Rain-gauge network evaluation and augmentation using geostatistics
Rain-gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an e...
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| Vydané v: | Hydrological processes Ročník 22; číslo 14; s. 2554 - 2564 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Chichester, UK
John Wiley & Sons, Ltd
01.07.2008
Wiley |
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| ISSN: | 0885-6087, 1099-1085 |
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| Abstract | Rain-gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an existing rain-gauge network is proposed in this study. Through variogram analysis, hourly rainfalls are shown to have higher spatial variability than annual rainfalls, with hourly Mei-Yu rainfalls having the highest spatial variability. A criterion using ordinary kriging variance is proposed to assess the accuracy of rainfall estimation using the acceptance probability defined as the probability that estimation error falls within a desired range. Based on the criterion, the percentage of the total area with acceptable accuracy Ap under certain network configuration can be calculated. A sequential algorithm is also proposed to prioritize rain-gauges of the existing network, identify the base network, and relocate non-base gauges. Percentage of the total area with acceptable accuracy is mostly contributed by the base network. In contrast, non-base gauges provide little contribution to Ap and are subject to removal or relocation. Using a case study in northern Taiwan, the proposed approach demonstrates that the identified base network which comprises of approximately two-thirds of the total rain-gauges can achieve almost the same level of performance (expressed in terms of percentage of the total area with acceptable accuracy) as the complete network for hourly Mei-Yu rainfall estimation. The percentage of area with acceptable accuracy can be raised from 56% to 88% using an augmented network. A threshold value for the percentage of area with acceptable accuracy is also recommended to help determine the number of non-base gauges which need to be relocated. Copyright © 2007 John Wiley & Sons, Ltd. |
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| AbstractList | Rain-gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an existing rain-gauge network is proposed in this study. Through variogram analysis, hourly rainfalls are shown to have higher spatial variability than annual rainfalls, with hourly Mei-Yu rainfalls having the highest spatial variability. A criterion using ordinary kriging variance is proposed to assess the accuracy of rainfall estimation using the acceptance probability defined as the probability that estimation error falls within a desired range. Based on the criterion, the percentage of the total area with acceptable accuracy Ap under certain network configuration can be calculated. A sequential algorithm is also proposed to prioritize rain-gauges of the existing network, identify the base network, and relocate non-base gauges. Percentage of the total area with acceptable accuracy is mostly contributed by the base network. In contrast, non-base gauges provide little contribution to Ap and are subject to removal or relocation. Using a case study in northern Taiwan, the proposed approach demonstrates that the identified base network which comprises of approximately two-thirds of the total rain-gauges can achieve almost the same level of performance (expressed in terms of percentage of the total area with acceptable accuracy) as the complete network for hourly Mei-Yu rainfall estimation. The percentage of area with acceptable accuracy can be raised from 56% to 88% using an augmented network. A threshold value for the percentage of area with acceptable accuracy is also recommended to help determine the number of non-base gauges which need to be relocated. Rain‐gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an existing rain‐gauge network is proposed in this study. Through variogram analysis, hourly rainfalls are shown to have higher spatial variability than annual rainfalls, with hourly Mei‐Yu rainfalls having the highest spatial variability. A criterion using ordinary kriging variance is proposed to assess the accuracy of rainfall estimation using the acceptance probability defined as the probability that estimation error falls within a desired range. Based on the criterion, the percentage of the total area with acceptable accuracy Ap under certain network configuration can be calculated. A sequential algorithm is also proposed to prioritize rain‐gauges of the existing network, identify the base network, and relocate non‐base gauges. Percentage of the total area with acceptable accuracy is mostly contributed by the base network. In contrast, non‐base gauges provide little contribution to Ap and are subject to removal or relocation. Using a case study in northern Taiwan, the proposed approach demonstrates that the identified base network which comprises of approximately two‐thirds of the total rain‐gauges can achieve almost the same level of performance (expressed in terms of percentage of the total area with acceptable accuracy) as the complete network for hourly Mei‐Yu rainfall estimation. The percentage of area with acceptable accuracy can be raised from 56% to 88% using an augmented network. A threshold value for the percentage of area with acceptable accuracy is also recommended to help determine the number of non‐base gauges which need to be relocated. Copyright © 2007 John Wiley & Sons, Ltd. Rain‐gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an existing rain‐gauge network is proposed in this study. Through variogram analysis, hourly rainfalls are shown to have higher spatial variability than annual rainfalls, with hourly Mei‐Yu rainfalls having the highest spatial variability. A criterion using ordinary kriging variance is proposed to assess the accuracy of rainfall estimation using the acceptance probability defined as the probability that estimation error falls within a desired range. Based on the criterion, the percentage of the total area with acceptable accuracy A p under certain network configuration can be calculated. A sequential algorithm is also proposed to prioritize rain‐gauges of the existing network, identify the base network, and relocate non‐base gauges. Percentage of the total area with acceptable accuracy is mostly contributed by the base network. In contrast, non‐base gauges provide little contribution to A p and are subject to removal or relocation. Using a case study in northern Taiwan, the proposed approach demonstrates that the identified base network which comprises of approximately two‐thirds of the total rain‐gauges can achieve almost the same level of performance (expressed in terms of percentage of the total area with acceptable accuracy) as the complete network for hourly Mei‐Yu rainfall estimation. The percentage of area with acceptable accuracy can be raised from 56% to 88% using an augmented network. A threshold value for the percentage of area with acceptable accuracy is also recommended to help determine the number of non‐base gauges which need to be relocated. Copyright © 2007 John Wiley & Sons, Ltd. |
| Author | Liou, Jun-Jih Lin, Yun-Ching Cheng, Ke-Sheng |
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| Keywords | geostatistics algorithms rainfall kriging probability variograms accuracy rain water case studies networks variogram analysis network augmentation contour maps performances errors network evaluation |
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| References_xml | – reference: Bogardi I, Bardossy A. 1985. Multicriterion network design using geostatistics. Water Resources Research 21: 199-208. – reference: St-Hilaire A, Ouarda TBMJ, Lachance M, Bobée B, Gaudet J, Gignac C. 2003. Assessment of the impact of meteorological network density on the estimation of basin precipitation and runoff: a case study. Hydrological Processes 17: 3561-3580. – reference: Bastin G, Lorent B, Duque C, Gevers M. 1984. Optimal estimation of the average areal rainfall and optimal selection of raingauge locations. Water Resources Research 20: 463-470. – reference: Journel AG, Huijbregts CJ. 1978. Mining Geostatistics. Academic Press: London. – reference: Bras RF, Rodriguez-Iturbe I. 1976. Network design for the estimation of areal mean rainfall events. Water Resources Research 12: 1185-1195. – reference: Cheng KS, Wei C, Cheng YB, Yeh HC. 2003. Effect of spatial variation characteristics on contouring of design storm depth. Hydrological Processes 17: 1755-1769. DOI: 10·1002/hyp.1209. – reference: Krajewski WF. 1987. Cokriging radar-rainfall and rain gage data. Journal of Geophysical Research 92: 9571-9580. – reference: Tsintikidis D, Georgakakos KP, Sperfslage JA, Smith DE, Carpenter TM. 2002. Precipitation uncertainty and raingauge network design within Folsom Lake watershed. Journal of Hydrologic Engineering 7: 175-184. – reference: Delhomme JP. 1978. Kriging in the hydrosciences. Advances in Water Resources 1: 251-266. – reference: Al-Zahrani M, Husain T. 1998. An algorithm for designing a precipitation network in the south-western region of Saudi Arabia. Journal of Hydrology 205: 205-216. – reference: Krstanovic PF, Singh VP. 1992b. Evaluation of rainfall network using entropy: II. Application. Water Resources Management 6: 295-314. – reference: Eagleson PS. 1967. Optimum density of rainfall networks. Water Resources Research 3: 1021-1033. – reference: Ali A, Lebel T, Amani A. 2003. Invariance in the spatial structure of Sahelian rain fields at climatological scales. Journal of Hydrometeorology 4: 996-1011. – reference: Hughes JP, Lettenmaier DP. 1981. Data requirements for kriging: estimation and network design. Water Resources Research 17: 1641-1650. – reference: Lebel T, Bastin G, Obled C, Creutin JD. 1987. On the accuracy of areal rainfall estimation: a case study. Water Resources Research 23: 2123-2134. – reference: Kassim AHM, Kottegoda NT. 1991. Rainfall network design through comparative kriging methods. Hydrological Sciences Journal 36: 223-240. – reference: De Marsily G. 1986. Quantitative Hydrogeology. Academic Press: Orlando, FL. – reference: Yeh HC, Chen GT-J, Liu WT. 2002. Kinematic characteristics of a Mei-yu front detected by the QuikSCAT oceanic winds. Monthly Weather Review 130: 700-711. – reference: Pardo-Igúzquiza E. 1998. 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| SubjectTerms | algorithms case studies Earth sciences Earth, ocean, space Exact sciences and technology geostatistics Hydrology Hydrology. Hydrogeology network augmentation network evaluation probability rain rain gauges Taiwan variance variogram analysis |
| Title | Rain-gauge network evaluation and augmentation using geostatistics |
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