A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series

Grillakis, Manolis; Polykretis, Christos; Manoudakis, Stelios; Seiradakis, Konstantinos D.; Alexakis, Dimitrios D.

doi:10.3390/w12082304

Cited by 8 publications

(8 citation statements)

References 35 publications

(45 reference statements)

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“…In this study, we used the name "empirical quantile mapping plus (EQM + )" to refer to the empirical quantile mapping applied to the outputs (values estimated by all six techniques). e study [31] obtained a better result (reduced mean absolute error) after applying quantile mapping on the outputs generated by other techniques. us, we applied it to evaluate its performance on the outputs obtained by other techniques.…”

Section: Empirical Quantile Mapping Plus (Eqm +mentioning

confidence: 91%

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Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Chinasho

Bedadi

Lemma

et al. 2021

Advances in Meteorology

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Meteorological stations, mainly located in developing countries, have gigantic missing values in the climate dataset (rainfall and temperature). Ignoring the missing values from analyses has been used as a technique to manage it. However, it leads to partial and biased results in data analyses. Instead, filling the data gaps using the reference datasets is a better and widely used approach. Thus, this study was initiated to evaluate the seven gap-filling techniques in daily rainfall datasets in five meteorological stations of Wolaita Zone and the surroundings in South Ethiopia. The considered gap-filling techniques in this study were simple arithmetic means (SAM), normal ratio method (NRM), correlation coefficient weighing (CCW), inverse distance weighting (IDW), multiple linear regression (MLR), empirical quantile mapping (EQM), and empirical quantile mapping plus (EQM+). The techniques were preferred because of their computational simplicity and appreciable accuracies. Their performance was evaluated against mean absolute error (MAE), root mean square error (RMSE), skill scores (SS), and Pearson’s correlation coefficients (R). The results indicated that MLR outperformed other techniques in all of the five meteorological stations. It showed the lowest RMSE and the highest SS and R in all stations. Four techniques (SAM, NRM, CCW, and IDW) showed similar performance and were second-ranked in all of the stations with little exceptions in time series. EQM+ improved (not substantial) the performance levels of gap-filling techniques in some stations. In general, MLR is suggested to fill in the missing values of the daily rainfall time series. However, the second-ranked techniques could also be used depending on the required time series (period) of each station. The techniques have better performance in stations located in higher altitudes. The authors expect a substantial contribution of this paper to the achievement of sustainable development goal thirteen (climate action) through the provision of gap-filling techniques with better accuracy.

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Section: Empirical Quantile Mapping Plus (Eqm +mentioning

confidence: 91%

“…e best performance of the machine learning process was stated for ANN [26,27], ordinary kriging [28,29], and Kernel approaches [30]. Besides, Grillakis et al [31] indicated the acceptable performance of empirical quantile mapping in filling the discontinued daily rainfall data in the Mediterranean island of Crete.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Chinasho

Bedadi

Lemma

et al. 2021

Advances in Meteorology

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“…With respect to previous research related to the filling of daily missing data by means of statistical methods (e.g., [27,[46][47][48][49][50]), our results have indicated slightly larger values of RMSE and MAE, but moderately lower values for CC, particularly for the MLR approach; of course, the distinct climate conditions, which drive the alternation between wet and dry states, as well as the occurrence of large rainfall amounts, may render this direct comparison unfair across different geographic regions. On the other hand, one should note that, in most studies, the filling procedures are applied to relatively shorter periods (i.e., less than a full water year), or in more densely gauged areas, in which a better description of the rainfall fields is possible.…”

Section: Daily Estimatesmentioning

confidence: 99%

Comparison of Methods for Filling Daily and Monthly Rainfall Missing Data: Statistical Models or Imputation of Satellite Retrievals?

Duarte

Formiga

Costa

2022

Water

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Accurate estimation of precipitation patterns is essential for the modeling of hydrological systems and for the planning and management of water resources. However, rainfall time series, as obtained from traditional rain gauges, are frequently corrupted by missing values that might hinder frequency analysis, hydrological and environmental modeling, and meteorological drought monitoring. In this paper, we evaluated three techniques for filling missing values at daily and monthly time scales, namely, simple linear regression, multiple linear regression, and the direct imputation of satellite retrievals from the Global Precipitation Measurement (GPM) mission, in rainfall gauging stations located in the Brazilian midwestern region. Our results indicated that, despite the relatively low predictive skills of the models at the daily scale, the satellite retrievals provided moderately more accurate estimates, with better representations of the temporal dynamics of the dry and wet states and of the largest observed rainfall events in most testing sites in comparison to the statistical models. At the monthly scale, the performance of the three methods was similar, but the regression-based models were unable to reproduce the seasonal characteristics of the precipitation records, which, at least to some extent, were circumvented by the satellite products. As such, the satellite retrievals might comprise a useful alternative for dealing with missing values in rainfall time series, especially in those regions with complex spatial precipitation patterns.

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“…The QM method effectively eliminates model bias, not only for mean and interannual variability but also for extreme events 24 , 29 – 31 . The QM bias correction method has also been developed and applied to the climate change impact studies in Italy 32 and Sweden 30 and was used to fill in missing climate data 33 .…”

Section: Introductionmentioning

confidence: 99%

Effects of Climate change on temperature and precipitation in the Lake Toba region, Indonesia, based on ERA5-land data with quantile mapping bias correction

Irwandi

Rosid

Mart

2023

Sci Rep

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Climate change is a serious problem that can cause global variations in temperature and rainfall patterns. This global variation can affect the water availability of lakes. In this study, trends in temperature and rainfall in the Lake Toba area for 40 years (1981–2020) were analyzed using ERA5-Land data corrected with observation station data utilizing the quantile mapping bias correction method. Corrected ERA5-Land data were used in this study to show spatial patterns and trends. The Mann–Kendall and Sen slope tests were carried out to see the magnitude of the trend. A comparison of temperature and rainfall against their baseline period (1951–1980) was also investigated. The results of this study show that climate change has affected the trend of increasing temperature and rainfall in the Lake Toba area, with an increase in temperature of 0.006 °C per year and an average rainfall of 0.71 mm per year. In general, significant changes in the increase of temperature and rainfall occurred in the last decade, with an increase in temperature of 0.24 °C and rainfall of 22%. The study of the impact of climate change expected to be useful for policymakers in managing water resources in the Lake Toba area.

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A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series

Cited by 8 publications

References 35 publications

Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Comparison of Methods for Filling Daily and Monthly Rainfall Missing Data: Statistical Models or Imputation of Satellite Retrievals?

Effects of Climate change on temperature and precipitation in the Lake Toba region, Indonesia, based on ERA5-land data with quantile mapping bias correction

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