A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield

Abstract

Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.

Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania

“…This deterioration was generally less pronounced in the smaller catchment (KC) but resulted, nevertheless, in streamflow simulations of poor performance when QM bias corrected satellite or reanalysis products were used as precipitation input. While QM previously has been applied in data limited environment by leveraging similarity of temporal precipitation distribution within a climate zone [30], the decrease in model performance due to using the QM bias corrected products could, to some extent, be expected in this current study. The poor performance is likely attributed to both a lack of representativeness of the rain gauge as well as a commensurability problem where the frequency distribution of observations from a single rain gauge is extrapolated to the whole catchment.…”

“…It has previously been successfully implemented as a bias correction technique for regional climate model data [29,70,71]. It has also succesfully been utilized for bias correcting GPDs using a sparse rain gauge network when rain gauges are matched to climate zones [30].…”

Uncertainty of Rainfall Products: Impact on Modelling Household Nutrition from Rain-Fed Agriculture in Southern Africa

“…Observations from weather radar systems and satellite-borne sensors, however, show deviations in estimated rainfall compared to in situ measurements from ground stations that have to be accounted for. These indirect measurements have to be calibrated with other sensors data (e.g., rain gauges) to obtain rainfall estimates [53]. Several techniques for bias correction are available, from simple linear adjustment methods where the adjustment of estimated rainfall is performed by considering the ratio between direct and indirect measurements to long-term static (e.g., arithmetic mean ratio, geometric mean ratio) and short-term dynamic adjustment factors [54].…”

“…It corrects the distributions of the RP's daily precipitation estimates (P rp ) with the distribution of observed daily precipitation at Okatana station (P os ) [60]. The QM technique is mainly applied and evaluated for calibration purposes in global and regional climate models, as well as hydrological contexts [53,[61][62][63][64].…”

“…Instead of relying on parametric distributions, empirical CDFs for the RPs' time series and Okatana station were used [53,60]. In this regard, the respective CDFs and the corresponding QM parameters were estimated on a monthly basis ( Figure 3).…”