Intercomparison of Bias-Correction Data Sources and Their Influence on Watershed-Specific Downscaling Climate Projections

2021 ◽  
Vol 64 (1) ◽  
pp. 203-220
Author(s):  
Aleksey Y. Sheshukov ◽  
Jungang Gao ◽  
Kyle R. Douglas-Mankin ◽  
Haw Yen
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Historical Data ◽  
Representative Concentration Pathway ◽  
Data Sources ◽  
Historical Period ◽  
Climate Projections ◽  
Weather Station ◽  
Future Projections ◽  
Data Source

HighlightsBias correction from three historical data sources (NCDC, PRISM, and NEXRAD) were assessed.Bias correction removed watershed-average bias in general circulation model (GCM) data for a historical period.Subbasin-specific variability was detected in future projections for each bias-correction data source.Data source had less impact on uncertainty in future projections than GCM or a representative concentration pathway.Uncertainty from bias-correction data sources was higher for precipitation than for temperature.Abstract. Climate projections developed by general circulation models (GCM) are often used in watershed modeling applications to project future hydrologic changes. In many models, the climate projections are downscaled to individual map units represented by grid cells or subbasins. Uncertainty of downscaled climate projections are a product of uncertainties arising mainly from the model itself, from the representative concentration pathway (RCP), and from the downscaling procedure. Other sources of uncertainty may include the historical observations used for GCM bias correction and data aggregation from GCM grids to map (often subbasin) units. This study evaluated effects of three sources of historical data (ground-based weather station network, NCDC, and two gridded datasets, NEXRAD and PRISM) on historical variability, and shifts and uncertainty in precipitation and temperature projections. Climate projections from six GCMs and three RCPs were evaluated in 54 subbasins of the Smoky Hill River watershed in the U.S. Central Great Plains. Bias correction of GCM projections reduced bias of watershed-average annual precipitation in the historical period to near zero, but subbasin-specific variability remained in future projections with little difference among bias-correction data sources. For minimum and maximum temperatures, the GCM ensemble statistics for basin-average and subbasin-specific future projections were similar for all bias-correction data sources. Increase in RCP forcing was found to widen the uncertainty in future projections. Overall, the uncertainty due to data source selection was smaller than the uncertainty due to GCM model and RCP forcing selection. The results demonstrate that statistical downscaling is essential to account for local climate factors within a watershed, and that both weather station-based and gridded bias-correction data sources can be used effectively, but that future climate projections may inherit the historical bias in a selected data source. These inherent uncertainties associated with application of GCMs in hydrological and geospatial modeling should be carefully considered for understanding climate projections when building watershed models and interpreting the results. Keywords: Bias correction, Climate change, Downscaling, GCM, Uncertainty, Watershed.

scholarly journals Projection of Droughts as Multivariate Phenomenon in the Rhine River

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2288
Author(s):  
Alejandro Chamorro ◽  
Tobias Houska ◽  
Shailesh Singh ◽  
Lutz Breuer
Keyword(s):  
General Circulation ◽  
Critical Role ◽  
General Circulation Models ◽  
Historical Period ◽  
Change Scenario ◽  
Climate Projections ◽  
Rhine River ◽  
Return Periods ◽  
Drought Duration ◽  
Future Projections

Drought is a complex phenomenon whose characterization is best achieved from a multivariate perspective. It is well known that it can generate adverse consequences in society. In this regard, drought duration, severity, and their interrelationship play a critical role. In a climate change scenario, drought characterization and the assessment of the changes in its pattern are essential for a proper quantification of water availability and managing strategies. The purpose of this study is to characterize hydrological droughts in the Rhine River in a multivariate perspective for the historical period and estimate the expected multivariate drought patterns for the next decades. Further, a comparison of bivariate drought patterns between historical and future projections is performed for different return periods. This will, first, indicate if changes can be expected and, second, what the magnitudes of these possible changes could be. Finally, the underlying uncertainty due to climate projections is estimated. Four Representative Concentration Pathways (RCP) are used along with five General Circulation Models (GCM). The HBV hydrological model is used to simulate discharge in both periods. Characterization of droughts is accomplished by the Standardized Runoff Index and the interdependence between drought severity and duration is modelled by a two-dimensional copula. Projections from different climate models show important differences in the estimation of the number of drought events for different return periods. This study reveals that duration and severity present a clear interrelationship, suggesting strongly the appropriateness of a bivariate model. Further, projections show that the bivariate interdependencies between drought duration and severity show clearly differences depending on GCMs and RCPs. Apart from the influence of GCMs and RCMs, it is found that return periods also play an important role in these relationships and uncertainties. Finally, important changes in the bivariate drought patterns between the historical period and future projections are estimated constituting important information for water management purposes.

scholarly journals Propagation of biases in humidity in the estimation of global irrigational water

2015 ◽  
Vol 6 (1) ◽  
pp. 81-132 ◽  
Author(s):  
Y. Masaki ◽  
N. Hanasaki ◽  
K. Takahashi ◽  
Y. Hijioka
Keyword(s):  
Relative Humidity ◽  
Bias Correction ◽  
General Circulation ◽  
Meteorological Data ◽  
General Circulation Models ◽  
Future Climate Change ◽  
Climate Projections ◽  
Future Projections ◽  
Water Abstraction ◽  
The Impact

Abstract. Future projections on irrigational water under a changing climate are highly dependent on meteorological data derived from general circulation models (GCMs). Since climate projections include biases, bias correction is widely used to adjust meteorological elements, such as the atmospheric temperature and precipitation, but less attention has been paid to biases in humidity. Hence, in many cases, raw GCM outputs have been directly used to analyze the impact of future climate change. In this study, we examined how the biases remaining in the humidity data of five GCMs propagate into the estimation of irrigational water demand and abstraction from rivers using the global hydrological model (GHM) H08. First, to determine the effects of humidity bias across GCMs, we used meteorological data sets to which a state-of-the-art bias correction method was applied except to the humidity. Uncorrected GCM outputs were used for the humidity. We found that differences in the monthly relative humidity of 11.7 to 20.4% RH (percent used as the unit of relative humidity) from observations across the GCMs caused the estimated irrigational water abstraction from rivers to range between 1217.7 and 1341.3 km3 yr−1 for 1971–2000. Differences in humidity also propagate into future projections. Second, sensitivity analysis with hypothetical humidity biases of ±5% RH added homogeneously worldwide revealed the large negative sensitivity of irrigational water abstraction in India and East China, which have high areal fractions of irrigated cropland. Third, we performed another set of simulations with bias-corrected humidity data to examine whether bias correction of the humidity can reduce uncertainties in irrigational water across the GCMs. The results showed that bias correction, even with a primitive methodology that only adjusts the monthly climatological relative humidity, helped reduce uncertainties across the GCMs. Although the GHMs have different sensitivities to atmospheric humidity because of the implementation of different types of potential evapotranspiration formulae, bias correction of the humidity should be included in hydrological analysis, particularly for the evaluation of evapotranspiration and irrigational water.

scholarly journals Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

2013 ◽  
Vol 17 (6) ◽  
pp. 2147-2159 ◽  
Author(s):  
E. P. Maurer ◽  
T. Das ◽  
D. R. Cayan
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Circulation Model ◽  
The United States ◽  
Historical Period ◽  
Late 20Th Century ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

scholarly journals Bias correction and post-processing under climate change

2011 ◽  
Vol 18 (6) ◽  
pp. 911-924 ◽  
Author(s):  
S. Vannitsem
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
General Circulation ◽  
Climate Changes ◽  
Climate Projections ◽  
Model Errors ◽  
Post Processing ◽  
Straightforward Application ◽  
Potential Impact ◽  
Temperature Climate

Abstract. The statistical and dynamical properties of bias correction and linear post-processing are investigated when the system under interest is affected by model errors and is experiencing parameter modifications, mimicking the potential impact of climate change. The analysis is first performed for simple typical scalar systems, an Ornstein-Uhlenbeck process (O-U) and a limit point bifurcation. It reveals system's specific (linear or non-linear) dependences of biases and post-processing corrections as a function of parameter modifications. A more realistic system is then investigated, a low-order model of moist general circulation, incorporating several processes of high relevance in the climate dynamics (radiative effects, cloud feedbacks...), but still sufficiently simple to allow for an extensive exploration of its dynamics. In this context, bias or post-processing corrections also display complicate variations when the system experiences temperature climate changes up to a few degrees. This precludes a straightforward application of these corrections from one system's state to another (as usually adopted for climate projections), and increases further the uncertainty in evaluating the amplitudes of climate changes.

scholarly journals Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs

2020 ◽  
Vol 12 (18) ◽  
pp. 7508 ◽  
Author(s):  
Young Hoon Song ◽  
Eun-Sung Chung ◽  
Mohammed Sanusi Shiru
Keyword(s):  
South Korea ◽  
Bias Correction ◽  
General Circulation ◽  
General Circulation Models ◽  
Coastal Areas ◽  
Historical Period ◽  
Monthly Precipitation ◽  
Opposite Pattern ◽  
Statistical Measures ◽  
Selection Of

This study quantified the uncertainties in historical and future average monthly precipitation based on different bias correction methods, General Circulation Models (GCMs), Representative Concentration Pathways (RCPs), projection periods, and locations within the study area (i.e., the coastal and inland areas of South Korea). The GCMs were downscaled using deep learning, random forest, and nine quantile mapping bias correction methods for 22 gauge stations in South Korea. Data from the Korean Meteorology Administration (1970–2005) were used as the reference data in this study. Two statistical measures, the standard deviation and interquartile range, were used to quantify the uncertainties. The probability distribution density was used to assess the similarity/variation in rainfall distributions. For the historical period, the uncertainty in the selection of bias correction methods was greater than that in the selection of GCMs, whereas the opposite pattern was observed for the projection period. The projection period had the lowest level of uncertainty in the selection of RCP scenarios, and for the future, the uncertainly related to the time period was slightly lower than that for the other sources but was much greater than that for the RCP selection. In addition, it was clear that the level of uncertainty of inland areas is much lower than that of coastal areas. The uncertainty in the selection of the GCMs was slightly greater than that in the selection of the bias correction method. Therefore, the uncertainty in the selection of coastal areas was intermediate between the selection of bias correction methods and GCMs. This paper contributes to an improved understanding of the uncertainties in climate change projections arising from various sources.

Sensitivity of Predicted Agro-Ecosystem Variables to Errors in Weather Input Data

2019 ◽  
Vol 62 (3) ◽  
pp. 627-640 ◽  
Author(s):  
Yao Zhang ◽  
Keith Paustian
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Input Data ◽  
Cropping Systems ◽  
Circulation Model ◽  
Ecosystem Model ◽  
Weather Data ◽  
Historical Period ◽  
Weather Station ◽  
Crop Modeling

Abstract. Statistically interpolated weather station data, outputs from climate reanalyses, and results from downscaled general circulation model (GCM) simulations are widely used to drive a variety of agro-ecosystem model applications, including regional- and national-scale crop modeling. In this study, we compared these three types of gridded weather datasets (total of nine datasets) with actual point-level weather station observations and analyzed the biases in predicted ecosystem variables of evapotranspiration (ET), crop grain yield, soil organic carbon (SOC) change, and soil N2O emissions using the process-based DayCent ecosystem model. As a reference system, we defined continuous corn cropping systems for three different regions in the U.S. Our results suggested that the predicted ecosystem variables can be highly sensitive to the sources of weather input data. Interpolated weather data from the PRISM and Daymet data products provided relatively accurate estimations of important ecosystem variables. Compared with the bias-corrected constructed analogs (BCCA) method, GCM results downscaled with the multivariate adaptive constructed analogs (MACA) method performed better for agro-ecosystem simulations under climate change; for datasets using BCCA, the rainfall frequency was positively biased and likely caused models to significantly underestimate solar radiation. For regional climate change studies that use a baseline simulation (historical period) for comparison, we suggest including the uncertainty of the baseline due to biases in the weather data in addition to the uncertainty in the projected weather data. Keywords: Agro-ecosystem model, DayCent model, Prediction bias, Weather data.

scholarly journals Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2312
Author(s):  
Joseph A. Daraio
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Stream Flow ◽  
General Circulation ◽  
Model Performance ◽  
General Circulation Models ◽  
Hydrologic Model ◽  
Historical Period ◽  
Hydrologic Models ◽  
Stream Flows

Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results.

scholarly journals Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

2013 ◽  
Vol 10 (2) ◽  
pp. 1657-1691 ◽  
Author(s):  
E. P. Maurer ◽  
T. Das ◽  
D. R. Cayan
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Circulation Model ◽  
The United States ◽  
Historical Period ◽  
Late 20Th Century ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10-yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

scholarly journals Hydrological Modeling to Evaluate Climate Model Simulations and Their Bias Correction

2018 ◽  
Vol 19 (8) ◽  
pp. 1321-1337 ◽  
Author(s):  
Kirsti Hakala ◽  
Nans Addor ◽  
Jan Seibert
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Climate Models ◽  
Hydrological Modeling ◽  
Climate Model ◽  
Snow Accumulation ◽  
Historical Period ◽  
Model Simulations ◽  
Process Oriented ◽  
Climate Model Simulations

Abstract Variables simulated by climate models are usually evaluated independently. Yet, climate change impacts often stem from the combined effect of these variables, making the evaluation of intervariable relationships essential. These relationships can be evaluated in a statistical framework (e.g., using correlation coefficients), but this does not test whether complex processes driven by nonlinear relationships are correctly represented. To overcome this limitation, we propose to evaluate climate model simulations in a more process-oriented framework using hydrological modeling. Our modeling chain consists of 12 regional climate models (RCMs) from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) forced by five general circulation models (GCMs), eight Swiss catchments, 10 optimized parameter sets for the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV), and one bias correction method [quantile mapping (QM)]. We used seven discharge metrics to explore the representation of different hydrological processes under current climate. Specific combinations of biases in GCM–RCM simulations can lead to significant biases in simulated discharge (e.g., excessive precipitation in the winter months combined with a cold temperature bias). Other biases, such as exaggerated snow accumulation, do not necessarily impact temperature over the historical period to the point where discharge is affected. Our results confirm the importance of bias correction; when all catchments, GCM–RCMs, and discharge metrics were considered, QM improved discharge simulations in the vast majority of all cases. Additionally, we present a ranking of climate models according to their hydrological performance. Ranking GCM–RCMs is most meaningful prior to bias correction since QM reduces differences between GCM–RCM-driven hydrological simulations. Overall, this work introduces a multivariate assessment method of GCM–RCMs, which enables a more process-oriented evaluation of their simulations.

Sign in / Sign up

Export Citation Format

Share Document