Quantile Mapping Bias Correction Method Research Articles

Effects of Climate change on temperature and precipitation in the Lake Toba region, Indonesia, based on ERA5-land data with quantile mapping bias correction

Climate change is a serious problem that can cause global variations in temperature and rainfall patterns. This global variation can affect the water availability of lakes. In this study, trends in temperature and rainfall in the Lake Toba area for 40 years (1981–2020) were analyzed using ERA5-Land data corrected with observation station data utilizing the quantile mapping bias correction method. Corrected ERA5-Land data were used in this study to show spatial patterns and trends. The Mann–Kendall and Sen slope tests were carried out to see the magnitude of the trend. A comparison of temperature and rainfall against their baseline period (1951–1980) was also investigated. The results of this study show that climate change has affected the trend of increasing temperature and rainfall in the Lake Toba area, with an increase in temperature of 0.006 °C per year and an average rainfall of 0.71 mm per year. In general, significant changes in the increase of temperature and rainfall occurred in the last decade, with an increase in temperature of 0.24 °C and rainfall of 22%. The study of the impact of climate change expected to be useful for policymakers in managing water resources in the Lake Toba area.

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.

Hydrological ensemble forecasting using a multi-model framework

Ensemble streamflow predictions (ESP) from a single hydrological model tend to under-sample the variability needed to provide a good representation of streamflow observations. ESPs typically end up being biased and under-dispersed, making them imprecise and less valuable in an operational context. In this study, a proposed methodology based on a multi-hydrological model approach is applied to address these issues. The forecast methodology includes eight lumped hydrological models whose states were updated through data assimilation using an Ensemble Kalman Filter (EnKF) to provide initial states to the models, allowing them to better sample uncertainty in the hydrometeorological observations and reduce initial condition errors. ESP forecasts are then driven by numerical weather prediction ensemble forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) for a nine-day lead-time on five snowmelt-dominated catchments in Quebec, Canada. These eight assimilated individual ESPs are then post-processed using a quantile mapping bias correction method, after which they are combined in a larger ensemble (known as a Grand Ensemble Streamflow Prediction (GESP)) using a multi-model approach. Results indicate that the proposed methodology allowed reducing both forecast biases and under-dispersion problems for all catchments and four periods corresponding to the seasonality of the streamflow. On their own, the data assimilation (DA) and post-processing manipulations allowed shifting the hydrological ensembles to better encompass the observed streamflow. The multi-model approach further improved the forecasts by increasing their reliability. Talagrand diagrams, supported by the Kolmogorov-Smirnov test for uniformity, as well as the Average Bin Distance to Uniformity (ABDU) and the Continuous Ranked Probability Skill Score (CRPSS), were chosen as performance metrics to evaluate the robustness of the methodology. These findings suggest that it is possible to improve hydrological short-term forecast reliability through a multi-model approach in an operational forecasting context.

Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections

Abstract. Dynamical downscaling of future projections of global climate model outputs can provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand in regional water resource planning processes. By explicitly modelling climate processes within and across global climate model grid cells for a region, dynamical downscaling can provide higher-resolution hydroclimate projections and independent (from historical time series), physically plausible future rainfall time series for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias-correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile–quantile mapping to rainfall dynamically downscaled by the NSW and ACT Regional Climate Modelling (NARCliM) Project in the state of Victoria, Australia, and examine the effect of this on (i) biases both before and after bias correction in different rainfall metrics, (ii) change signals in metrics in comparison to the bias and (iii) the effect of bias correction on wet–wet and dry–dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile–quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. Representing quantile–quantile mapping in this way demonstrates that any quantile mapping bias-correction method is unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias-correct dynamical downscaling rainfall outputs.

Future projections of Malaysia daily precipitation characteristics using bias correction technique

Abstract The quantile mapping (QM) bias correction algorithm was applied to bias-correcting the daily rainfall over the Malaysia based on the APHRODITE gridded observed dataset. The quality of the bias correction in reducing the biases of regional climate models in frequency (FREQ), mean intensity (SDII) and 90th percentile (Q90) of the daily rainfall was assessed. The result shows that QM performs well in correcting the rainfall statistics. The QM bias correction method was then applied to seven Regional Climate Models (RCMs) projection output from the CORDEX Southeast Asia (CORDEX-SEA) dataset, to obtain high resolution rainfall change scenarios over the Malaysia, based on the IPCC's representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The ensemble mean of seven RCMs suggests that the future projection expected a decrease in the rainfall frequencies over the Malaysia toward the 21st century for both RCP scenarios. Meanwhile, the rainfall intensity and extremes over the Peninsular Malaysia (East Malaysia) is likely to decrease (increase) especially during the winter (summer and autumn) months by the end of the century. A remarkable decrement of rainfall frequency and extremes was found in western Peninsular Malaysia, with changes of around −4 to −8% with respect to the historical period driven by the RCP4.5 and RCP8.5 scenario respectively.

High-Resolution, Integrated Hydrological Modeling of Climate Change Impacts on a Semi-Arid Urban Watershed in Niamey, Niger

This study evaluates the impact of climate change on water resources in a large, semi-arid urban watershed located in the Niamey Republic of Niger, West Africa. The watershed was modeled using the fully integrated surface–subsurface HydroGeoSphere model at a high spatial resolution. Historical (1980–2005) and projected (2020–2050) climate scenarios, derived from the outputs of three regional climate models (RCMs) under the regional climate projection (RCP) 4.5 scenario, were statistically downscaled using the multiscale quantile mapping bias correction method. Results show that the bias correction method is optimum at daily and monthly scales, and increased RCM resolution does not improve the performance of the model. The three RCMs predicted increases of up to 1.6% in annual rainfall and of 1.58 °C for mean annual temperatures between the historical and projected periods. The durations of the minimum environmental flow (MEF) conditions, required to supply drinking and agricultural water, were found to be sensitive to changes in runoff resulting from climate change. MEF occurrences and durations are likely to be greater from 2020–2030, and then they will be reduced for the 2030–2050 statistical periods. All three RCMs consistently project a rise in groundwater table of more than 10 m in topographically high zones, where the groundwater table is deep, and an increase of 2 m in the shallow groundwater table.

Climate change impacts on Canadian yields of spring wheat, canola and maize for global warming levels of 1.5 °C, 2.0 °C, 2.5 °C and 3.0 °C

Science-based assessments of climate change impacts on cropping systems under different levels of global warming are essential for informing stakeholders which global climate targets and potential adaptation strategies may be effective. A comprehensive evaluation of climate change impacts on Canada’s crop production under different levels of global warming is currently lacking. The DayCent, DNDC and DSSAT models were employed to estimate changes in crop yield and production for three prominent crops including spring wheat, canola and maize in current agricultural regions of Canada. Four warming scenarios with global mean temperature changes of 1.5 °C, 2.0 °C, 2.5 °C and 3.0 °C above the pre-industrial level were investigated. Climate scenarios from 20 Global Climate Models, included in the Coupled Model Intercomparison Project Phase 5 and downscaled with a multivariate quantile mapping bias correction method, were used to drive the crop simulation models. Simulated yield changes demonstrate a potentially positive impact on spring wheat and canola yields at all four temperature levels, particularly when shifting planting date is considered in the simulations. There was less consensus for the currently utilized short-season maize cultivars, as yields were only projected to increase by DNDC compared to a slight decrease by DayCent and a slight increase up to 2.5 °C followed by a decrease at 3.0 °C by DSSAT. These findings indicate that climate at the global warming levels up to 3.0 °C above the pre-industrial level could be beneficial for crop production of small grains in Canada. However, these benefits declined after warming reached 2.5 °C.

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

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.

Bias correction and downscaling of future RCM precipitation projections using a MOS‐Analog technique

AbstractIn this study we assess the suitability of a recently introduced analog‐based Model Output Statistics (MOS) downscaling method (referred to as MOS‐Analog) for climate change studies and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider daily precipitation output from an ensemble of Regional Climate Models provided by the ENSEMBLES project. The reanalysis‐driven Regional Climate Model (RCM) data provide the historical data (with day‐to‐day correspondence with observations induced by the forcing boundary conditions) to conduct the analog search of the control (20C3M) and future (A1B) global climate model (GCM)‐driven RCM values. First, we show that the MOS‐Analog method outperforms the raw RCM output in the control 20C3M scenario (period 1971–2000) for all considered regions and precipitation indices, although for the worst‐performing models the method is less effective. Second, we show that the MOS‐Analog method broadly preserves the original RCM climate change signal for different future periods (2011–2040, 2041–2070, 2071–2100), except for those indices related to extreme precipitation. This could be explained by the limitation of the analog method to extrapolate unobserved precipitation records. These results suggest that the MOS‐Analog is a spatially consistent alternative to standard bias correction methods, although the limitation for extreme values should be taken with caution in cases where this aspect is relevant for the problem.

Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

Abstract. Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼ 200 km) to 1∕8° ( ∼ 12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼ 50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis.

Applying bias correction for merging rain gauge and radar data

Weather radar provides areal rainfall information with very high temporal and spatial resolution. Radar data has been implemented in several hydrological applications despite the fact that the data suffers from varying sources of error. Several studies have attempted to propose methods for solving these problems. Additionally, weather radar usually underestimates or overestimates the rainfall amount. In this study, a new method is proposed for correcting radar data by implementing the quantile mapping bias correction method. Then, the radar data is merged with observed rainfall by conditional merging and kriging with external drift interpolation techniques. The merging product is analysed regarding the sensitivity of the two investigated methods to the radar data quality. After implementing bias correction, not only did the quality of the radar data improve, but also the performance of the interpolation techniques using radar data as additional information. In general, conditional merging showed greater sensitivity to radar data quality, but performed better than all the other interpolation techniques when using bias corrected radar data. Furthermore, a seasonal variation of interpolation performances has in general been observed. A practical example of using radar data for disaggregating stations from daily to hourly temporal resolution is also proposed in this study.

Analysis of precipitation extremes with the assessment of regional climate models over the Willamette River Basin, USA

AbstractAn appropriate, rapid and effective response to extreme precipitation and any potential flood disaster is essential. Providing an accurate estimate of future changes to such extreme events due to climate change are crucial for responsible decision making in flood risk management given the predictive uncertainties. The objective of this article is to provide a comparison of dynamically downscaled climate models simulations from multiple model including 12 different combinations of General Circulation Model (GCM)–regional climate model (RCM), which offers an abundance of additional data sets. The three major aspects of this study include the bias correction of RCM scenarios, the application of a newly developed performance metric and the extreme value analysis of future precipitation. The dynamically downscaled data sets reveal a positive overall bias that is removed through quantile mapping bias correction method. The added value index was calculated to evaluate the models' simulations. Results from this metric reveal that not all of the RCMs outperform their host GCMs in terms of correlation skill. Extreme value theory was applied to both historic, 1980–1998, and future, 2038–2069, daily data sets to provide estimates of changes to 2‐ and 25‐year return level precipitation events. The generalized Pareto distribution was used for this purpose. The Willamette River basin was selected as the study region for analysis because of its topographical variability and tendency for significant precipitation. The extreme value analysis results showed significant differences between model runs for both historical and future periods with considerable spatial variability in precipitation extremes. Copyright © 2012 John Wiley & Sons, Ltd.