Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments
Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments
复制标题
DOI：
10.5194/hess-24-2253-2020
复制
发表时间：
2020-05-08
影响因子：
6.3
通讯作者：
Beighley, Edward
中科院分区：
文献类型：
--
作者：
Feng, Dongmei;Beighley, Edward
评估气候变化对水文系统的影响对于制定水资源管理、风险控制和生态系统保护做法的适应和减缓战略至关重要。这种评估通常是利用水文模型的输出来完成的，该模型被迫与未来的降水和温度预测相结合。用于水文模型组件的算法（例如，径流产生）可能会给模拟的水文变量带来重大的不确定性。在这里，开发了一个建模框架，集成了多个径流生成算法与路由模型和相关的参数优化。该框架能够识别水文模型组件和气候强迫以及相关参数化的不确定性。三个根本不同的径流生成方法，径流系数法（RCM，概念），可变渗透能力（维克，物理基础，渗透过剩），和简单的TOPMODEL（STP，物理基础，饱和过剩），耦合到山坡河流路由模型，模拟地表/地下径流和径流。在加州的圣巴巴拉县进行的一项案例研究显示，2月和3月的地表径流增加，但其他月份的径流减少，雨季延迟（3天，中位数）和缩短（6天，中位数），日流量增加，特别是在极端情况下（例如，100-年洪水流量，Q（100））。贝叶斯模型平均分析表明，这种增加的可能性可高达85%。对于径流和排放的预测变化，大气环流模式（GCM）和排放情景是两个主要的不确定性来源，约占总不确定性的一半。对于季节性的变化，GCM和水文模型是两个主要的不确定性贡献者（类似于35%）。相比之下，水文模型参数对这些水文变量变化的总不确定性的贡献相对较小（
Assessing impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control, and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used for the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties into the simulated hydrologic variables. Here, a modeling framework was developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches, runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically based, infiltration excess), and simple-TOPMODEL (STP, physically based, saturation excess), were coupled with the Hillslope River Routing model to simulate surface/subsurface runoff and streamflow. A case study conducted in Santa Barbara County, California, reveals increased surface runoff in February and March but decreased runoff in other months, a delayed (3 d, median) and shortened (6 d, median) wet season, and increased daily discharge especially for the extremes (e.g., 100-year flood discharge, Q(100)). The Bayesian model averaging analysis indicates that the probability of such an increase can be up to 85 %. For projected changes in runoff and discharge, general circulation models (GCMs) and emission scenarios are two major uncertainty sources, accounting for about half of the total uncertainty. For the changes in seasonality, GCMs and hydrologic models are two major uncertainty contributors (similar to 35 %). In contrast, the contribution of hydrologic model parameters to the total uncertainty of changes in these hydrologic variables is relatively small (