| 马拉河流域植被生态需水特征及估算 |
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| 引用本文： | 朱婉怡,张振克,郭新亚,冯首铭,蒋大亮,江飞.马拉河流域植被生态需水特征及估算[J].生态学报,2023,43(18):7523-7535. |
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| 作者姓名： | 朱婉怡 张振克 郭新亚 冯首铭 蒋大亮 江飞 |
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| 作者单位： | 南京大学地理与海洋科学学院, 南京 210023;南京大学非洲研究所, 南京 210023 |
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| 基金项目： | 国家重点研发计划项目（2018YFE0105900） |
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| 摘 要： | 生态需水是生态用水控制和区域生态环境恢复建设的基本依据。马拉河流域拥有世界著名的生态系统，植被生态需水占流域总需水量的很大一部分。基于1980—2020年ERA5气象数据、叶面积指数(LAI)与世界土壤数据库数据，采用Penman-Monteith法计算了马拉河流域四个季节(短旱季、长雨季、长旱季、短雨季)植被生态需水量的时空变化特征。在此基础上，使用支持向量机(SVM)、随机森林(RF)和卷积神经网络(CNN)3种机器学习方法与7个环境因子(气温、降水、10 m风速、LAI、太阳辐射、相对湿度、地形)建立了回归模型，分别估算了2011—2020年逐年不同季节的植被生态需水量，并与Penman-Monteith法计算结果进行时间序列拟合度和空间相似性的比较。结果表明：马拉河流域植被生态需水量在过去40年所有季节都呈现为波动变化，植被生态需水量长雨季>长旱季>短雨季>短旱季，长雨季的植被生态需水量约为短旱季的1.5倍。不同季节均呈现出上下游高、中游低的植被生态需水量空间分布格局。LAI为最大的正影响因子，风速为最大的负影响因子。就不同方法估算的植被生态需水量准确性而言，...
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| 关 键 词： | 马拉河流域 植被生态需水量 支持向量机(SVM) 随机森林(RF) 卷积神经网络(CNN) 估算 |
| 收稿时间： | 2022/9/18 0:00:00 |
| 修稿时间： | 2023/2/3 0:00:00 |
Characteristics and estimation of vegetation ecological water demand in the Mara River Basin |
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| ZHU Wanyi,ZHANG Zhenke,GUO Xiny,FENG Shouming,JIANG Daliang,JIANG Fei.Characteristics and estimation of vegetation ecological water demand in the Mara River Basin[J].Acta Ecologica Sinica,2023,43(18):7523-7535. |
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| Authors: | ZHU Wanyi ZHANG Zhenke GUO Xiny FENG Shouming JIANG Daliang JIANG Fei |
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| Affiliation: | School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China;Institute of African Studies, Nanjing University, Nanjing 210023, China |
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| Abstract: | Ecological water demand (EWD) is the basis for regionally ecological water control and environment restoration. The Mara River Basin has a world famous ecosystem, and the vegetation EWD accounts for a large part of the total water demand in the Basin. Based on the ERA5 meteorological data, leaf index data (LAI) data from 1980 to 2020, and HSWD world soil data, we calculated the vegetation EWD temporal and spatial variation characteristics in the four seasons (short dry season, long rainy season, long dry season, and short rainy season) of Mara River Basin by using the Penman-Monteith method. Then, three different machine learning methods of support vector machine (SVM), random forest (RF), convolutional neural network (CNN) and seven environmental factors (temperature, precipitation, 10 m wind speed, LAI, solar radiation, relative humidity and terrain) were used to establish regression models. Thus, the vegetation EWD in different seasons from 2011 to 2020 was estimated by the machine learning regression models, and the estimation results were compared with the results calculated by the Penman-Monteith method in the fitting degree of time change series and the similarity in the space. The results showed that the EWD of vegetation in the Mara River Basin showed fluctuating trends in all seasons in the past 40 years. The vegetation EWD in different seasons from the most to the least was long rainy season, long dry season, short rainy season, and short dry season, the EWD in the long rainy season was about 1.5 times of that in the short dry season. The EWD in the upstream and downstream was larger than that in the midstream in all the seasons. LAI was the largest positive influence factor and wind speed was the largest negative influence factor. In terms of the accuracy of vegetation EWD estimation, RF performed the best, which mainly reflected in the minimum estimation error of the maximum, mean and minimum values, and the highest fitting degree of time change series. In the space, the best performance reflected in the most similar spatial distribution, and the smallest relative error. However, the estimation of SVM was relatively the worst. RF was the most suitable method for estimating vegetation EWD in the Mara River Basin. In this study, the three different machine learning methods were used to estimate the vegetation EWD in different seasons in the Mara River Basin, and the results can provide technical reference for the EWD estimation. |
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| Keywords: | Mara River Basin vegetation ecological water demand support vector machine (SVM) random forest (RF) convolution neural network (CNN) estimation |
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