scholarly journals Comparison of different statistical downscaling models and future projection of areal mean precipitation of a river basin under climate change effect

2021 ◽  
Author(s):  
A. Guven ◽  
A. Pala
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Gene Expression Programming ◽  
Circulation Model ◽  
Support Vector ◽  
Group Method ◽  
Monthly Precipitation ◽  
Weather Factors

Abstract Investigation of the hydrological impacts of climate change at the local scale requires the use of a statistical downscaling technique. In order to use the output of a Global Circulation Model (GCM) model, downscaling technique is used. In this study, statistical downscaling of monthly areal mean precipitation of Göksun River basin in Turkey was carried out using the Group Method of Data Handling (GMDH), Support Vector Machines (SVM) and Gene-expression Programming (GEP) techniques. Large-scale weather factors are used for a basin with monthly areal mean precipitation (PM) record from 1971 to 2000 for training and testing periods. The R2-value for precipitation in SVM, GEP and GMDH models are 0.62, 0.59, and 0.6 respectively, for testing periods. The results showed that SVM has the best model performance than the other proposed downscaling models, however, AIC values showed the GEP model has the lowest AIC value. The simulated results for CGCM3 A1B and A2 scenarios show a similarity in their average precipitation prediction. Generally, both scenarios anticipate a decrease in the average monthly precipitation during the simulated periods. Therefore, the results of future projections show that the mean precipitation might decrease during the period of 2021–2100.

scholarly journals Investigation of impact of climate change on small catchments using different climate models and statistical approaches

2021 ◽  
Author(s):  
A. Guven ◽  
A. Pala ◽  
M. Sheikhvaisi
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Gene Expression Programming ◽  
Monthly Precipitation ◽  
Global Circulation Models ◽  
Systems Model

Abstract The use of a statistical downscaling technique is needed to investigate the hydrological consequences of climate change on the local hydropower capacity. Global Circulation Models (GCMs) are crucial tools used in various simulations for potential climate change effects, including precipitation and temperature. Statistical downscaling methods comprise the improvement of relations between the large-scale climatic parameters and the local variables. This study presents the trend analysis of the observed variables compared to the statistically downscaled emission scenarios that are adopted from the Canadian Second Generation Earth Systems Model (CanESM2) in the basin of Göksu River which is located in Turkey. The key purpose of the research is to evaluate both the predicted monthly precipitation and the projections of GCMs within the three simulated scenarios of RCP2.6, RCP4.5, and RCP8.5 by Gene Expression Programming (GEP). In addition, the findings of statistical downscaling of monthly mean precipitation will be compared to the Linear Regression model (LR). The R-value is 0.827 and 0.755 for precipitation of the GEP model for the periods of calibrating and validation. In comparison with the LR model for the validation and calibration periods (1971–2005), the results of the GEP model prove its applicability in projecting the data of the monthly mean rainfall. Generally, in the simulated periods of 2021–2100, the mentioned scenarios forecast a decline in the monthly mean precipitation in the basin. Moreover, the scenario of RCP8.5 projects more suitably for the case study than expected under the scenarios of the RCP4.5 and RCP2.6. The mean statistically downscaled CanESM2 model was compared with the trend analysis of the areal mean precipitation (PM) over the case study area, and the trend was shown decreasing. However, the RCP 8.5 scenario has the more quasi-asymptotic for trend.

scholarly journals Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
L. Campozano ◽  
D. Tenelanda ◽  
E. Sanchez ◽  
E. Samaniego ◽  
J. Feyen
Keyword(s):  
River Basin ◽  
Statistical Downscaling ◽  
General Circulation ◽  
General Circulation Models ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Local Conditions ◽  
Southern Ecuador ◽  
Monthly Total Precipitation ◽  
Precipitation Estimates

Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador’s main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons.

scholarly journals Identification of dominant sources of sea level pressure for precipitation forecasting over Wales

2012 ◽  
Vol 15 (3) ◽  
pp. 1002-1021 ◽  
Author(s):  
Azadeh Ahmadi ◽  
Dawei Han
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Principal Component ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Climate Variables ◽  
Forward Selection ◽  
Precipitation Forecasting ◽  
Variable Combination

Downscaling methods are utilized to assess the effects of large scale atmospheric circulation on local hydrological variables such as precipitation and runoff. In this paper, a methodology of statistical downscaling using a support vector machine (SVM) approach is presented to simulate and predict the precipitation using general circulation model (GCM) data. Due to the complexity and issues related to finding a relationship between the large scale climatic parameters and local precipitation, the climate variables (predictors) affecting monthly precipitation variations over Wales are identified using a combination of the methods including the principal component analysis (PCA), fuzzy clustering, backward selection, forward selection, and Gamma test (GT). The effectiveness of those tools is illustrated through their implementations in the case study. It has been found that although the GT itself fails to identify the best input variable combination, it provides useful and narrowed-down options for further exploration. The best input variable combination is achieved by the GT and forward selection method. This approach can be a useful way for assessing the impacts of climate variables on precipitation forecasting.

scholarly journals Assessing the climate change impact on hydrological response in the Gorganrood River Basin, Iran

2017 ◽  
Vol 9 (3) ◽  
pp. 421-433 ◽  
Author(s):  
Hamed Rouhani ◽  
Marayam Sadat Jafarzadeh
Keyword(s):  
Climate Change ◽  
River Basin ◽  
General Circulation ◽  
Assessment Tool ◽  
Circulation Model ◽  
Low Flow ◽  
Future Climate Change ◽  
Monthly Precipitation ◽  
Climate Change Scenarios ◽  
Annual Scale

Abstract A general circulation model (GCM) and hydrological model SWAT (Soil and Water Assessment Tool) under forcing from A1B, B1, and A2 emission scenarios by 2030 were used to assess the implications of climate change on water balance of the Gorganrood River Basin (GRB). The results of MPEH5C models and multi-scenarios indicated that monthly precipitation generally decreases while temperature increases in various parts of the basin with the magnitude of the changes in terms of different stations and scenarios. Accordingly, seasonal ET will decrease throughout the GRB over the 2020s in all seasons except in summer, where a slight increase is projected for A1B and A2 scenarios. At annual scale, average quick flow and average low flow under the B1, A1B, and A2 scenarios are projected to decrease by 7.3 to 12.0% from the historical levels. Over the ensembles of climate change scenarios, the simulations project average autumn total flow declines of ∼10% and an overall range of 6.9 to 13.2%. In summer, the components of flow at the studied basin are expected to increase under A2 and A1B scenarios but will slightly decrease under B1 scenario. The study result addresses a likelihood of inevitable future climate change.

scholarly journals Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts

2009 ◽  
Vol 9 (3) ◽  
pp. 879-894 ◽  
Author(s):  
L. Vasiliades ◽  
A. Loukas ◽  
G. Patsonas
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Standardized Precipitation Index ◽  
Circulation Model ◽  
Economic Losses ◽  
Monthly Precipitation ◽  
Statistical Downscaling Method ◽  
Downscaling Method

Abstract. Despite uncertainties in future climates, there is considerable evidence that there will be substantial impacts on the environment and human interests. Climate change will affect the hydrology of a region through changes in the timing, amount, and form of precipitation, evaporation and transpiration rates, and soil moisture, which in turn affect also the drought characteristics in a region. Droughts are long-term phenomena affecting large regions causing significant damages both in human lives and economic losses. The most widely used approach in regional climate impact studies is to combine the output of the General Circulation Models (GCMs) with an impact model. The outputs of Global Circulation Model CGCMa2 were applied for two socioeconomic scenarios, namely, SRES A2 and SRES B2 for the assessment of climate change impact on droughts. In this study, a statistical downscaling method has been applied for monthly precipitation. The methodology is based on multiple regression of GCM predictant variables with observed precipitation developed in an earlier paper (Loukas et al., 2008) and the application of a stochastic timeseries model for precipitation residuals simulation (white noise). The methodology was developed for historical period (1960–1990) and validated against observed monthly precipitation for period 1990–2002 in Lake Karla watershed, Thessaly, Greece. The validation indicated the accuracy of the methodology and the uncertainties propagated by the downscaling procedure in the estimation of a meteorological drought index the Standardized Precipitation Index (SPI) at multiple timescales. Subsequently, monthly precipitation and SPI were estimated for two future periods 2020–2050 and 2070–2100. The results of the present study indicate the accuracy, reliability and uncertainty of the statistical downscaling method for the assessment of climate change on hydrological, agricultural and water resources droughts. Results show that climate change will have a major impact on droughts but the uncertainty introduced is quite large and is increasing as SPI timescale increases. Larger timescales of SPI, which, are used to monitor hydrological and water resources droughts, are more sensitive to climate change than smaller timescales, which, are used to monitor meteorological and agricultural droughts. Future drought predictions should be handled with caution and their uncertainty should always be evaluated as results demonstrate.

scholarly journals Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, China

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jiaming Liu ◽  
Di Yuan ◽  
Liping Zhang ◽  
Xia Zou ◽  
Xingyuan Song
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Support Vector ◽  
Simulation Accuracy ◽  
Downscaling Method ◽  
Hanjiang River

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA) method combined with three statistical downscaling methods, which are support vector machine (SVM), BCC/RCG-Weather Generators (BCC/RCG-WG), and Statistics Downscaling Model (SDSM), is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB). The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error) shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.

scholarly journals Statistical downscaling of precipitation using inclusive multiple modelling (IMM) at two levels

2021 ◽  
Author(s):  
Sina Sadeghfam ◽  
Rahman Khatibi ◽  
Tara Moradian ◽  
Rasoul Daneshfaraz
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Fuzzy Model ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Local Data ◽  
Neuro Fuzzy ◽  
Level 1 ◽  
Environmental Prediction ◽  
Level 2

Abstract Topical research on hydrological impacts of climate change in terms of downscaling of monthly precipitation is investigated in this paper by formulating an inclusive multiple modelling (IMM) strategy. IMM strategies manage multiple models at two levels and the paper uses statistical downscaling model, Sugeno fuzzy logic and support vector machine at Level 1 and feeds their outputs to a neuro-fuzzy model at Level 2. In the downscaling stage, large-scale NCEP (National Centres for Environmental Prediction)/NCAR (National Centre for Atmospheric Research) data for a station with local data record from 1961 to 2005 are used for training and testing Level 1 models, which are found to be ‘fit-for-purpose’, but the variations between them signify some room for improvements. The model at Level 2 combines outputs of those at Level 1 and produces Level 2 results, which are over the Level 1 models in terms of dispersion of residual errors. In this way, IMM provides a more defensible modelling strategy for application in the projection stage. The comparison between observed and projected precipitation indicates that precipitation will be likely to reduce compared with observed precipitation in cold seasons (October–February), but the projected precipitation will be likely to increase slightly in wet seasons (April and May).

Impacts of climate change on irrigation water requirements for rice–wheat cultivation in Bagmati River Basin, Nepal

2013 ◽  
Vol 4 (4) ◽  
pp. 422-439 ◽  
Author(s):  
S. Shrestha ◽  
B. Gyawali ◽  
U. Bhattarai
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Irrigation Water ◽  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Growth Stages ◽  
Water Requirements ◽  
Bagmati River ◽  
Impacts Of Climate Change

This study highlights the spatial and temporal impacts of climate change on rice–wheat cropping systems, focusing on irrigation water requirement (IWR) in the Bagmati River Basin of Nepal. The outputs from a general circulation model (HadCM3) for two selected scenarios (A2 and B2) of IPCC and for three time periods (2020s, 2050s, and 2080s) have been downscaled and compared to a baseline climatology. CROPWAT 8.0 model is used to estimate the water requirements. IWRs show different trends in different physiographic regions and different growth stages of rice and wheat. A decreasing trend of IWRs in the Mid Hills and the High Hills indicates that farmer-based small irrigation schemes are sufficient to meet the requirements. However, in the Terai region, where there is an increasing trend in IWRs, the deficit volume of water needs to be supplied from potential large-scale irrigation schemes.

Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand

2021 ◽  
Author(s):  
Kanawut Chattrairat ◽  
Waranyu Wongseree ◽  
Adisorn Leelasantitham
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Performance Criteria ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Support Vector ◽  
Seasonal Temperature ◽  
Statistical Downscaling Method ◽  
Downscaling Method

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

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