Liu Jingshi, Kang Shichang, Hewitt Kenneth, Hu Linjin, Xianyu Li
Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.
State key laboratory of cryospheric sciences, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences(CAS), Lanzhou, China.
Sci Rep. 2018 Nov 23;8(1):17291. doi: 10.1038/s41598-018-35600-3.
The discharge of one of the world's largest river - Indus River was reported to be increasing that was not supported by the Karakoram (KK) glacier expansion. A major hydrometric bias was ignored, which seemed similar to the montage that the Himalayan glaciers would disappear. This study proposed a framework for quantifying the bias resulting from inaccurate data affecting hydrologic studies on the Indus. We constructed a statistical model by converting the rating curves of rivers into air temperature (T) - discharge (Q) curves from an adjacent catchment in China where flow measurement was carried out using a standard method. We found that most flow data for the Indus were much greater than the error limits of T-Q curves estimated by daily data, a greater bias occurred in recent decades when discharge increased, the higher the flow was, the larger the bias was. The estimated mean annual and maximum monthly bias was 22.5% and 210%, respectively. These biases indicated that discharge increase in the Indus probably resulted from the large errors of hydrometrics without a scientific basis. We suggested a montage bias was needed in the hydrologic science of KK's rivers that may strongly affect water resource management.
据报道,世界上最大的河流之一——印度河的流量在增加,但这与喀喇昆仑(KK)冰川扩张的情况并不相符。一个主要的水文测量偏差被忽视了,这似乎类似于喜马拉雅冰川将会消失的拼凑说法。本研究提出了一个框架,用于量化因数据不准确而影响印度河水文研究的偏差。我们通过将河流的水位流量关系曲线转换为来自中国相邻流域的气温(T)-流量(Q)曲线,构建了一个统计模型,在中国的该相邻流域,流量测量采用标准方法。我们发现,印度河的大多数流量数据远大于根据日数据估算的T-Q曲线的误差范围,近几十年来,当流量增加时偏差更大,流量越高,偏差越大。估计的年平均偏差和月最大偏差分别为22.5%和210%。这些偏差表明,印度河流量的增加可能是由于没有科学依据的水文测量的巨大误差导致的。我们建议,在KK河流的水文科学中需要一种拼凑偏差,这可能会强烈影响水资源管理。