Average Rainfall Estimation: Methods Performance Comparison in the Brazilian Semi-Arid

Barbalho, Fernando Duarte; Silva, Gabriela F. N. da; Formiga, Klebber Teodomiro Martins

doi:10.4236/jwarp.2014.62014

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…They also calculated the accuracy of the AAR on areas that lack density of rain gauge by using the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method. Cheng et al [5] evaluated and augmented rain gauge network using geostatistical approach and Barbalho et al [28] compared the estimation techniques of AAR such as Thiessen polygon method, IDW (Inverse Distance Weighting), Kriging Method (KM), Multiquadric Equations Method (ME). According to Barbalho et al [28], TM, IDW and ME Method are to be shown with good performance to obtain AAR.…”

Section: Introductionmentioning

confidence: 99%

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A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation

Lee

Kim

Jun

2018

Water

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Estimating the AAR (Areal Average Rainfall) is an essential process when determining the accurate amount of available water resources and building the input data which is integral to the Rainfall-Runoff Analysis. To estimate the AAR, using rain gauge networks that are spatially well distributed is ideal. In this study, the spatial characteristics of the rain gauge networks for the five major river basins in South Korea are considered and the amount of influence the spatial distribution has on the estimation of the AAR is evaluated. For this purpose, the estimation error for AAR is calculated for two cases. The first case (Analysis 1) compares the value of the estimation error of the AAR from two different basins where one has well distributed rain gauges while the other does not. The second case (Analysis 2) estimates the estimation error of two different rain gauge distributions for the same basin. The spatial characteristic of the rain gauge network is evaluated by using the NNI (Nearest Neighbour Index), while the Arithmetic Mean Method, Thiessen Method and the Estimation Theory are applied to calculate the AAR. From Analysis 1, we are able to prove that the estimation error of the AAR is relatively small in the basins with that have spatially well distributed rain gauge networks whereas the estimation error is relatively large when the spatial distribution of the rain gauge network is clustered. Also, results from Analysis 2 showed that not only is the spatial distribution of the rain gauge networks important, but that the density has a significant influence on accurately calculating the AAR. The results from this study can be applied towards the ideal establishment of the rain gauge networks.

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Section: Introductionmentioning

confidence: 99%

“…Whereas KM, tested with two variograms models, had a not expected unstable behaviour. True mean areal rainfall and its distributions will always remain unknown variable even various techniques of AAR estimation exist [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation

Lee

Kim

Jun

2018

Water

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“…Meanwhile, the parametric model is a combination of deterministic and stochastic models. In the stochastic model, there are several methods used to calculate rainfall, including: arithmetic method, polygon, and comparison method [25], [26]. These three methods function for forecasting rainfall in an area.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia

Ruhiat¹

2022

J. meas. eng.

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In a period of ten years, from 2011-2020 rainfall in Indonesia is relatively high, with annual rainfall between 460.5-4,627.4 mm. The high rainfall has implications for flooding in several provinces. During this period, almost every year several areas in Banten Province experienced floods. To predict areas of Banten Province that have the potential for flooding, forecasts of rainfall and the potential for repeated occurrences of high rainfall are carried out. In making the forecast, observations were made at the Serang Meteorological Station, the Budiarto Curug Meteorological Station, the South Tangerang Climatology Station, and the Tangerang Geophysics Station. Rainfall data from the four stations were analyzed by Fourier transform, Gumbel method and Mononobe method. Distribution analysis results obtained rainfall in Banten Province between 0.0-607.9 mm with the length of rainy days per month between 0-26 days. Then, the results of the Fourier transform analysis; Banten Province included a monsoon rain pattern with unimodial rainfall. Furthermore, the results of the analysis of the Gumbel method and the Mononobe method, Banten Province included the category of moderate rain and tended to be heavy, even extreme. Based on the results of the analysis using these two methods, in 2025 in Banten Province, it is predicted that 11 % heavy rain, 3 % very heavy rain and 1 % extreme rain are predicted. In that year, it is predicted that there will be 65 sub-districts in Banten Province that have the potential for flooding. The sub-districts that have the potential for flooding are mostly located in Serang Regency, Serang City, Tangerang City, and South Tangerang City. This potential flood is predicted to occur in: January, February, March, April, May, October and November.

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“…(USDA) to estimate the missing rainfall data by considering the reciprocal of the inverse of distances between the target station and the surrounding stations (Barbalho et al 2014). The formula of IDW method is as follows:…”

Section: Inverse Distance Weighting (Idw)mentioning

confidence: 99%

Missing rainfall data estimation—an approach to investigate different methods: case study of Baghdad

Abdullah

Al‐Ansari

2022

Arab J Geosci

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The missing of the meteorological data in Iraq is common due to malfunction of measuring devices, security status, and human effects. The study tested 17 missing precipitation data estimation methods in Baghdad city as a case study, where, all the surrounding stations around Baghdad experienced the missing of data for various reasons, and some of the missing data are for a full year record. The methods examined in this study are based on different approaches, some of the methods are based upon the distances to the targeted station, others are upon regression factors, and there are also methods that combine several factors. There are also other types of missing data filling methods which depend on imputation and artificial intelligence. The investigation of the most accurate method to find the missing data will assist researchers and decision makers to fill the gap in their analysis in one of the most vulnerable countries in terms of drought and climate changes impacts. Results showed that Expectation Maximization (EM) method utilization has the best results with the least errors, and Multiple Linear Regression (MLR) method was ranked the second best method. In general, all of the applied methods had resulted acceptable interpolations, and it was clear that the combined methods have low significance on the results in comparison with others. All of these findings are limited to the study area meteorological and spatial conditions.

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Average Rainfall Estimation: Methods Performance Comparison in the Brazilian Semi-Arid

Cited by 12 publications

References 24 publications

A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation

A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation

Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia

Missing rainfall data estimation—an approach to investigate different methods: case study of Baghdad

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