scholarly journals Bias Correction of Climate Model’s Precipitation Using the Copula Method and Its Application in River Basin Simulation

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 600 ◽  
Author(s):  
Georgia Lazoglou ◽  
Christina Anagnostopoulou ◽  
Charalampos Skoulikaris ◽  
Konstantia Tolika
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Monthly Precipitation ◽  
Distributed Hydrological Model ◽  
Copula Method ◽  
Corrected Data ◽  
Precipitation Analysis ◽  
Obs Data

During the last few decades, the utilization of the data from climate models in hydrological studies has increased as they can provide data in the regions that lack raw meteorological information. The data from climate models data often present biases compared to the observed data and consequently, several methods have been developed for correcting statistical biases. The present study uses the copula for modeling the dependence between the daily mean and total monthly precipitation using E-OBS data in the Mesta/Nestos river basin in order to use this relationship for the bias correction of the MPI climate model monthly precipitation. Additionally, both the non-corrected and bias corrected data are tested as they are used as the inputs to a spatial distributed hydrological model for simulating the basin runoff. The results showed that the MPI model significantly overestimates the E-OBS data while the differences are reduced sufficiently after the bias correction. The outputs from the hydrological models were proven to coincide with the precipitation analysis results and hence, the simulated discharges in the case of copula corrected data present an increased correlation with the observed flows.

Climate model bias correction for nonstationary conditions

2020 ◽  
Vol 47 (3) ◽  
pp. 326-336
Author(s):  
Mohammad Madani ◽  
Vinod Chilkoti ◽  
Tirupati Bolisetti ◽  
Rajesh Seth
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Bias Reduction ◽  
Historical Period ◽  
Monthly Precipitation ◽  
Model Bias ◽  
New Methods ◽  
Climate Model Bias

In most of the climate change impact assessment studies, climate model bias is considered to be stationary between the control and scenario periods. Few methods are found in the literature that addresses the issue of nonstationarity in correcting the bias. To overcome the shortcomings reported in these approaches, three new methods of bias correction (NBC_μ, NBC_σ, and NBC_bs) are presented. The methods are improvised versions of previous techniques relying on distribution mapping. The methods are tested using split sample approach over 50-year historical period for nine climate stations in Ontario, using six regional climate models. The average bias reduction improvement by new methods, in mean daily and monthly precipitation, was found to be 73.9%, 74.3%, and 77.4%, respectively, higher than that obtained by the previous methods (eQM 67.7% and CNCDFm_NP 64.1%). Thus, the methods are found to be more effective in accounting for nonstationarity in the model bias.

scholarly journals Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps

2018 ◽  
Author(s):  
Christian Reszler ◽  
Matthew Blasie Switanek ◽  
Heimo Truhetz
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Flash Flood ◽  
Regional Climate ◽  
Eastern Alps ◽  
Small Scale ◽  
Distributed Hydrological Model ◽  
Flood Generation

Abstract. Small scale floods are a consequence of high precipitation rates in small areas that can occur along frontal activity and convective storms. This situation is expected to become more severe due to a warming climate, when single precipitation events resulting from deep convection become more intense (Super Clausius-Clapeyron effect). Regional climate model (RCM) evaluations and inter-comparisons have shown that there is evidence that an increase in regional climate model resolution and in particular, at the convection permitting scale, will lead to a better representation of the spatial and temporal characteristics of heavy precipitation at small and medium scales. In this paper, the benefit of grid size reduction and bias correction in climate models are evaluated in their ability to properly represent flood generation in small and medium sized catchments. The climate models are coupled with a distributed hydrological model. The study area is the Eastern Alps, where small scale storms often occur along with heterogeneous rainfall distributions leading to a very local flash flood generation. The work is carried out in a small multi-model (ensemble) framework using two different RCMs (CCLM and WRF) in different grid sizes. Bias correction is performed by the use of the novel Scaled Distribution Mapping (SDM) method. The results show, that for small catchments (

scholarly journals Climate model bias correction and the role of timescales

2010 ◽  
Vol 7 (5) ◽  
pp. 7863-7898 ◽  
Author(s):  
J. O. Haerter ◽  
S. Hagemann ◽  
C. Moseley ◽  
C. Piani
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Correction Method ◽  
Statistical Properties ◽  
Statistical Bias ◽  
Negative Effects ◽  
Climate Model Bias ◽  
Corrected Data ◽  
Statistical Bias Correction

Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However the improvements to the statistical properties of the data are limited to the specific time scale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made of persistence of the bias over the largest timescales. We examine the effects of mixing fluctuations on different time scales and suggest an improved statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

scholarly journals Multivariate bias corrections of climate simulations: Which benefits for which losses?

2020 ◽  
Author(s):  
Bastien François ◽  
Mathieu Vrac ◽  
Alex J. Cannon ◽  
Yoann Robin ◽  
Denis Allard
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Temporal Structure ◽  
Climate Projections ◽  
Dimensional Changes ◽  
Climate Simulations ◽  
Temporal Structures ◽  
Corrected Data ◽  
Temporal Dependencies

Abstract. Climate models are the major tools to estimate climate variables evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last two decades, in order to adjust simulations according to historical records and obtain climate projections with appropriate statistical attributes. Most of the existing and popular BC methods are univariate, i.e., correcting one physical variable and one location at a time, and thus can fail to reconstruct inter-variable, spatial or temporal dependencies of the observations. These remaining biases in the correction can then affect the subsequent analyses. This has led to further research on multivariate aspects for statistical post-processing BC methods. Recently, some multivariate bias correction (MBC) methods have been proposed, with different approaches to restore multidimensional dependencies. However, these methods are not well apprehended yet by researchers and practitioners due to differences in their applicability and assumptions, therefore leading potentially to different results. This study is intended to intercompare four existing MBCs to provide end-users with aid in choosing such methods for their applications. For evaluation and illustration purposes, these methods are applied to correct simulation outputs from one climate model through a cross-validation methodology, which allows for the assessment of inter-variable, spatial and temporal criteria. Then, a second methodology is performed for assessing the ability of the MBC methods to account for the multi-dimensional evolutions of the climate model. Additionally, two reference datasets are used to assess the influence of their spatial resolution on (M)BC results. Most of the methods reasonably correct inter-variable and inter-site correlations. However, none of them adjust correctly the temporal structure as they generate bias corrected data with usually weak temporal dependencies compared to observations. Major differences are found concerning the applicability and stability of the methods in high-dimensional contexts, and in their capability to reproduce the multi-dimensional changes of the model. Based on these conclusions, perspectives for MBC developments are suggested, such as methods to adjust not only multivariate correlations but also temporal structures and allowing to account for multi-dimensional evolutions of the model in the correction.

scholarly journals Evaluation and selection of CORDEX-SA datasets and bias correction methods for a hydrological impact study in a humid tropical river basin, Kerala

2021 ◽  
Author(s):  
M. S. Saranya ◽  
V. Nair Vinish
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Circulation Model ◽  
Climate Variables ◽  
Tropical River ◽  
Hydrological Impact ◽  
Selection Of

Abstract It is well recognised that the performance of climate model simulations and bias correction methods is region specific, and, therefore, careful validation should always be performed. This study evaluates the performance of five general circulation model–regional climate model (GCM–RCM) combinations selected from CORDEX–SA datasets over a humid tropical river basin in Kerala, India, for climate variables such as precipitation, maximum and minimum temperatures. This involves ranking of the selected climate models based on an EDAS (Evaluation Based on Distance from Average Solution) method and the selection of an appropriate bias correction method for the selected three climate variables. A range of indices are used to evaluate the performance of the bias-corrected climate models to simulate observed climate data. Finally, the hydrological impact of the bias-corrected ranked models is assessed by simulating streamflow over the river basin using individual models and different combinations of models based on rank. According to the findings, hydrological simulation using an average of all GCM–RCM pairs provides the best model output in simulating streamflow, with an NSE value of 0.72. The results confirm the importance of a multimodel ensemble for improving the reliability and minimising the uncertainty of climate predictions for impact studies.

scholarly journals Trends in projections of standardized precipitation indices in a future climate in Poland

2016 ◽  
Vol 20 (5) ◽  
pp. 1947-1969 ◽  
Author(s):  
Marzena Osuch ◽  
Renata J. Romanowicz ◽  
Deborah Lawrence ◽  
Wai K. Wong
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Standardized Precipitation Index ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Projections ◽  
Monthly Precipitation

Abstract. Possible future climate change effects on dryness conditions in Poland are estimated for six climate projections using the standardized precipitation index (SPI). The time series of precipitation represent six different climate model runs under the selected emission scenario for the period 1971–2099. Monthly precipitation values were used to estimate the SPI for multiple timescales (1, 3, 6, 12, and 24 months) for a spatial resolution of 25 km for the whole country. Trends in the SPI were analysed using the Mann–Kendall test with Sen's slope estimator for each grid cell for each climate model projection and aggregation scale, and results obtained for uncorrected precipitation and bias corrected precipitation were compared. Bias correction was achieved using a distribution-based quantile mapping (QM) method in which the climate model precipitation series were adjusted relative to gridded precipitation data for Poland. The results show that the spatial pattern of the trend depends on the climate model, the timescale considered and on the bias correction. The effect of change on the projected trend due to bias correction is small compared to the variability among climate models. We also summarize the mechanisms underlying the influence of bias correction on trends in precipitation and the SPI using a simple example of a linear bias correction procedure. In both cases, the bias correction by QM does not change the direction of changes but can change the slope of trend, and the influence of bias correction on SPI is much reduced. We also have noticed that the results for the same global climate model, driving different regional climate model, are characterized by a similar pattern of changes, although this behaviour is not seen at all timescales and seasons.

scholarly journals Climate model bias correction and the role of timescales

2011 ◽  
Vol 15 (3) ◽  
pp. 1065-1079 ◽  
Author(s):  
J. O. Haerter ◽  
S. Hagemann ◽  
C. Moseley ◽  
C. Piani
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Correction Method ◽  
Statistical Properties ◽  
Statistical Bias ◽  
Negative Effects ◽  
Climate Model Bias ◽  
Corrected Data ◽  
Statistical Bias Correction

Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However, the improvements to the statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a statistical bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

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 Development of a daily gridded precipitation data set for the Middle East

2008 ◽  
Vol 12 ◽  
pp. 165-170 ◽  
Author(s):  
A. Yatagai ◽  
P. Xie ◽  
P. Alpert
Keyword(s):  
Middle East ◽  
Climate Models ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Data Set ◽  
Key Points ◽  
Fertile Crescent ◽  
Precipitation Analysis ◽  
The Impact

Abstract. We show an algorithm to construct a rain-gauge-based analysis of daily precipitation for the Middle East. One of the key points of our algorithm is to construct an accurate distribution of climatology. One possible advantage of this product is to validate high-resolution climate models and/or to diagnose the impact of climate changes on local hydrological resources. Many users are familiar with a monthly precipitation dataset (New et al., 1999) and a satellite-based daily precipitation dataset (Huffman et al., 2001), yet our data set, unlike theirs, clearly shows the effect of orography on daily precipitation and other extreme events, especially over the Fertile Crescent region. Currently the Middle-East precipitation analysis product is consisting of a 25-year data set for 1979–2003 based on more than 1300 stations.

scholarly journals Evaluation of Future Climate and Potential Impact on Streamflow in the Upper Nan River Basin of Northern Thailand

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Miyuru B. Gunathilake ◽  
Yasasna V. Amaratunga ◽  
Anushka Perera ◽  
Imiya M. Chathuranika ◽  
Anura S. Gunathilake ◽  
...  
Keyword(s):  
River Basin ◽  
Minimum Temperature ◽  
Climate Models ◽  
Hydrological Modeling ◽  
Winter Season ◽  
Future Climate ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Northern Thailand ◽  
The Impact

Water resources in Northern Thailand have been less explored with regard to the impact on hydrology that the future climate would have. For this study, three regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX) of Coupled Model Intercomparison Project 5 (CMIP5) were used to project future climate of the upper Nan River basin. Future climate data of ACCESS_CCAM, MPI_ESM_CCAM, and CNRM_CCAM under Representation Concentration Pathways RCP4.5 and RCP8.5 were bias-corrected by the linear scaling method and subsequently drove the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) to simulate future streamflow. This study compared baseline (1988–2005) climate and streamflow values with future time scales during 2020–2039 (2030s), 2040–2069 (2050s), and 2070–2099 (2080s). The upper Nan River basin will become warmer in future with highest increases in the maximum temperature of 3.8°C/year for MPI_ESM and minimum temperature of 3.6°C/year for ACCESS_CCAM under RCP8.5 during 2080s. The magnitude of changes and directions in mean monthly precipitation varies, with the highest increase of 109 mm for ACESSS_CCAM under RCP 4.5 in September and highest decrease of 77 mm in July for CNRM, during 2080s. Average of RCM combinations shows that decreases will be in ranges of −5.5 to −48.9% for annual flows, −31 to −47% for rainy season flows, and −47 to −67% for winter season flows. Increases in summer seasonal flows will be between 14 and 58%. Projection of future temperature levels indicates that higher increases will be during the latter part of the 20th century, and in general, the increases in the minimum temperature will be higher than those in the maximum temperature. The results of this study will be useful for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset negative impacts of future changes in climate. In addition, the results will also be valuable for agriculturists and hydropower planners.

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