作 者 ：熊伟,杨红龙,冯颖竹
期 刊 ：生态学报 2010年 30卷 18期 页码：5050~5058
Keywords:crop model, regional simulation, weather interpolation,
摘 要 ：作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时，遇到的问题之一就是输入模型的空间数据质量问题，研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响，可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型，将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中，模拟了50km×50km网格水平下1961—1990年我国玉米生产状况，并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较，以了解区域模拟中，不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明：(1)作物模型区域应用时，所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求，采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征，与网格调查平均产量之间具有极显著的相关关系，但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2) 采用不同内插天气数据，在进行作物区域模拟时，各方法的模拟结果之间呈极显著的相关关系，但这些模拟结果之间，在全国大部分地区是差异显著。
Abstract:Crop regional simulation has emerged as a new scope for crop model application. It has been used in studies of climate change impact assessment, precision farming, food security and agricultural policy assessment etc. Key obstacles keeping the regional simulation from widely application are availability and quality issues of high resolution daily weather data, and, the diverse methods for generating high-resolution daily weather data from coarse weather observations. Various methods exist in interpolating from randomly distributed weather observation sites to high resolution grid weather data, but how the methods affect the crop regional simulation is rarely known. Evaluating the sensitivity and uncertainty of simulation to method of weather interpolation can help us to identify an appropriate interpolation method for the regional simulation. Based on observed daily weather data from 650 weather observation sites distributed across China, we use three types of interpolation approach, geometrical (choose method of Nearest Neighbor, NN), geographical statistic (choose method of Bivariate Interpolation, BI), and regional climate model interpolation (We use PRECIS Baseline run, BS) to generate gridded (50 km×50 km) daily weather data for whole China, and input them into CERES-Maize crop model. Maize yield is simulated from 1961-1990 and its spatial variability is generated with each interpolation method. Differences in results due to various interpolation methods are measured through (1) comparison of simulated yields to census yields, (2) identifying sensitivity of simulated yields to various interpolation approaches. The census yields from 1980 to 1990 are compared to corresponding simulation yields with each daily weather dataset as input. The comparisons demonstrate interpolated daily weather data with different methods are all able to produce reasonable projections in terms of spatial patterns of yield variability. The spatial patterns simulated by inputting the three interpolation methods are roughly identical to census one, indicating the reliability of the interpolation methods for crop simulation use. Simulated yields are correlate to census yield significantly (P<0.05) in all cases, suggesting the feasibility of using interpolated weather to replace observed weather if observations were not available. Difference exits between census yields and simulated yields, the differences due to different selection on interpolated weather data are within 8%, implying the limited impacts caused by different weather interpolation methods. Sensitivity analysis is operated through correlation analysis for any two of the three simulation results, it proves that there are significant correlations between any two of the three simulation results, but statistically speaking, yields/phonologies are different when comparing any two pairs within the three simulation results. These differences are also significant for most of the maize planting regions. This highlights that caution must be taken before choosing interpolation method for regional crop simulation, particularly in the case of forecasting exact local yield. We make recommendations for selection of interpolation method for crop regional simulation. According to their different characteristics of the methods and the observation data availability, geometrical interpolation is a best solution given the availability and accessibility of nicely distributed and large number of observed weather site, geographical statistic interpolation can be used if regional simulation happens in large flat regions, interpolation by regional climate model is an alternative when attentions were put on spatial variability or without observations.
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