Process-based snowmelt modeling: does it require more input data than temperature-index modeling?
Process-based snowmelt modeling: does it require more input data than temperature-index modeling?
复制标题
DOI：
10.1016/j.jhydrol.2004.05.002
复制
发表时间：
2005-01
影响因子：
6.4
通讯作者：
M. Walter;E. Brooks;D. Mccool;L. G. King;M. Molnau;J. Boll
中科院分区：
文献类型：
--
作者：
M. Walter;E. Brooks;D. Mccool;L. G. King;M. Molnau;J. Boll
模拟寒冷地区的雪水文仍然是许多水文环境模型的一个问题。温度指数法是常用的方法，通常是合理的主持下，基于过程的模型需要太多的输入数据。为了验证这一说法，我们使用了一个基于过程的物理模型来模拟美国境内四个地点的融雪，使用的是从测得的每日最高和最低温度估计的能量分量，即只使用温度指数模型所需的相同数据。结果表明，观测值与预测值吻合较好，平均R2>0.9。我们使用最适合数据的简单温度指数模型重复了模拟，结果较差，R2<0.8。在一个研究中心，我们应用了基于过程的模型，而没有进行实质性的参数估计，这些结果与使用温度估计参数获得的结果之间没有显著差异（α=0.05），尽管预测的比能量预算分量相对较差（R2<0.8）。这些结果鼓励在水文模型中使用机械融雪建模方法，特别是在分布式水文模型中，景观雪分布可以由环境能量预算的空间分布组件控制。
Modeling snow hydrology for cold regions remains a problematic aspect of many hydro-environmental models. Temperature-index methods are commonly used and are routinely justified under the auspices that process-based models require too many input data. To test this claim, we used a physical, process-based model to simulate snowmelt at four locations across the conterminous US using energy components estimated from measured daily maximum and minimum temperature, i.e. using only the same data required for temperature-index models. The results showed good agreement between observed and predicted snow water equivalents, average R2>0.9. We duplicated the simulations using a simple temperature-index model best fitted to the data and results were poorer, R2<0.8. At one site we applied the process-based model without substantial parameter estimation, and there were no significant (α=0.05) differences between these results and those obtained using temperature-estimated parameters, despite relatively poorly predicted specific energy budget components (R2<0.8). These results encourage the use of mechanistic snowmelt modeling approaches in hydrological models, especially in distributed hydrological models for which landscape snow distribution may be controlled by spatially distributed components of the environmental energy budget.