基于VIC和MLP-ANN模型重建中国陆地水储量数据
P46; 基于GRACE重力卫星的产品数据为大尺度的陆地水储量研究提供了重要支撑,但由于数据长度有限,无法满足长序列研究需求.基于气象和水文观测数据,利用可变下渗容量曲线(VIC)模型在中国十大水资源分区构建了流域水循环模型,基于模型输出的土壤水和雪水储量,并结合气象观测数据,构建了基于多层感知器的人工神经网络模型(MLP-ANN),重建了中国地区1980-2018年高分辨率(0.25°)的陆地水储量距平(TWSA)月尺度数据集,并利用2003-2018年的GRACE数据对重建的TWSA进行评估.结果表明:①VIC模型总体具有较好的模拟效果,且湿润流域的模拟精度优于半干旱流域;②重建的TWSA...
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| Published in | 水科学进展 Vol. 35; no. 5; pp. 711 - 725 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
长江保护与绿色发展研究院,江苏南京 210098%济南大学水利与环境学院,山东济南 250022%暨南大学生命科学技术学院,广东 广州 510632
01.11.2024
中国地质大学(北京)水资源与环境学院,北京 100083%河海大学水灾害防御全国重点实验室,江苏南京 210098 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-6791 |
| DOI | 10.14042/j.cnki.32.1309.2024.05.003 |
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| Abstract | P46; 基于GRACE重力卫星的产品数据为大尺度的陆地水储量研究提供了重要支撑,但由于数据长度有限,无法满足长序列研究需求.基于气象和水文观测数据,利用可变下渗容量曲线(VIC)模型在中国十大水资源分区构建了流域水循环模型,基于模型输出的土壤水和雪水储量,并结合气象观测数据,构建了基于多层感知器的人工神经网络模型(MLP-ANN),重建了中国地区1980-2018年高分辨率(0.25°)的陆地水储量距平(TWSA)月尺度数据集,并利用2003-2018年的GRACE数据对重建的TWSA进行评估.结果表明:①VIC模型总体具有较好的模拟效果,且湿润流域的模拟精度优于半干旱流域;②重建的TWSA在空间分布上与GRACE数据高度一致,可以较好地捕捉到绝大部分流域TWSA的年际变化特征及趋势;③1980-2018年,TWSA在华北平原、辽东、松花江西部、西南及西北部分地区呈显著下降趋势(＞5 mm/a),而显著上升趋势主要集中在西部的少部分地区(＞20 mm/a).重建的TWSA数据可为中国地区的水文气象研究提供数据支撑. |
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| AbstractList | P46; 基于GRACE重力卫星的产品数据为大尺度的陆地水储量研究提供了重要支撑,但由于数据长度有限,无法满足长序列研究需求.基于气象和水文观测数据,利用可变下渗容量曲线(VIC)模型在中国十大水资源分区构建了流域水循环模型,基于模型输出的土壤水和雪水储量,并结合气象观测数据,构建了基于多层感知器的人工神经网络模型(MLP-ANN),重建了中国地区1980-2018年高分辨率(0.25°)的陆地水储量距平(TWSA)月尺度数据集,并利用2003-2018年的GRACE数据对重建的TWSA进行评估.结果表明:①VIC模型总体具有较好的模拟效果,且湿润流域的模拟精度优于半干旱流域;②重建的TWSA在空间分布上与GRACE数据高度一致,可以较好地捕捉到绝大部分流域TWSA的年际变化特征及趋势;③1980-2018年,TWSA在华北平原、辽东、松花江西部、西南及西北部分地区呈显著下降趋势(＞5 mm/a),而显著上升趋势主要集中在西部的少部分地区(＞20 mm/a).重建的TWSA数据可为中国地区的水文气象研究提供数据支撑. |
| Abstract_FL | The product data based on GRACE gravity satellites provide important support for large-scale terrestrial water storage(TWS)research.However,these data cannot meet the needs of long-term sequence research because of the limited data length.Based on meteorological and hydrological observation data,a variable infiltration capability(VIC)model was constructed in the ten water resource zones in China.Based on the soil water and snow water storage output from the VIC model and meteorological observation data,an artificial neural network model based on a multilayer perceptron was developed to reconstruct a long-term(1980-2018),high-resolution(0.25° × 0.25°)monthly TWS anomaly(TWSA)dataset in China.The reconstructed TWSA data were evaluated using GRACE data from 2003 to 2018.The results demonstrate the following:① The VIC model exhibits overall good simulation performance,with better performance in humid basins than in semi-arid basins.② The reconstructed TWSA dataset is highly consistent with the GRACE data at the spatial scale and can effectively capture interannual variations and evolution trends of the TWSA in most basins,especially in humid basins.③ From 1980 to 2018,the TWSA exhibited a significant downward trend(＞5 mm/a)in the North China Plain,Eastern Liaoning,Southern Songhua,and Southwest and Northwest parts of China,while a significant upward trend(＞20 mm/a)was mainly concentrated in some regions of Western China.The TWSA data constructed can provide data support for hydrological and meteorological research in China. |
| Author | 武传号 巨佳丽 胡晓农 龚郑洁 |
| AuthorAffiliation | 中国地质大学(北京)水资源与环境学院,北京 100083%河海大学水灾害防御全国重点实验室,江苏南京 210098;长江保护与绿色发展研究院,江苏南京 210098%济南大学水利与环境学院,山东济南 250022%暨南大学生命科学技术学院,广东 广州 510632 |
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| Author_FL | GONG Zhengjie HU Xiaonong WU Chuanhao JU Jiali |
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| DocumentTitle_FL | Reconstruction of terrestrial water storage data in China based on VIC and MLP-ANN models |
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| Keywords | VIC模型 十大水资源分区 artificial neural network 陆地水储量 人工神经网络 GRACE data reconstruction 数据重建 VIC model ten water resource zones terrestrial water storage |
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| Title | 基于VIC和MLP-ANN模型重建中国陆地水储量数据 |
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