In this first worldwide synthesis of in situ and satellite‐derived lake data, we find that lake summer surface water temperatures rose rapidly (global mean = 0.34°C decade−1) between 1985 and 2009. ...Our analyses show that surface water warming rates are dependent on combinations of climate and local characteristics, rather than just lake location, leading to the counterintuitive result that regional consistency in lake warming is the exception, rather than the rule. The most rapidly warming lakes are widely geographically distributed, and their warming is associated with interactions among different climatic factors—from seasonally ice‐covered lakes in areas where temperature and solar radiation are increasing while cloud cover is diminishing (0.72°C decade−1) to ice‐free lakes experiencing increases in air temperature and solar radiation (0.53°C decade−1). The pervasive and rapid warming observed here signals the urgent need to incorporate climate impacts into vulnerability assessments and adaptation efforts for lakes.
Key Points
Lake surface waters are warming rapidly but are spatially heterogeneous
Ice‐covered lakes are typically warming at rates greater than air temperatures
Both geomorphic and climate factors influence lake warming rates
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In this study, an artificial neural network (ANN) based real-time predictive control and optimization algorithm for a chiller based cooling system was developed and applied to an ...actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, we set the cooling tower's condenser water outlet temperature and the chiller's chilled water outlet temperature as the system control variables. To evaluate the algorithm performance, we compared and analyzed the electric consumption and the COP when the chilled and condenser water temperatures were controlled conventionally and controlled based on the ANN. As a result, the ANN model's accuracy was high, with a Cv(RMSE) of 4.9%. In addition, the ANN based control algorithm's energy analysis showed that the average energy saving rate for the chiller was 24.7% and that the total average energy saving effect for the chiller and cooling towers was 7.4%. The results confirmed that the proposed MPC algorithm could contribute to improved HVAC energy efficiency in commercial buildings.
Deep learning (DL) models can accurately predict many hydrologic variables including streamflow and water temperature; however, these models have typically predicted hydrologic variables ...independently. This study explored the benefits of modeling two interdependent variables, daily average streamflow and daily average stream water temperature, together using multi‐task DL. A multi‐task scaling factor controlled the relative contribution of the auxiliary variable's error to the overall loss during training. Our experiments examined the improvement in prediction accuracy of the multi‐task approach using paired streamflow and water temperature data from sites across the conterminous United States. Our results showed that for 56 out of 101 sites, the best performing multi‐task models performed better overall than the single‐task models in terms of Nash‐Sutcliffe efficiency for predicting streamflow with single‐site models. For 43 sites, the best multi‐task, single‐site models made no significant difference in predicting streamflow. The multi‐task approach had a smaller effect when applied to a model trained with data from 101 sites together, significantly improving performance for only 17 sites. The multi‐task scaling factor was consequential in determining to what extent the multi‐task approach was beneficial. A naïve selection of this factor led to significantly worse‐performing models for 3 of 101 sites when predicting streamflow as the primary variable, and 47 of 53 sites when predicting stream temperature as the primary variable. We conclude that a multi‐task approach can make more accurate predictions by leveraging information from interdependent hydrologic variables, but only for some sites, variables, and model configurations.
Key Points
A single deep learning model was used to predict both water temperature and streamflow
The best configured single‐site multi‐task models improved streamflow predictions for most sites tested
A naïve implementation of multi‐task learning was detrimental to water temperature predictions
Main text
The purpose of this comparison was to compare the results of the participating laboratories at the triple point of water temperature (TPW) and assess the uncertainty on the practical ...realization of triple point of water temperature by the participant laboratories and also to support the Calibration and Measurement Capabilities (CMC) entries of the participating laboratories for this fixed point. This comparison was initiated as a EURAMET project with project number 1357. Initially, the participants of the comparison included the Metrology Institutes of Albania (DPM), F.Y.R Macedonia (BOM), Montenegro (MBM) and Serbia (DMDM). But at the later stages of the comparison, these countries understandably chose to leave the comparison instead of repeating the measurements due to an unexpected problem. On the other hand, participants from GULFMET organization, Emirates Metrology Institute (EMI) and National Measurement and Calibration Center at Saudi Standards, Metrology and Quality Organization of the Kingdom of Saudi Arabia (SASO NMCC) and also Jordan National Metrology Institute (JNMI) were included in the comparison after having approvals of each participating laboratory. Then the comparison was registered as Key Comparison with the name EURAMET.T-K7-4 in BIPM KCDB. Finally, two loops was combined together and five institutes performed the comparison with the single circulating TPW Cell. This report presents the results of the TPW comparison and gives detailed information about the measurements made at TUBİTAK UME and participating laboratories.
To reach the main text of this paper, click on
Final Report
. Note that this text is that which appears in Appendix B of the BIPM key comparison database
https://www.bipm.org/kcdb/
.
The final report has been peer-reviewed and approved for publication by the CCT, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Shrinking pupal cocoons of
Rhyacophila lezeyi
were often found during summer in Shibukuro Stream, a highly acidic mountain stream in northern Japan (pH = 2.82 on average). We performed both field ...surveys and laboratory rearing experiments to clarify the mechanisms of
R. lezeyi
cocoon shrinkage. The
R. lezeyi
cocoon shrinkage proportion increased in years with high stream water temperatures and was related to water temperatures before and after pupation at the study site. Approximately 90% of the prepupae and pupae inside the shrinking cocoons died during the rearing experiment, implying that cocoon shrinkage caused by high water temperature strongly influenced
R. lezeyi
pupal survival. Laboratory experiments showed that
R. lezeyi
’s pupal cocoon membranes were semi-permeable and that the cocoon fluids were always hyperosmotic, indicating that water molecules can continuously enter the cocoon fluids from the stream water until the turgor of the cocoon wall is reached. However, the shrinking cocoons showed lower fluid volume and higher osmolarity than the normal turgescent cocoons. The reduction of osmotic gradient across the membrane during decreased stream flow due to less precipitation and/or the damage to the cocoon membrane and pupal body from high and fluctuating water temperatures and low pH are possible mechanisms for
R. lezeyi
pupal cocoon shrinkage.
This paper presents a model for predicting the water temperature of the reservoir incorporating with solar radiation to analyze and evaluate the water temperature of large high-altitude reservoirs in ...western China. Through mutual information inspection, the model shows that the dependent variable has a good correlation with water temperature, and it is added to the sample feature training model. Then, the measured water temperature data in the reservoir for many years are used to establish the support vector regression (SVR) model, and genetic algorithm (GA) is introduced to optimize the parameters, so as to construct an improved support vector machine (M-GASVR). At the same time, root-mean-square error, mean absolute error, mean absolute percentage error, and Nash–Sutcliffe efficiency coefficient are used as the criteria for evaluating the performance of SVR model, ANN model, GA-SVR model, and M-GASVR model. In addition, the M-GASVR model is used to simulate the water temperature of the reservoir under different working conditions. The results show that ANN model is the worst among the four models, while GA-SVR model is better than SVR model in terms of metric, and M-GASVR model is the best. For non-stationary sequences, the prediction model M-GASVR can well predict the vertical water temperature and water temperature structure in the reservoir area. This study provides useful insights into the prediction of vertical water temperature at different depths of reservoirs.
Water temperature impacts many processes in rivers, and it is determined by various environmental factors. This study proposed an extreme learning machine (ELM)-based model to predict daily water ...temperature for rivers. Air temperature (
T
a
), discharge (
Q
) and the day of the year (DOY) were used as predictors. Three rivers characterized by different hydrological conditions were investigated to test the modeling performances and the model results were compared with multilayer perceptron neural network (MLPNN) and simple multiple linear regression (MLR) models. Results showed that inclusion of three inputs as predictors (
T
a
,
Q
and the DOY) yielded the best modeling accuracy for all the developed models. It was also found that
Q
played a minor role and
T
a
and DOY are the most important explanatory variables for river water temperature predictions. Additionally, sigmoidal and radial basis activation functions within the ELM model performed the best for river water temperature forecasting. ELM and MLPNN models outperformed MLR model, and ELM model with sigmoidal and radial basis activation functions performed comparably to MLPNN model. Overall, results indicated that the ELM model developed in this study can be effectively used for river water temperature predictions.
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