Evaluation of Reanalysis Precipitation Data and Potential Bias Correction Methods for Use in Data-Scarce Areas

Garibay, Victoria M.; Gitau, Margaret W.; Kiggundu, Nicholas; Moriasi, Daniel N.; Mishili, Fulgence

doi:10.1007/s11269-021-02804-8

Cited by 15 publications

(5 citation statements)

References 49 publications

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“…Rain gauges remain the most accurate and reliable measurements, even if they are subject to systematic and human-made errors [5,6]; thus, investments in the national hydrometeorological networks are essential for adequate water and natural resource planning and management. Our results indicate that both reanalysis datasets are inaccurate in reproducing the rainfall estimations from rain gauges in the Sinú River basin; however, the literature suggests that a point-to-point approach [39], the use of bias correction techniques [78,79], mathematical regression, and geostatistics [8] might improve their performance. These adjustments might allow them to serve as boundary conditions or create scenarios for integrated modeling of natural processes, as they offer consistent time series of multiple hydrometeorological variables [17,56,76,80].…”

Section: Discussionmentioning

confidence: 81%

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

et al. 2021

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Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

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Section: Discussionmentioning

confidence: 81%

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

et al. 2021

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“…The results revealed that the bias of the two sets of historical bias‐corrected MME could be ignored for most regions of China. Many studies have demonstrated that the bias correction method improved the accuracy of CMIP6 models in estimating precipitation as well (Garibay et al, 2021; Su et al, 2018). However, the bias correction process could not revise the shortcomings of GCMs, where the models could not simulate the ascending vertical atmospheric motion in the mountainous regions of the Tibetan Plateau (Wu et al, 2017); thus, the historical bias remained concentrated in western China.…”

Section: Discussionmentioning

confidence: 99%

“…The SD was applied based on the fundamental assumption that the fine‐scale distribution of the large‐scale climate determined by topographic and climatic features is stable (Su et al, 2018; Wang & Chen, 2013). Many approaches, such as the first‐order conservative remapping scheme and sliding window approach, have been used to bias‐correct the outputs of GCMs (Gao et al, 2020; Garibay et al, 2021; Su et al, 2018). By applying the cumulative distribution function (CDF) on models and observations for the base period, Li et al (2010) developed a new quantile‐based mapping technique, that is, equidistant CDF matching (EDCDFm) method, to bias‐correct models' monthly outputs.…”

Section: Introductionmentioning

confidence: 99%

Does CMIP6 inspire more confidence in projecting precipitation over China?

Tian

Zhang

et al. 2023

Intl Journal of Climatology

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Despite significant advancements in recent versions of general circulation models (GCMs), uncertainties persist in simulations of both historical and future precipitation. Notably, disparities exist between the simulated precipitation before 2015 and the projected precipitation in Coupled Model Intercomparison Project Phase 6 (CMIP6) models. In this study, the accuracy of projected precipitation in 12 CMIP6 models was evaluated before and after applying SD and EDCDFm methods, by comparing with observed precipitation data during 2015–2020. This study aims to address the gap in the comprehensive evaluation of projected precipitation against observational data. The results of the study showed that: (1) Downscaling and bias correction improved the skill of CMIP6 models in simulating spatial distribution of precipitation during 1961–2014, especially in east China. However, bias‐corrected projected precipitation during 2015–2020 exhibited a dry bias over most of China. (2) Bias correction improved the capability of CMIP6 models in simulating historical monthly variations in precipitation, but did not address the issue of projected precipitation following the simulated seasonal cycles. (3) The improvement of projected precipitation after bias correction was limited during 2015–2020, and for CanESM5, IPSL‐CM6A‐LR, and MIROC6 models, bias correction even exacerbated the bias of projected precipitation over the Song–Liao River, Yangtze River, and Southeast River basins. (4) Bias‐corrected CMIP6 models generally underestimated the increase in precipitation after 2015, but projected precipitation over China was still expected to increase during 2015–2099 after bias correction. These findings emphasize the need for more precise strategic suggestions to improve the projection of precipitation.

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“…Precipitation data was obtained for the grid point closest to the weather stations used to create the hydrological model and was bias corrected for each station. The bias correction method used was the empire quantile mapping (QUANT) because this method has been found to have a good performance in the bias correction of precipitation data (Enayati et al, 2021;Garibay et al, 2021). Bias corrected data were compared to observed data for 2006-2015 to assess the representativeness of the model data.…”

Section: Description Of Scenariosmentioning

confidence: 99%

Urban FEW Nexus Model for the Otun River Watershed

Torres

Gitau

Lara-Borrero

et al. 2023

Preprint

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The food-energy-water (FEW) nexus has emerged as an alternative to improve the management of the resources used in the food, energy, and water systems. However, there are limited case studies in the Latin American and Caribbean region where a FEW nexus methodology has been implemented. This region will greatly benefit from a FEW nexus approach since more than half of the electricity comes from hydropower, and even though the region has excess in water resources, these resources are not equally distributed and they are poor managed. In this study, an urban FEW nexus framework was used in the Otun River Watershed (ORW) to evaluate the changes in food, energy, and water demand for four scenarios (base scenario, year 2017; scenario 1, year 2035-population growth; scenario 2, year 2035-population growth and reduction in locally produced food; scenario 3, year 2035-population growth and increase in electricity generated locally). Additionally, regional climate models (RCMs) were used to estimate the water availability in the ORW from 2030-2039. The results show that water demand could increase by 16% and energy demand will increase roughly 15% for scenario 2, while water demand in scenario 3 will likely remain unchanged in relation to the current conditions (base scenario). As the Latin America and Caribbean region is the most urbanized region in the world, the modeling tool developed as part of this study may be implemented in other urban watersheds in the Andean Region.

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Evaluation of Reanalysis Precipitation Data and Potential Bias Correction Methods for Use in Data-Scarce Areas

Cited by 15 publications

References 49 publications

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Does CMIP6 inspire more confidence in projecting precipitation over China?

Urban FEW Nexus Model for the Otun River Watershed

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