scholarly journals Does glacial retreat impact benthic chironomid communities? A case study from Rocky Mountain National Park, Colorado

2021 ◽  
Vol 3 (12) ◽  
Author(s):  
Danielle R. Haskett Jennings
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
National Park ◽  
Rocky Mountain ◽  
Environmental Variable ◽  
Rocky Mountain National Park ◽  
Surface Water Temperature ◽  
Running Water ◽  
Glacial Meltwater ◽  
Lake Catchment

AbstractThe aim of this study was to determine which environmental variables are responsible for modern benthic chironomid distributions in a glacial setting. The chironomid communities from nine alpine lakes were assessed, and forty-three individual taxa were extracted and identified. Surface water temperature and nitrate were strongly and negatively correlated (−0.82, p = 0.007), suggesting that glacial meltwater (the driver that explains both surface water temperature (SWT) (°C) and nitrate (NO3 + NO2-N)) is the environmental variable that explains the most variance (15%). On average, lakes receiving glacial meltwater were 2.62 °C colder and contained 66% more NO3 + NO2-N than lakes only receiving meltwater from snow. The presence of taxa from the tribe Diamesinae indicates very cold input from running water, and these taxa may be used as a qualitative indicator species for the existence of glacial meltwater within a lake catchment. Heterotrissocladius, Diamesa spp., and Pseudodiamesa were present in the coldest lakes. Chironomus, Diplocladius, and Protanypus were assemblages found in cold lakes affiliated with the littoral zone or alpine streams. The modern benthic chironomid communities collected from the alpine of subalpine lakes of Rocky Mountain National Park, Colorado, represent a range of climatic and trophic influences and capture the transition from cold oligotrophic lakes to warmer and eutrophic conditions.

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 Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

Impact of climate warming on the surface water temperature of plateau lake

2021 ◽  
Author(s):  
Zongqi Peng ◽  
Jiaying Yang ◽  
Yi Luo ◽  
Kun Yang ◽  
Chunxue Shang
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Climate Warming ◽  
Surface Water Temperature ◽  
Plateau Lake
Sign in / Sign up

Export Citation Format

Share Document