Comparison of spatial interpolation methods for annual and seasonal rainfall in two hotspots of biodiversity in South America

Cerón, Wilmar Loaiza; Andreoli, Rita V.; Kayano, Mary Toshie; Canchala, Teresita; Carvajal-Escobar, Yesid; Souza, Rodrigo Augusto Ferreira de

doi:10.1590/0001-3765202120190674

Cited by 24 publications

(16 citation statements)

References 58 publications

(58 reference statements)

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“…(2) Average of the resulting variograms. The set of variograms obtained from Step (1) have a similar structure. On average, the curve increases until it reaches a maximal value at the sixth neighbor (h = 6 months) and then decreases in a quasi-symmetrical way, as shown in Figure 3.…”

Section: Curve Fittingmentioning

confidence: 97%

“…As a traditional geostatistical tool, variograms have been well-utilized for rainfall purposes, showing attributes that quantify and model the correlation and the variability of spacial or time structures represented by the rainfall dataset [1][2][3][4]. From a mathematical point of view, a variogram is an estimator that models how the values of a time series are correlated at different time scales, indicating the variability of the series [5,6], so that the lower the correlation, the higher the variability.…”

Section: Introductionmentioning

confidence: 99%

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A Quadratic–Exponential Model of Variogram Based on Knowing the Maximal Variability: Application to a Rainfall Time Series

et al. 2021

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Variogram models are a valuable tool used to analyze the variability of a time series; such variability usually entails a spherical or exponential behavior, and so, models based on such functions are commonly used to fit and explain a time series. Variograms have a quasi-periodic structure for rainfall cases, and some extra steps are required to analyze their entire behavior. In this work, we detailed a procedure for a complete analysis of rainfall time series, from the construction of the experimental variogram to curve fitting with well-known spherical and exponential models, and finally proposed a novel model: quadratic–exponential. Our model was developed based on the analysis of 6 out of 30 rainfall stations from our case study: the Río Bravo–San Juan basin, and was constructed from the exponential model while introducing a quadratic behavior near to the origin and taking into account the fact that the maximal variability of the process is known. Considering a sample with diverse Hurst exponents, the stations were selected. The results obtained show robustness in our proposed model, reaching a good fit with and without the nugget effect for different Hurst exponents. This contrasts to previous models, which show good outcomes only without the nugget effect.

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Section: Curve Fittingmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A Quadratic–Exponential Model of Variogram Based on Knowing the Maximal Variability: Application to a Rainfall Time Series

et al. 2021

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“…Given these considerations and given the focus of the work, we analyze annual rainfall data. However, we emphasize that this approach constitutes one possibility of analysis, and others such as monthly, seasonal and/or cumulative annual analysis could be considered [11,32,45,46]. The plots of the exploratory analyses are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…According to Pereira Britto et al [8], even though the state of Rio Grande do Sul is industrially developed, its economy is dependent on agriculture, and despite the great advances that have taken place in this sector during the last years, agricultural activity and crop yields depend on the occurrence of rainfall. The state has a large part of its territory in the La Plata Basin, which has a sparse and irregular rainfall network [11]. Therefore, in order to have knowledge about the regime or behavior of precipitation in these locations, it is necessary to spatially distribute the precipitation.…”

Section: Introductionmentioning

confidence: 99%

The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

et al. 2021

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An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

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“…According to Puertas et al [52] and Cerón et al [53], the region has a mean annual rainfall of 1597 mm (±224 mm) and a mean air temperature of 21.3 • C (±0.5 • C). Thus, rainfall and temperature present an inverse behavior with a bimodal intra-annual variability with two rainy periods in the March-April-May (MAM) and September-October-November (SON) quarters, as well as two less rainy periods during the December-January-February (DJF) and June-July-August (JJA) quarters.…”

Section: Study Areamentioning

confidence: 99%

Spatio-Temporal Variability of Hydroclimatology in the Upper Cauca River Basin in Southwestern Colombia: Pre- and Post-Salvajina Dam Perspective

et al. 2021

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The Cauca River rises in the Colombian Andes and is the main tributary of the Magdalena River, which drains to the Caribbean Sea. The La Balsa station monitors the Upper Cauca basin and is located just downstream of La Salvajina hydroelectric facility. At this station, the discharge time series for November–January during 1950–2019 shows a statistically significant downward break, and change of distribution after 1986 has been documented after La Salvajina started operation. We assessed the spatio-temporal variability of hydroclimatology in the upper Cauca River basin during the pre- and post-Salvajina dam periods to better understand this break. Post-Salvajina, low (high) discharge events are linked to negative (positive) precipitation and soil moisture anomalies that are greater in magnitude and extension than those recorded in the pre-Salvajina period in response to the more intense El Niño events (more intense and frequent central La Niña events) after 1986. Therefore, it is necessary to consider possible future rainfall scenarios and non-infrastructure measures (i.e., reforestation, territorial planning, integrated watershed management, etc.) to mitigate floods and droughts impacts. The contribution of this study is to provide evidence for the need for foresight in the design of any structural or non-structural flood measures.

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Comparison of spatial interpolation methods for annual and seasonal rainfall in two hotspots of biodiversity in South America

Cited by 24 publications

References 58 publications

A Quadratic–Exponential Model of Variogram Based on Knowing the Maximal Variability: Application to a Rainfall Time Series

A Quadratic–Exponential Model of Variogram Based on Knowing the Maximal Variability: Application to a Rainfall Time Series

The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

Spatio-Temporal Variability of Hydroclimatology in the Upper Cauca River Basin in Southwestern Colombia: Pre- and Post-Salvajina Dam Perspective

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