Bias Correction Methods Research Articles

Investigating the Accuracies in Short-Term Weather Forecasts and Its Impact on Irrigation Practices

The effectiveness of irrigation schedules and crop growth models is largely dependent on the accuracies associated with the most uncertain input parameter, viz. weather forecasts. This research is focused on quantifying the inaccuracies associated with the India Meteorological Department (IMD) issuing short-term weather forecasts (with 1–5 days lead time), and their propagation into irrigation models, with an objective to select the optimal parameters for use with simulation. While precipitation (P) forecasts were directly used, remaining meteorological forecasts were converted to reference evapotranspiration (ET0) forecasts. The effectiveness of the ‘P’ and ‘ET0’ forecasts was found to be low at all lead times. We applied two popular bias correction methods: linear scaling (LS) and empirical quantile mapping (EQM), and observed a marginal improvement in forecast skill. Bias-corrected, forecast-driven irrigation scenarios, along with conventional irrigation (that ignores weather forecast), and a hypothetical perfect 5-day forecast-based irrigation (as reference) system were tested on a water-intensive paddy crop for two growing seasons (S1: monsoon and S2: winter). Conventional irrigation resulted in the highest use of irrigation water (820 mm in S1, 880 mm in S2) and percolation loss (1,140 mm in S1, 680 mm in S2), while achieving a low relative yield (0.88 in S1, 0.87 in S2). LS-corrected forecasts outperformed other scenarios with 20.24%±4.21% and 1.25%±1.51% savings in irrigation costs for S1 and S2, respectively. While IMD forecasts greatly improved irrigation schedules in the monsoon season, their usage for winter crops was found to be trivial. Our findings conclude that LS-corrected IMD forecasts were moderately reliable over multiple lead times and can serve as a valuable addition to irrigation scheduling, provided the contribution of ‘P’ to the total water balance is significant.

Application of Recurrent Neural Networks to Model Bias Correction: Idealized Experiments With the Lorenz‐96 Model

AbstractSystematic biases in numerical weather prediction models cause forecast deviation from reality. While model biases also affect data assimilation and degrade the analysis accuracy, observation information incorporated through data assimilation can provide information for detecting and alleviating such biases. In this study, the application of machine learning to model bias correction is demonstrated, emphasizing the effectiveness of recurrent neural networks. Idealized experiments are performed using the two‐scale coupled Lorenz‐96 model as the true system and single Lorenz‐96 model as the imperfect forecast model, to compare the effectiveness of bias correction methods based on various architectures of neural networks and simple linear regression. The neural networks generally outperformed linear regression, and recurrent neural networks showed the best ability in finding the systematic bias component from the analysis increment data. Bias correction using the recurrent neural networks also gives the most significant improvement in reducing the error growth rate in extended range forecasts. The results suggest that including past time series of the forecast variables improve model bias correction when limited information of the observation is incorporated through data assimilation.

A Modification to Phase Estimation for Distributed Scatterers in InSAR Data Stacks

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.

Adjusting for publication bias in meta-analysis via inverse probability weighting using clinical trial registries.

Publication bias is a major concern in conducting systematic reviews and meta-analyses. Various sensitivity analysis or bias-correction methods have been developed based on selection models, and they have some advantages over the widely used trim-and-fill bias-correction method. However, likelihood methods based on selection models may have difficulty in obtaining precise estimates and reasonable confidence intervals, or require a rather complicated sensitivity analysis process. Herein, we develop a simple publication bias adjustment method by utilizing the information on conducted but still unpublished trials from clinical trial registries. We introduce an estimating equationfor parameter estimation in the selection function by regarding the publication bias issue as a missing data problem under the missing not at random assumption. With the estimated selection function, we introduce the inverse probability weighting (IPW) method to estimate the overall mean across studies. Furthermore, the IPW versions of heterogeneity measures such as the between-study variance and the I2 measure are proposed. We propose methods to construct confidence intervals based on asymptotic normal approximation as well as on parametric bootstrap. Through numerical experiments, we observed that the estimators successfully eliminated bias, and the confidence intervals had empirical coverage probabilities close to the nominal level. On the other hand, the confidence interval based on asymptotic normal approximation is much wider in some scenarios than the bootstrap confidence interval. Therefore, the latter is recommended for practicaluse.

Age-level bias correction in brain age prediction

The predicted age difference (PAD) between an individual’s predicted brain age and chronological age has been commonly viewed as a meaningful phenotype relating to aging and brain diseases. However, the systematic bias appears in the PAD achieved using machine learning methods. Recent studies have designed diverse bias correction methods to eliminate it for further downstream studies. Strikingly, here we demonstrate that bias still exists in the PAD of samples with the same age even after kind of correction. Therefore, current PAD may not be taken as a reliable phenotype and more investigations are needed to solve this fundamental defect. To this end, we propose an age-level bias correction method and demonstrate its efficacy in numerical experiments.

Independent Bias Correction Method for Satellite Observation Data Introduced to CO<sub>2</sub> Flux Inversion

Satellite observations are expected to play an important role in studying carbon fluxes. However, it is necessary to properly remove spatiotemporal bias from these observations. In this study, we estimated the spatiotemporal bias in satellite XCO2 data by making an inversion of in-situ observations. Compared to the GOSAT XCO2 concentrations derived from this analysis, the global average difference is −1.26 ppm, with a difference of −0.76 ppm over the land and −1.54 ppm over the ocean. We then developed a method to correct the bias between the satellite XCO2 and the in-situ inversed XCO2 data. Using this bias correction method, we performed an inverse analysis using in-situ and satellite observations and found that the error relative to the independent observations decreased mainly in the middle and upper troposphere compared to the in-situ inversion. Compared to previous studies, the introduction of satellite observations also suppressed large variations in the regional CO2 flux, except in regions with few observations. After correction using the proposed method, the estimated CO2 flux can be used to provide improved estimates of regional CO2 fluxes; however, the results also show a need for increasing the CO2 observation sites and improving the analysis system.

Spatio-temporal variation of precipitation projection based on bias-adjusted CORDEX-SA regional climate model simulations for arid and semi-arid region

Global warming significantly affects the hydrological cycle that influences hydrological systems worldwide. Knowledge of precipitation variation under a changing climate can provide effective ways to deal with its impacts on water resource management. Hence, in this study, spatiotemporal variability of precipitation under future climatic scenarios was studied using suitable regional climate models (RCMs) extracted from the South Asia Coordinated Regional Climate Downscaling Experiments (CORDEX-SA) based on the control period (1976-2005). Additionally, this research attempted to evaluate the changes in precipitation patterns in Iran by integrating output from a set of RCMs extracted from CORDEX-SA with selected bias-correction (BC) methods, namely quantile ranking-based bias correction (QRBC), linear scaling (LS), empirical quantile mapping (EQM), Gamma quantile mapping (GQM), and Gamma-Pareto quantile mapping (GPQM). Regarding the findings, the performance of RCMs varied based on the climates of regions. Moreover, QRBC played an essential role in improving the output of RCMs to simulate precipitation values. Under RCP4.5 and RCP8.5, the projection of seasonal precipitation indicated that a slight wetting pattern might occur across the northern area in all seasons, especially in the southern regions. Whereas the southern area exhibited a strong wetting tendency in autumn, and the maximum percentage changes (PC) of precipitation were projected to increase by 35% for the mid (2040-2070) and far future (2070-2100) periods, based on RCP4.5 and RCP8.5, respectively. This study will be helpful in understanding the uncertainties in CORDEX-SA RCM projections of climate changes in Iran. Using the outcomes of this study, strategies can be adopted to better manage water resources for various regions of Iran.

Predicting Groundwater Level Using Climate Change Scenarios in the Southern Part of Mali

Groundwater is mainly demanded in all the activities for the population of the southern part especially in the Koda catchment, the studied area. These resources are affected by various factors especially climate change. Therefore, knowing the impact of projected climate change on groundwater recharge is an important issue for water resources management, especially for those responsible for the Koda catchment. In this work, the impact of climate change on groundwater resources in the study area in Mali, West Africa is investigated. The Hydrogeological modeling was performed using the Gardenia model, and the monthly precipitation and temperature data were used as the Baseline. These data considered the past 30-year period (1987-2016) and the projections for the next 30 years (2021-2050). Projected precipitation and air temperatures, extracted from the Rossby Centre regional Atmospheric climate model (RCA 4) statistically downscaled from the GCM-IHEC-EC-EARTH and the GCM-MPI-M-MPI-ESM-LR under the Representative Concentration Pathways RCP 4.5 and RCP 8.5 and corrected with the Multiscale Quantile Mapping bias correction method, were used as input data to the gardenia model. Potential evapotranspiration (PET) values estimated from Blaney Criddle method and groundwater levels measured in three piezometers were used to calibrate the Gardenia model. The outputs display the reduction of groundwater level in the three piezometers in the Koda catchment for all the two Regional Climate Models (RCMs) during the periods of rainy season from July to October. From the results of GCM IHEC-EC-EARTH, the projected decline in GWL reaches 1.09 m for the RCP 4.5 and it up to 1.26 m for the RCP 8.5 in the study area while the GCM MPI-M-MPI-ESM-LR presentes the decline in groundwater level (GWL) during winter season from about 0.62 m for the RCP 4.5 up to 1.93 m for the RCP 8:5. Both RCMs project a reduction trend of groundwater recharge over time. It is noticeable that this decline is greater in RCP8.5 for all the three piezometers. The results also show that the average groundwater recharge (90 mm) in the future (2021-2050) is lower (180 mm) than that of the current drought (1987-2016), which could lead to severe drought events. The projected impacts of climate change would have a significant impact on groundwater in the period of 2029-2039; this situation could have a negative impact the socioeconomic activities especially on agriculture, which depends on water resources. The results will help also to take some adaptation measures to climate change, the famers could have a possibility to know the period of groundwater recharge where they have more water infiltration therefore, where to seek crops that need less or more water. The study area presents numerous potential of groundwater, the results could be a tool for groundwater management and to determine the favorable sites to implant new boreholes.

Enhancement of RegCM4.7‐CLM precipitation and temperature by improved bias correction methods over Central Africa

AbstractPrecipitation and temperature projections from Regional Climate Models (RCMs) over Central Africa (CA) are of great importance. However, several studies have already shown that the data from RCMs cannot be directly used for climate impact studies on a local scale because of systematic biases that characterize them. Therefore, RCM simulations must be preprocessed in order to make them more representative of climate at a local scale. The present study focuses on improving temperature and precipitation simulations from the RegCM4.7 RCM over CA. For this purpose, two correction methods are used: The adjusted Linear Scaling and Variance (Va) methods. Corrected and uncorrected precipitation and near‐surface temperature are compared with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the fifth generation of ECMWF reanalysis data (ERA5), respectively. Comparison of the performances of both methods was made during various seasons not only over the whole CA region but also over two sub‐domains (Zones 1 and 2) having different characteristics. This was done on the basis of the mean bias and Root Mean Square Error. Results show that the precipitation from RegCM4 is tainted with huge wet biases compared to CHIRPS. Overall, the analysis suggests that the Va method is the most suitable for reducing the biases of RegCM4.7 simulations, particularly for precipitation irrespective of regions or seasons. However, it has been found that both methods fail to improve temperature biases in the Inter‐Tropical Convergence Zone.

A System Optimization Scheme for Bias Correction of Polarimetric Phased-Array Radar

With the change in the spatial angle, the cross-polarization isolation (CPI) of polarimetric phased-array radar (PPAR) changes as well, destroying the estimation of the target polarization scattering matrix (PSM). To correct the bias in PPAR, this article comprehensively designs the transmitting antenna, receiving antenna, and signal waveform and proposes a bias correction method based on system optimization. First, for the transmitting antenna of PPAR, the second-order cone program (SOCP) model is proposed to optimize the weighting coefficient. With the SOCP-based beamforming method, not only beam pattern in any spatial angle can be achieved, but also arbitrary polarization state can be precisely configured. Then, for the wideband receiving signal with a certain beamwidth, an angle estimation method based on eigenvalue decomposition is proposed in this article, which can effectively cure the challenges introduced by the beamwidth and signal bandwidth. Subsequently, for the signal waveform, the phase code is designed to measure all the elements of PSM in simultaneous transmission and simultaneous reception (STSR) mode, which could eliminate the biases of the moving speed and the second-order cross-polarization error. Finally, this article compares with other methods based on differential reflectivity, and experiments show that the factors such as the spatial angle, array structure, antenna beamwidth, signal bandwidth, motion speed, and signal-to-noise ratio (SNR) have the least influence on the present method in this article.

Physical responses of Baiu extreme precipitation to future warming: Examples of the 2018 and 2020 western Japan events

Baiu (Meiyu) is a continuous and large-scale rainfall phenomenon in East Asia, whose extreme events have caused many disasters in recent years. Global warming will accelerate the global water cycle and tend to increase the risks of extreme precipitation events. However, to date, the response of Baiu extreme rainfall to future climate warming is still not well investigated. In this study, by conducting the ensemble experiments with a very high resolution (2 km) regional climate model and pseudo warming technique, we reproduced two Baiu extreme rainfall events in 2018 and 2020 that caused severe losses in western Japan, and investigated its response to a +4K warmer climate. We found that climate warming will apparently increase the intensity of the peak Baiu precipitation, and the increasing rates can be much larger than the well-known Clausius-Clapeyron (C–C) scaling (∼7% K−1) with the maximum rate reaching 16.7% K−1 for Shikoku in the 2018 event. This increase in peak precipitation is jointly caused by the increased atmospheric moisture and the accelerated vertical air motion. However, both super and under C–C scaling of changes are expected for total precipitation, with the range from 1.5% K−1 to 17.4% K−1 for different regions and events. The rainfall change rates are jointly influenced by moisture transport direction and elevation, topography, and vertical air motion. The moisture inflow with lower elevation tends to cause a larger rainfall increase. It should also be noted that our regional simulations underestimate the extreme precipitation when reproducing the two events, which is caused by the bias in the GCM data. Implementing bias correction methods or future improvement of GCM simulations could be the solutions for future study. Overall, this study points out the risk of extreme Baiu rainfall under future global warming, and highlights the different increasing rates of extreme Baiu rainfall due to specific climate conditions.

A Novel Bias Correction Method for Extreme Events

When one is using climate simulation outputs, one critical issue to consider is the systematic bias affecting the modelled data. The bias correction of modelled data is often used when one is using impact models to assess the effect of climate events on human activities. However, the efficacy of most of the currently available methods is reduced in the case of extreme events because of the limited number of data for these low probability and high impact events. In this study, a novel bias correction methodology is proposed, which corrects the bias of extreme events. To do so, we extended one of the most popular bias correction techniques, i.e., quantile mapping (QM), by improving the description of extremes through a generalised extreme value distribution (GEV) fitting. The technique was applied to the daily mean temperature and total precipitation data from three seasonal forecasting systems: SEAS5, System7 and GCFS2.1. The bias correction efficiency was tested over the Southern African Development Community (SADC) region, which includes 15 Southern African countries. The performance was verified by comparing each of the three models with a reference dataset, the ECMWF reanalysis ERA5. The results reveal that this novel technique significantly reduces the systematic biases in the forecasting models, yielding further improvements over the classic QM. For both the mean temperature and total precipitation, the bias correction produces a decrease in the Root Mean Squared Error (RMSE) and in the bias between the simulated and the reference data. After bias correcting the data, the ensemble forecasts members that correctly predict the temperature extreme increases. On the other hand, the number of members identifying precipitation extremes decreases after the bias correction.

A deep learning method for real-time bias correction of wind field forecasts in the Western North Pacific

Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short) can provide a basis for the establishment of maritime-disaster warning systems, but they contain some systematic biases. The fifth-generation EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by about 5 days. To overcome this issue, a spatiotemporal deep-learning method could be used for nonlinear mapping between EC and ERA5 data, which would improve the quality of EC wind forecast data in real time. In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved “double-encoder forecaster” architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model. The study area was the western North Pacific (WNP), and real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021, divided into four seasons. Compared with the original EC forecasts, after correction using the MT-DETrajGRU model the wind speed and wind direction biases in the four seasons were reduced by 8–11% and 9–14%, respectively. In addition, the proposed method modelled the data uniformly under different weather conditions. The correction performance under normal and typhoon conditions was comparable, indicating that the data-driven mode constructed here is robust and generalizable.

Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia

Abstract This study investigates the utility of satellite-based rainfall products and the performance of bias correction methods in one of the sub-basins of the Upper Blue Nile Basin (Main Beles basin). Four satellite rainfall products are used: Climate Prediction Center (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 (TMPA 3B42V7), and Climate Forecast System Reanalysis (CFSR). The performance of the satellite rainfall products (SRPs) was compared using three bias correction methods (Delta, Empirical Quantile Mapping (EQM), and Quantile Mapping (QM)) on five metrological stations. Six statistical criteria were used to evaluate these methods on the period 2003–2016 at daily and monthly scales. The results showed that SRPs and bias correction methods of CMORPH_QM (r = 0.538) and TMPA_3B42V7_EQM (r = 0.95) data showed good performance, while PERSIANN_EQM (r = 0.348) and PERSIANN_Delta (r = 0.83) performed worst at daily and monthly time scales, respectively. This study assessed the importance of SRPs and bias correction methods to use in data scarce regions for water resources planning and other related sectors.

Daily precipitation and temperature for 2021–2050 over China: Multiple RCMs and emission scenarios corrected by a trend‐preserving method

AbstractDaily climate data are extremely important to drive ecological models, but the outputs of climate models are often deviated from the observations. The bias correction methods mostly highlight the target statistical parameters (e.g., mean and variance) for a target period, but perturb the simulated change trend and variability. In this study, we utilize a trend‐preserving bias correction method that can simultaneously consider the frequency and intensity of climatic variables to adjust the daily maximum and minimum temperature, and precipitation (Tmax, Tmin, and Pre) from CORDEX‐EA regional climate models for the period of 2021–2050 over China. Results show that: (a) the bias correction method greatly improves the spatial patterns of simulated Tmax, Tmin, and Pre since the corrected data has higher correlation and smaller differences across the whole domain. The differences in mean Tmax, Tmin, and Pre between climate models and observations have been reduced from −4.5 ± 6.41°C, −2.06 ± 0.58°C, 177.25 ± 51.55 mm before correction to 0.01 ± 0.02°C, −0.01 ± 0.02°C, −26.22 ± 6.39 mm after correction, respectively. In addition, the simulated change trend and climate variability at all time scales are well preserved. (b) Spatially, the warming rates in the west would be higher than in the east, and they are larger under the most severe future emissions scenario (RCP8.5) relative to the low‐mid emissions scenario (RCP4.5). The Pre change would mainly occur over east China with the significant upward trend. The dataset presented in this article has been published in the Network Common Data Form (NetCDF) File Format at https://doi.org/10.5281/zenodo.5058620.

Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China

The 2-m temperature is one of the important meteorological elements, and improving the accuracy of medium- and long-term forecasts of the 2-m temperature is important. The similarity forecasting method is widely used as a calibration technique in the statistical postprocessing of numerical weather prediction (NWP). In this study, the analogue ensemble averaging method is used to correct the deterministic forecast of the 2-m temperature with a forecast lead time from 180 h to 348 h using the CMA-GEPS model. The bias, mean absolute error (MAE), and root mean square error (RMSE) are used as the evaluation metrics. In comparison with NWP, the systematic error of the model for 2-m temperature is effectively reduced during each forecast period when using the analogue ensemble averaging method. In addition, the differences in forecast errors between regions are reduced, and the accuracy of 2-m temperature forecasts over complex terrain, especially in Southwest China, Northwest China, and North China, is improved using this method. In the future, there is certainly potential to apply the analogue ensemble averaging method to the bias correction of medium- and long-term forecasts of more meteorological elements.

A Deep Learning‐Based Bias Correction Method for Predicting Ocean Surface Waves in the Northwest Pacific Ocean

AbstractOcean waves, especially extreme waves, are vital for air‐sea interaction and shipping. However, current wave models still have significant biases. Based on a numerical wave model and a deep learning model, a BU‐Net by adding batch normalization layers to a U‐Net, we accurately predict the significant wave height (SWH) of the Northwest Pacific Ocean. For each day in 2017–2021, we conducted a 3‐day hindcast experiment using WAVEWATCH3 (WW3) to obtain the SWH forecasts at lead times of 24, 48, and 72 hr, forced by GFS real‐time forecast surface winds. After using BU‐Net, the mean Root Mean Squared Errors (RMSEs) of the SWH forecast from WW3 at lead times of 24, 48, and 72 hr are reduced by 40%, 38%, and 30%, respectively. During typhoon passages, the drop percentages of RMSEs all exceed 20% for three lead times. Therefore, combining numerical models and deep learning is very promising in wave forecasting.

Species distribution modelling of the endangered Mahogany Glider (Petaurus gracilis) reveals key areas for targeted survey and conservation

AbstractThe Mahogany Glider (Petaurus gracilis) is one of the most endangered marsupials in Australia. Its known distribution is an approximately 120 km strip of fragmented coastal woodland in north‐east Queensland, from north of Townsville to the Tully area. Records are clustered in a number of well‐surveyed areas, with significant areas of lowland habitat unsurveyed. Around 30% of historic records fall in areas that were subsequently cleared for farmland, and ongoing clearing and fragmentation of lowland sclerophyll forest continues within the potential distribution. Resolving the distribution is an urgent requirement to guide conservation but Mahogany Gliders are difficult to detect in the field. Species distribution modelling offers a technique for estimating the fine‐scale distribution and for targeting further field survey and conservation efforts. We used known occurrence records (N = 481) to predict the distribution of Mahogany Gliders across the Wet Tropics bioregion. We used climatic, topographic and other environmental predictors to generate distribution models using Maxent and Random Forest algorithms, each with two bias correction methods. The predictions revealed that many unknown populations may exist within the currently defined distribution and in important areas beyond this (e.g. Hinchinbrook Island). There was reasonable congruence between models, and we include syntheses of the models to present the most likely current distribution. The most important predictor variables across the models were precipitation seasonality (high seasonality), elevation (generally <100 m), soil type (hydrosols) and vegetation type (including Eucalyptus and Melaleuca woodlands). Our results identify core habitat and reveal key areas that require targeted field surveys. Importantly, the predicted suitable habitat is highly fragmented and ongoing conservation efforts need to improve habitat connectivity and limit further fragmentation.

Investigating the Impact of the Spatiotemporal Bias Correction of Precipitation in CMIP6 Climate Models on Drought Assessments

Precipitation of future climate models is critical for the assessments of future drought but contains large systematic biases over the Tibetan Plateau. Although the common precipitation bias correction method, quantile mapping has achieved remarkable results in terms of temporal bias correction, it does not consider the spatial distribution of bias. Furthermore, the extent to which precipitation bias affects drought estimation remains unclear. In our study, we take the Qinghai–Tibet Plateau (QHTP) as the case study and quantify the impact of corrected precipitation bias for seven Coupled Model Intercomparison Project Phase 6 (CMIP6) models on drought assessment in historical and future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). To improve the accuracy of drought prediction, potential evapotranspiration (PET) was also corrected. Firstly, the histogram matching-quantile mapping (HQ) algorithm considering spatial correction is established to correct precipitation and PET. Then, we quantified the effects of precipitation and potential evapotranspiration correction on the change of drought intensity, and finally analyzed the spatiotemporal trends of precipitation, PET, and SPEI over the QHTP in the future. The results show that the HQ method can effectively improve the simulation ability of the model, especially the simulation accuracy of the ensemble model. After correction, the average annual total precipitation (TP) declined by 64.262% in 99.952% of QHTP, the average PET increased in 11.902% of the area and decreased in 88.098% of the area, while the intensity of the drought in 81.331% of the area increased by 2.875% and the 18.669% area decreased by 1.139%. Therefore, the uncorrected simulation data overestimated the future increase trend in precipitation and underestimated the future decrease trend in SPEI. The trend of HQ-corrected TP increased by 3.730 mm/10a, 7.190 mm/10a, and 12.790 mm/10a, and the trend of SPEI (TP and PET corrected) decreased by 0.143/100a, 0.397/100a, and 0.675/100a, respectively. Therefore, quantifying the changing relationship between precipitation bias correction and drought assessments is useful for understanding regional climate change.

Simulation and Evaluation of Water Resources Management Scenarios Under Climate Change for Adaptive Management of Coastal Agricultural Watersheds

The main objective of this paper is to analyze the impact of climate change on water resources management and groundwater quantity and quality in the coastal agricultural Almyros Basin, in Greece. Intensive groundwater abstractions for irrigation and nitrogen fertilization for crop production maximization, have caused a large water deficit, nitrate pollution, as well as seawater intrusion in the Almyros aquifer system. Multi-model climate projections for Representative Concentration Pathways (RCPs 4.5 and 8.5) from the Med-CORDEX database for precipitation and temperature have been used to evaluate the impacts of climate change on the study area. The multi-model climate projections have been bias-corrected with Delta, Delta change of Mean and Variance, Quantile Delta Change, Quantile Empirical Mapping, and Quantile Gamma Mapping methods, and statistically tested to find the best GCM/RCM multi-model ensemble. Simulation of coastal water resources has been performed using an Integrated Modelling System (IMS) that contains connected models of surface hydrology (UTHBAL), groundwater hydrology (MODFLOW), nitrate leaching/crop growth (REPIC), nitrate pollution (MT3DMS), and seawater intrusion (SEAWAT). The results indicate that the best climate multi-model ensemble consists of three (3) climate models for both RCP4.5 and RCP8.5 using the Quantile Empirical Mapping bias-correction method. The IMS was applied for historical and future periods with observed and simulated meteorological inputs (e.g. precipitation and temperature) and various irrigation and agronomic scenarios and water storage works development (i.e. reservoirs). The results indicate that at least deficit irrigation and deficit irrigation along with rain-fed cultivation schemes, combined with or without the development and operation of reservoirs, should be applied to overcome the degradation of groundwater quality and quantity in the study basin. Based on the findings of this work, the water resources management should be adaptive to tackle the water resources problems of the Almyros Basin.