Climate change and urbanization threaten streams and the biodiversity that rely upon them worldwide. Emissions of greenhouse gases are causing air and sea surface temperatures to increase, and even ...small areas of urbanization are degrading stream biodiversity, water quality and hydrology. However, empirical evidence of how increasing air temperatures and urbanization together affect stream temperatures over time and their relative influence on stream temperatures is limited. This study quantifies changes in stream temperatures in a region in South-East Australia with an urban-agricultural-forest landcover gradient and where increasing air temperatures have been observed. Using Random Forest models we identify air temperature and urbanization drive increasing stream temperatures and that their combined effects are larger than their individual effects occurring alone. Furthermore, we identify potential mitigation measures useful for waterway managers and policy makers. The results show that both local and global solutions are needed to reduce future increases to stream temperature.
Floating Algae Blooms in the East China Sea Qi, Lin; Hu, Chuanmin; Wang, Mengqiu ...
Geophysical research letters,
28 November 2017, 2017-11-28, 20171128, Letnik:
44, Številka:
22
Journal Article
Recenzirano
Odprti dostop
A floating algae bloom in the East China Sea was observed in Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in May 2017. Using satellite imagery from MODIS, Visible Infrared Imaging ...Radiometer Suite, Geostationary Ocean Color Imager, and Ocean Land Imager, and combined with numerical particle tracing experiments and laboratory experiments, we examined the history of this bloom as well as similar blooms in previous years and attempted to trace the bloom source and identify the algae type. Results suggest that one bloom origin is offshore Zhejiang coast where algae slicks have appeared in satellite imagery almost every February–March since 2012. Following the Kuroshio Current and Taiwan Warm Current, these “initial” algae slicks are first transported to the northeast to reach South Korea (Jeju Island) and Japan coastal waters (up to 135°E) by early April 2017, and then transported to the northwest to enter the Yellow Sea by the end of April. The transport pathway covers an area known to be rich in Sargassum horneri, and spectral analysis suggests that most of the algae slicks may contain large amount of S. horneri. The bloom covers a water area of ~160,000 km2 with pure algae coverage of ~530 km2, which exceeds the size of most Ulva blooms that occur every May–July in the Yellow Sea. While blooms of smaller size also occurred in previous years and especially in 2015, the 2017 bloom is hypothesized to be a result of record‐high water temperature, increased light availability, and continuous expansion of Porphyra aquaculture along the East China Sea coast.
Plain Language Summary
A massive floating algae bloom in the East China Sea was first captured in Moderate Resolution Imaging Spectroradiometer satellite imagery in mid‐May 2017. Both its size and location are unprecedented. Several means have been used to identify the algae type and bloom origin, including the use of multisource satellite imagery, numerical particle tracing experiments, and laboratory experiments. While multiple origins are possible, the bloom could be tracked to Zhejiang coastal waters where the “initial” algae slicks back in February were transported to the northeast following the Kuroshio Current and Taiwan Warm Current to reach South Korea (Jeju Island) and Japan coastal waters by early April 2017, and then transported to the northwest to enter the Yellow Sea by end of April. Spectral analysis and historical field surveys suggested that the bloom may be dominated by Sargassum horneri, while expanded seaweed aquaculture and record‐high water temperature and increased surface light may have contributed to the unprecedented bloom, which covered a water area of ~160,000 km2 with pure algae coverage of ~530 km2, both exceeding the maximum size of most Ulva blooms in the Yellow Sea.
Key Points
An unprecedented massive floating algae bloom in the East China Sea is discovered, which appears to be Sargassum horneri
The bloom is thought to be a result of increased water temperature, light, and expanded seaweed aquaculture along the ECS coast
Bloom origin is traced back to coastal waters off Zhejiang coast, with a “hot spot” identified, yet other origins cannot be ruled out
In modeling species distributions and population dynamics, spatially‐interpolated climatic data are often used as proxies for real, on‐the‐ground measurements. For shallow freshwater systems, this ...practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections – particularly among pleustonic and epilimnetic organisms. Using a global database of millions of daily satellite‐derived lake surface water temperatures (LSWT), I trained machine learning models to correct for the correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high‐resolution global maps of air‐LSWT offsets, corresponding uncertainty measures and derived LSWT‐based bioclimatic layers for use by the scientific community. I then compared the performance of these LSWT layers and air temperature‐based layers in population dynamic and ecological niche models (ENM). While generally high, the correspondence between air temperature and LSWT was quite variable and often nonlinear depending on the spatial context. These LSWT predictions were better able to capture the modeled population dynamics and geographic distributions of two common aquatic plant species. Further, ENM models trained with LSWT predictors more accurately captured lab‐measured thermal response curves. I conclude that these predicted LSWT temperatures perform better than raw air temperatures when used for population projections and environmental niche modeling, and should be used by practitioners to derive more biologically‐meaningful results. These global LSWT predictions and corresponding error estimates and bioclimatic layers have been made freely available to all researchers in a permanent archive.
•A new hybrid model by coupling WT and ANN is developed to forecast water temperature in rivers.•Four mother wavelets (Daubechies, Symlet, discrete Meyer and Haar) are considered to develop the ...WT-ANN hybrid model.•The hybrid models perform much better than the regular ANN model in both normal and heat wave conditions.•The hybrid model with discrete Meyer mother wavelet performs the best, superior to the others.
Accurate and reliable water temperature forecasting models can help in environmental impact assessment as well as in effective fisheries management in river systems. In this paper, a hybrid model that couples discrete wavelet transforms (WT) and artificial neural networks (ANN) is proposed for forecasting water temperature. Four mother wavelets, including Daubechies, Symlet, discrete Meyer and Haar, are considered to develop the WT-ANN hybrid model. The hybrid model is applied to forecast daily water temperature on the Warta River in Poland. Time series of daily water temperatures in eight river gauges as well as daily air temperatures of seven meteorological stations are used for forecasting daily water temperature. The performance of this WT-ANN hybrid model is evaluated by comparing the results with those obtained from linear and non-linear regression models as well as a traditional ANN model. The results show that the WT-ANN models perform well in simulating and forecasting river water temperature time series, and outperform the linear, non-linear and traditional ANN models. The superior performance of the WT-ANN models is particularly observed for extreme weather conditions, such as heat waves and drought. Among the four mother wavelets applied, the discrete Meyer performs the best, slightly better than the Daubechies at level 10 and Symlet, while the Haar mother wavelet has the lowest accuracy. In addition, the model performance improves with an increase in the decomposition level, indicating the importance of the choice of decomposition level. The outcomes of this study have important implications for water temperature forecasting and ecosystem management of rivers.
Clean district heating systems are needed in large cities, especially in Northern China. This paper presents a low carbon district heating system that features a low return water temperature, use of ...low grade waste heat as the main heat source, long distance heat transmission with a large temperature difference, distributed peak heating load addition by natural gas and heat-power decoupling with heat pump and thermal storage. This Low carbon district heating 2025 system is suitable for large cities, large waste heat sources, high heating densities and the utilization of using existing large heating networks in almost all the cities of Northern China. These clean heating systems in Chinese cities have great potential to reduce energy use, reduce emissions and improve the district heating system economics.
Display omitted
•Low carbon district heating 2025 system is designed for large cities, large waste heat sources, high heating densities.•It describes a district heating system which uses low-grade thermal energy.•It features with a large temperature difference for heat transmission.•It solves heat-power decoupling with heat pump and thermal storage.•It reduces the energy consumption and emissions by 80% compared to coal boilers.
•An adaptive control method for supply water temperature is proposed.•Model parameters are identified by the new control method quickly and accurately.•Coefficient of performance of experimental unit ...increases by 21.16%.•A decrease of 34.24% in building energy consumption is achieved.
Air source heat pumps (ASHPs) have been widely used for heating in buildings. While in practice, the setting value of its supply water temperature is always far higher than the theoretical value owing to the delayed regulation, and this results in the low coefficient of performance. To solve this problem, an adaptive control method for the supply water temperature was proposed in present work. This control method could predict the best setting value of supply water temperature based on the heat-balance of the heat exchange amount in indoor fan-coils and building heating load, and the least squares method was used to achieve the adaptive identification of parameters. By adopting this control method in a field ASHP system, four sets of experiments were conducted. Results showed that this control method could quickly and accurately adapt to the actual project, and adjust the water temperature according to heating demand. When employing the new control method, the average supply water temperature was reduced by 8.4 °C, and lower supply water temperature made the coefficient of performance of ASHP unit increased by 21.16%; the building energy consumption was reduced by 34.24%, and the power consumption of the ASHP unit was reduced by 38.20%.
Water temperature and streamflow intermittency are critical parameters influencing aquatic ecosystem health. Low‐cost temperature loggers have made continuous water temperature monitoring relatively ...simple but determining streamflow timing and intermittency using temperature data alone requires significant and subjective data interpretation. Electrical resistance (ER) sensors have recently been developed to overcome the major limitations of temperature‐based methods for the assessment of streamflow intermittency. This technical note introduces the STIC (Stream Temperature, Intermittency, and Conductivity logger); a robust, low‐cost, simple to build instrument that provides long‐duration, high‐resolution monitoring of both relative conductivity (RC) and temperature. Simultaneously collected temperature and RC data provide unambiguous water temperature and streamflow intermittency information that is crucial for monitoring aquatic ecosystem health and assessing regulatory compliance. With proper calibration, the STIC relative conductivity data can be used to monitor specific conductivity.
Key points
High‐resolution, long‐duration, intermittent stream flow, and temperature monitoring
Provides relative conductivity information and can estimate specific conductivity
Simple, low‐cost, robust design, operates when frozen or buried in sediment
Water temperature is critical for the ecology of lakes. However, the ability to predict its spatial and seasonal variation is constrained by the lack of a thermal classification system. Here we ...define lake thermal regions using objective analysis of seasonal surface temperature dynamics from satellite observations. Nine lake thermal regions are identified that mapped robustly and largely contiguously globally, even for small lakes. The regions differed from other global patterns, and so provide unique information. Using a lake model forced by 21
century climate projections, we found that 12%, 27% and 66% of lakes will change to a lower latitude thermal region by 2080-2099 for low, medium and high greenhouse gas concentration trajectories (Representative Concentration Pathways 2.6, 6.0 and 8.5) respectively. Under the worst-case scenario, a 79% reduction in the number of lakes in the northernmost thermal region is projected. This thermal region framework can facilitate the global scaling of lake-research.
Water temperature is a controlling indicator of river habitat since many physical, chemical and biological processes in rivers are temperature dependent. Highly precise and reliable predictions of ...water temperature are important for river ecological management. In this study, a hybrid model named BP_PSO3, based on the BPNN (back propagation neural network) optimized by the PSO (particle swarm optimization) algorithm, is proposed for water temperature prediction using air temperature (Ta), discharge (Q) and day of year (DOY) as input variables. The performance of the BP_PSO3 model was compared with that of the BP_PSO1 (with Ta as the input) and BP_PSO2 (with Ta and Q as the inputs) models to evaluate the importance of the inputs. In addition, a comparison among the BPNN, RBFNN (radial basis function neural network), WNN (wavelet neural network), GRNN (general regression neural network), ELMNN (Elman neural network), and BP_PSO-based models was carried out based on the MAE, RMSE, NSE and R2. The eight artificial intelligence models were examined to predict the water temperature at the Cuntan and Datong stations in the Yangtze River. The results indicated that the hybrid BPNN-PSO3 model had a stronger ability to forecast water temperature under both normal and extreme drought conditions. Optimization by the PSO algorithm and the inclusion of Q and DOY could help capture river thermal dynamics more accurately. The findings of this study could provide scientific references for river water temperature forecasting and river ecosystem protection.
Display omitted
•A novel hybrid prediction model is proposed for water temperature forecasting.•The PSO algorithm is utilized to optimize the BPNN network.•The BP_PSO3 model outperformed than BPNN, RBFNN, WNN, GRNN and ELMNN models.
The temperature of river water plays a crucial role in many physical, chemical, and aquatic ecological processes. Despite the importance of having detailed information on this environmental variable ...at locally relevant scales (≤50 km), high‐resolution simulations of water temperature on a large scale are currently lacking. We have developed the dynamical 1‐D water energy routing model (DynWat), that solves both the energy and water balance, to simulate river temperatures for the period 1960–2014 at a nominal 10‐km and 50‐km resolution. The DynWat model accounts for surface water ion, reservoirs, riverine flooding, and formation of ice, enabling a realistic representation of the water temperature. We present a novel 10‐km water temperature data set at the global scale for all major rivers, lakes, and reservoirs. Validated results against 358 stations worldwide indicate a decrease in the simulated root‐mean‐square error (0.2 °C) and bias (0.7 °C), going from 50‐ to 10‐km simulations. We find an average global increase in water temperature of 0.16 °C per decade between 1960 and 2014, with more rapid warming toward 2014. Results show increasing trends for the annual daily maxima in the Northern Hemisphere (0.62 °C per decade) and the annual daily minima in the Southern Hemisphere (0.45 °C per decade) for 1960–2014. The high‐resolution modeling framework not only improves the model performance, it also positively impacts the relevance of the simulations for regional‐scale studies and impact assessments in a region without observations. The resulting global water temperature data set could help to improve the accuracy of decision‐support systems that depend on water temperature estimates.
Key Points
Development of a simulated high-resolution global water temperature data set and high‐resolution physically based model is presented
Increased spatial resolution results in a better performance against global in situ observations
An average increase of 0.16 degrees Celsius per decade is found for global water temperature between 1960 and 2014
PDF
