Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka

Plouffe, Cameron C F; Robertson, Colin; Chandrapala, Lalith

doi:10.1016/j.envsoft.2015.01.011

Cited by 74 publications

(46 citation statements)

References 31 publications

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“…Therefore, in situ gauge data usually cannot meet the requirements of applications that depend on high spatial–temporal resolution precipitation data, such as hydrologic simulations. Moreover, due to high spatial and temporal variability and precipitation uncertainty (Tustison et al, ; Hrachowitz & Weiler, ), satisfactory and continuously distributed precipitation data are difficult to obtain even if adopting spatial interpolation (Li & Heap, ; Teegavarapu et al, ; Plouffe et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and hydrologic validation of TMPA satellite precipitation product downstream of the Pearl River Basin, China

Wang

Zhong

2017

Hydrological Processes

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With high spatio‐temporal resolution and wide coverage, satellite‐based precipitation products can potentially fill the deficiencies of traditional in situ gauge precipitation observations and provide an alternative data source for ungauged areas. However, due to the relatively poor accuracy and high uncertainty of satellite‐based precipitation products, it remains necessary to assess the quality and applicability of the products for each investigated area. This study evaluated the accuracy and error of the latest Tropical Rainfall Measuring Mission Multi‐satellites Precipitation Analysis 3B42‐V7 satellite‐based precipitation product and validated the applicability of the product for the Beijiang and Dongjiang River Basins, downstream of the Pearl River Basin in China. The study first evaluated the accuracy, error, and bias of the 3B42‐V7 product during 1998–2006 at daily and monthly scale via comparison with in situ observations. The study further validated the applicability of the product via hydrologic simulation using the variable infiltration capacity hydrological model for three hydrological stations in the Beijiang River Basin, considering two scenarios: a streamflow simulation with gauge‐calibrated parameters (Scenario I) and a simulation after recalibration with the 3B42‐V7 product (Scenario II). The results revealed that (a) the 3B42‐V7 product produced acceptable accuracy both at the daily scale and high accuracy at the monthly scale while generally tending to overestimate precipitation; (b) the product clearly overestimated the frequency of no rainfall events at the grid cell scale and light rainfall (<1 mm/day) events at the region scale and also overestimated the amount of heavy rain (25–50 mm/day) and hard rain (≥50 mm/day) events; (c) under Scenario I, the 3B42‐V7 product performed poorly at three stations with gauge‐calibrated parameters; under Scenario II, the recalibrated model provided significantly improved performance of streamflow simulation with the 3B42‐V7 product; (d) the variable infiltration capacity model has the ability to reveal the hydrological characteristics of the karst landform in the Beijiang Basin when using the 3B42‐V7 product.

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Section: Introductionmentioning

confidence: 99%

“…Moreover, due to high spatial and temporal variability and precipitation uncertainty (Tustison et al, 2001;Hrachowitz & Weiler, 2009), satisfactory and continuously distributed precipitation data are difficult to obtain even if adopting spatial interpolation (Li & Heap, 2008;Teegavarapu et al, 2012;Plouffe et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluation and hydrologic validation of TMPA satellite precipitation product downstream of the Pearl River Basin, China

Wang

Zhong

2017

Hydrological Processes

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“…Early examples of using SPC for model assessment include Cliff (1970) and Sokal and Wartenberg (1983), who both characterized model outputs with spatial autocorrelation statistics. Spatially explicit model assessment is critical as it can reveal patterns in error structures not evident in error statistics (e.g., Plouffe, Robertson, & Chandrapala, 2015). SPC for model assessment has seen more recent interest in the area of categorical spatial data (e.g., land cover maps; FIGURE 2 Four dimensions of spatial pattern important for spatial pattern comparison Hagen-Zanker & Martens, 2008;Visser & de Nijs, 2006).…”

Section: Types Of Questionsmentioning

confidence: 99%

Comparing spatial patterns

Long

Robertson

2017

Geography Compass

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The comparison of spatial patterns is a fundamental task in geography and quantitative spatial modelling. With the growth of data being collected with a geospatial element, we are witnessing an increased interest in analyses requiring spatial pattern comparisons (e.g., model assessment and change analysis). In this paper, we review quantitative techniques for comparing spatial patterns, examining key methodological approaches developed both within and beyond the field of geography. We highlight the key challenges using examples from widely known datasets from the spatial analysis literature. Through these examples, we identify a problematic dichotomy between spatial pattern and process—a widespread issue in the age of big geospatial data. Further, we identify the role of complex topology, the interdependence of spatial configuration and composition, and spatial scale as key (research) challenges. Several areas ripe for geographic research are discussed to establish a consolidated research agenda for spatial pattern comparison grounded in quantitative geography. Hierarchical scaling and the modifiable areal unit problem are highlighted as ideas which can be exploited to identify pattern similarities across spatial and temporal scales. Increased use of “time‐aware” comparisons of spatial processes are suggested, which properly account for spatial evolution and pattern formation. Simulation‐based inference is identified as particularly promising for integrating spatial pattern comparison into existing modelling frameworks. To date, the literature on spatial pattern comparison has been fragmented, and we hope this work will provide a basis for others to build on in future studies.

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“…Hegstad and Omre, 2001;Craig et al, 2001), atmospheric dispersion (e.g. Politis and Robertson, 2004;Konda et al, 2010;Reggente et al, 2014) and climatology (Rougier et al, 2009b;Qin et al, 2013;Castruccio et al, 2014;Plouffe et al, 2015). In contrast, statistical emulation is relatively unknown to the computational wind engineering (CWE) community.…”

Section: Introductionmentioning

confidence: 96%