研究地下水位的动态对国计民生具有重大意义Groundwater气候条件.ppt

A Statistical Method for Recovering the Depth to Shallow Groundwater Table in China 袁 星 谢正辉，梁妙玲 中国科学院大气物理研究所 xyuan@mail.iap.ac.cn 2006.08.10 Background Data Methodology Validation Application Summary * * 在全球总水量中，海洋占97%以上，偏远而难以利用的两极冰帽及冰川约占2%，其余不到1%才是人类可取用的水资源，而其中地下水的储存总量居冠。 地下水的过量开采会造成地下水位的大幅下降，引起地面沉降。地下水位过高会对农作物生长不利，造成渍害。因此，研究地下水位的动态对国计民生具有重大意义。 Groundwater 气候条件、植被地形和人类活动的变化能引起地下水埋深时空分布的变化；反之，大尺度地下水埋深的变化，导致土壤含水量、地表径流和基流的改变，进一步影响下垫面的蒸散发和低层大气感热和潜热的分配，从而对气候产生影响。 估计浅层地下水埋深变化对水资源研究、陆面过程模拟、陆地生态系统及陆气相互作用的研究具有重要意义。 Transfer function-noise (TFN) models parameter transfer method. Purpose To recover monthly data for the depth to shallow groundwater table since 1961 in continental China. Scheme Data Meteorological Data (1961-2000) Daily time-series of precipitation, maximum temperature, and minimum temperature are obtained by interpolating station values from 740 meteorological stations in China. Soil Data The soil texture information is derived from Food and Agriculture Organization dataset (FAO). Groundwater Phreatic data from monitor wells. Locations of the meteorological stations Locations of wells interpolated into 60×60 km2 grids MethodologyⅠ: Calibration TFN model Input: precipitation surplus (precipitation minus potential evapotranspiration). The instantaneous evaporative demand (mm/s) is calculated following Jarvis and McNaughton (1986): TFN model State-space representation A linear discrete stochastic system State equation Measurement equation If no observation taken at time t If there is an observation at time t Recursive application of the Kalman filter Running the Kalman filter for the calibration period with a parameter set resulting in the following objective function: Using SCE-UA (shuffled complex evolution method developed at The University of Arizona) method to minimize the objective function. Identification of TFN model Transform data to improve normality and stationarity, and determine parameters which will be calibrated. Representation of