Bias Correction Methods Research Articles

Two deep learning-based bias-correction pathways improve summer precipitation prediction over China

As most global climate models (GCM) suffer from large biases in simulating/predicting summer precipitation over China, it is of great importance to develop suitable bias-correction methods. This study proposes two pathways of bias-correction with deep learning (DL) models incorporated. One is the deterministic pathway (DP), in which the bias correction is directly applied to the precipitation forecasts. The other one, namely the probability pathway (PP), corrects the forecasted precipitation anomalies using a conditional probability method before being added to the observational climatology. These two pathways have been applied to correct the precipitation forecasts based on a GCM prediction system Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0). The applications of DL models in the both pathways yield higher resolution of corrected predictions than the uncorrected ones. Both pathways improve summer precipitation predictions at 4-month lead. Moreover, the DP correction shows a better performance in predicting extreme precipitation, while the PP is proficient in correcting the spatial pattern of precipitation anomalies over China. The present results highlight the importance of the application of appropriate correction strategy for different prediction purposes.

Universally consistent estimation of the reach

The reach of a set M⊂Rd, also known as condition number when M is a manifold, was introduced by Federer in 1959. The reach is a central concept in geometric measure theory, set estimation, manifold learning, among others areas. We introduce a universally consistent estimate of the reach, just assuming that the reach is positive. Under an additional assumption we provide rates of convergence. We also show that it is not possible to determine, based on a finite sample, if the reach of the support of a density is zero or not. We provide a small simulation study and a bias correction method for the case when M is a manifold.

Spatial and Temporal Analysis of Extreme Precipitation under Climate Change over Gandaki Province, Nepal

This paper presents a research study of expected precipitation extremes across the Gandaki Province, Nepal. The study used five indices to assess extreme precipitation under climate change. Precipitation output of two Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase Six (CMIP6) were used to characterize the future precipitation extremes during the rainfall season from June to September (JJAS) and overall days of the year. To characterize extreme precipitation events, we used daily precipitation under the SSP2–4.5 and SSP5–8.5 scenarios from the Beijing Climate Center and China Meteorological Administration, China; and Meteorological Research Institute (MRI), Japan. Considering large uncertainties with GCM outputs and different downscaling (including bias correction) methods, direct use of GCM outputs were made to find change in precipitation pattern for future climate. For 5-, 10-, 20-, 50-, and 100-year return periods, observed and projected 24 h and 72 h annual maximum time series were used to calculate the return level. The result showed an increase in simple daily intensity index (SDII) in the near future (2021–2040) and far future (2081–2100), with respect to the base-year (1995–2014). Similarly, heavy precipitation days (R50 mm), very heavy precipitation days (R100 mm), annual daily maximum precipitation (RX1day), and annual three-day maximum precipitation (RX3day) indices demonstrated an increase in extreme precipitation toward the end of the 21st century. A comparison of R50 mm and R100 mm values showed an extensive (22.6% and 63.8%) increase in extreme precipitation days in the near future and far future. Excessive precipitation was forecasted over Kaski, Nawalparasi East, Syangja, and the western half of the Tanahun region. The expected increase in extreme precipitation may pose a severe threat to the long-term viability of social infrastructure, as well as environmental health. The findings of these studies will provide an opportunity to better understand the origins of severe events and the ability of CMIP6 model outputs to estimate anticipated changes. More research into the underlying physical factors that modulate the occurrence of extreme incidences expected for relevant policies is suggested.

Harmonized gap-filled datasets from 20 urban flux tower sites

Abstract. A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).

Bias correction of Sentinel-2 with unmanned aerial vehicle multispectral data for use in monitoring walnut fruit forest in western Tien Shan, Kyrgyzstan

The scope of our work is to compare Sentinel-2 and unmanned aerial vehicles (UAV) imagery from Western Tian Shan for monitoring applications in walnut fruit forests. Because acquiring field data on walnut fruit forests is difficult due to access limitations in the Tian Shan, remote sensing offers a unique opportunity to increase spatial and temporal coverage of ecological parameters. Sentinel-2 satellites launched in 2015 have been providing images at spatial resolutions of 10 to 20 m; however, difficulties remain as information retrieved from Sentinel pixels result from a mixture of objects that affect reflectance signals. We used submeter multispectral UAV images to assess the sensitivity of Sentinel-2 normalized difference vegetation index (NDVI) to subpixel vegetation. Results showed that Sentinel-2 data overestimates NDVI in regions with open terrain and grass, and underestimates NDVI in areas with trees. By implementing a bias correction method, the accuracy of the Sentinel-2 derived NDVI increased; R2 values increased from 0.59 to 0.88 (p value < 0.001). We also showed that drought index derived from Sentinel-2 vegetation condition index (VCI) is well correlated with the ground-based standard precipitation index (SPI). Using this bias correction method, on average the correlation increased by 3% between VCI and SPI.

Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China

Accurate precipitation (P) short-term forecasts are important for engineering studies and water allocation. This study evaluated a method for bias correction of the Numerical Weather Prediction (NWP) of Global Ensemble Forecast System V2 forecasts based on the extreme gradient boosting (XGBoost) model (M3) and 689 meteorological stations in seven different climatic regions of China. The method used a common deviation correction for multiple meteorological factors to forecast P for 1–8 d ahead. It was also compared with the equidistant cumulative distribution functions matching a single weather factor (EDCDFm, M1) and the XGBoost model (M2). The M3 method had the best forecast performance. M1, M2, and M3 methods had an average root mean square error (RMSE) ranging from 2.292–17.049 mm, 1.844–18.835 mm, and 1.819–13.608 mm, respectively. The performance of each method tended to decrease as the lead time was extended. The average false alarm ratio (increased from 55.3%, 52.8% and 50.1% to 75.8%, 82.3% and 76.0%, respectively) and miss ratio (increased from 60.9%, 53.5% and 50.3% to 76.6%, 77.7% and 71.2%, respectively) also increased with an increased lead time for all methods. The forecast performance trended downwards from northwest to southeast China. However, each method’s significance in forecasting P’s determination coefficient showed a contrary pattern to the forecast accuracy. There was a general underestimation across the methods. The best performance for forecasting P was achieved in winter, with root mean square error values of 2.0–3.4 mm, followed in order by autumn > spring > summer. Factor P contributed the most to forecast P after bias correction of the XGBoost model (average Gain, Cover, and Frequency values of 0.55, 0.45, and 0.29, respectively). In summary, satisfactory performance could be obtained using the XGBoost model combined with multi-factor bias correction for NWP data to forecast daily P.

Performance Evaluation of Bias Correction Methods and Projection of Future Precipitation Changes Using Regional Climate Model over Thanjavur, Tamil Nadu, India

The use of regional climate models in climate change risk assessments is uncertain because of the threat of biases. Prior to using simulations, some corrections must be made. The aim of this study is to evaluate the effectiveness of simple and complex correction approaches on CORDEX (Coordinated Regional Climate Downscaling Experiment) precipitation datasets over Thanjavur in Tamil Nadu, India. The various bias correction approaches were assessed using the two-sample cross-validation technique, and the best method was chosen using statistical metrics such as the correlation coefficient, root mean square error, and percent bias for correcting the future scenario datasets. According to the findings, all methods significantly improved raw simulated estimations after correction. The Delta Change method was used to adjust the RCP2.6 and RCP8.5 scenarios datasets for this study region because it produced good agreement (0.98) and low wet bias (1%) against observed precipitation. In addition, this study found that the annual mean precipitation projections showed an increase in precipitation intensity of between 4.2 and 6.8% under the RCP2.6 scenario and between 14 and 20.1% under the RCP8.5 scenario, compared to observed data (1976–2005). Furthermore, the analysis showed that most years in the twenty-first century will experience heavy precipitation, resulting in floods. This research has offered reliable knowledge about future precipitation to environmentalists, urban administrators, and policy-makers, enable them to make plausible strategies to mitigate the detrimental effects of climate change.

Bias Correction and Evaluation of Precipitation Data from the CORDEX Regional Climate Model for Monitoring Climate Change in the Wadi Chemora Basin (Northeastern Algeria)

This study aims to provide a brief overview of four regional climate model (RCM) estimations for (Daily, Monthly, Seasonal, and Annual) averaged precipitation over the Wadi Chemora Basin in northeastern Algeria for the historical period (1970–2005) and future forecasts (2006–2100). Data from seven ground stations were compared to data from four RCMs: RCA4 driven by ICHEC-EC-EARTH and NOAA-GFDL-GFDL-ESM2M from MENA-CORDEX domain with intermediate resolution (25 km, 0.22°) and ALADIN and RegCM4 from MED-CORDEX domain with high resolution (12 km, 0.11°). In most time steps (Annual, Seasonal, Monthly, and Daily), the raw RCMs overestimated precipitation, but their performance improved significantly after applying gamma quantile mapping (GQM) bias correction method. The bias-corrected projections indicate decreases of seasonal rainfall for the near future (2010–2039), mid-century (2040–2069), and end of century (2070–2100) periods. Overall decreases in all seasons lead to the projected decrease in annual rainfall of an average of 66% by the end of the 21st century.

Using high-resolution global climate models from the PRIMAVERA project to create a European winter windstorm event set

Abstract. PRIMAVERA (process-based climate simulation: advances in high-resolution modelling and European climate risk assessments) was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data. The data are available at https://doi.org/10.5281/zenodo.6492182. To create the storm footprints for the event set, the storms in the PRIMAVERA models are identified through tracking. A method is developed to separate the winds from storms occurring in the domain at the same time. The wind footprints are bias corrected and converted to 3 s gusts onto a uniform grid using quantile mapping. The distribution of the number of model storms per season as a function of estimated loss is consistent with re-analysis, as are the total losses per season, and the additional event set data greatly reduce uncertainty on return period magnitudes. The event set also reproduces the temporally clustered nature of European windstorms. Since the event set is generated from global climate models, it can help to quantify the non-linear relationship between large-scale climate indices such as the North Atlantic Oscillation (NAO) and windstorm damage. Although we find only a moderate positive correlation between extended winter NAO and storm damage in northern European countries (consistent with re-analysis), there is a large change in risk of extreme seasons between negative and positive NAO states. The intensities of the most severe storms in the event set are, however, sensitive to the gust conversion and bias correction method used, so care should be taken when interpreting the expected damages for very long return periods.

Evaluation of the spatial characteristics of climate scenarios based on statistical and dynamical downscaling for impact assessments in Japan

AbstractRepresentation of spatial dependence on temporal variations in climate scenarios is quite important when performing impact assessments in various sectors, including water resource management, agriculture, and energy. Although the complex terrain of Japan is closely related to the formation of regional climates, the spatial aspects of climate scenarios have not been investigated. To examine how well spatial dependency on temporal variations in precipitation is represented, we analysed the dependence of the correlation of daily precipitation time series for station pairs on the geographical distance between stations in several downscaled products for Japan. Although the bias correction (BC) method using quantile mapping was sufficient for removing the inherent biases at each station, we found minimal improvement in the spatial dependency. The performance was strongly dependent on the grid spacing of the parent model when the downscaling process was simple; it could not be improved by applying BC to the model output statistics. When analogue‐type statistical downscaling was applied, the observed spatial pattern of interstation correlations was much better reproduced; this was comparable to the results obtained using a dynamical downscaling technique with grid spacing of a few kilometres. Our results emphasize the importance of selecting appropriate data to represent the spatial variability of meteorological variables because mean bias‐based or accessibility‐based data selection may produce misleading results. The combination of an analogue method with BC of model output statistics has the potential to effectively represent the marginal and spatial aspects of climate scenarios.

Development of CMIP6‐Based Climate Scenarios for Japan Using Statistical Method and Their Applicability to Heat‐Related Impact Studies

AbstractClimate scenario data set are indispensable for assessing future climate impacts. In this study, we developed statistically downscaled climate scenarios in Japan using modified bias correction method based on five general circulation models selected from the Coupled Model Intercomparison Project (CMIP) Phase 6 to facilitate impact assessments and adaptation strategies. Modification of time window of the original correction method results in successful agreement with the observed seasonal change of variables in each grid. The original CMIP6 models have a relatively small bias compared to CMIP5 models. The advantage of CMIP6‐based bias‐corrected scenarios is its availability for multiple global circulation models, which covered wide uncertainty in CMIP6 ensembles, with various emissions scenarios for representative concentration pathway (RCP) including RCP4.5, RCP2.6, and RCP8.5. Several temperature‐related indices derived from the CMIP6‐based climate scenarios agreed well with observations. The number of extremely hot days and nights increased nonlinearly in the future with additional global warming. An increase in the global warming level from 1 to 2°C above the early 1900s would increase the probability of the number of extremely hot days per year exceeding the 2018 case by 4.1 times. The development of bias‐corrected climate scenarios facilitates the study of various climate impacts on a CMIP6 basis.

Climate change impact on flood hazard over Italy

This study assesses future projections of flood hazards across the Italian river basins. For this purpose, sub-daily river discharge for the entire Italian territory was simulated with the CETEMPS hydrological model (CHyM) using as climate forcing both observational datasets and regional climate simulations completed with the ICTP Regional Climate Model (RegCM4). First, simulated precipitation and river discharge were evaluated against observational products, showing both models good performance in simulating Italian precipitation and discharge characteristics. Then, projections of these variables were explored for two time slices, near (2020–2049) and far (2070–2099) future, under the RCP8.5 emission scenario, with RegCM4 driven by output from a global climate model projection. The RegCM4 outputs were also bias-adjusted by means of the N-dimension multivariate bias correction (MBCn) method. We found that, although the bias correction leads to a benefit in capturing mean precipitation, the improvement is less clear for extreme precipitation. In terms of discharge, however, bias correction has a noticeable impact on mean discharge as well as average yearly peak flow and hourly peak flow at return periods of 10, 50, and 100 years. Nevertheless, for a large part of the Italian territory, projected changes in mean precipitation and discharge do not show significant differences when bias correction is applied, while for extreme events only ∼30 % of river points show significant sensitivity to bias correction. Under the RCP8.5 scenario, an increase in yearly peak flow is evident over most of the Italian peninsula, with values increasing from 30 % to more than 100 % for the mid and far time slices respectively, especially over the Po River and along the eastern coasts of Italy. Similar conclusions are found for the hourly peak flows of 10, 50, and 100-year return periods. These indicators thus suggest a general increase in flood hazard expected over the region.

Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties With Bias Correction Techniques

AbstractDecomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures.

On the Benefits of Bias Correction Techniques for Streamflow Simulation in Complex Terrain Catchments: A Case-Study for the Chitral River Basin in Pakistan

This work evaluates the suitability of linear scaling (LS) and empirical quantile mapping (EQM) bias correction methods to generate present and future hydrometeorological variables (precipitation, temperature, and streamflow) over the Chitral River Basin, in the Hindukush region of Pakistan. In particular, LS and EQM are applied to correct the high-resolution statistically downscaled dataset, NEX-GDDP, which comprises 21 state-of-the-art general circulation models (GCMs) from the coupled model intercomparison project phase 5 (CMIP5). Raw and bias-corrected NEX-GDDP simulations are used to force the (previously calibrated and validated) HBV-light hydrological model to generate long-term (up to 2100) streamflow projections over the catchment. Our results indicate that using the raw NEX-GDDP leads to substantial errors (as compared to observations) in the mean and extreme streamflow regimes. Nevertheless, the application of LS and EQM solves these problems, yielding much more realistic and plausible streamflow projections for the XXI century.

Climate variability impacts on runoff projection under quantile mapping bias correction in the support CMIP6: An investigation in Lushi basin of China

In this study, we modelled the XAJ (Xin’anjiang) hydrologic model parameters for the Lushi basin of China, an important tributary of the Yellow River. Time series of daily precipitation, evaporation, and runoff data from 1976 to 1995 were used to calibrate the model, while the data from 1996 to 2000 were used for validation. Thereafter, the future climate variability was projected from 2021 to 2100 for 0.22° grids under CMIP6 CORDEX-East-Asia (Coordinated Regional Climate Downscaling Experiment-East-Asia) RCP2.6 and RCP8.5 climate scenarios. The non-parametric quantile mapping (QM) bias correction method was used to minimize the large differences between climate variability directly derived from the regional circulation model (RCM) and the historical observations, and the results were also compared with the baseline period (1976–2000). Overall, when compared to the baseline case, results showed a decline in the annual precipitation by 7.22 % and 5.01 %, while an increase was observed for the annual evaporation by 2.03 % and 3.58 % under RCP2.6 and RCP8.5 climate scenarios in the 21st century. Annual runoff projection by XAJ model indicate reduction, with 9.3 % and 31.2 % fall for short-term (2021–2040), 31.5 % and 41.2 % fall for mid-term (2051–2070) and 32.3 % and 77.4 % fall for long-term (2081–2100), respectively. The extreme streamflow projects increase during the dry period (November to June) with 76.5 m3/s and 84.6 m3/s under RCP2.6 and RCP8.5 compared to 45.6 m3/s from the baseline period. In contrast, there is a decrease during the wet period (July to October) by 390 m3/s and 237 m3/s compared to 405 m3/s in the baseline data of the same period. Such projections become more pronounced toward the end of the 21st century, and the reduction is likely to continue if future climate projections occur. The hydrological parameters can be applied to adjacent and similar landscape basins for disaster forecasting and early warning. The QM method is effective in bias correction. The findings can also be used for flood and drought mitigation planning and water resources management in the Yellow River Basin.

The New Bias Correction Method for Daily Extremes Precipitation over South Korea using CMIP6 GCMs

The New Bias Correction Method for Daily Extremes Precipitation over South Korea using CMIP6 GCMs

Modeling the atmospheric dispersion of SO2 from Mount Nyiragongo

Mount Nyiragongo, an active volcano, is the most dominant natural source of sulphur dioxide (SO2) in Africa. While a number of studies have employed atmospheric models to simulate the dispersion of SO2 from this mountain, prior to this study, no attempt has been made to use deep learning to bias correct the model's estimates. Here, the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) was used to simulate massive SO2 plumes degassed from this mountain between September 2014 and August 2015. Satellite observations by the Ozone Monitoring Instrument (OMI) showed that the SO2 spread to over 500 km from the volcano site. A deep convolutional autoencoder algorithm (WRF-DCA) was then applied to reduce the bias that WRF-Chem showed against the OMI observations. Finally, the correction performance of WRF-DCA was compared with a conventional bias correction method, linear scaling (WRF-LS). The performance of WRF-Chem, WRF-DCA, and WRF-LS was analyzed using three metrics, that is, the normalized mean bias (NMB), the root mean square error (RMSE), and Pearson's correlation coefficient (R). The results showed that WRF-Chem overestimated SO2 at locations near the volcano site and underestimated SO2 at locations further away from the volcano site. It generated an overall average NMB of −0.61 against the OMI observations. Respectively, WRF-DCA and WRF-LS reduced this bias by an average of 0.25 (40.9%) and 0.21 (34.4%). Furthermore, although both methods also reduced the RMSE and improved the correlation, WRF-DCA consistently performed better than WRF-LS. This study demonstrates the advantage that deep learning can provide in estimating volcanic SO2 emissions.

A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI.

Quantitative Precipitation Estimation (QPE) Rainfall from Meteorology Radar over Chi Basin

This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property.

(Digital Presentation) Evaluation of Two Methods of Bias Correction of Precipitations on Improvement of Hydrological Simulation:Case of Chiffa Basin-Northern Algeria-

This work aim to assess the “basic-quantile”and“gamma-mapping” bias correction methods in order to improve hydrological simulations of the Chiffa watershed. The results are provided by the conceptual rainfall-runoff GR2M model in the R environment, coupled with the outputs of rainfall data simulations from the RCA4 model of CORDEX-AFRICA forced by two global circulation models (MPI-ESM-LR and CRNM-CM5) under two Representative Concentration Pathways (RCP) RCP4.5 and RCP 8.5 .The validation of GR2M hydrological model on the Chiffa basin using Nash criteria made it possible to simulate future flows over the period 2074-2099. The results showed that the future flows simulated from raw rainfall data are not in adequacy with the evolution of future rainfall simulations whereas the flows simulated from the corrected rainfall data shown better results, which highlights the importance of bias correction for hydrological impact studies. Finally, the regional climate models simulate a 9% decrease in winter, and a 6% increase in spring under RCP4.5 by the end of the 21st century Figure 1