Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

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Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-35-2007 ◽

2007 ◽

Vol 4 (1) ◽

pp. 35-74 ◽

Cited By ~ 14

Author(s):

D. Sharma ◽

A. Das Gupta ◽

M. S. Babel

Keyword(s):

River Basin ◽

Bias Correction ◽

Hydrological Modeling ◽

Correction Method ◽

Hydrologic Model ◽

Global Climate Models ◽

Model Parameters ◽

Bias Correction Method ◽

Spatial Disaggregation ◽

Grid Nodes

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Runoff Predicting and Variation Analysis in Upper Ganjiang Basin under Projected Climate Changes

Sustainability ◽

10.3390/su11215885 ◽

2019 ◽

Vol 11 (21) ◽

pp. 5885 ◽

Cited By ~ 3

Author(s):

Chao Deng ◽

Weiguang Wang

Keyword(s):

Resource Management ◽

Water Resource ◽

Water Resource Management ◽

Hydrological Modeling ◽

Hydrologic Model ◽

Global Climate Models ◽

Balance Model ◽

Model Parameters ◽

The Mean ◽

Parameter Approach

Catchment runoff is significantly affected by climate condition changes. Predicting the runoff and analyzing its variations under future climates play a vital role in water security, water resource management, and the sustainable development of the catchment. In traditional hydrological modeling, fixed model parameters are usually used to transfer the global climate models (GCMs) to runoff, while the hydrologic model parameters may be time-varying. It is more appropriate to use the time-variant parameter for runoff modeling. This is achieved by incorporating the time-variant parameter approach into a two-parameter water balance model (TWBM) through the construction of time-variant parameter functions based on the identified catchment climate indicators. Using the Ganjiang Basin with an outlet of the Dongbei Hydrological Station as the study area, we developed time-variant parameter scenarios of the TWBM model and selected the best-performed parameter functions to predict future runoff and analyze its variations under the climate model projection of the BCC-CSM1.1(m). To synthetically assess the model performance improvements using the time-variant parameter approach, an index Δ was developed by combining the Nash–Sutcliffe efficiency, the volume error, the Box–Cox transformed root-mean-square error, and the Kling–Gupta efficiency with equivalent weight. The results show that the TWBM model with time-variant C (evapotranspiration parameter) and SC (water storage capacity of catchment), where growing and non-growing seasons are considered for C, outperformed the model with constant parameters with a Δ value of approximately 5% and 10% for the calibration and validation periods, respectively. The mean annual values of runoff predictions under the four representative concentration pathways (RCPs) exhibited a decreasing trend over the future three decades (2021–2050) when compared to the runoff simulations in the baseline period (1982–2011), where the values were about −9.9%, −19.5%, −16.6%, and −11.4% for the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The decreasing trend of future precipitation exerts impacts on runoff decline. Generally, the mean monthly changes of runoff predictions showed a decreasing trend from January to August for almost all of the RCPs, while an increasing trend existed from September to November, along with fluctuations among different RCPs. This study can provide beneficial references to comprehensively understand the impacts of climate change on runoff prediction and thus improve the regional strategy for future water resource management.

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Estimating monthly rainfall in rural river basins under climate change: an improved bias-correcting statistical downscaling approach

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-6847-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 6847-6896

Author(s):

D. L. Jayasekera ◽

J. J. Kaluarachchi

Keyword(s):

Climate Change ◽

General Circulation Model ◽

River Basin ◽

Bias Correction ◽

General Circulation ◽

Circulation Model ◽

Correction Method ◽

Annual Rainfall ◽

Bias Correction Method ◽

Time Space

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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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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Application of Statistical Bias correction method to the Yoshino River Basin

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

10.2208/jscejhe.70.i_193 ◽

2014 ◽

Vol 70 (4) ◽

pp. I_193-I_198 ◽

Cited By ~ 2

Author(s):

CHOTHANDA NYUNT ◽

TOSHIO KOIKE ◽

AKIO YAMAMOTO ◽

TOSHIHORO NEMOTO ◽

MASARU KITSUREGAWA

Keyword(s):

River Basin ◽

Bias Correction ◽

Correction Method ◽

Statistical Bias ◽

Bias Correction Method ◽

Statistical Bias Correction

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DEVELOPMENT OF BIAS CORRECTION METHOD FOR TROPICAL CYCLONES IN GLOBAL CLIMATE MODEL AND FUTURE CHANGE PROJECTION OF STORM SURGE USING AMR MODEL

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

10.2208/kaigan.74.i_607 ◽

2018 ◽

Vol 74 (2) ◽

pp. I_607-I_612 ◽

Cited By ~ 1

Author(s):

Yosuke YAMAMOTO ◽

Nobuthito MORI ◽

Marc KJERLAND

Keyword(s):

Tropical Cyclones ◽

Storm Surge ◽

Bias Correction ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Correction Method ◽

Future Change ◽

Bias Correction Method

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Calibration transfer of partial least squares jet fuel property models using a segmented virtual standards slope-bias correction method

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2011.09.014 ◽

2012 ◽

Vol 110 (1) ◽

pp. 64-73 ◽

Cited By ~ 15

Author(s):

Mohamed F. Abdelkader ◽

John B. Cooper ◽

Christopher M. Larkin

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Bias Correction ◽

Correction Method ◽

Jet Fuel ◽

Calibration Transfer ◽

Fuel Property ◽

Bias Correction Method

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Comparing bias correction methods in downscaling meteorological variables for hydrologic impact study in an arid area in China

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-12659-2014 ◽

2014 ◽

Vol 11 (11) ◽

pp. 12659-12696 ◽

Cited By ~ 4

Author(s):

G. H. Fang ◽

J. Yang ◽

Y. N. Chen ◽

C. Zammit

Keyword(s):

Water Resources ◽

River Basin ◽

Bias Correction ◽

Meteorological Data ◽

Hydrologic Model ◽

Temperature Correction ◽

Arid Area ◽

Tarim River ◽

Tarim River Basin ◽

Impact Study

Abstract. Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River Basin, Northwest China, and expected to be vulnerable to climate change. Regional Climate Models (RCM) have been proved to provide more reliable results for regional impact study of climate change (e.g. on water resources) than GCM models. However, it is still necessary to apply bias correction before they are used for water resources research due to often considerable biases. In this paper, after a sensitivity analysis on input meteorological variables based on Sobol' method, we compared five precipitation correction methods and three temperature correction methods to the output of a RCM model with its application to the Kaidu River Basin, one of the headwaters of the Tarim River Basin. Precipitation correction methods include Linear Scaling (LS), LOCal Intensity scaling (LOCI), Power Transformation (PT), Distribution Mapping (DM) and Quantile Mapping (QM); and temperature correction methods include LS, VARIance scaling (VARI) and DM. These corrected precipitation and temperature were compared to the observed meteorological data, and then their impacts on streamflow were also compared by driving a distributed hydrologic model. The results show: (1) precipitation, temperature, solar radiation are sensitivity to streamflow while relative humidity and wind speed are not, (2) raw RCM simulations are heavily biased from observed meteorological data, which results in biases in the simulated streamflows, and all bias correction methods effectively improved theses simulations, (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g. SD, percentile values) while LOCI method performed best in terms of the time series based indices (e.g. Nash–Sutcliffe coefficient, R2), (4) for temperature, all bias correction methods performed equally well in correcting raw temperature. (5) For simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with these of corrected precipitation, i.e. PT and QM methods performed equally best in correcting flow duration curve and peak flow while LOCI method performed best in terms of the time series based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other area and other models.

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