Analysis of Rainfall-Runoff Characteristics on Bias Correction Method of Climate Change Scenarios

Abstract. This study extended the work of Kim et al. (2008) to generate future rainfall under climate change using a discrete-time/space Markov chain based on historical conditional probabilities. A bias-correction method is proposed by fitting suitable statistical distributions to transform rainfall from the general circulation model (GCM) scale to watershed scale. The demonstration example used the Nam Ngum River Basin (NNRB) in Laos which is a rural river basin with high potential for hydropower generation and significant rain-fed agriculture supporting rural livelihoods. This work generated weekly rainfall for a 100 yr period using historical rainfall data from 1961 to 2000 for ten selected weather stations. The bias-correction method showed the ability to reduce bias of the mean values of GCMs when compared to the observed mean amount at each station. The simulated rainfall series is perturbed using the delta change estimated at each station to project future rainfall for the Special Report on Emission Scenarios (SRES) A2. GCMs consisting of third generation coupled general circulation model (CGCM3.1 T63) and European center Hamburg model (ECHAM5) projected an increasing trend of mean annual rainfall in the NNRB. Seasonal rainfall percent changes showed an increase in the wet and dry seasons with the highest increase in the dry season mean rainfall of about 31% from 2051 to 2090. While the GCM projections showed good results with appropriate bias corrections, the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model significantly underestimated historical behavior and produced higher mean absolute errors compared to the corresponding GCM predictions.

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Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Proceedings ◽

10.3390/ecws-3-05809 ◽

2018 ◽

Vol 7 (1) ◽

pp. 14 ◽

Cited By ~ 1

Author(s):

Enrique Soriano ◽

Luis Mediero ◽

Carlos Garijo

Keyword(s):

Climate Change ◽

Bias Correction ◽

Correction Method ◽

Flood Frequency ◽

Precipitation Extremes ◽

Bias Correction Method ◽

Simulated Precipitation ◽

The Future ◽

The Impact ◽

Frequency Curves

Annual maximum daily rainfalls will change in the future because of climate change, according to climate projections provided by EURO-CORDEX. This study aims at understanding how the expected changes in precipitation extremes will affect the flood behavior in the future. Hydrological modeling is required to characterize the rainfall-runoff process adequately in a changing climate to estimate flood changes. Precipitation and temperature projections given by climate models in the control period usually do not fit the observations in the same period exactly from a statistical point of view. To correct such errors, bias correction methods are used. This paper aims at finding the most adequate bias correction method for both temperature and precipitation projections, minimizing the errors between observed and simulated precipitation and flood frequency curves. Four catchments located in central western Spain have been selected as case studies. The HBV hydrological model has been calibrated, using the observed precipitation, temperature, and streamflow data available at a daily scale. Expected changes in precipitation extremes are usually smoothed by the reduction of soil moisture content due to expected increases in temperatures and decreases in mean annual precipitation. Consequently, rainfall is the most significant input to the model and polynomial quantile mapping is the best bias correction method.

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Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction

Agronomy ◽

10.3390/agronomy11050990 ◽

2021 ◽

Vol 11 (5) ◽

pp. 990

Author(s):

Joana Martins ◽

Helder Fraga ◽

André Fonseca ◽

João Andrade Santos

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Model ◽

Correction Method ◽

Maximum Temperature ◽

Lower Sensitivity ◽

Climate Projections ◽

Quantile Mapping ◽

Bias Correction Method ◽

Wine Region

The implications of weather and climate extremes on the viticulture and winemaking sector can be particularly detrimental and acquire more relevance under a climate change context. A four-member ensemble of the Regional Climate Model-Global Climate Model chain simulations is used to evaluate the potential impacts of climate change on indices of extreme temperature and precipitation, as well as on agroclimatic indices of viticultural suitability in the Douro Wine Region, Portugal, under current and future climate conditions, following the RCP8.5 anthropogenic radiative forcing scenario. Historical (1989–2005) and future (2051–2080) periods are considered for this purpose. Although model outputs are bias-corrected to improve the accuracy of the results, owing to the sensitivity of the climatic indicators to the specific bias correction method, the performance of the linear and quantile mapping methods are compared. The results hint at the importance of choosing the most accurate method (quantile mapping), not only in replicating extremes events but also in reproducing the accumulated agroclimatic indices. Significant differences between the bias correction methods are indeed found for the number of extremely warm days (maximum temperature > 35 °C), number of warm spells, number of warm spell days, number of consecutive dry days, the Dryness Index, and growing season precipitation. The Huglin Index reveals lower sensitivity, thus being more robust to the choice of the method. Hence, an unsuitable bias correction method may hinder the accuracy of climate change projections in studies heavily relying on derived extreme indices and agroclimatic indicators, such as in viticulture. Regarding the climate change signal, significant warming and drying trends are projected throughout the target region, which is supported by previous studies, but also accompanied by an increase of intensity, frequency, and duration of extreme events, namely heatwaves and dry spells. These findings thereby corroborate the need to adopt timely and effective adaptation strategies by the regional winemaking sector to warrant its future sustainability and enhance climate resilience.

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

10.20944/preprints201912.0329.v1 ◽

2019 ◽

Author(s):

Abdou Boko Boubacar ◽

Konaté Moussa ◽

Nicaise Yalo ◽

Steven J. Berg ◽

Andre R. Erler ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Hydrological Modeling ◽

Correction Method ◽

Groundwater Table ◽

Annual Rainfall ◽

Urban Watershed ◽

Bias Correction Method ◽

The Impact ◽

Semi Arid

This study evaluated the impact of climate change on water resources in a large semi-arid urban watershed located in Niamey Republic of Niger, West Africa. The watershed was modeled using the fully integrated surface-subsurface HydroGeoSpheremodel at a high spatial resolution. Historical (1980-2005) and projected (2020-2050) climate scenario derived from the outputs of three Regional Climate Models (RCM) under the 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 predict 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 agriculture water, were found to be sensitive to changes in runoff resulting from climate change. MEF occurrences and durations are likely to be greater for (2020-2030), and then they will be reduced for (2030-2050). All three RCMs consistently project a rise in groundwater table of more than 10 meters in topographically high zones where the groundwater table is deep and an increase of 2 meters in the shallow groundwater table.

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DEVELOPMENT OF STATISTICAL BIAS CORRECTION METHOD FOR CLIMATE MODEL TYPHOONS AND ENSAMBLE FUTURE STORM SURGE PROJECTION DUE TO CLIMATE CHANGE

Journal of Japan Society of Civil Engineers Ser B2 (Coastal Engineering) ◽

10.2208/kaigan.71.i_1507 ◽

2015 ◽

Vol 71 (2) ◽

pp. I_1507-I_1512

Author(s):

Tomohiro YASUDA ◽

Nariaki KATAHIRA ◽

Nobuhito MORI ◽

Hajime MASE ◽

Yoko SHIBUTANI

Keyword(s):

Climate Change ◽

Storm Surge ◽

Bias Correction ◽

Climate Model ◽

Correction Method ◽

Statistical Bias ◽

Bias Correction Method ◽

Statistical Bias Correction

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Bias correction method for climate change impact assessments in the Philippines

Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering) ◽

10.2208/jscejhe.69.i_19 ◽

2013 ◽

Vol 69 (4) ◽

pp. I_19-I_24 ◽

Cited By ~ 4

Author(s):

Cho Thanda NYUNT ◽

Toshio KOIKE ◽

Pactricia Ann J. SANCHEZ ◽

Akio YAMAMOTO ◽

Toshihoro NEMOTO ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Change Impact ◽

Correction Method ◽

The Philippines ◽

Bias Correction Method ◽

Change Impact

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

Water ◽

10.3390/w12020364 ◽

2020 ◽

Vol 12 (2) ◽

pp. 364 ◽

Cited By ~ 1

Author(s):

Boubacar Abdou Boko ◽

Moussa Konaté ◽

Nicaise Yalo ◽

Steven J. Berg ◽

Andre R. Erler ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Regional Climate ◽

Correction Method ◽

Groundwater Table ◽

Annual Rainfall ◽

Urban Watershed ◽

Bias Correction Method ◽

The Impact ◽

Semi Arid

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.

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Selection of Effective GCM Bias Correction Methods and Evaluation of Hydrological Response under Future Climate Scenarios

Climate ◽

10.3390/cli8100108 ◽

2020 ◽

Vol 8 (10) ◽

pp. 108

Author(s):

Yaogeng Tan ◽

Sandra M. Guzman ◽

Zengchuan Dong ◽

Liang Tan

Keyword(s):

Climate Change ◽

Water Resources ◽

Bias Correction ◽

Global Climate ◽

Global Climate Model ◽

Swat Model ◽

Correction Method ◽

Climate Change Scenarios ◽

Hydrological Variables ◽

Precipitation And Temperature

Global climate change is presenting a variety of challenges to hydrology and water resources because it strongly affects the hydrologic cycle, runoff, and water supply and demand. In this study, we assessed the effects of climate change scenarios on hydrological variables (i.e., evapotranspiration and runoff) by linking the outputs from the global climate model (GCM) with the Soil and Water Assessment Tool (SWAT) for a case study in the Lijiang River Basin, China. We selected a variety of bias correction methods and their combinations to correct the lower resolution GCM outputs of both precipitation and temperature. Then, the SWAT model was calibrated and validated using the observed flow data and corrected historical GCM with the optimal correction method selected. Hydrological variables were simulated using the SWAT model under emission scenarios RCP2.6, RCP4.5, and RCP8.5. The results demonstrated that correcting methods have a positive effect on both daily precipitation and temperature, and a hybrid method of bias correction contributes to increased performance in most cases and scenarios. Based on the bias corrected scenarios, precipitation annual average, temperature, and evapotranspiration will increase. In the case of precipitation and runoff, projection scenarios show an increase compared with the historical trends, and the monthly distribution of precipitation, evapotranspiration, and runoff shows an uneven distribution compared with baseline. This study provides an insight on how to choose a proper GCM and bias correction method and a helpful guide for local water resources management.

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Impact of Bias-Correction Methods in Assessing the Potential Flood Frequency Change in the Bago River

Journal of Disaster Research ◽

10.20965/jdr.2020.p0288 ◽

2020 ◽

Vol 15 (3) ◽

pp. 288-299

Author(s):

Ralph Allen E. Acierto ◽

Akiyuki Kawasaki ◽

Win Win Zin ◽

◽

Keyword(s):

Climate Change ◽

Frequency Analysis ◽

Bias Correction ◽

Correction Method ◽

Flood Frequency ◽

Climate Projections ◽

Frequency Change ◽

Annual Maximum ◽

Quantile Mapping ◽

Bias Correction Method

The increasing flood risks in the Bago River due to rapid urbanization and climate change have great implications on the local development and quality of life in the basin. Therefore, the current flood hazard and potential future changes in flooding due to climate change must be assessed. This study investigates the potential flood frequency change in the Bago River and its sensitivity to the bias-correction method used in climate projections from the downscaled Global Climate Model (GCM) output. A pseudo-global warming method using MIROC5 RCP 8.5 was employed to produce 12-km 30-y historical and future climate projections. Empirical quantile mapping (EQM), gamma quantile mapping (GQM), and the multiplicative scaling method (SCM) were used for bias-correcting the rainfall input of the water-energy budget distributed hydrological model (WEB-DHM). The impacts of bias-correction methods used in reproducing the annual maximum series in the frequency analysis are sensitive to the trend of potential future changes in flood discharge frequency estimation. All methods exhibited decreases in the flood peak discharge for 50-yr and 100-yr flood predictions, which may primarily be due to the MIROC5 GCM used. However, the variation in the magnitude of the change is wide. This demonstrates the uncertainty of the frequency analysis for flood magnitude due to the employed bias-correction method. This uncertainty has significant implications on risk quantification conducted using downscaled climate projections. The effect of the uncertainty of the bias-correction method on the annual maximum rainfall time series should be communicated properly when conducting risk and hazard assessment studies.

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