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长江上游干暖河谷地区气候和土地利用变化的水文响应
Congcong Li, Yanpeng Cai, Zhong Li, Qianqian Zhang, Lian Sun, Xinyi Li, Pengxiao Zhou
工程（英文） ›› 2022, Vol. 19 ›› Issue (12) : 24-39.
PDF(4802 KB)
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长江上游干暖河谷地区气候和土地利用变化的水文响应
Hydrological Response to Climate and Land Use Changes in the Dry–Warm Valley of the Upper Yangtze River
中国西南山区干暖河谷的水文过程具有独特的特点，已引起世界科学界的广泛关注。鉴于该地区是长江上游生态系统脆弱和水资源冲突严重的地区，需系统地识别气候和土地利用变化的水文响应。本研究以安宁河流域的干暖河谷为研究对象，采用MIKE SHE模型进行校准。随后，利用长短期记忆网络模型（LSTM）和传统多模式集成均值（MMEM）方法对31个全球气候模式（GCM）进行气候预测。采用元胞自动机-马尔可夫模型，综合考虑气候、社会和经济条件等，对土地利用的空间格局进行预测。将生成的4组气候预测和3组土地利用预测数据交叉输入MIKE SHE模型，以预测2021—2050年的水文响应变化。针对日尺度模拟的率定期和第一个验证期，决定性系数（R）分别为0.85和0.87，纳什效率系数分别为0.72和0.73；先进的LSTM方法对日尺度气温和月尺度降水的预测效果优于传统的MMEM方法；RCP8.5下的月平均气温预测值略高于RCP4.5，这与6~10月月平均降水量的变化相反；径流量和实际蒸散发（ET）的变化对气候变化的敏感性高于对土地利用变化的敏感性；研究区径流量变化与ET变化无显著相关性。本研究可以提供复杂变化环境下的一系列水文响应，从而有助于关键地区水资源随机不确定性和优化管理。
The hydrological process in the dry–warm valley of the mountainous area of southwest China has unique characteristics and has attracted scientific attention worldwide. Given that this is an area with fragile ecosystems and intensive water resource conflicts in the upper reaches of the Yangtze River, a systematic identification of its hydrological responses to climate and land use variations needs to be performed. In this study, MIKE SHE was employed and calibrated for the Anning River Basin in the dry–warm valley. Subsequently, a deep learning neural network model of the long short-term memory (LSTM) and a traditional multi-model ensemble mean (MMEM) method were used for an ensemble of 31 global climate models (GCMs) for climate projection. The cellular automata–Markov model was implemented to project the spatial pattern of land use considering climatic, social, and economic conditions. Four sets of climate projections and three sets of land use projections were generated and fed into the MIKE SHE to project hydrologic responses from 2021 to 2050. For the calibration and first validation periods of the daily simulation, the coefficients of determination (R) were 0.85 and 0.87 and the Nash–Sutcliffe efficiency values were 0.72 and 0.73, respectively. The advanced LSTM performed better than the traditional MMEM method for daily temperature and monthly precipitation. The average monthly temperature projection under representative concentration pathway 8.5 (RCP8.5) was expected to be slightly higher than that under RCP4.5; this is contrary to the average monthly precipitation from June to October. The variations in streamflow and actual evapotranspiration (ET) were both more sensitive to climate change than to land use change. There was no significant relationship between the variations in streamflow and the ET in the study area. This work could provide general variation conditions and a range of hydrologic responses to complex and changing environments, thereby assisting with stochastic uncertainty and optimizing water resource management in critical regions.
干暖河谷 / 水文模拟 / 多气候模式集成 / 气候变化 / 土地利用变化
Dry–warm valley / Hydrologic simulation / Multi-ensemble GCMs / Climate change / Land use variations
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