Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.
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Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication
Abstract. Global water models (GWMs) simulate the terrestrial watercycle on the global scale and are used to assess the impacts of climatechange on freshwater systems. GWMs are developed within different modellingframeworks and consider different underlying hydrological processes, leadingto varied model structures. Furthermore, the equations used to describevarious processes take different forms and are generally accessible onlyfrom within the individual model codes. These factors have hindered aholistic and detailed understanding of how different models operate, yetsuch an understanding is crucial for explaining the results of modelevaluation studies, understanding inter-model differences in theirsimulations, and identifying areas for future model development. This studyprovides a comprehensive overview of how 16 state-of-the-art GWMs aredesigned. We analyse water storage compartments, water flows, and humanwater use sectors included in models that provide simulations for theInter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). Wedevelop a standard writing style for the model equations to enhance modelintercomparison, improvement, and communication. In this study, WaterGAP2used the highest number of water storage compartments, 11, and CWatM used 10compartments. Six models used six compartments, while four models (DBH,JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments.WaterGAP2 simulates five human water use sectors, while four models (CLM4.5,CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. Weconclude that, even though hydrological processes are often based on similarequations for various processes, in the end these equations have beenadjusted or models have used different values for specific parameters orspecific variables. The similarities and differences found among the modelsanalysed in this study are expected to enable us to reduce the uncertaintyin multi-model ensembles, improve existing hydrological processes, andintegrate new processes.
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- Award ID(s):
- 1752729
- NSF-PAR ID:
- 10324963
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 14
- Issue:
- 6
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 3843 to 3878
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/gmd-14-3843-2021
