We investigated the effects of climate change on rainfed rice yield using the CERES-rice crop growth model and identified suitable agro-adaptation measures to address the threats induced by climate change on rice production in Northeast Thailand. The crop physiological data from field experiments conducted in the region was used to calibrate the model. Future climate scenarios for the periods 2020-2029, 2050-2059 and 2080-2089 were developed using the global climate model ECHAM4 and downscaled using the regional climate model PRECIS. Results indicated a decline in the rice yield in the region by 17.81, 27.59 and 24.34% for the 2020s, 2050s and 2080s, respectively, compared to the average yield during 1997-2006 under the ECHAM4 A2 climate change scenario. Simulation experiments suggested that for a given temperature, the yields of 2 rice varieties, KDML105 and RD6, increase with increases in CO 2 concentration. On the contrary, increases in temperature reduce the yield at a constant CO 2 concentration. The overall decrease in yield under future climatic conditions can be mitigated significantly by proper nutrient management and altering planting dates. Hybrid rice cultivars having a high temperature tolerance can also help to address the challenges imposed by future climate.KEY WORDS: Agro-adaptation · CERES-rice · Climate change · ECHAM4 A2 scenario · Forecasting · Northeast Thailand · Rice yield Resale or republication not permitted without written consent of the publisherClim Res 46: [137][138][139][140][141][142][143][144][145][146] 2011 more than half of the world's population. Since the 1980s there has been a decline in per capita rice production and productivity in Asia; therefore, the effect of climate change is a major concern to rice production in the region, which accounts for > 80% of the world's production and consumption (FAO 2004). Due to limited development of irrigation, a significant portion of this production is contributed by rainfed areas in Asia that are particularly susceptible to climate change.Several modeling studies revealed that an increase in CO 2 and temperature will significantly alter the production of wheat (e.g. in China, Thomson et al. 2006), maize (e.g. in North Central China, Tao & Zhang 2010 and rice (e.g. in Japan, Horie 2005; China, Erda et al. 2005; Thailand, Buddhaboon et al. 2004; Lao PDR, Inthavon et al. 2004; and India, Krishnan et al. 2007). However, studies on the impacts of future climate change on rice yield in most of the Southeast Asian countries are still very limited, especially under rainfed conditions, where earlier but less reliable forecasts may actually be more valuable than those that are more accurate but late (Sivakumar 2006). This needs particular attention, as modeling studies have projected crop yield losses even with minimal warming in the tropics (Easterling et al. 2007).To assess the vulnerability of agriculture to climate change it is necessary to consider the role of adaptation, as appropriate adaptation can greatly reduce the ma...
Eight rainfall bias correction techniques were compared over the Chindwin River basin in Myanmar to improve hydrological simulation at multiple timescales using two approaches, viz. monthly and annual. The techniques included linear scaling, parametric quantile mapping using linear, scale, power and exponential assymptotic transfer functions and nonparametric quantile mapping using empirical, robust regression and smoothing splines interpolation methods. Three global climate models (GCMs), wet, near-normal and dry in nature to estimate mean rainfall at the country and the basin scales were selected from a set of 13 GCMs. The rainfall bias correction factors for each GCM were generated from the control period 1981-1999 and verified over 2000-2005. Application of bias correction techniques resulted in reduction of biases and improved the flow simulations. These techniques showed better performance statistics in simulating daily, monthly and seasonal flows under the monthly approach, where correction factors were generated and applied separately for different months. The inconsistencies in magnitude and seasonality of flows were addressed under the monthly approach while only the biases related to magnitude were corrected under the annual approach. Linear scaling followed by parametric (linear and power transformation) and nonparametric empirical quantile mapping methods yielded a very good hydrological performance at all temporal scales when applied under the monthly approach. Parametric quantile mapping with scaling function yielded least efficiency under the annual approach for all temporal scales. These results are expected to be valid for other river basins in the region showing similar strong rainfall seasonality. K E Y W O R D Sbias correction, Chindwin River basin, climate models, ensemble, improved hydrological simulation, Myanmar
This study analyzes temperature projections in the Koshi River Basin in Nepal using data obtained from ten General Circulation Models (GCMs) for three IPCC Special Range of Emission Scenarios (SRES): B1, A1B and A2. Low resolution data of minimum and maximum temperature obtained from the selected GCMs was downscaled using the statistical downscaling model Long Ashton Research Station Weather Generator (LARS‐WG) for ten stations located in two physiographic regions of the study area: the Middle Mountains (1500–2700 m) and the Siwalik Hills (700–1500 m). The projected temperature and differences in projections among individual GCM projections for changes in the mean value of seasonal and annual Tmin and Tmax are presented for three future periods: 2011–2030 (2020s), 2046–2065 (2055s) and 2080–2099 (2090s). We also analyzed the baseline period and future Tmin and Tmax data through seven indices, as recommended by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Results show that the LARS‐WG model performs well when downscaling Tmin and Tmax. An increase in seasonal as well as mean annual minimum and maximum temperature is projected for all three future periods. Projected warming, as well as the differences among projections from different GCMs, increases with time for each of the three scenarios. The cold years during the 2055s and 2090s are expected to be hotter than the hot years during the baseline period. The increase in temperature, as well as the range of uncertainty, is expected to be higher in the Mountains than in the Hills. The number of summer days and tropical nights is expected to increase during all three future periods. The temperature of the coldest day, coldest night, warmest day and warmest night is also expected to increase in both the regions during all three future periods.
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