基于STELLA和气候变化情景的灌区农业供需水量模拟

为了评估气候变化对灌区农业供需水量的影响,基于系统动力学软件STELLA(structure thinking experimental learning laboratory with animation)建立了宝鸡峡灌区供需水量计算模型,结合灌区气象水文数据,利用非一致性水文频率计算原理,预估了未来可供水量和气候变化情景,模拟了气候变化情景下不同规划水平年灌区农业供需水状况。结果表明:灌区主要水源渭河径流在1989年发生跳跃变化,跃幅为-14.25亿m3,各典型年预估径流量(1989-2030年)比原序列(1954-2010年)对应值减少40%~55%,导致农业可供水量锐减;在α=0.05的显著水平上,1981-2010年灌区降水下降不显著,平均气温、最高气温显著上升而相对湿度和风速显著下降,其他因子无明显趋势;灌区未来各典型年农业需水量2030年比2010年增加1.08~2.19亿m3,水资源供需平衡指数上升9.06%~14.46%,说明灌区农业供需水平衡状况受气候变化影响显著,必须在规划、设计和管理中予以考虑并采取积极的应对措施。研究结果为气候变化条件下灌区农业水资源合理配置提供了参考。

Simulation of agricultural water supply and demand at irrigation district under climate change using STELLA

Abstract: Climate change has significant influences on irrigation water demand (IWD) and available irrigation water supply (IWS), which poses grave challenges to farmers and administrators of agriculture and water resources. Due to the complexity and uncertainties from climate, agriculture and water systems, only a few studies have combined these systems together, especially at irrigation district scale. Furthermore, most climate change scenarios (CCS) are continuous time series with uncertainties, meanwhile the corresponding CCS of the typical hydrological years are deficient. Thus it is difficult for the administrators from water sectors and agriculture to make positive responses to climate change. The object of the study was to provide an approach to estimate and assess the influences of climate change on IWS and IWD systems in irrigation district. The study area was a typical northern irrigation district of China, the Baojixia Irrigation District. The model considering IWD, IWS and CCS was developed using system dynamics software i.e. structure thinking experimental learning laboratory with animation (STELLA). There were four function modules: IWDM, IWSM, CCSM and WSDBI. CCSM included the reference crop evapotranspiration (ET0) and the climate factors of future typical hydrological years. Climate factors (i.e. precipitation, air temperature, wind speed, relative humidity and sunshine duration) were predicted using historical data and hydrological frequency calculation principle of inconsistent series. ET0 was calculated by Penman-Monteith equation from the Food and Agriculture Organization of the United Nations (FAO) based on the predicted climate factors. IWDM calculated the irrigation water demands of main crops (wheat, maize, cotton, cole and apple). Crop water requirement was calculated by a simple soil water balance model including effective precipitation, crop coefficient (Kc) and ET0. Net irrigation water demand (NIWD) was the sum of crop water requirements multiplied by their planting areas. IWD was NIWD divided by coefficient of irrigation water effective utilization. IWS was the available irrigation water supply which consisted of ground water and surface water (runoff of Weihe River and reservoirs). The runoff was estimated by historical data and hydrological frequency calculation principle of inconsistent series. The outputs were IWD, IWS and a water supply-demand balance index (WSDBI, i.e. the ratio of IWD and IWS), which were used to evaluate the effects of climate change. The model was verified by historical hydro-meteorological and agriculture data from 2000 to 2010. After validation the predicted CCS and IWS were used as inputs for the simulation of the future irrigation water balance conditions. The conclusions of this research are: 1) The water supply and demand model based on STELLA has a good performance and can be a steady approach for agriculture and water resource simulation. The mean relative errors for IWD and WSDBI are both 3.13%. The mean absolute errors for IWD and WSDBI are 3.9?10-7 m3 and 0.06, respectively. 2) The runoff of Weihe River at Linjiacun station had a significant downward trend at the 0.05 level from 1954 to 2010. It was a jump type of change with its jump range of about -1.43?10-9 m3 occurred in 1989. Compared with the runoff values of different typical hydrological years from 1954 to 2010, the predicted values from 1989 to 2030 were declined by 40%-55% which caused a large reduction of agricultural water supply. The mean temperature and maximum temperature significantly increased meanwhile relative humidity and wind speed significantly decreased at the 0.05 level during 1981-2010. 3) Compared with 2010, the simulation values of IWD in 2030 for wet year (P=25%), normal year (P=50%) and dry year (P=75%) increased by 1.08?10-8, 1.29?10-8 and 1.21?10-8 m3 respectively, meanwhile the corresponding values of WSDBI increased by 14.46%, 11.70% and 9.06% respectively. The simulation indicated that the IWD and WSDBI increased dramatically under the synthetic action of meteorological factors in future. The supply could not meet the demands except wet years and the situation could be worse over the time, so effective measures should be taken in water resource and agriculture planning and management. This research can provide the reference for assessing the effect of climate change on agricultural water supply and demand in the irrigation district.