中国农业气象 ›› 2023, Vol. 44 ›› Issue (11): 1043-1056.doi: 10.3969/j.issn.1000-6362.2023.11.006

• 农业气象灾害 栏目 • 上一篇    下一篇

基于干旱风险的西藏青稞保险费率厘定及预测

史继清,甘臣龙,郭艺楠,杜军,周刊社   

  1. 1.西藏自治区气候中心,拉萨 850000;2.日喀则国家气候观象台,日喀则 857000;3.墨竹工卡县气象局,拉萨 850000; 4.西藏自治区气象信息网络中心,拉萨 850000;5.西藏高原大气环境科学研究所,拉萨 850000
  • 收稿日期:2023-01-05 出版日期:2023-11-20 发布日期:2023-11-15
  • 通讯作者: 甘臣龙,工程师,主要从事地面气象观测、农业气象方面研究。 E-mail:176585235@qq.com
  • 作者简介:史继清,E-mail:549923050@qq.com
  • 基金资助:
    西藏自治区自然科学基金(XZ202001ZR0033G);西藏自治区科技重点研发计划(XZ202001ZY0023N);第 二次青藏高原综合科学考察研究项目(2019QZKK0105;2019QZKK0106)

Determination and Prediction of Insurance Premium Rate of Highland Barley in Tibet Based on Drought Risk

SHI Ji-qing, GAN Chen-long, GUO Yi-nan, DU Jun, ZHOU Kan-she   

  1. 1.Tibet Climate Center, Lhasa 850000, China; 2. Shigatse National Climate Oberservatory, Shigatse 857000; 3.China Maizhokunggar Meteological Bureau, Lhasa 850000; 4.Information and Internet Center of Tibet Meteorological Bureau, Lhasa 850000; 5. Tibet Plateau Atmospheric Environmental Science Research Institute, Lhasa 850000
  • Received:2023-01-05 Online:2023-11-20 Published:2023-11-15

摘要: 以西藏青稞主要种植区为例,基于致灾因子危险性指数、承灾体暴露性指数、承灾体易损性指数和防灾减灾能力构建干旱灾害综合风险评估模型,并进行干旱灾害风险评估及区划,利用非参数法厘定各县青稞的纯保险费率,在风险区划结果基础上修正纯保险费率,再结合改进GM(1,1)模型和R/S方法预测未来修正纯保险费率。结果表明:(1)基于干旱致灾因子危险性指数和承灾体易损性指数的风险等级呈中部区域低两边高的趋势,基于干旱承灾体暴露性指数、防灾减灾能力和干旱灾害综合风险指数的风险等级由东向西有逐渐加重的趋势。(2)各县青稞保险的纯保险费率水平介于1.07%~9.79%,相差不大;修正后的纯保险费率介于1.86%~17.02%,相差较大。(3)基于干旱致灾因子危险性指数、承灾体暴露性指数和承灾体易损性指数修正下的纯保险费率空间分布呈中部高两边低的特点,基于干旱防灾减灾能力和干旱灾害综合风险指数修正下的纯保险费率呈现中部高、局部高和两边低的特征。说明科学合理的厘定纯保险费率应考虑多种干旱指数的综合影响。(4)首次利用改进GM(1,1)模型和R/S方法预测西藏青稞主要种植区未来修正纯保险费率的预测值有微弱的上升,上升率为0.21个百分点·10a1。构建的综合风险指数总体能客观反映西藏青稞干旱灾害风险水平,区划结果可为纯保险费率的修正提供基础支撑,进而为提高青稞农业保险服务的精确性提供科学依据。

关键词: MCI指数, 干旱灾害综合风险评估模型, 非参数, GM(1,1)模型, R/S方法

Abstract: Taking the main highland barley growing areas in Tibet as study region, a comprehensive risk assessment model of drought disasters was established based on hazard index of disaster-causing factors, exposure index of disaster-bearing body, vulnerability index of disaster-bearing body and disaster prevention and reduction ability of disaster-bearing body. The pure insurance premium rate of highland barley was determined by non-parametric method, and the pure insurance rate was revised based on the results of risk regionalization, the modified GM (1,1) model and R/S method were combined to predict the future revised pure insurance premium rate. The results show that: (1) based on the risk index of drought hazard factor and vulnerability index of disaster-bearing body, risk grade shows a trend of high on both sides of the middle low, the risk grade based on the exposure index, the ability of disaster prevention and mitigation, and the comprehensive risk index of drought disaster has a tendency to increase gradually from east to west. (2) The pure insurance premium rate of highland barley insurance in each station has little difference (between 1.07%-9.79%), but the modified pure insurance premium rate has a big difference (between 1.86%-17.02%). (3) Based on the risk index, exposure index, and vulnerability index of drought, the spatial distribution of pure insurance premium rate shows the characteristics of high middle, low two sides, however, the pure insurance premium rate based on the drought disaster prevention and mitigation capability correction and the drought disaster comprehensive risk index shows the characteristics of middle high, local high and both sides low. This showed that the scientific and reasonable determination of pure insurance rates should consider the combined effects of multiple drought indices. (4) For the first time, the modified GM (1,1) model and R/S method were used to predict the future revised pure insurance rate of highland barley in Tibet (the rate of increase was 0.21 percent points·10y−1). The comprehensive risk index can objectively reflect the drought risk level of highland barley in Tibet, and the result of regionalization can provide basic support for revising the pure insurance premium rate, and then to improve the precision of highland barley agricultural insurance services to provide a scientific basis.

Key words: MCI index, Comprehensive risk assessment model of drought disaster, Nonparametric, GM(1,1)model, R/S method