1971-2017年北极大河入海径流（各径流成分）数据集

该数据集包含北极两条大河（北美：Mackenzie，欧亚：Lena）的观测及模拟的入海径流量及各径流成分（总径流、冰川径流、融雪径流、降雨径流）的组成，时间分辨率为月。该数据是利用项目组制作的气象驱动场数据驱动发展的VIC-CAS模型，利用观测的径流及遥感积雪数据进行校正和验证，校正和验证期径流的模拟的Nash效率系数均达到0.85以上，模型也能较好地模拟积雪的空间分布和年内、年际变化。 该数据可用于分析长期的流域径流的组成及变化原因，加深对北极大河径流变化的理解。

This dataset contains observed and simulated oceanward runoff discharges and the composition of each runoff component (total runoff, glacial runoff, snowmelt runoff, rainfall runoff) for two major Arctic rivers: the Mackenzie River (North America) and the Lena River (Eurasia), with a monthly temporal resolution.

The dataset is generated using the VIC-CAS model driven by meteorological forcing data developed by the project team, and it was calibrated and validated against observed runoff and remote sensing-derived snow cover data. The Nash-Sutcliffe efficiency coefficients of the simulated runoff during both the calibration and validation periods exceeded 0.85. Furthermore, the model can well reproduce the spatial distribution, intra-annual and inter-annual variations of snow cover.

This dataset can be used to analyze the long-term composition and driving factors of watershed runoff changes, thereby enhancing the understanding of runoff variations in major Arctic rivers.

搜集汇总

背景与挑战

背景概述

该数据集提供了1971年至2017年北极两条大河（北美Mackenzie和欧亚Lena）的月尺度入海径流量及各径流成分（包括总径流、冰川径流、融雪径流和降雨径流）的观测和模拟数据，基于VIC-CAS模型生成并经过严格校正验证（Nash效率系数超过0.85），适用于分析长期径流组成变化及其原因，以增强对北极大河径流变化机制的理解。