Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M

Okkan, Umut; Fıstıkoğlu, Okan

doi:10.1007/s00704-013-1005-y

Cited by 49 publications

(27 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This procedure with formulations can be found in the study by Ines and Hansen (). This methodology was also recently used by Okkan and Fistikoglu () in their downscaling work. This methodology consists of following steps: CDFs were obtained with monthly observed and 20C3M forecasts produced by ensemble procedure for the 1979–1999 period using the selected probability plotting position formulas. For a given value of obtained output under 20C3M scenario, the value of CDF was computed. Corresponding to CDF value of ensemble model, the observed flow values were obtained from the CDFs of observed data. The forecasted inflows were replaced by the observed data, thus computed, having the same CDF values. The CDFs of forecasted and observed inflows, obtained for the 1979–1999 period and based on them the corrections were applied to flow values obtained with ensemble procedure for future periods. …”

Section: Resultsmentioning

confidence: 99%

“…This procedure with formulations can be found in the study by Ines and Hansen (2006). This methodology was also recently used by Okkan and Fistikoglu (2014) in their downscaling work. This methodology consists of following steps:…”

Section: C3m Scenario Results and Bias Correctionmentioning

confidence: 99%

“…To the best of our knowledge, a work addressing the application of statistical learning methods to downscale streamflow in Turkey has not been published in the hydrology literature. Moreover, only a few studies on downscaling of GCM simulations directly to flow have been carried out in the hydrology literature (Landman et al, 2001;Cannon and Whiteld, 2002;Ghosh and Mujumdar, 2008;Mujumdar and Ghosh, 2008;Tisseul et al, 2010;Sachindra et al, 2013), while predicting flows through GCMs and hydrological models have already been carried out (Dibike and Coulibaly, 2005;Gebremeskel et al, 2005;Ozkul, 2009;Okkan and Fistikoglu, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

Okkan

İnan

2014

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

ABSTRACT:In this study, statistical downscaling of general circulation model (GCM) simulations to monthly inflows of Kemer Dam in Turkey under A1B, A2, and B1 emission scenarios has been performed using machine learning methods, multi-model ensemble and bias correction approaches. Principal component analysis (PCA) has been used to reduce the dimension of potential predictors of National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. Then, the reasonable GCMs were selected by investigating the rank correlations between the selected predictors in NCEP/NCAR reanalysis data and those in GCMs for 20C3M scenario between periods 1979 and 1999. Upon the training of feedforward neural network (FFNN), least squares support vector machine (LSSVM) and relevance vector machine (RVM) downscaling models, the general performance of the downscaled predictions using NCEP/NCAR reanalysis data for Kemer watershed showed that the trained RVM model produced adequate results. The effectiveness of RVM model was illustrated by its integration with 20C3M scenario between periods 1979 and 1999 and A1B, A2, and B1 future climate scenarios between periods 2010 and 2039. Afterwards, the flow forecasts were obtained by building a multi-model ensemble through the selected GCMs followed by a bias correction approach. Finally, the significance of the probable changes in trends was identified through statistical tests based on the corrected forecasts. Results showed that decreasing flows trends in winter, spring and fall seasons have been foreseen over the study area for the period between 2010 and 2039.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: C3m Scenario Results and Bias Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

Okkan

İnan

2014

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Projected changes of hydrometeorological variables for different basins in Turkey under SRES of AR4 have been obtained from the simulation of GCMs in version three of the Coupled Model Inter‐comparison Project, CMIP3 (e.g. Okkan and Fistikoglu, ; Okkan and Inan, , ) However, in the present work, projections derived from statistical downscaling techniques are introduced based on CMIP5 data in the hope of providing a reference for investigating the probable climatic change impacts on the precipitation regime in the Basin under different RCPs.…”

Section: Introductionmentioning

confidence: 99%

“…Wilby et al, 1998;Hessami et al, 2008), weather generator (Semenov, 2008) and artificial neural networks can be preferred. Successful downscaling studies carried out with artificial neural networks (ANNs) under different climate change scenarios can be found in the literature (Goyal and Ojha, 2012;Okkan and Fistikoglu, 2014;Okkan, 2015;Okkan and Inan, 2015b).…”

Section: Introductionmentioning

confidence: 99%

Downscaling of monthly precipitation using CMIP5 climate models operated under RCPs

Okkan

Kırdemir

2016

Meteorological Applications

Self Cite

View full text Add to dashboard Cite

Downscaling of general circulation model (GCM) outputs extracted from CMIP5 datasets to monthly precipitation for the Gediz Basin, Turkey, under Representative Concentration Pathways (RCPs) was performed by statistical downscaling models, multi-GCM ensemble and bias correction. The output databases from 12 GCMs were used for the projections. To determine explanatory predictor variables, the correlation analysis was applied between precipitation observed at 39 meteorological stations located over the Basin and potential predictors of ERA-Interim reanalysis data. After setting both artificial neural networks and least-squares support vector machine-based statistical downscaling models calibrated with determined predictor variables, downscaling models producing the most suitable results were chosen for each meteorological station. The selected downscaling model structure for each station was then operated with historical and future scenarios RCP4.5, RCP6.0 and RCP8.5. Afterwards, the monthly precipitation forecasts were obtained from a multi-GCM ensemble based on Bayesian model averaging and bias correction applications. The statistical significance of the foreseen changes for the future period 2015-2050 was investigated using Student's t test. The projected decrease trend in precipitation is significant for the RCP8.5 scenario, whereas it is less significant for the RCP4.5 and RCP6.0 scenarios.

show abstract

Downscaling rainfall using deep learning long short‐term memory and feedforward neural network

Anh

Van

Dang

et al. 2019

Intl Journal of Climatology

View full text Add to dashboard Cite

Choosing downscaling techniques is crucial in obtaining accurate and reliable climate change predictions, allowing for detailed impact assessments of climate change at regional and local scales. Traditional statistical methods are likely inefficient in downscaling precipitation data from multiple sources or complex data patterns, so using deep learning, a form of nonlinear models, could be a promising solution. In this study, we proposed to use deep learning models, the so-called long short-term memory and feedforward neural network methods, for precipitation downscaling for the Vietnamese Mekong Delta. Model performances were assessed for 2036-2065 period, using original climate projections from five climate models under the Coupled Model Intercomparison Project Phase 5, for two Representative Concentration Pathway scenarios (RCP 4.5 and RCP 8.5). The results exhibited that there were good correlations between the modelled and observed values of the testing and validating periods at two long-term meteorological stations (Can Tho and Chau Doc). We then analysed extreme indices of precipitation, including the annual maximum wet day frequency (Prcp), 95th percentile of precipitation (P95p), maximum 5-day consecutive rain (R5d), total number of wet days (Ptot), wet day precipitation (SDII) and annual maximum dry day frequency (Pcdd) to evaluate changes in extreme precipitation events. All the five models under the two scenarios predicted that precipitation would increase in the wet season (June-October) and decrease in the dry season (November-May) in the future compared to the present-day scenario. On average, the means of multiannual wet season precipitation would increase by 20.4 and 25.4% at Can Tho and Chau Doc, respectively, but in the dry season, these values were projected to decrease by 10 and 5.3%. All the climate extreme indices would increase in the period of 2036-2065 in comparison to the baseline. Overall, the developed downscaling models can successfully reproduce historical rainfall patterns and downscale projected precipitation data.

show abstract

Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M

Cited by 49 publications

References 38 publications

Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

Downscaling of monthly precipitation using CMIP5 climate models operated under RCPs

Downscaling rainfall using deep learning long short‐term memory and feedforward neural network

Contact Info

Product

Resources

About