Reconstructing meteorological time series to quantify the uncertainties of runoff simulation in the ungauged Qira River Basin using data from multiple stations

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• 作者：Jie Xue ; Dongwei Gui ; Jiaqiang Lei ; Xinlong Feng…
• 刊名：Theoretical and Applied Climatology
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：126
• 期：1-2
• 页码：61-76
• 全文大小：2,860 KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Meteorology and Climatology
  Atmospheric Protection, Air Quality Control and Air Pollution
  Climate Change
  Waste Water Technology, Water Pollution Control, Water Management and Aquatic Pollution
  
• 出版者：Springer Wien
• ISSN：1434-4483
• 卷排序：126

文摘

The existence and development of oases in arid plain areas depends mainly on the runoff generated from alpine regions. Quantifying the uncertainties of runoff simulation under climatic change is crucial for better utilization of water resources and management of oases in arid areas. In the present study, based on the ungauged Qira River Basin in Xinjiang, China, a modified version of the Delta statistical downscaling method was applied to reconstruct the monthly mean temperature (MMT), monthly accumulated precipitation (MAP), and monthly accumulated evaporation (MAE) of two target stations. Then, the uncertainty in runoff simulation, implemented using the Three-Layered Feedforward Neural Network model with the Back-Propagation learning algorithm, was quantified. The modified Delta method reproduced the MMT, MAP, and MAE time series of the two target stations very well during the calibrated periods, and the reconstructed uncertainty ranges were small among reconstructed datasets using data from 12 observation stations. The monthly accumulated runoff simulated by the reconstructed MMT, MAP, and MAE as input variables of the model possessed unpredictable uncertainty. Although the use of multi-data ensembles in model inputs are considered an effective way to minimize uncertainties, it could be concluded that, in this case, the efficiency of such an approach was limited because of errors in the meteorological data and the deficiency of the model’s structure. The uncertainty range in the runoff peak was unable to capture the actual monthly runoff. Nevertheless, this study represents a significant attempt to reproduce historical meteorological data and to evaluate the uncertainties in runoff simulation through multiple input ensembles in an ungauged basin. It can be used as reference meteorological data for researching long-term climate change and producing runoff forecasts for assessing the risk of droughts and/or floods, as well as the existence and management of plain oases in the Qira River Basin.