase in heavy and extreme precipitation). Annual precipitation decreased significantly in the mid-Yangtze River and upstream Pearl River (due to decrease in light, moderate, and heavy precipitation). Seasonally, precipitation decreased only in autumn; this was attributable to a decrease in light and moderate precipitation. Results show that the distribution of precipitation intensity over China has shifted to intense categories since the 1960s, there has been an increase in moderate precipitation in Northwestern and Northern China, and an increase in extreme precipitation in Southeastern China. This shift was detected in all seasons, especially in summer. Precipitation extremes were investigated in the categories of extreme precipitation and results show that the risk of flood has been exacerbated over the past half-century in the Huaihe River, the mid-and lower Yangtze River, the Pearl River, and southeastern rivers.

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Section: Spatial Interpolationmentioning

confidence: 99%

Daily Precipitation Changes over Large River Basins in China, 1960–2013

Jia

Zhu

2016

Water

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“…The spatial density of rain gauge networks is rarely sufficient to capture the spatial and temporal variability of the precipitation at small scales (McMillan et al, 2012;Volkmann, Lyon, Gupta, & Troch, 2010). The use of interpolation techniques that combine point and distributed rainfall measurements (i.e., radar or satellite data) partially overcomes this drawback (Bargaoui & Chebbi, 2009;Haberlandt, 2007), but radar and satellite data are not always available at the appropriate temporal and spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

Fraga

Cea

Puertas

2018

Hydrological Processes

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This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km2 located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.

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“…A few authors have compared the efficiency of different interpolation techniques with kriging, cokriging, and kriging with external drift (Hoeksema et al, 1989;Boezio et al, 2006;Pardo-Igúzquiza and Chica-Olmo, 2007;Ahmadi and Sedghamiz, 2008;Bargaoui and Chebbi, 2008). Kriging using DEM information as an external drift seems to be the most efficient methodology for unconfined aquifer units (Desbarats et al, 2002;Rivest et al, 2008), which is in agreement with the high correlation between hydraulic head and soil surface in such systems (Tóth, 1962).…”

Section: Experimental Site and Datamentioning

confidence: 68%

Hydraulic head and groundwater <sup>111</sup>Cd content interpolations using empirical Bayesian kriging (EBK) and geo-adaptive neuro-fuzzy inference system (geo-ANFIS)

Sağır¹,

Kurtuluş²

2017

WSA

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In this study, hydraulic head and 111 Cd interpolations based on the geo-adaptive neuro-fuzzy inference system (Geo-ANFIS) and empirical Bayesian kriging (EBK) were performed for the alluvium unit of Karabağlar Polje in Muğla, Turkey. Hydraulic head measurements and Cd content of groundwater. Both models were applied on the same case study: alluvium of Karabağlar Polje, which covers an area of 25 km 2 in Muğla basin, in the southwest of Turkey. The ANFIS method (called ANFIS XY ) uses two reduced centred pre-processed inputs, which are cartesian coordinates (XY). Geo-ANFIS is tested on a 100-random-data subset of 8 data among 42, with the remaining data used to train and validate the models. ANFIS XY and EBK were then used to interpolate hydraulic head and heavy metal distribution, on a 50 m 2 grid covering the study area for ANFIS XY , while a 100 m 2 grid was used for EBK. Both EBK-and ANFIS XY -simulated hydraulic head and 111 Cd distributions exhibit realistic patterns, with RMSE < 9 m and RMSE < 8 µg/L, respectively. In conclusion, EBK can be considered as a better interpolation method than ANFIS XY for both parameters.

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Comparison of two kriging interpolation methods applied to spatiotemporal rainfall

Cited by 128 publications

References 22 publications

Daily Precipitation Changes over Large River Basins in China, 1960–2013

Daily Precipitation Changes over Large River Basins in China, 1960–2013

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

Hydraulic head and groundwater <sup>111</sup>Cd content interpolations using empirical Bayesian kriging (EBK) and geo-adaptive neuro-fuzzy inference system (geo-ANFIS)

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