scholarly journals Impact Analysis of Univariate and Multivariate Bias Correction on Rice Irrigation Water Needs in Jiangxi Province, China

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 381
Author(s):  
Wido Hanggoro ◽  
Jing Yuanshu ◽  
Leila Cudemus ◽  
Jing Zhihao
Keyword(s):  
Irrigation Water ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Jiangxi Province ◽  
Observation Data ◽  
Climate Variables ◽  
Calibration Period ◽  
Significant Difference ◽  
Rice Irrigation

Regional climate models (RCMs) provide an improved representation of climate information as compared to global climate models (GCMs). However, in climate-agricultural impact studies, accurate and interdependent local-scale climate variables are preferable, but both RCMs and GCMs are still subjected to bias. This study compares univariate bias correction (UBC) and multivariate bias correction (MBC) method to simulate rice irrigation water needs (IWNs) in Jiangxi Province, China. This research uses the daily output of Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) forced with ERAINT (ECMWF ERA Interim) data and 13 Jiangxi ground-based observations, and the observation data are reference data with 1989–2005 defined as a calibration period and 2006–2007 as a validation period. The result shows that UBC and MBC methods favorably bias-corrected all climate variables during the calibration period, but still no significant difference is noted between the two methods. However, the UBC ignores the relationship between climate variables, while MBC preserves the climate variables’ interdependence which affect subsequent analyses. In rice IWNs simulation analysis, MBC has better skill at correcting bias compare to UBC in ETo (evapotranspiration) and Peff (effective rainfall) components. Nonetheless, both methods have a low ability to correct extreme values bias. Overall, both techniques successfully reduce bias, even though they are still less effective for precipitation compared to maximum and minimum temperature, relative humidity and windspeed.

scholarly journals Evaluation of SRE Scenarios for Penang, Selangor and Johor in Peninsular Malaysia using PRECIS Regional Climate Model (RCM)

2018 ◽  
Vol 65 ◽  
pp. 05020
Author(s):  
Kah Seng Chin ◽  
Kok Weng Tan
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Peninsular Malaysia ◽  
Observation Data ◽  
Future Projections ◽  
Monthly Average ◽  
Average Maximum ◽  
Maximum Temperatures ◽  
Historical Observation

Climate change is unambiguous as there is much evidence from around the world showing that changes have already occurred. This phenomenon is in response to an array of human activities, notably the release of greenhouse gases; an understanding of the rate, mode and scale of this change is now of literally vital importance to society. Researchers utilize climate models to study the dynamics of our changing climate and also to make future projections. Climate models are basic representation of many interactions within the Earth’s climate which includes the atmosphere, land surface, oceans and ice. These models are typically quantitative in nature and range from simple depictions of the climate to very complex ones. In this present study, downscaled PRECIS regional climate models (RCMs) were used to project the average minimum and average maximum temperatures and average precipitation for Penang, Selangor and Johor in Peninsular Malaysia. The RCM projections for these three states were developed based on ECHAM4 A2 and ECHAM5 A1B scenarios for the years 1980 to 2069 and ECHAM4 B2 scenario for the years 2010 to 2069. Bias correction will be applied to the simulated historical data to remove common systematic model errors. Historical observation data of monthly average minimum and maximum temperatures and monthly average rainfall from the Malaysian Meteorological Department (MMD) will be used in the bias correction. Finally, a RCM scenario which matches with the historical observation data of the three states for future projections will be recommended.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

scholarly journals New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

2019 ◽  
Vol 58 (12) ◽  
pp. 2617-2632 ◽  
Author(s):  
Qifen Yuan ◽  
Thordis L. Thorarinsdottir ◽  
Stein Beldring ◽  
Wai Kwok Wong ◽  
Shaochun Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Data Product ◽  
Temporal Features ◽  
Stochastic Downscaling

AbstractIn applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

The role of conceptual hydrologic model calibration in climate change impact on water resources assessment

2015 ◽  
Vol 7 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Andrijana Todorovic ◽  
Jasna Plavsic
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
High Sensitivity ◽  
Regional Climate Models ◽  
Baseline Period ◽  
Hydrologic Model ◽  
Calibration Period ◽  
Rainfall Runoff Model ◽  
Parameter Values

Assessment of climate change (CC) impact on hydrologic regime requires a calibrated rainfall-runoff model, defined by its structure and parameters. The parameter values depend, inter alia, on the calibration period. This paper investigates influence of the calibration period on parameter values, model efficiency and streamflow projections under CC. To this end, a conceptual HBV-light model of the Kolubara River catchment in Serbia is calibrated against flows observed within 5 consecutive wettest, driest, warmest and coldest years and in the complete record period. The optimised parameters reveal high sensitivity towards calibration period. Hydrologic projections under climate change are developed by employing (1) five hydrologic models with outputs of one GCM–RCM chain (Global and Regional Climate Models) and (2) one hydrologic model with five GCM–RCM outputs. Sign and magnitude of change in projected variables, compared to the corresponding values simulated over the baseline period, vary with the hydrologic model used. This variability is comparable in magnitude to variability stemming from climate models. Models calibrated over periods with similar precipitation as the projected ones may result in less uncertain projections, while warmer climate is not expected to contribute to the uncertainty in flow projections. Simulations over prolonged dry periods are expected to be uncertain.

scholarly journals Performance of SEACLID/CORDEX-SEA multi-model experiments in simulating temperature and rainfall in Vietnam

2019 ◽  
Vol 41 (4) ◽  
pp. 374-387 ◽  
Author(s):  
Nguyen Thi Tuyet ◽  
Ngo Duc Thanh ◽  
Phan Van Tan
Keyword(s):  
Southeast Asia ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Red River ◽  
Observation Data ◽  
Red River Delta ◽  
North Central ◽  
Climate Downscaling

The study examined the performance of six regional climate experiments conducted under the framework of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA) project and their ensemble product (ENS) in simulating temperature at 2 m (T2m) and rainfall (R) in seven climatic sub-regions of Vietnam. The six experiments were named following the names of their driving Global Climate Models (GCMs), i.e., CNRM, CSIRO, ECEA, GFDL, HADG and MPI. The observation data for the period 1986–2005 from 66 stations in Vietnam were used to compare with the model outputs. Results showed that cold biases were prominent among the experiments and ENS well reproduced the seasonal cycle of temperature in the Northeast, Red River Delta, North Central and Central Highlands regions. For rainfall, all the experiments showed wet biases and CSIRO exhibited the best. A scoring system was elaborated to objectively rank the performance of the experiments and the ENS experiment was reported to be the best.

scholarly journals Impact of climate change on hydrological conditions in a tropical West African catchment using an ensemble of climate simulations

2017 ◽  
Vol 21 (4) ◽  
pp. 2143-2161 ◽  
Author(s):  
Yacouba Yira ◽  
Bernd Diekkrüger ◽  
Gero Steup ◽  
Aymar Yaovi Bossa
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
West African ◽  
Climate Projections ◽  
Impact Of Climate Change ◽  
Budget Analysis ◽  
High Uncertainty

Abstract. This study evaluates climate change impacts on water resources using an ensemble of six regional climate models (RCMs)–global climate models (GCMs) in the Dano catchment (Burkina Faso). The applied climate datasets were performed in the framework of the COordinated Regional climate Downscaling Experiment (CORDEX-Africa) project.After evaluation of the historical runs of the climate models' ensemble, a statistical bias correction (empirical quantile mapping) was applied to daily precipitation. Temperature and bias corrected precipitation data from the ensemble of RCMs–GCMs was then used as input for the Water flow and balance Simulation Model (WaSiM) to simulate water balance components.The mean hydrological and climate variables for two periods (1971–2000 and 2021–2050) were compared to assess the potential impact of climate change on water resources up to the middle of the 21st century under two greenhouse gas concentration scenarios, the Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results indicate (i) a clear signal of temperature increase of about 0.1 to 2.6 °C for all members of the RCM–GCM ensemble; (ii) high uncertainty about how the catchment precipitation will evolve over the period 2021–2050; (iii) the applied bias correction method only affected the magnitude of the climate change signal; (iv) individual climate models results lead to opposite discharge change signals; and (v) the results for the RCM–GCM ensemble are too uncertain to give any clear direction for future hydrological development. Therefore, potential increase and decrease in future discharge have to be considered in climate change adaptation strategies in the catchment. The results further underline on the one hand the need for a larger ensemble of projections to properly estimate the impacts of climate change on water resources in the catchment and on the other hand the high uncertainty associated with climate projections for the West African region. A water-energy budget analysis provides further insight into the behavior of the catchment.

Performance evaluation of bias adjustment methods for wave climate projections in the Mediterranean Sea

2021 ◽  
Author(s):  
Andrea Lira Loarca ◽  
Giovanni Besio
Keyword(s):  
Bias Correction ◽  
Temporal Variability ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Wave Climate ◽  
Mapping Method ◽  
Climate Projections ◽  
Bias Adjustment ◽  
Distribution Mapping

<p>Global and regional climate models are the primary tools to investigate the climate system response to different scenarios and therefore allow to make future projections of different atmospheric variables which are used as input for wave generation models to assess future wave climate. Adequate projections of future wave climate are needed in order to analyze climate change impacts and hazards in coastal areas such as flooding and erosion with waves being the predominant factor with varied temporal variability. </p><p>Bias adjustment methods are commonly used for climate impact variables dealing with systematic errors (biases) found in global and regional climate models.  While bias correction techniques are extended in the climate and hydrological impact modeling scientific communities, there is still a lack of consensus regarding their use in sea climate variables (Parker & Hill, 2017; Lemos et al, 2020; Lira-Loarca et at, 2021)</p><p>In these work we assess the performance of different bias-adjustment methods such as the Empirical Gumbel Quantile Mapping (EGQM) method as a standard method which takes into the account the extreme values of the distribution takes, the Distribution Mapping method using Stationary Mixture Distributions (DM-stMix) allowing for a better representation of each variable in the mean regime and tails and the Distribution Mapping method using Non-Stationary Mixture Distributions (DM-nonstMix) as an improved methods which allows to take into account the temporal variability of wave climate according to different baseline periods such as monthly, seasonal, yearly and decadal. The performance of the different bias adjustment methods will be analyzed with particular interest on the futural temporal behavior of wave climate. The advantages and drawbacks of each bias adjustment method as well as their complexity will be discussed.</p><p> </p><p><em>References:</em></p><ul><li>Lemos, G., Menendez, M., Semedo, A., Camus, P., Hemer, M., Dobrynin, M., Miranda, P.M.A. (2020). On the need of bias correction methods for wave climate projections, Global and Planetary Change, 186, 103109.</li> <li><span>Lira-Loarca, A., Cobos, M., Besio, G., Baquerizo, A. (2021) Projected wave climate temporal variability due to climate change. Stoch Environ Res Risk Assess.</span></li> <li><span>Parker, K. & Hill, D.F. (2017) Evaluation of bias correction methods for wave modeling output, Ocean Modelling 110, 52-65</span></li> </ul><p><br><br></p>

scholarly journals Influence of Bias Correction Methods on Simulated Köppen−Geiger Climate Zones in Europe

Climate ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 18 ◽  
Author(s):  
Beáta Szabó-Takács ◽  
Aleš Farda ◽  
Petr Skalák ◽  
Jan Meitner
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Generalized Pareto Distribution ◽  
Regional Climate Models ◽  
Climate Classification ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Simulated Precipitation ◽  
Precipitation And Temperature

Our goal was to investigate the influence of bias correction methods on climate simulations over the European domain. We calculated the Köppen−Geiger climate classification using five individual regional climate models (RCM) of the ENSEMBLES project in the European domain during the period 1961−1990. The simulated precipitation and temperature data were corrected using the European daily high-resolution gridded dataset (E-OBS) observed data by five methods: (i) the empirical quantile mapping of precipitation and temperature, (ii) the quantile mapping of precipitation and temperature based on gamma and Generalized Pareto Distribution of precipitation, (iii) local intensity scaling, (iv) the power transformation of precipitation and (v) the variance scaling of temperature bias corrections. The individual bias correction methods had a significant effect on the climate classification, but the degree of this effect varied among the RCMs. Our results on the performance of bias correction differ from previous results described in the literature where these corrections were implemented over river catchments. We conclude that the effect of bias correction may depend on the region of model domain. These results suggest that distribution free bias correction approaches are the most suitable for large domain sizes such as the pan-European domain.

scholarly journals A method to preserve trends in quantile mapping bias correction of climate modeled temperature

2017 ◽  
Vol 8 (3) ◽  
pp. 889-900 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Mapping Bias ◽  
Temperature Signal ◽  
Change Impact

Abstract. Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

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