Chapter 2: Review of Literature……………………………………………………... 9-52
2.8 General Circulation Models/Global Climatic Models
2.8.1 Approaches for downscaling of GCMs
The approaches, which have been proposed for downscaling GCMs, can be broadly classified into two categories: dynamic downscaling and statistical downscaling and are the most commonly used methods in the one-way coupling of GCM and hydrological models (Bergstrom et al., 2001; Fowler et al., 2007; Pinto et al., 2010).
2.8.1.1 Dynamic downscaling
In the dynamic downscaling approach, a Regional Climate Model (RCM) is embedded into GCM. The RCM is a numerical model in which GCMs are used to fix boundary conditions.
Despite of having clear physical meanings, the major disadvantages of RCM are its complicated design and high computational cost. Another drawback associated with RCM is that expanding the region or moving to a slightly different region requires redoing of the entire experiment (Crane and Hewitson, 1998). Because of the fact that RCM is nested in a GCM, the overall quality of dynamically downscaled RCM output is tied to the accuracy of the large-scale forcing of the GCM and its biases (Seaby et al., 2013). The RCM outputs bear some systematic errors, despite the fact that it recovers important regional-scale features that are underestimated in coarse resolution GCMs. Hence, RCM outputs require bias correction as well as further downscaling to higher resolution. Moreover, the quality of RCM outputs depends on the driving GCM information. For example, if the GCM misplaces storm tracks, there will be errors in the RCM’s precipitation climatology (Wilby et al., 2009). The grid-box size of an RCM is typically greater than 10 km, which is too coarse for hydrological impact studies (Benestad, 2009). To obtain higher resolution results, statistical methods are used in lieu of RCMs, or the RCM output is further downscaled via statistical means. The most
commonly used RCMs in downscaling studies are, U.S. Regional Climate Model Version 3(RegCM3), Canadian regional Climate Model (CRCM), U.K. Met Office Hadley Centre’s Regional Climate Model Version 3(HadRM3), German Regional Climate Model (REMO), Dutch Regional Atmospheric Climate Model (RACMO) and German HIRHAM which combines the dynamics of the High resolution Limited Area Model (HIRLAM) and European Centre-Hamburg (ECHAM) models.
2.8.1.2 Statistical downscaling
Statistical downscaling involves deriving empirical relationships that transform large-scale features of the GCM (predictors) to regional-scale variables (predictands) such as precipitation, temperature and streamflow. Since it is based on statistical relationships between predictors and predictands, the statistical downscaling approach requires less computational time. Hewitson and Crane (1996) enumerated three main assumptions involved in statistical downscaling. These are, (i) the predictors are variables of relevance and are realistically modeled by the host GCM; (ii) the empirical relationship is valid also under altered climatic conditions and (iii) the predictors employed fully present the climate change signal.
As per Musau et al., (2013), the statistical downscaling methods are categorized into following three different categories:
1) Transfer functions: In this technique, linear and non-linear methods are used to infer the relationship between predictors and predictands (Hashmi et al., 2011; Guo et al., 2011). This predictor-predictand relationship then can be verified by using multiple linear regressions (Winkler et al., 1997), principal component analysis (Huth, 2004), canonical correlation analysis (WASA, 1998) on artificial neural network and singular value decomposition (Jeong et al., 2012).
2) Weather typing: This method is based on classification of the large scale atmospheric structure into ‘weather types’ and then associating local meteorological variables with each of these types (Cheng et al., 2011b). However, since this method does not assume continuous relationship between large-scale circulation and local climate, there is a chance of potential loss of information (Chen et al., 2012).
3) Weather generators: In this technique, parameter values are perturbed according to the changes projected by climate models (Zhang, 2007; Jones et al., 2009). This method is useful in evolution of synthetic series of unlimited length (Yang et al., 2005). Two
types of weather generators are Markov chain approach and spell-length approach (Karl et al., 1990).
Various researchers conducted various studies using various statistical downscaling techniques. Dibike and Coulibaly (2005) applied a stochastic based and a regression based statistical downscaling technique along with two hydrological models to simulate the future flow regime in a catchment. Prudhomme and Davies (2009) used a lumped conceptual rainfall-runoff model, three GCMs and two downscaling techniques to study the impact of climate change on river flows. Chiew et al., (2010) used SIMHYD rainfall-runoff model with daily rainfall which was downscaled from three GCMs using five downscaling models to simulate runoff. Segui et al., (2010) evaluated the uncertainty related to climate change impacts on water resources by applying a distributed hydrological model and three different downscaling techniques. Chen et al., (2011) used SSVM (Smooth Support Vector Machine) and SDSM (Statistical Downscaling Model) to compare the difference in water balance simulations in upper Hanjiang basin in China. The A2 emission scenarios from CGCM3 and HadCM3 were used as input to the statistical downscaling models.They found that for the same GCM, the simulated runoff varied greatly when rainfall provided by different downscaling techniques were used as the inputs to the hydrological models. Tripathi et al., (2006) proposed a Support Vector Machine (SVM) approach for statistical downscaling of precipitation at monthly time scale. The climatic parameters of Coupled Global Climate Model (CGCM2) have been used as predictors. They showed that SVMs provide a promising alternative to conventional ANN for statistical downscaling and are suitable for conducting climate impact studies. SVM has found wide applications in the field of pattern recognition and time series analysis (Vapnik, 1995, 1998). Hu et al., (2013) compared three statistical downscaling methods: SDSM, Generalized linear model for daily climate and Non- homogeneous Hidden Markov Model for downscaling summer precipitation from a network of 14 stations over the Yellow River region using National Centre for Environmental Prediction (NCEP) reanalysis data and GCMs CGCM3 and ECHAM5.
2.8.1.3 Downscaling by Artificial Neural Network (ANN) model
Artificial Neural Network (ANN) based downscaling techniques are widely used because of their ability to capture non-linear relationship between predictors and predictand (Solecki and Oliveri, 2004; Tatli et al., 2004).
Karamouz et al., (2009) conducted a study in the south-eastern part of Iran using field and GCM data with the Statistical Downscaling Model (SDSM) and the ANN model to predict rainfall. The results showed that SDSM outperformed the ANN model.
Mendes and Marengo (2010) compared ANN with an autocorrelation statistical downscaling model to downscale five coupled GCMs for a region in the Amazon Basin. Their results indicated that ANN model outperformed the other statistical models for downscaling of daily precipitation.
Chadwick et al., (2011) used an ANN approach for downscaling GCM temperature and precipitation and applied this approach to a nested RCM over Europe. They found that the ANN was able to recreate RCM output fields from GCM input data for both temperature and rainfall.
Hoai et al., (2011) used an empirical-statistical downscaling method for prediction of precipitation using feed-forward multilayer perception (MLP) neural network for the Bon River basin in Central Vietnam. The downscaled precipitation was then used as input to the runoff model for flood prediction. Their results revealed that the precipitation predicted by MLP outperformed the precipitation directly obtained from model outputs or that downscaled using multiple linear regression.
Samadi et al., (2012) conducted a study in Karkeh catchment, west Iran to determine the change in streamflow for the period 2040-2069 using a hybrid conceptual hydrological model in conjunction with model outcomes from HadCM3 GCM. They performed two downscaling techniques, Statistical Downscaling Model and ANN. The results suggested a significant decrease of streamflow in both downscaling projections, particularly in winter.
Chitra and Thampi (2013) applied an advanced non-linear bias correction method to ANN based downscaling models to obtain projections of monthly precipitation at two rain gauge stations, one in the Chaliyar river basin located in the humid tropics in Kerala,India and other located close to it. The predictors were extracted from National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) and CGCM3 GCM models. The performance of the models was reasonably good except for a few extremes.
Okkan and Fistikoglu (2013) applied Levenberge- Marquardt algorithm based feed forward neural networks (LM-FFNN) to downscale GCM CGCM3 for present day and future climates for precipitation and temperature over the Thatali River basin in Turkey. They used parametric hydrological model GR2M to observe the impacts of temperature and
precipitation on runoff. Their findings indicated that the near future runoff was likely to decrease by 32% leading to a reduction in water supply.
Pour et al., (2014) proposed a genetic programming (GP) based logistic regression method for downscaling of rainfall over the east coast of Peninsular Malaysia. The results were compared with those obtained using ANN and SDSM. It was found that models derived using GP can predict both annual and seasonal rainfall more accurately compared to ANN and SDSM.
Vu et al., (2016) utilized ANN method to downscale climate models for rainy season at meteorological site locations in Bangkok, Thailand. The downscaled results showed good agreement against station precipitation with a correlation coefficient of 0.8 and Nash- Sutcliffe efficiency of 0.65. The final downscaled results showed an increasing trend of precipitation for rainy season over Bangkok by the end of the 21st century.
Kueh and Kuok (2016) proposed an ANN approach to forecast future precipitation through spatial downscaling for which the bat neural network (BatNN) was developed and benchmarked with the traditional Scaled Conjugate Gradient Neural Network (SCGNN).
Their results revealed that BatNN outperformed its benchmark in terms of prediction accuracy.