scholarly journals Simulation of the hydrodynamic behaviour of a Mediterranean reservoir under different climate change and management scenarios

2017 ◽  
Vol 77 (1) ◽  
Author(s):  
Jordi Prats ◽  
Marie-José Salençon ◽  
Magali Gant ◽  
Pierre-Alain Danis
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Water Level ◽  
Input Data ◽  
Reference Period ◽  
Management Scenarios ◽  
Elevated Water ◽  
Elevated Water Level ◽  
Bottom Outlet

One of the most important current issues in the management of lakes and reservoirs is the prediction of global climate change effects to determine appropriate mitigation and adaptation actions. In this paper we analyse whether management actions can limit the effects of climate change on water temperatures in a reservoir. For this, we used the model EOLE to simulate the hydrodynamic and thermal behaviour of the reservoir of Bimont (Provence region, France) in the medium term (2036-2065) and in the long term (2066-2095) using regionalised projections by the model CNRM-CERFACS-CNRM-CM5 under the emission scenarios RCP 4.5 and RCP 8.5. Water temperature projections were compared to simulations for the reference period 1993-2013, the longest period for which we had year-long data for both hydrology and meteorology. We calibrated the model using profile measurements for the period 2010-2011 and we carried an extensive validation and assessment of model performance. In fact, we validated the model using profile measurements for 2012-2014, obtaining a root mean square error of 1.08°C and mean bias of -0.11°C, and we assured the consistency of model simulations in the long term by comparing simulated surface temperature to satellite measurements for 1999-2013. We assessed the effect using synthetic input data instead of measured input data by comparing simulations made using both kinds of data for the reference period. Using synthetic data resulted in slightly lower (-0.3°C) average and maximum epilimnion temperatures, a somewhat deeper thermocline, and slightly higher evaporation (+7%). To investigate the effect of different management strategies, we considered three management scenarios: i) bottom outlet and present water level; ii) bottom outlet and elevated water level; and iii) surface outlet and elevated water level. According to the simulations, the reservoir of Bimont will have a low rate of warming of the epilimnion of 0.009-0.024 °C·yr-1, but a rapid hypolimnion warming of 0.013-0.028°C·yr-1. The increase in surface temperatures will augment evaporation. However, the length of the stratification period and the thermocline depth are not expected to change. Elevating the water level and using a surface outlet in the reservoir of Bimont, would result in reductions of surface temperature of a similar magnitude as the expected increase because of climate change.

scholarly journals Projecting marine fish production and catch potential in Bangladesh in the 21st century under long-term environmental change and management scenarios

2015 ◽  
Vol 73 (5) ◽  
pp. 1357-1369 ◽  
Author(s):  
Jose A. Fernandes ◽  
Susan Kay ◽  
Mostafa A. R. Hossain ◽  
Munir Ahmed ◽  
William W. L. Cheung ◽  
...  
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Low Income ◽  
Nutrient Loading ◽  
Global Climate Model ◽  
Productive Capacity ◽  
Fish Production ◽  
Management Scenarios ◽  
Bombay Duck

Abstract The fisheries sector is crucial to the Bangladeshi economy and wellbeing, accounting for 4.4% of national gross domestic product and 22.8% of agriculture sector production, and supplying ca. 60% of the national animal protein intake. Fish is vital to the 16 million Bangladeshis living near the coast, a number that has doubled since the 1980s. Here, we develop and apply tools to project the long-term productive capacity of Bangladesh marine fisheries under climate and fisheries management scenarios, based on downscaling a global climate model, using associated river flow and nutrient loading estimates, projecting high-resolution changes in physical and biochemical ocean properties, and eventually projecting fish production and catch potential under different fishing mortality targets. We place particular interest on Hilsa shad (Tenualosa ilisha), which accounts for ca. 11% of total catches, and Bombay duck (Harpadon nehereus), a low price fish that is the second highest catch in Bangladesh and is highly consumed by low-income communities. It is concluded that the impacts of climate change, under greenhouse emissions scenario A1B, are likely to reduce the potential fish production in the Bangladesh exclusive economic zone by <10%. However, these impacts are larger for the two target species. Under sustainable management practices, we expect Hilsa shad catches to show a minor decline in potential catch by 2030 but a significant (25%) decline by 2060. However, if overexploitation is allowed, catches are projected to fall much further, by almost 95% by 2060, compared with the Business as Usual scenario for the start of the 21st century. For Bombay duck, potential catches by 2060 under sustainable scenarios will produce a decline of <20% compared with current catches. The results demonstrate that management can mitigate or exacerbate the effects of climate change on ecosystem productivity.

scholarly journals A Hydrological Modeling Approach for Assessing the Impacts of Climate Change on Runoff Regimes in Slovakia

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3358
Author(s):  
Patrik Sleziak ◽  
Roman Výleta ◽  
Kamila Hlavčová ◽  
Michaela Danáčová ◽  
Milica Aleksić ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Hydrological Modeling ◽  
Performance Metrics ◽  
Climate Change Scenarios ◽  
Reference Period ◽  
Changing Climate ◽  
Time Horizons ◽  
The Future

The changing climate is a concern with regard to sustainable water resources. Projections of the runoff in future climate conditions are needed for long-term planning of water resources and flood protection. In this study, we evaluate the possible climate change impacts on the runoff regime in eight selected basins located in the whole territory of Slovakia. The projected runoff in the basins studied for the reference period (1981–2010) and three future time horizons (2011–2040, 2041–2070, and 2071–2100) was simulated using the HBV (Hydrologiska Byråns Vattenbalansavdelning) bucket-type model (the TUW (Technische Universität Wien) model). A calibration strategy based on the selection of the most suitable decade in the observation period for the parameterization of the model was applied. The model was first calibrated using observations, and then was driven by the precipitation and air temperatures projected by the KNMI (Koninklijk Nederlands Meteorologisch Instituut) and MPI (Max Planck Institute) regional climate models (RCM) under the A1B emission scenario. The model’s performance metrics and a visual inspection showed that the simulated runoff using downscaled inputs from both RCM models for the reference period represents the simulated hydrological regimes well. An evaluation of the future, which was performed by considering the representative climate change scenarios, indicated that changes in the long-term runoff’s seasonality and extremality could be expected in the future. In the winter months, the runoff should increase, and decrease in the summer months compared to the reference period. The maximum annual daily runoff could be more extreme for the later time horizons (according to the KNMI scenario for 2071–2100). The results from this study could be useful for policymakers and river basin authorities for the optimum planning and management of water resources under a changing climate.

Global long-term land surface temperature for NOAA AVHRR: extension from 1981-2000 to 1981-2020

2021 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Global Scale ◽  
Mean Bias Error ◽  
Bias Error ◽  
Important Indicator ◽  
Noaa Avhrr

<p>As an important indicator of land-atmosphere energy interaction, land surface temperature (LST) plays an important role in the research of climate change, hydrology, and various land surface processes. Compared with traditional ground-based observation, satellite remote sensing provides the possibility to retrieve LST more efficiently over a global scale. Since the lack of global LST before, Ma et al., (2020) released a global 0.05 ×0.05  long-term (1981-2000) LST based on NOAA-7/9/11/14 AVHRR. The dataset includes three layers: (1) instantaneous LST, a product generated based on an ensemble of several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST at 14:30 solar time; and (3) monthly averages of ODC LST. To meet the requirement of the long-term application, e.g. climate change, the period of the LST is extended from 1981-2000 to 1981-2020 in this study. The LST from 2001 to 2020 are retrieved from NOAA-16/18/19 AVHRR with the same algorithm for NOAA-7/8/11/14 AVHRR. The train and test results based on the simulation data from SeeBor and TIGR atmospheric profiles show that the accuracy of the RF-SWA method for the three sensors is consistent with the previous four sensors, i.e. the mean bias error and standard deviation less than 0.10 K and 1.10 K, respectively, under the assumption that the maximum emissivity and water vapor content uncertainties are 0.04 and 1.0 g/cm<sup>2</sup>, respectively. The preliminary validation against <em>in-situ</em> LST also shows a similar accuracy, indicating that the accuracy of LST from 1981 to 2020 are consistent with each other. In the generation code, the new LST has been improved in terms of land surface emissivity estimation, identification of cloud pixel, and the ODC method in order to generate a more reliable LST dataset. Up to now, the new version LST product (1981-2020) is under generating and will be released soon in support of the scientific research community.</p>

scholarly journals Statistics and Analysis of the Relations between Rainstorm Floods and Earthquakes

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Baodeng Hou ◽  
Yongxiang Wu ◽  
Jianhua Wang ◽  
Kai Wu ◽  
Weihua Xiao
Keyword(s):  
Climate Change ◽  
Input Data ◽  
Computational Results ◽  
Occurrence Time ◽  
The Past ◽  
The Earth ◽  
Water Vapors ◽  
Transmission Zone ◽  
Chinese Scholars

The frequent occurrence of geophysical disasters under climate change has drawn Chinese scholars to pay their attention to disaster relations. If the occurrence sequence of disasters could be identified, long-term disaster forecast could be realized. Based on the Earth Degassing Effect (EDE) which is valid, this paper took the magnitude, epicenter, and occurrence time of the earthquake, as well as the epicenter and occurrence time of the rainstorm floods as basic factors to establish an integrated model to study the correlation between rainstorm floods and earthquakes. 2461 severe earthquakes occurred in China or within 3000 km from China and the 169 heavy rainstorm floods occurred in China over the past 200+ years as the input data of the model. The computational results showed that although most of the rainstorm floods have nothing to do with the severe earthquakes from a statistical perspective, some floods might relate to earthquakes. This is especially true when the earthquakes happen in the vapor transmission zone where rainstorms lead to abundant water vapors. In this regard, earthquakes are more likely to cause big rainstorm floods. However, many cases of rainstorm floods could be found after severe earthquakes with a large extent of uncertainty.

May long-term historical hydrological data be misleading for flood frequency analysis in current conditions of climate change?

2020 ◽  
Author(s):  
Alexandra Fedorova ◽  
Nataliia Nesterova ◽  
Olga Makarieva ◽  
Andrey Shikhov
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Economic Damage ◽  
Irkutsk Region ◽  
Extreme Flood ◽  
The Town

<p>In June 2019, the extreme flash flood was formed on the rivers of the Irkutsk region originating from the East Sayan mountains. This flood became the most hazardous one in the region in 80 years history of observations.</p><p>The greatest rise in water level was recorded at the Iya River in the town of Tulun (more than 9 m in three days). The recorded water level was more than 5 m above the dangerous mark of 850 cm and more than 2.5 m above the historical maximum water level which was observed in 1984.</p><p>The flood led to the catastrophic inundation of the town of Tulun, 25 people died and 8 went missing. According to preliminary assessment, economic damage from the flood in 2019 amounted up to half a billion Euro.</p><p>Among the reasons for the extreme flood in June 2019 that are discussed are heavy rains as a result of climate change, melting of snow and glaciers in the mountains of the East Sayan, deforestation of river basins due to clearings and fires, etc.</p><p>The aim of the study was to analyze the factors that led to the formation of a catastrophic flood in June 2019, as well as estimate the maximum discharge of at the Iya River. For calculations, the deterministic distributed hydrological model Hydrograph was applied. We used the observed data of meteorological stations and the forecast values ​​of the global weather forecast model ICON. The estimated discharge has exceeded previously observed one by about 50%.</p><p>The results of the study have shown that recent flood damage was caused mainly by unprepared infrastructure. The safety dam which was built in the town of Tulun just ten years ago was 2 meters lower than maximum observed water level in 2019. This case and many other cases in Russia suggest that the flood frequency analysis of even long-term historical data may mislead design engineers to significantly underestimate the probability and magnitude of flash floods. There are the evidences of observed precipitation regime transformations which directly contribute to the formation of dangerous hydrological phenomena. The details of the study for the Irkutsk region will be presented.</p>

Non-linear, long-term evolution of sea surface temperature across the World Ocean

2020 ◽  
Author(s):  
Benjamin Martinez-Lopez
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
World Ocean ◽  
Sea Surface ◽  
Second Derivative ◽  
Term Evolution ◽  
The World ◽  
Non Linear

<p>Sea surface temperature (SST) is the only oceanic parameter on which depend heat fluxes between ocean and atmosphere and, therefore, SST is one of the key factors that influence climate and its variability. Over the twentieth century, SSTs have significantly increased around the global ocean, warming that has been attributed to anthropogenic climate change, although it is not yet clear how much of it is related to natural causes and how much is due to human activities. A considerable part of available literature regarding climate change has been built based on the global or hemispheric analysis of surface temperature trends. There are, however, some key open questions that need to be answered and for this task estimates of long-term SST trend patterns represent a source of valuable information. Unfortunately, long-term SST trend patterns have large uncertainties and although SST constitutes one of the most-measured ocean variables of our historic records, their poor spatial and temporal sampling, as well as inhomogeneous measurements technics, hinder an accurate determination of long-term SST trends, which increases their uncertainty and, therefore, limit their physical interpretation as well as their use in the verification of climate simulations.<br>Most of the long-term SST trend patterns have been built using linear techniques, which are very usefull when they are used to extract information of measurements satisfying two key assumptions: linearity and stationarity. The global warming resulting of our economic activities, however, affect the state of the World Ocean and the atmosphere inducing changes in the climate that may result in oscillatory modes of variability of different frequencies, which may undergo non-stationary and non-linear evolutions. In this work, we construct long-term SST trend patterns by using non-linear techniques to extract non-linear, long-term trends in each grid-point of two available global SST datasets: the National Oceanic and Atmospheric Administration Extended Reconstructed SST (ERSST) and from the Hadley Centre sea ice and SST (HadISST). The used non-linear technique makes a good job even if the SST data are non-linear and non-stationary. Additionally, the nonlinearity of the extracted trends allows the use of the first and second derivative to get more information about the global, long-term evolution of the SST fields, favoring thus a deeper understanding and interpretation of the observed changes in SST. Particularly, our results clearly show, in both ERSST and HadISST datasets, the non-uniform warming observed in the tropical Pacific, which seems to be related to the enhanced vertical heat flux in the eastern equatorial Pacific and the strengthening of the warm pool in the western Pacific. By using the second derivative of the nonlinear SST trends, emerges an interesting pattern delimiting several zones in the Pacific Ocean which have been responded in a different way to the impose warming of the last century.</p>

scholarly journals Wave Climate Associated With Changing Water Level and Ice Cover in Lake Michigan

2021 ◽  
Vol 8 ◽  
Author(s):  
Chenfu Huang ◽  
Longhuan Zhu ◽  
Gangfeng Ma ◽  
Guy A. Meadows ◽  
Pengfei Xue
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Lake Michigan ◽  
Linear Trend ◽  
Ice Cover ◽  
Wave Climate ◽  
Water Levels ◽  
Regional Warming ◽  
Model Simulations

Detailed knowledge of wave climate change is essential for understanding coastal geomorphological processes, ecosystem resilience, the design of offshore and coastal engineering structures and aquaculture systems. In Lake Michigan, the in-situ wave observations suitable for long-term analysis are limited to two offshore MetOcean buoys. Since this distribution is inadequate to fully represent spatial patterns of wave climate across the lake, a series of high-resolution SWAN model simulations were performed for the analysis of long-term wave climate change for the entirety of Lake Michigan from 1979 to 2020. Model results were validated against observations from two offshore buoys and 16 coastal buoys. Linear regression analysis of significant wave height (Hs) (mean, 90th percentile, and 99th percentile) across the entire lake using this 42-year simulation suggests that there is no simple linear trend of long-term changes of Hs for the majority (>90%) of the lake. To address the inadequacy of linear trend analysis used in previous studies, a 10-year trailing moving mean was applied to the Hs statistics to remove seasonal and annual variability, focusing on identifying long-term wave climate change. Model results reveal the regime shifts of Hs that correspond to long-term lake water level changes. Specifically, downward trends of Hs were found in the decade of 1990–2000; low Hs during 2000–2010 coincident with low lake levels; and upward trends of Hs were found during 2010–2020 along with rising water levels. The coherent pattern between the wave climate and the water level was hypothesized to result from changing storm frequency and intensity crossing the lake basin, which influences both waves (instantly through increased wind stress on the surface) and water levels (following, with a lag through precipitation and runoff). Hence, recent water level increases and wave growth were likely associated with increased storminess observed in the Great Lakes. With regional warming, the decrease in ice cover in Lake Michigan (particularly in the northernmost region of the lake) favored the wave growth in the winter due to increased surface wind stress, wind fetch, and wave transmission. Model simulations suggest that the basin-wide Hs can increase significantly during the winter season with projected regional warming and associated decreases in winter ice cover. The recent increases in wave height and water level, along with warming climate and ice reduction, may yield increasing coastal damages such as accelerating coastal erosion.

scholarly journals A New Look at the Variance of Summertime Temperatures over Land

2020 ◽  
Vol 33 (13) ◽  
pp. 5465-5477 ◽  
Author(s):  
Lucas R. Vargas Zeppetello ◽  
David S. Battisti ◽  
Marcia B. Baker
Keyword(s):  
Climate Change ◽  
United States ◽  
Heat Flux ◽  
Surface Temperature ◽  
Shortwave Radiation ◽  
Climatic Warming ◽  
Temperature Variance ◽  
Central United States ◽  
Very High

AbstractThe increasing frequency of very high summertime temperatures has motivated growing interest in the processes determining the probability distribution of surface temperature over land. Here, we show that on monthly time scales, temperature anomalies can be modeled as linear responses to fluctuations in shortwave radiation and precipitation. Our model contains only three adjustable parameters, and, surprisingly, these can be taken as constant across the globe, notwithstanding large spatial variability in topography, vegetation, and hydrological processes. Using observations of shortwave radiation and precipitation from 2000 to 2017, the model accurately reproduces the observed pattern of temperature variance throughout the Northern Hemisphere midlatitudes. In addition, the variance in latent heat flux estimated by the model agrees well with the few long-term records that are available in the central United States. As an application of the model, we investigate the changes in the variance of monthly averaged surface temperature that might be expected due to anthropogenic climate change. We find that a climatic warming of 4°C causes a 10% increase in temperature variance in parts of North America.

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