Evaluating and modelling the impacts of climate change and reference datasets on river water temperatures for a hydropower system with two outlets

2020 ◽  
Author(s):  
Philippe Gatien
Keyword(s):  
Climate Change ◽  
Water Temperature ◽  
Air Temperature ◽  
River Flow ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Delta Method ◽  
Weather Data ◽  
Monthly Basis ◽  
Reanalysis Dataset

<p>Water temperature modelling has become an essential tool in the management of ectotherm species downstream of dams in North American rivers. The main objective of this project is to compare different datasets and their ability to adequately simulate water temperatures in the Nechako River, (B.C., Canada) downstream of a major dam where the flow is not managed for hydroelectric production, but spills are programmed to cool the downstream reaches. This will ultimately lead to a reassessment of water management in the context of climate change to ensure the survival of fish migrating or living in the reaches located downstream of the dam during warm periods.</p><p>Water in the Nechako River stems from the Nechako reservoir at the Skins lake spillway and flows into river through a series of lakes prior to reaching Finmoore, where federal regulations stipulate that water temperatures must be maintained below 20 °C. The river has multiple tributaries on it’s 250 km journey including the Nautley river. The river flow is simulated using a 1D unsteady flow simulation and lateral inflows using HEC-RAS.</p><p>Water temperature simulations are then conducted using different datasets. The first is a series of observed meteorological data spanning from 2017 to present day from two different weather stations near the river. The second dataset is ERA5, a reanalysis product that’s gridded every 0.25°. Eleven stations nearest to the river were extracted over the same period as the observations. Both datasets were used to calibrate five parameters (dust coefficient, three wind function parameters and the Richardson number) three times using the mean absolute error (MAE), Nash-Sutcliffe coefficient (NS) and root mean squared error (RMSE) by comparing the observed and simulated temperatures near Finmoore.</p><p>Individual calibrations were performed over each available summer from early June to late August and then validated over the rest of the data to ensure the robustness of the results.</p><p>Overall, the reanalysis dataset outperformed the available observations for thermal representation of the river.</p><p>To further understand the thermal model, a sensitivity analysis was performed on the different inputs (inflow water temperature, air temperature, wind speed, etc.). The model showed very little sensitivity to the characteristics of the inflow (temperature, volume) as the point of interest was so far downstream. In fact, environmental factors such as air temperature had a greater impact on water temperature than upstream conditions at the reservoir spillway. This effect seems to be mostly attributable to Cheslatta Lake with its long water residence time that can reach upwards of three days.</p><p>The potential effects of climate change on water temperature were then investigated by modifying existing weather data like air temperature with the delta method on a monthly basis using the RCP8.5 emission scenario. Water temperatures increased throughout by roughly 2.5°C downstream, near Finmoore.</p><p> </p>

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 Bioclimatic conditions and thermal seasons of the year in Szczecin

2021 ◽  
Vol 94 (2) ◽  
pp. 283-299
Author(s):  
Agnieszka Mąkosza
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Meteorological Data ◽  
Thermal Conditions ◽  
Thermal Spring ◽  
Early Spring ◽  
Climate Index ◽  
Relative Air Humidity ◽  
Thermal Climate ◽  
Bioclimatic Conditions

Climate change is an empirical fact evidenced by subsequent IPCC reports. The observed climate change is also manifested in the altered date of occurrence and duration of the seasons in a year. Variability of thermal conditions due to climate warming will have its toll on the bioclimatic conditions. The assessment of bioclimatic conditions was conducted with the use of Universal Thermal Climate Index (UTCI). The present elaboration is based on hourly values of the following meteorological elements: air temperature, relative air humidity, wind speed and cloud cover. The meteorological data were obtained from the Institute of Meteorology and Water Management – National Research Institute (IMGW-PIB) in Szczecin and cover the period 2000-2019. Variability of bioclimatic conditions is considered per periods corresponding to thermal seasons of the year as identified by the Gumiński (1948) method on the basis of monthly air temperature values. The analysed UTCI values with respect to thermal seasons indicate that mean UTCI values in the period 2000-2019 representative for thermal summer amount to 22.6°C, thermal spring 9,9°C, thermal autumn 8.4°C, thermal winter -10.4°C, early spring -4.6°C, and early winter -7.9°C. For the periods with identified lack of thermal winter, mean UTCI value was -6.6°C. The aim of the present paper is an attempt to assess the variability of biothermal conditions as calculated using the UTCI index against the thermal seasons of the year in Szczecin.

scholarly journals A hierarchical model of daily stream temperature for regional predictions

2018 ◽  
Author(s):  
Daniel J Hocking ◽  
Kyle O'Neil ◽  
Benjamin H Letcher
Keyword(s):  
Water Temperature ◽  
Air Temperature ◽  
Hierarchical Model ◽  
Mean Squared Error ◽  
Thermal Sensitivity ◽  
Stream Temperature ◽  
Eastern United States ◽  
Validation Data ◽  
Temperature Records ◽  
Use Factors

Stream temperature is an important exogenous factor influencing populations of stream organisms such as fish, amphibians, and invertebrates. Many states regulate stream protections based on temperature. Therefore, stream temperature models are important, particularly for estimating thermal regimes in unsampled space and time. To help meet this need, we developed a hierarchical model of daily stream temperature and applied it across the eastern United States. Our model accommodates many of the key challenges associated with daily stream temperature models including the lagged response of water temperature to changes in air temperature, incomplete and widely-varying observed time series, spatial and temporal autocorrelation, and the inclusion of predictors other than air temperature. We used 248,517 daily stream temperature records from 1,352 streams to fit our model and 100,909 records were withheld for model validation. Our model had a root mean squared error of 0.61 C for the fitted data and 2.03 C for the validation data, indicating excellent fit and good predictive power for understanding regional temperature patterns. We then used our model to predict daily stream temperatures from 1980 - 2015 for all streams <200 km2 from Maine to Virginia. From these, we calculated derived stream metrics including mean July temperature, mean summer temperature, and the thermal sensitivity of each stream reach to changes in air temperature. Although generally water temperature follows similar latitudinal and altitudinal patterns as air temperature, there are considerable differences at the reach scale based on landscape and land-use factors.

scholarly journals Trends In Climate Variables (Temperature And Rainfall) And Local Perceptions Of Climate Change In Lamu, Kenya

2020 ◽  
Vol 13 (3) ◽  
pp. 102-109
Author(s):  
Maingey Yvonne ◽  
Gilbert Ouma ◽  
Daniel Olago ◽  
Maggie Opondo
Keyword(s):  
Climate Change ◽  
Local Community ◽  
Linear Trend ◽  
Meteorological Data ◽  
Household Survey ◽  
Weather Data ◽  
Climate Variables ◽  
Local Perceptions ◽  
Maximum Temperatures ◽  
Negative Impacts

Community  adaptation to the negative impacts of climate change benefits from an analysis of both the trends in climate variables and people’s perception of climate change. This paper contends that members of the local community have observed changes in temperature  and rainfall patterns and that these perceptions can be positively correlated with meteorological records. This is particularly useful for remote regions like Lamu whereby access to weather data is spatially and temporally challenged. Linear trend analysis is employed to describe the change in temperature and rainfall in Lamu using monthly data obtained from the Kenya Meteorological Department (KMD) for the period 1974–2014. To determine local perceptions and understanding of the trends, results from a household survey are presented. Significant warming trends have been observed in the study area over the period 1974–2014. This warming is attributed to a rise in maximum temperatures. In contrast to temperature, a clear picture of the rainfall trend has not emerged. Perceptions of the local community closely match the findings on temperature, with majority of the community identifying a rise in temperature over the same period. The  findings suggest that the process of validating community perceptions of trends with historical meteorological data analysis can promote adaptation planning that is inclusive and responsive to local experiences.

scholarly journals The thermal response of small and shallow lakes to climate change: new insights from 3D hindcast modelling

2021 ◽  
Vol 12 (2) ◽  
pp. 439-456
Author(s):  
Francesco Piccioni ◽  
Céline Casenave ◽  
Bruno Jacques Lemaire ◽  
Patrick Le Moigne ◽  
Philippe Dubois ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Temperature ◽  
Shallow Lakes ◽  
Thermal Regime ◽  
Meteorological Data ◽  
Thermal Response ◽  
Thermal Conditions ◽  
3D Analysis ◽  
Sharp Separation

Abstract. Small, shallow lakes represent the majority of inland freshwater bodies. However, the effects of climate change on such ecosystems have rarely been quantitatively addressed. We propose a methodology to evaluate the thermal response of small, shallow lakes to long-term changes in the meteorological conditions through model simulations. To do so, a 3D thermal-hydrodynamic model is forced with meteorological data and used to hindcast the evolution of an urban lake in the Paris region between 1960 and 2017. Its thermal response is assessed through a series of indices describing its thermal regime in terms of water temperature, thermal stratification, and potential cyanobacteria production. These indices and the meteorological forcing are first analysed over time to test the presence of long-term monotonic trends. 3D simulations are then exploited to highlight the presence of spatial heterogeneity. The analyses show that climate change has strongly impacted the thermal regime of the study site. Its response is highly correlated with three meteorological variables: air temperature, solar radiation, and wind speed. Mean annual water temperature shows a considerable warming trend of 0.6 ∘C per decade, accompanied by longer stratification and by an increase in thermal energy favourable to cyanobacteria proliferation. The strengthening of thermal conditions favourable for cyanobacteria is particularly strong during spring and summer, while stratification increases especially during spring and autumn. The 3D analysis allows us to detect a sharp separation between deeper and shallower portions of the basin in terms of stratification dynamics and potential cyanobacteria production. This induces highly dynamic patterns in space and time within the study site that are particularly favourable to cyanobacteria growth and bloom initiation.

scholarly journals Analysis of Long-Term Measured Exterior Air Temperature in Zilina

2018 ◽  
Vol 14 (2) ◽  
pp. 124-131 ◽  
Author(s):  
Daniela Jurasova
Keyword(s):  
Climate Change ◽  
Temperature Measurement ◽  
Air Temperature ◽  
Weather Data ◽  
Time Data ◽  
Long Time ◽  
Experimental Laboratory ◽  
Weather Stations ◽  
Selection Of

Abstract The climate change assumes nowadays on significance. Weather data may be available on various time scales – long-term, minutes, hours, days, periods, years. Measurements of air temperature are realized for a long time. Data in Slovakia are available from few weather stations of Slovak Hydrometeorological Institute (SHMI). For long-term and wide-ranging measurement of various parameters this can be complicated and expensive. This paper is focused on temperature measurement near the experimental laboratory. Estimation of daily, monthly and yearly mean temperatures is done in different ways. Results from experimental temperature measurement, in a way of selection of unusual extremes are presented. Shorter recording intervals describe the temperature courses in a more pertinent way.

scholarly journals The response of small and shallow lakes to climate change: new insights from hindcast modelling

2020 ◽  
Author(s):  
Francesco Piccioni ◽  
Céline Casenave ◽  
Bruno Jacques Lemaire ◽  
Patrick Le Moigne ◽  
Philippe Dubois ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Temperature ◽  
Biomass Production ◽  
Thermal Regime ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Meteorological Data ◽  
Thermal Response ◽  
Production Model ◽  
Ecological Value

Abstract. Small and shallow water bodies are a dominant portion of inland freshwaters. However, the effects of climate change on such ecosystems have rarely been quantitatively adressed. We propose a methodology to evaluate the thermal response of a small and shallow lake to long-term changes in the meteorological conditions, through model simulations. To do so, a 3D hydrodynamic model is forced with meteorological data and used to hindcast the evolution of a urban lake in the Paris region between 1960 and 2017. Its thermal response is analyzed through the definition of a series of indices describing its thermal regime in terms of water temperature, thermal stratification and tendency to biomass production. Model results and meteorological forcing are analyzed over time to test the presence of monotonic trends and 3D simulations are exploited to highlight spatial patterns in the dynamics of stratification. The thermal regime of the study site underwent significant changes. Its response was highly correlated with three meteorological variables: air temperature, solar radiation and wind speed. Mean annual water temperature showed a considerable warming trend of 0.6 °C/dec, accompanied by longer stratification and by an increase of thermal energy available for biomass production. Water warming was significant during all four seasons, with maxima in Spring and Summer, while stratification and energy for phytoplankton growth increased especially during Spring and Autumn. Stratification only established in the deeper areas of the water body, possibly inducing heterogeneity in the release of nutrient from the sediment and in the development of harmful algal blooms. Numerous similar ecosystems might be experiencing analogous changes, and appropriate management policies are needed to preserve their ecological value.

scholarly journals DETERMINATION OF SOME MARINE METEOROLOGICAL EXTREME VALUES AND ITS RELATION WITH DISTRIBUTION OF SURFACE WATER RESOURCES ON THE LY SON ISLAND

2018 ◽  
Vol 17 (4) ◽  
pp. 353-363
Author(s):  
Bui Xuan Thong ◽  
Le Tuan Dat ◽  
Truong Viet Chau
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Resources ◽  
Sea Level ◽  
Air Temperature ◽  
Extreme Values ◽  
Meteorological Data ◽  
Water Volume ◽  
Surface Water Resources ◽  
Per Capita

Climate change in terms of marine meteorological extreme values has direct impact on distribution of surface water resources on the island. Based on a series of marine meteorological data collected in the period 1985 - 2012 at the Ly Son station we have determined some extreme values such as maximum precipitation, evaporation, air temperature, sea level and other oceanographic elements. The present study tries to reveal some relationships between marine meteorological extreme values and distribution of surface water resources under condition of Ly Son island. The precipitation of < 50 mm, 50 - 100 mm and > 100 mm has the frequency of 57.8%, 20.7%, and 21.5% respectively. Due to climate change, the air temperature has the increasing tendency for all three states of medium, maximum and minimum values. Sea level and other oceanographic phenomena also have the increasing tendency. The calculation results show that the average annual surface runoff is 13.9 million m3/year and the water volume per capita reaches 678 m3/person/year. According to criteria of International Water Resources Association, a country with a water volume per capita off less than 4,000 m3/person/year is considered as country of water shortage.

scholarly journals Models to estimate river temperature. Example for Danube, at Oltenița in the context of climate change and anthropic impact

2021 ◽  
Vol 286 ◽  
pp. 04003
Author(s):  
Daniela-Elena Gogoașe-Nistoran ◽  
Cristina Sorana Ionescu ◽  
Ioana Opriș
Keyword(s):  
Water Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Logistic Function ◽  
Danube River ◽  
Weather Data ◽  
River Temperature ◽  
Anthropic Impact ◽  
Long Term Trend

Daily variation of Danube River temperature measured at Oltenitț gauging station over 9 years (2008-2016) was analysed in comparison with the air temperature measured by satellite in the same location between 1979-2020. Air temperature shows a nearly 2°C increase over the 40-years period, which can be attributed to both climate warming and anthropic impact. Water temperature was modeled with a sinusoidal function and variation with discharge was discussed. Long-term trend of hourly surface air temperature variation was obtained from Open Weather data. Air - water temperature dependency was fitted with a logistic function with good approximation. Resulting correlations help predict water temperature as a function of satellite - measured air temperature.

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