Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

Yang, Xihua; Xie, Xuejian; Liu, De Li; Ji, Fei; Wang, Lin

doi:10.1155/2015/563629

Cited by 116 publications

(53 citation statements)

References 30 publications

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“…The results show that all the precipitation products had a better performance in the monthly streamflow simulation compared to the daily streamflow simulation. The IMERG_F is the only product that categorized a "very good" performance, with NSE, R 2 and RB values of 0.86, 0.9 and −7.1%, respectively. In addition, the IMERG_E outperformed the IMERG_L in the monthly streamflow simulation, indicating the reprocess scheme within the IMERG_L is less beneficial in this tropical basin.…”

Section: Hydrological Assessmentmentioning

confidence: 99%

Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia

et al. 2018

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Abstract:The rapid development of Satellite Precipitation Products (SPPs) has heightened the need for a hydro-meteorological assessment of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products in different climate and geographical regions. Reliability of the IMERG early (IMERG_E), late (IMERG_L) and final (IMERG_F) run products in precipitation estimations was evaluated over the Kelantan River Basin, Malaysia from 12 March 2014 to 31 December 2016. The three IMERG products were then incorporated into a calibrated Soil and Water Assessment Tool (SWAT) model to assess their reliability in streamflow simulations. Overall, monthly precipitation variability is well captured by the three SPPs. The IMERG_F exhibited a smaller systematic bias (RB = 7.14%) compared to the IMERG_E (RB = −10.42%) and IMERG_L (RB = −17.92%) in daily precipitation measurement. All the three SPPs (NSE = 0.66~0.71 and R 2 = 0.73~0.75) performed comparably well as precipitation gauges (NSE = 0.74 and R 2 = 0.79) in the daily streamflow simulation. However, the IMERG_E and IMERG_L showed a significant underestimation of daily streamflow by 27.6% and 36.3%, respectively. The IMERG_E and IMERG_F performed satisfactory in streamflow simulation during the 2014-2015 flood period, with NSE and R 2 values of 0.5~0.51 and 0.62~0.65, respectively. With a better peak flow capture ability, the IMERG_F outperformed the near real-time products in cumulative streamflow measurement. The study has also shown that the point-to-pixel or pixel-to-pixel comparison schemes gave comparable conclusions. Future work should focus on the development of a standardized GPM hydro-meteorological assessment framework, so that a fair comparison among IMERG validation studies can be conducted.

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Section: Hydrological Assessmentmentioning

confidence: 99%

Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia

et al. 2018

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“…To date, many studies have been conducted on spatial interpolation of rainfall at a regional and national scale in Australia (Gyasi‐Agyei, ; Hancock & Hutchinson, ; Hutchinson, ; Jeffrey et al, ; Johnson et al, ; Jones, Wang, & Fawcett, ; Li & Shao, ; Woldemeskel, Sivakumar, & Sharma, ; Yang et al, ). However, none of these studies was conducted at a local or catchment scale.…”

Section: Introductionmentioning

confidence: 99%

“…In view of that, kriging has become the most widely used geostatistical method for spatial interpolation of rainfall. The ability of kriging to produce spatial predictions of rainfall has been distinguished in many studies (e.g., Adhikary et al, 2016a;Goovaerts, 2000;Jeffrey, Carter, Moodie, & Beswick, 2001;Lloyd, 2005;Moral, 2010;Yang, Xie, Liu, Ji, & Wang, 2015). The major advantage of kriging is that it takes into account the spatial correlation between data points and provides unbiased estimates with a minimum variance.…”

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confidence: 99%

Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments

Adhikary

Muttil

Yilmaz

2017

Hydrological Processes

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Rainfall data in continuous space provide an essential input for most hydrological and water resources planning studies. Spatial distribution of rainfall is usually estimated using ground-based point rainfall data from sparsely positioned rain-gauge stations in a rain-gauge network. Kriging has become a widely used interpolation method to estimate the spatial distribution of climate variables including rainfall. The objective of this study is to evaluate three geostatistical (ordinary that among the geostatistical methods, the OCK method is found to be the best interpolator for estimating spatial rainfall distribution in both the catchments with the lowest prediction error between the observed and estimated monthly rainfall. Thus, this study demonstrates that the use of elevation as an auxiliary variable in addition to rainfall data in the geostatistical framework can significantly enhance the estimation of rainfall over a catchment.

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“…We used a test set of CSD values to assess surface modeling efficacy of the following interpolation methods: inverse distance weighted, local and global polynomials, radial basis functions or spline, and kriging. Those models that produced lower mean absolute error (MAE) and root mean square errors (RMSE), and produced surface models that appeared to reflect environmental patterns reasonably well were deemed as most suitable (Yang et al ). Of these, ordinary kriging using a spherical semivariogram model, with a maximum searching neighborhood of two to seven neighbors ( k ), in four diagonal sectors produced the best results and was used for the creation of all subsequent contour maps of CSD or rCSD using all 147 contour nodes.…”

Section: Materials and Proceduresmentioning

confidence: 99%

“…Kriging is analogous to regression analysis in that it produces a statistical model to predict data patterns (a spatial surface in this case) and the differences between model‐predicted values and observed values are used to assess error. In comparing different interpolation models using the kriging method for any given CSD or rCSD, those models that produced the lowest RMSE, mean error, and MAE and had mean standardized errors (MSEs) near zero were deemed as superior (Yang et al ). Since RMSE is scale dependent, there is no standardized reference for a “good” fit; it can only be used to compare among interpolation models based on the same data scale and sample size.…”

Section: Materials and Proceduresmentioning

confidence: 99%

Characterizing estuarine salinity patterns with event duration and frequency of reoccurrence approaches

Johns

Heger

2018

Limnology & Ocean Methods

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Salinity is widely recognized as a dominant environmental variable in estuarine systems. However, drawing clear causal relationships between salinity and estuarine floral or faunal patterns of abundance, population status, or vigor is often confounded by the great spatial and temporal variation exhibited by salinity. Averages, or other statistics, over monthly, seasonal, or longer periods are often used, but may fail to capture patterns of ecological significance. This study introduces a novel approach to characterize estuarine salinity based on deriving events of specific magnitude and duration from a long-term record; events that are also amenable to analysis of the frequency that they reoccur. With sufficient point data for salinity over the spatial domain of an estuary, these variables can be mapped and contoured to reveal spatial patterns of potential ecological significance. Model-predicted salinity for a large estuary system of the Texas coast was used to develop and test these new techniques. While this system has great variation in salinity, both temporally and spatially, we find that this variability can be characterized into well-defined and reoccurring salinity-based events. Furthermore, the frequency of reoccurrence of those events over a long-term record reveals patterns not captured by more common techniques such as averaging over similar time frames. These novel techniques provide an integrated spatial-temporal approach to salinity pattern portrayal which may be useful for examining short-term (daily-monthly) estuary processes and longer-term (yearly-decadal) limits to estuary species distributions and habitat composition.

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Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

Cited by 116 publications

References 30 publications

Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia

Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia

Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments

Characterizing estuarine salinity patterns with event duration and frequency of reoccurrence approaches

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