Adapting boreal streams to climate change: effects of riparian vegetation on water temperature and biological assemblages

2016 ◽  
Vol 35 (3) ◽  
pp. 984-997 ◽  
Author(s):  
Richard K. Johnson ◽  
Karin Almlöf
Keyword(s):  
Climate Change ◽  
Water Temperature ◽  
Riparian Vegetation ◽  
Climate Change Effects ◽  
Boreal Streams

scholarly journals Can riparian vegetation shade mitigate the expected rise in stream temperatures due to climate change during heat waves in a human-impacted pre-alpine river?

2018 ◽  
Vol 22 (1) ◽  
pp. 437-461 ◽  
Author(s):  
Heidelinde Trimmel ◽  
Philipp Weihs ◽  
David Leidinger ◽  
Herbert Formayer ◽  
Gerda Kalny ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Temperature ◽  
Riparian Vegetation ◽  
Climate Models ◽  
Heat Waves ◽  
Stream Water ◽  
Temperature Shift ◽  
River System ◽  
Turbulent Energy ◽  
Source Model

Abstract. Global warming has already affected European rivers and their aquatic biota, and climate models predict an increase of temperature in central Europe over all seasons. We simulated the influence of expected changes in heat wave intensity during the 21st century on water temperatures of a heavily impacted pre-alpine Austrian river and analysed future mitigating effects of riparian vegetation shade on radiant and turbulent energy fluxes using the deterministic Heat Source model. Modelled stream water temperature increased less than 1.5 ∘C within the first half of the century. Until 2100, a more significant increase of around 3 ∘C in minimum, maximum and mean stream temperatures was predicted for a 20-year return period heat event. The result showed clearly that in a highly altered river system riparian vegetation was not able to fully mitigate the predicted temperature rise caused by climate change but would be able to reduce water temperature by 1 to 2 ∘C. The removal of riparian vegetation amplified stream temperature increases. Maximum stream temperatures could increase by more than 4 ∘C even in annual heat events. Such a dramatic water temperature shift of some degrees, especially in summer, would indicate a total shift of aquatic biodiversity. The results demonstrate that effective river restoration and mitigation require re-establishing riparian vegetation and emphasize the importance of land–water interfaces and their ecological functioning in aquatic environments.

Assessment of aquifers groundwater storage for the mitigation of climate change effects

2016 ◽  
Vol 39 ◽  
pp. 89-92 ◽  
Author(s):  
Luca Alberti ◽  
Martino Cantone ◽  
Loris Colombo ◽  
Gabriele Oberto ◽  
Ivana La Licata
Keyword(s):  
Climate Change ◽  
Groundwater Storage ◽  
Climate Change Effects

Estimating Climate Change Effects on Installation Energy Use

2012 ◽  
Author(s):  
Ronald Filadelfo ◽  
Jonathon Mintz ◽  
Daniel Carvell ◽  
Alan Marcus
Keyword(s):  
Climate Change ◽  
Energy Use ◽  
Climate Change Effects

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals Reassessing Existing Reservoir Supply Capacity and Management Resilience under Climate Change and Sediment Deposition

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1819
Author(s):  
Eleni S. Bekri ◽  
Polychronis Economou ◽  
Panayotis C. Yannopoulos ◽  
Alexander C. Demetracopoulos
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Vital Role ◽  
Sediment Deposition ◽  
Global Climate Models ◽  
Exceedance Probability ◽  
Climate Change Effects ◽  
Management Scenario ◽  
Supply Capacity

Freshwater resources are limited and seasonally and spatially unevenly distributed. Thus, in water resources management plans, storage reservoirs play a vital role in safeguarding drinking, irrigation, hydropower and livestock water supply. In the last decades, the dams’ negative effects, such as fragmentation of water flow and sediment transport, are considered in decision-making, for achieving an optimal balance between human needs and healthy riverine and coastal ecosystems. Currently, operation of existing reservoirs is challenged by increasing water demand, climate change effects and active storage reduction due to sediment deposition, jeopardizing their supply capacity. This paper proposes a methodological framework to reassess supply capacity and management resilience for an existing reservoir under these challenges. Future projections are derived by plausible climate scenarios and global climate models and by stochastic simulation of historic data. An alternative basic reservoir management scenario with a very low exceedance probability is derived. Excess water volumes are investigated under a probabilistic prism for enabling multiple-purpose water demands. Finally, this method is showcased to the Ladhon Reservoir (Greece). The probable total benefit from water allocated to the various water uses is estimated to assist decision makers in examining the tradeoffs between the probable additional benefit and risk of exceedance.

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