Abstract
A variety of methods are available to estimate future solar radiation (SR) scenarios at spatial scales that are appropriate for local climate change impact assessment. However, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. Three methodologies to guide the estimation of SR are discussed in this study, namely: Case 1: SR is measured, Case 2: SR is measured but sparse and Case 3: SR is not measured. In Case 1, future SR scenarios are derived using several downscaling methodologies that transfer the simulated large-scale information of global climate models to a local scale ( measurements). In Case 2, the SR was first estimated at the local scale for a longer time period using sparse measured records, and then future scenarios were derived using several downscaling methodologies. In Case 3: the SR was first estimated at a regional scale for a longer time period using complete or sparse measured records of SR from which SR at the local scale was estimated. Finally, the future scenarios were derived using several downscaling methodologies. The lack of observed SR data, especially in developing countries, has hindered various climate change impact studies. Hence, this was further elaborated by applying the Case 3 methodology to a semi-arid Malaprabha reservoir catchment in southern India. A support vector machine was used in downscaling SR. Future monthly scenarios of SR were estimated from simulations of third-generation Canadian General Circulation Model (CGCM3) for various SRES emission scenarios (A1B, A2, B1, and COMMIT). Results indicated a projected decrease of 0.4 to 12.2 W m(-2) yr(-1) in SR during the period 2001-2100 across the 4 scenarios. SR was calculated using the modified Hargreaves method. The decreasing trends for the future were in agreement with the simulations of SR from the CGCM3 model directly obtained for the 4 scenarios.
Highlights
Solar radiation (SR; see Table 1 for further abbreviations) plays an important role in hydrological, ecological, biological, and physical processes (Rivington et al 2005, Biggs et al 2007, Teuling et al 2009, Wild 2009)
This study showed that CC is expected to increase in the future for A1B, A2, and B1 scenarios, whereas no trend was discerned for the COMMIT scenario
In Case 2, the long-term SR was first estimated at the local scale using sparse measured records, and future scenarios were derived using several downscaling methodologies
Summary
Solar radiation (SR; see Table 1 for further abbreviations) plays an important role in hydrological, ecological, biological, and physical processes (Rivington et al 2005, Biggs et al 2007, Teuling et al 2009, Wild 2009). The lack of observed SR data, especially in developing countries, has hindered various climate change impact studies in agricultural and hydrological sectors (Iizumi et al 2012), indicating a need. ANN Artificial neural networks C Penalty term CC Cloud cover. CCCma Canadian Center for Climate Modeling and Analysis. CGCM3 Third-generation Canadian general circulation model CP
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