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Evaluation of a regional climate model for impact assessment of climate change on crop productivity in the tropics

机译:评价对热带作物生产率的影响评价区域气候模型

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Regional climate models (RCMs) are considered to be more useful than general circulation models for assessing impacts of climate change scenarios in agriculture. In this communication, the climatic outputs of an RCM-PRECIS (providing regional climates for impact studies) model were analysed by comparing its baseline simulation daily weather data on temperature and precipitation patterns with the observed weather for the corresponding period (1960-1990) in order to find out the bias in the model. Results showed that model could simulate the mean weather parameters on an aggregated scale, but could not satisfactorily represent spatio-temporal variations. There exists a bias towards higher precipitation along with more intense warm and cold events in the baseline simulation. In order to quantify the impacts of the PRECIS model biasness in baseline simulations on crop performance, rice (kharif season) and wheat (rabi season) yields were simulated using the observed weather and the PRECIS baseline weather for several locations representing the Indo-Gangetic Plains. With more extreme weather parameters in the baseline simulated data, the grain yields of rice and wheat were reduced, even causing wheat crop failure in several years as against none observed. The results indicated that using PRECIS baseline daily weather may cause bias in crop performance assessments. Since the bias in baseline will be carried forward in the assessment of future climatic impacts, there is a need to develop more reliable regional climate scenarios for the Indian region.
机译:区域气候模型(RCMS)被认为比普通流通模式更有用,用于评估气候变化情景在农业中的影响。在这种通信中,通过将其基线模拟日常天气数据与观察到的天气(1960-1990)的天气相比,通过比较其基线模拟日常天气数据来分析RCM-PRECIS(提供影响研究的区域气候)模型的气候输出。为了找出模型中的偏差。结果表明,模型可以模拟汇总规模的平均天气参数,但不能令人满意地代表时空变化。在基线模拟中存在更高的沉淀和更强烈的温暖和冷的事件存在偏差。为了量化基线模拟对作物性能的基线模拟中的Precis模型偏见的影响,使用观察到的天气和特殊的基线天气来模拟稻米(kharif季节)和小麦(拉比季节)产量,该天气是代表印陀螺的几个地点。在基线模拟数据中具有更极端的天气参数,降低了米和小麦的粮食产量,即使在几年内导致小麦作物失败,甚至没有观察到。结果表明,使用Precis基线日常天气可能导致作物性能评估中的偏差。由于基线中的偏差将在未来气候影响的评估中进行,因此需要开发印度地区更可靠的区域气候情景。

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