Predictions of un-gauged sub-basins in Canadian prairies dominated by boreal forest

流域水文模型大多是数据驱动的。 这项研究将开发一套技术/工具来预测无资料流域（PUB），这是国际水文科学协会（IAHS）于2003年发起的一项倡议。 研究地点的选择是区域尺度，阿萨巴斯卡河流域（ARB）在加拿大大草原占主导地位的北方森林（BF），施加强烈的影响，陆地表面和大气之间的热量和水通量。 通过野外数据收集、修改土壤-植被-大气传输模型（SVAT）（例如MISBA），我们可以科学地了解半干旱加拿大大草原（尤其是BF）的气候、景观以及水文过程的控制。 为了限制无资料流域预测中的不确定性，我们将尝试通过两阶段过程来最大限度地减少局地尺度气候输入中的不确定性，这些输入是从降尺度GCM尺度、欧洲中期天气预报中心（ECMWF）ERA-40再分析数据和/或NASA的NCEP再分析数据以及区域尺度观测数据（例如，ARB）中获得的，例如，GCM-区域尺度和区域-局地尺度。 接下来，我们将修改MISBA的模型结构，以适应大草原的水文和气候条件，并将另一个称为SWAP的SVAT中的BF冬季特征添加到MISBA中。 最后，我们将在地理上转移模型参数校准的一些测量的本地规模的子流域位于ARB内或附近的水文相似的未测量的子流域。 我们期望通过一些随机方案调整模型参数，然后将其转移到无资料流域，其性能将被定量评估。 这种技术的结合将提高我们预测未测量流域的能力，并更好地准备评估相对未知的河流流域的未来发展。

Hydrologic models built for predicting streamflow of river basins are mostly data driven.  This study will develop a suite of techniques/tools to predict for un-gauged basins (PUB), an initiative started by the International Association of Hydrologic Sciences (IAHS) in 2003.  Study sites to be chosen are such as the regional scale, Athabasca River Basin (ARB) in the Canadian Prairies dominated by the Boreal Forest (BF) that exerts strong influence on the heat and water fluxes between land surface and the atmosphere.  Through field data collections, modifying soil-vegetation-atmosphere transfer models (SVAT) such as the MISBA, we can gain scientific understanding of climate, landscape and controls on hydrologic processes of the semi-arid Canadian Prairies, especially the BF.  To constrain uncertainties in un-gauged basin predictions, we will attempt to minimize uncertainty in local-scale climate input obtained from downscaling GCM scale, re-analysis data of ERA-40 of the European Centre for Mid-range Weather Forecasts (ECMWF) and/or NCEP re-analysis data of NASA, and regional scale observed data of say, ARB by a 2-stage process, e.g., GCM-regional scale and regional-local scale.  Next, we will modify MISBA's model structure to the Prairies' hydrologic and climatic conditions, and adding winter features of BF from another SVAT called SWAP to MISBA with modifications.  Lastly, we will geographically transfer model parameters calibrated for some gauged local-scale sub-basins located within or near ARB to the un-gauged sub-basin that are hydrologically similar.  We expect to adjust the model parameters through some stochastic schemes before transferring them to the un-gauged basin whose performance will be assessed quantitatively.  This combination of techniques will improve our ability to predict for un-gauged basins, and better prepared to assess future developments of relatively unknown river basins.