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Prediction of chemicals runoff m watershed based on the combined water and mass transfer model

基于水传质组合模型的流域化学品径流预测

基本信息

批准号：
13838006
负责人：
YUASA Akira
金额：
$ 2.24万
依托单位：
Gifu University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13838006/
关键词：
watershed river basin agrochemical run off model farm work uncertainty Monte Carlo simulation シミュレーション除草剤地理情報気象情報

项目摘要

A model analysis of chemicals runoff in watershed was constructed, based on the combination of chemicals transfer model and water transfer model. Watershed area was divided into grids of 1km x 1km and each grid was further divided vertically and horizontally to 13 compartments. The Chikugo River basin was analyzed in which agrochemical compounds were monitored frequently (five times per week). Runoff of agrochemicals used in paddy field was predicted.Data of land utilization were collected from digital map and information published by the National Geographical Survey Institute. Weather information was obtained from the AMEDAS data. River flow rate was estimated from the observed values at the observation points and the reserved volume and discharge flow rate at the reservoir dam.Data on farm work such as date and place of agrochemical sprinkling and spraying, date of rice transplanting, water management schedule are regarded uncertain. Characteristics of the agrochemical compound such as adsorption coefficient and biological decomposition rate constant are also regarded uncertain. The value of these parameters was set using Monte Carlo method based on the probability distribution of matters taking place. The conventional simulation by a deterministic algorithm was also applied for comparison. As a result, the following results were obtained.(1)Estimated concentration level of agrochemicals by the Monte Carlo simulation are more dosed to the actual observed value in rivers than that estimated by the deterministic algorithm. Thus, the Monte Carlo simulation was proved to give much accurate prediction. (2)It was indicated that runoff of an agrochemical compound can probably be estimated if the total amount of consumption of the compound in the whole watershed area is given, even though the precise schedule and accurate amount of agrochemical sprinkling in each paddy field are unknown.

将化学物质迁移模型与水迁移模型相结合，建立了流域化学物质径流模型分析模型。将流域区域划分为1 KM×1 KM的网格，并将每个网格在垂直和水平方向上进一步划分为13个隔层。对奇库戈河流域进行了分析，对其中的农用化合物进行了频繁监测(每周五次)。利用美国国家地理调查局发布的数字地图和信息收集土地利用数据，对水田农用化学品径流进行预测。天气信息从AMEDAS数据中获得。根据观测点的观测值和水库大坝的蓄水量和流量推算河流流量，农用化学品喷洒的日期和地点、水稻插秧期、水分管理时间表等农业生产数据被认为是不确定的。农用化学品的吸附系数和生物分解速率常数等特性也被认为是不确定的。这些参数的值是根据事件发生的概率分布使用蒙特卡罗方法设定的。采用确定性算法的常规模拟也用于比较。结果表明：(1)与确定性算法相比，蒙特卡罗模拟估算的农药浓度水平更接近河流的实际观测值。因此，蒙特卡罗模拟被证明给出了更准确的预测。(2)给出了农药在整个流域范围内的总消耗量，即可估算出农药的径流量，即使不知道每一块稻田的农药喷洒的精确时间表和准确的喷洒数量。