Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments
<p>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 force...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-05-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/24/2253/2020/hess-24-2253-2020.pdf |
| Summary: | <p>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, <span class="inline-formula"><i>Q</i><sub>100</sub></span>). 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 (<span class="inline-formula">∼35</span> %). In contrast, the contribution of
hydrologic model parameters to the total uncertainty of changes in these
hydrologic variables is relatively small (<span class="inline-formula"><i><</i>6</span> %), limiting the
impacts of hydrologic model parameter equifinality in climate change impact
analysis. This study provides useful information for practices associated
with water resources, risk control, and ecosystem conservation and for
studies related to hydrologic model evaluation and climate change impact
analysis for the study region as well as other Mediterranean regions.</p> |
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| ISSN: | 1027-5606 1607-7938 |