Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments

Adhikary, Sajal Kumar; Muttil, Nitin; Yilmaz, Abdullah Gokhan

doi:10.1002/hyp.11163

Cited by 104 publications

(56 citation statements)

References 48 publications

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“…Based on visual inspection and quantitative appraisal both for interpolated values and standard errors, it is suggested that CK (with soil cohesion as covariate) performs better than IDW and OK in estimating spatial distribution of soil depth to hardpan in Western Central Java. This is consistent with the aim of Cokriging model development (Myers 1984) and its implementation 5 (Minnitt and Deutsch 2014;Adhikary et al 2017;Xie et al 2018).…”

Section: Interpolation Performancesupporting

confidence: 81%

Initial Assessment of Landslide Prone Area using Soil Properties

Yanto

Apriyono

Santoso

et al. 2019

Preprint

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Initial assessment of landslide prone area is important in designing landslide mitigation measures. This study, a part 5 of our study on developing landslide spatial model, presents initial signal of landslide prone area. In here, we use soil depth to hardpan to assess landslide prone area in Western Central Java, a relatively small region where 23% of Indonesian landslide occurs. To this end, we interpolated soil depth to hardpan in a regular grid from irregularly distributed data. To do this, we employed three different methods: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Co-Kriging (CK). For the latter, we experimented with several potential covariates. To determine the best fitting model, several tests on model 10 performance and its corresponding errors were done. Error measures used in this study are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), while statistical measures employed are Standard Deviation, Variance, Interquartile Range (IQR), Mean Absolute Deviation and MedianAbsolute Deviation. The result shows that CK with covariate of slope and soil cohesion is the best fitting model and exhibits clear pattern related to recorded landslide disaster sites. We found that 64% of landslide disaster events occur in the area having 15 soil depth to hardpan of 5 -10 m. Moreover, 84% of landslide occurrences happen in regions where soil depth to hardpan ranges from 5 to 15 m. Hence, we suggest that landslide prone area is an area possessing soil depth to hardpan of 5-15 m. This finding is advantageous for policy makers in planning and designing efforts for landslide mitigation. IntroductionIndonesia has a large number of landslide disaster occurrences. In last decade, 3,924 landslide disaster events were recorded 20 with 1,404 events (~36%) of them occurred in the Central Java province (BNPB 2018). Out of those numbers, 908 landslide events occurred in western part of the area. In this region, landslide disaster events have caused 211 casualties, nearly 20,000 people suffered and more than 7,000 house damaged. Moreover, the number of natural disaster in Indonesia shows increasing trend from 924 events in 2008 to 2,862 events in 2017 which accounts for more than threefold during the last decade (BNPB 2018). Considering the impact of global climate change, the number could be larger and the impact could be worsening 25 (Banholzer et al. 2014). Hence, appropriate mitigation measures in particular locations are critical. Accordingly, understanding landslide prone area is helpful in designing policy for appropriate mitigation efforts.Landslide occurs when safety factor (SF), defined as a ratio of shear strength and acting force (gravitational force, seepage pressure, etc) of soil layer disrupted (Das 1998). The higher the slope stability (SF > 1) the lower the possibility of landslide

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Section: Interpolation Performancesupporting

confidence: 81%

Initial Assessment of Landslide Prone Area using Soil Properties

Yanto

Apriyono

Santoso

et al. 2019

Preprint

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“…Considering the topography and sparse rain gauge stations, we adopted a geostatistical approach, co-kriging for estimation of areal average gauge-based precipitation. A detailed description and procedure of co-kriging are presented in [29,30]. Several statistical indices (continuous evaluation indices and categorical indices) were adhered for investigation of accuracy (of TMPA precipitation products) in terms of consistency and discrepancy [25,31].…”

Section: Methods For Accuracy Assessmentmentioning

confidence: 99%

Evaluation and Comparison of TRMM Multi-Satellite Precipitation Products With Reference to Rain Gauge Observations in Hunza River Basin, Karakoram Range, Northern Pakistan

et al. 2017

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Abstract:The performance evaluation of satellite-based precipitation products at local and regional scales is crucial for modification in satellite-based precipitation retrieval algorithms, as well as for the provision of guidance during the selection of substitute precipitation data. This study evaluated the performances of three Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products (3B42V6, 3B42RT and 3B42V7) with a reference to rain gauge observations in the Hunza River basin, northern Pakistan. Multi-spatial (pixel and basin) and temporal (daily, monthly, seasonal and annual) resolutions were considered for performance evaluation of TMPA products. Results revealed that the spatial pattern of observed precipitation over the basin was adequately captured by 3B42V7 but misplaced by 3B42V6 and 3B42RT. All TMPA products were unable to capture the intense precipitation events. On the daily time scale, the performance of TMPA products was very poor over both spatial scales. 3B42V6 underestimated the precipitation (31.25% and 44.27% on pixel and basin scales, respectively). By contrast, 3B42RT significantly overestimated the precipitation (47.91% and 38.62% on pixel and basin scales, respectively), while 3B42V7 showed overestimation (17.30%) on pixel scale and slight underestimation (6.24%) on the basin scale. On the seasonal scale, TMPA products showed significant biases with observed precipitation data. We found that the TMPA products performed relatively better on monthly and annual time scales and overall performance of 3B42V7 product was better than that of 3B42V6 and 3B42RT. The bias in 3B42V7 was improved by 85.90% compared with 3B42V6 and by 116.16% compared with 3B42RT. Thus, it is concluded that the TMPA products were unreliable to capture the intense precipitation events and retain high errors on daily and seasonal scales. Therefore, caution should be considered while using these precipitation estimates as a substitute data in hydrology, meteorology and climatology studies in Hunza River basin. However, due to the reasonable performance of monthly and annual 3B42V7 estimates, these can be used as an acceptable substitute for applications in the region.

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“…On the other hand, the performance of the CK method could be ascribed to the very frequent application of an elevation correction factor in the case of the CK-based dataset. The CK method was found to be useful for regionalization of hydrological signatures [5,51]. It solved a problem of hydrological modeling in that the precipitation data were poorly resolved in space and could not capture heterogeneous orographic effects [52].…”

Section: Analysis Of Runoff Process By Spatial Interpolation Of Precimentioning

confidence: 99%

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

Cheng

Wang

Engel

et al. 2017

Water

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Accurate assessment of spatial and temporal precipitation is crucial for simulating hydrological processes in basins, but is challenging due to insufficient rain gauges. Our study aims to analyze different precipitation interpolation schemes and their performances in runoff simulation during light and heavy rain periods. In particular, combinations of different interpolation estimates are explored and their performances in runoff simulation are discussed. The study was carried out in the Pengxi River basin of the Three Gorges Basin. Precipitation data from 16 rain gauges were interpolated using the Thiessen Polygon (TP), Inverse Distance Weighted (IDW), and Co-Kriging (CK) methods. Results showed that streamflow predictions employing CK inputs demonstrated the best performance in the whole process, in terms of the Nash-Sutcliffe Coefficient (NSE), the coefficient of determination (R 2 ), and the Root Mean Square Error (RMSE) indices. The TP, IDW, and CK methods showed good performance in the heavy rain period but poor performance in the light rain period compared with the default method (least sophisticated nearest neighbor technique) in Soil and Water Assessment Tool (SWAT). Furthermore, the correlation between the dynamic weight of one method and its performance during runoff simulation followed a parabolic function. The combination of CK and TP achieved a better performance in decreasing the largest and lowest absolute errors compared to any single method, but the IDW method outperformed all methods in terms of the median absolute error. However, it is clear from our findings that interpolation methods should be chosen depending on the amount of precipitation, adaptability of the method, and accuracy of the estimate in different rain periods.

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Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments

Cited by 104 publications

References 48 publications

Initial Assessment of Landslide Prone Area using Soil Properties

Initial Assessment of Landslide Prone Area using Soil Properties

Evaluation and Comparison of TRMM Multi-Satellite Precipitation Products With Reference to Rain Gauge Observations in Hunza River Basin, Karakoram Range, Northern Pakistan

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

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