无常规水文监测高寒湿地纳帕海水量波动模拟分析

论文以无常规水文监测高寒湿地纳帕海为例,基于流域产汇流的时滞效应,建立了湿地区气候因子(日累计降水量)与水文因子(湿地明水量)之间的经验关联模型,以模拟湿地水文情势的波动。研究首先通过对1990-2011 年不同时相的48 期纳帕海湿地Landsat TM/ETM+遥感数据进行解译,提取明水景观变化信息;再利用研究区1988-2011 年逐日降水数据经过统计计算后生成的不同时间步长日累计降水量与48 期明水面积序列进行回归分析,筛选出最佳时间步长日累计降水量并获得其与明水面积之间的经验关联模型;进而借助纳帕海湿地明水面积与明水量之间的经验方程建立湿地明水量-最佳日累计降水量关联模型。模拟结果表明:8-9 月是纳帕海湿地的主汛期,9 月多年平均明水量可达866.11×10⁴ m³;干、湿季明水量差异较大,干季平均明水量为95.91×10⁴ m³。上述模拟结果能够与遥感影像解译所获取的信息和实地调查信息保持基本一致,说明此模型能够较好地模拟研究区水文情势波动,为无监测高寒湿地水文情势研究提供了一种新的思路,同时也为该地区湿地洪灾防范提供了数据基础和方法指导。

The Simulation ofWater Storage Fluctuation in Ungauged SubalpineWetlands—Napahai in Northwest Yunnan,China

The present study took the Napahai ungauged subalpine wetlands as an example and built an empirical correlation model between the climatic factor and the hydrological factor based on the time lag effect of basin runoff. This model took advantages of the climatic factor to simulate the fluctuation of hydrological factor. Forty-eight scenes of Landsat Thematic Mapper/ Enhanced Thematic Mapper Plus (TM/ETM+) images from 1990 to 2011 were catalogued to extract the open water areas (OWA) in the wetlands. Then the accumulated daily precipitation (ADP) levels at different time steps were generated by statistically computing the daily rainfall data in the study area. Through regression analysis between OWA and ADP at different time steps, optimal accumulated daily precipitation (OADP) was screened out and the empirical correlation model of OADP-OWA was also obtained. And then with the help of empirical equation between open water storage (OWS)-OWA in Napahai wetlands, the correlation model of OWS-OADP was established. The simulated results showed that the hydro-regime of Napahai kept stable in the dry season (November to April) and fluctuated sharply in the rainy season (May to October) on the inter-annual time scale. On the intra-annual time scale, the average monthly OWS raised in July, got maximum of 8.66×10⁶ m³ in September, then rapidly declined in October, and kept stable of 0.95×10⁶ m³ from November to July in the next year. September was the most urgent month for protecting the surrounding villages from flooding. This is basically consistent with the data from field surveys and remote sensing image interpretation information. It is suggested that the above model could simulate the fluctuation trends of the OWS in Napahai wetlands and could effectively reduce the influence of data deficiency on the quantitative simulation of hydrological regime in wetlands. The above model provides a new vision for hydrological studies of ungauged subalpine wetlands, and offers the basic data and method for preventing the flood in this area.