Bias Correction Method of High-Resolution Satellite-Based Precipitation Product for Peninsular Malaysia

Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use without evaluation and often without bias correction. The bias correction of SBP remained a challenge for atmospheric scientists. In this study, the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GsMap, CHIRPS, PERSIANN-CDS and PERSIANN-CSS in replicating observed daily rainfall at 364 stations over Peninsular Malaysia was evaluated. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the amount of rainfall during rainfall events. The performance of different widely used ML algorithms for classification and regression were evaluated to select the suitable algorithms. IMERG showed better performance, showing a higher correlation coefficient (R2) of 0.57 and Kling-Gupta Efficiency (KGE) of 0.5 compared to the other products. The performance of random forest (RF) was better than the k-nearest neighbourhood (KNN) for both classification and regression. RF classified the rainfall events with a skill score of 0.38 and estimated the rainfall amount of a rainfall event with the modified Index of Agreement (md) of 0.56. Comparison of IMERG and bias-corrected IMERG (BIMERG) revealed an average reduction in RMSE by 55% in simulating observed rainfall. The proposed bias correction method performed much better when compared with the conventional bias correction methods such as linear scaling and quantile regression. The BIMERG could reliably replicate the spatial distribution of heavy rainfall events, indicating its potential for hydro-climatic studies like flood and drought monitoring in the study area.

Impact of Bias Correction on Present and Mid-Future Rainfall Projections over Senegal

In this study, we analyze the impact of bias correction models on present and future precipitation and extremes rainfall events over Senegal. The commonly used linear scaling (LS) bias correction method has been applied on four (4) regional climate models (RCMs) of the Coordinated Regional Climate Downscaling Experiment (CORDEX) program. The linear scaling bias correction method was firstly calibrated and validated during the 1976-1990 and 1991-2005 periods, respectively. The comparison with the observed data revealed that the linear scaling method significantly improves the mean and the extreme precipitations during the validation period. The RCMs generally simulate a decrease of rainfall in the mid-twenty-first century under the RCP8.5 greenhouse gas concentration pathway compared to the reference period (1976-2005), except for the CCLM4 and the RCA4 models which show respectively a slight increase overall Senegal and the east of the country. The changes in precipitation indices such as the number of wet days (R1mm) and mean frequency of heavy rainfall events (R20mm) follows that mean precipitation change distribution. Almost uncorrected RCMs (except RCA4) predict during the near future an increase in of the mean intensity of daily rainfall events (SDII), the mean intensity of precipitation events above the 95th Percentile (R95PTOT) and the mean maximum dry spells length (CDD), whereas a decrease in the mean maximum wet spells length (CWD) is projected. After applying the LS bias correction, the spatial distribution patterns are not so much modified in all the models but the magnitude of the climate change signal is either amplified or moderated depending on the considered variables.

A Simple and Efficient Method for Correction of Basin-Scale Evapotranspiration on the Tibetan Plateau

Evapotranspiration (ET) is one of the important components of the global hydrologic cycle, energy exchange, and carbon cycle. However, basin scale actual ET (hereafter ETa) is difficult to estimate accurately. We present an evaluation of four actual ET products (hereafter ETp) in seven sub-basins in the Tibetan Plateau. The actual ET calculated by the water balance method (hereafter ETref) was used as the reference for correction of the different ETp. The ETref and ETp show obvious seasonal cycles, but the ETp overestimated or underestimated the ET of the sub-basins in the Tibetan Plateau. A simple and effective method was proposed to correct the basin-scale ETp. The method was referred to as ratio bias correction, and it can effectively remove nearly all biases of the ETp. The proposed method is simpler and more effective in correcting the four ETp compared with the gamma distribution bias correction method. The reliability of the ETp is significantly increased after the ratio bias correction. The ratio bias correction method was used to correct the ETp in the seven sub-basins in the Tibetan Plateau, and regional ET was significantly improved. The results may help improve estimation of the ET of the Tibetan Plateau and thereby contribute to a better understanding of the hydrologic cycle of the plateau.

Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction

The implications of weather and climate extremes on the viticulture and winemaking sector can be particularly detrimental and acquire more relevance under a climate change context. A four-member ensemble of the Regional Climate Model-Global Climate Model chain simulations is used to evaluate the potential impacts of climate change on indices of extreme temperature and precipitation, as well as on agroclimatic indices of viticultural suitability in the Douro Wine Region, Portugal, under current and future climate conditions, following the RCP8.5 anthropogenic radiative forcing scenario. Historical (1989–2005) and future (2051–2080) periods are considered for this purpose. Although model outputs are bias-corrected to improve the accuracy of the results, owing to the sensitivity of the climatic indicators to the specific bias correction method, the performance of the linear and quantile mapping methods are compared. The results hint at the importance of choosing the most accurate method (quantile mapping), not only in replicating extremes events but also in reproducing the accumulated agroclimatic indices. Significant differences between the bias correction methods are indeed found for the number of extremely warm days (maximum temperature > 35 °C), number of warm spells, number of warm spell days, number of consecutive dry days, the Dryness Index, and growing season precipitation. The Huglin Index reveals lower sensitivity, thus being more robust to the choice of the method. Hence, an unsuitable bias correction method may hinder the accuracy of climate change projections in studies heavily relying on derived extreme indices and agroclimatic indicators, such as in viticulture. Regarding the climate change signal, significant warming and drying trends are projected throughout the target region, which is supported by previous studies, but also accompanied by an increase of intensity, frequency, and duration of extreme events, namely heatwaves and dry spells. These findings thereby corroborate the need to adopt timely and effective adaptation strategies by the regional winemaking sector to warrant its future sustainability and enhance climate resilience.