Effects of the Lake Sobradinho Reservoir (Northeastern Brazil) on the Regional Climate

Ekhtiari, Nikoo; Grossman‐Clarke, Susanne; Koch, Hagen; Souza, Werônica Meira de; Donner, Reik V.; Volkholz, Jan

doi:10.3390/cli5030050

Cited by 15 publications

(16 citation statements)

References 20 publications

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“…For surface and orography, ECOCLIMAP II (Faroux et al, 2013) and SRTM (Shuttle Radar Topography Mission, Jarvis et al, 2008) databases were used, respectively, both updated with the inclusion of the Alqueva reservoir by Policarpo et al (2017). All model domains had 68 vertical levels starting with 20 m up to 22 km, including 36 levels for the lower atmospheric level (2 km).…”

Section: Meso-nh Atmospheric Modelmentioning

confidence: 99%

Breeze effects at a large artificial lake: summer case study

Iakunin

Salgado

Potes

2018

Hydrol. Earth Syst. Sci.

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Abstract. Natural lakes and big artificial reservoirs can affect the weather regime of surrounding areas but, usually, consideration of all aspects of this impact and their quantification is a difficult task. The Alqueva reservoir, the largest artificial lake in western Europe, located on the south-east of Portugal, was filled in 2004. It is a large natural laboratory that allows the study of changes in surface and in landscape and how they affect the weather in the region. This paper is focused on a 3-day case study, 22–24 July 2014, during which an intensive observation campaign was carried out. In order to quantify the breeze effects induced by the Alqueva reservoir, two simulations with the mesoscale atmospheric model Meso-NH coupled to the FLake freshwater lake model has been performed. The difference between the two simulations lies in the presence or absence of the reservoir on the model surface. Comparing the two simulation datasets, with and without the reservoir, net results of the lake impact were obtained. Magnitude of the impact on air temperature, relative humidity, and other atmospheric variables are shown. The clear effect of a lake breeze (5–7 m s−1) can be observed during daytime on distances up to 6 km away from the shores and up to 300 m above the surface. The lake breeze system starts to form at 09:00 UTC and dissipates at 18:00–19:00 UTC with the arrival of a larger-scale Atlantic breeze. The descending branch of the lake breeze circulation brings dry air from higher atmospheric layers (2–2.5 km) and redistributes it over the lake. It is also shown that despite its significant intensity the effect is limited to a couple of kilometres away from the lake borders.

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Section: Meso-nh Atmospheric Modelmentioning

confidence: 99%

Breeze effects at a large artificial lake: summer case study

Iakunin

Salgado

Potes

2018

Hydrol. Earth Syst. Sci.

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“…Carroll et al (2009) combined the Shuttle Radar Topography Missions (SRTM) Water Body Data (SWBD) with 250 m Moderate Resolution Imaging Spectrometer (MODIS) reflectance data to produce a global static map of surface water for circa [2000][2001][2002]. Global Landsat-based static surface water datasets include the 3 arcsec (∼ 90 m) Water Body Map L. Li et al: A new dense 18-year time series of surface water fraction estimates (G3WBM: Yamazaki et al, 2015) and the Global Land Cover Facility (GLCF) inland surface water dataset at a 30 m resolution for 2000 (Feng et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region

Skidmore

Vrieling

2019

Hydrol. Earth Syst. Sci.

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Abstract. Detailed knowledge on surface water distribution and its changes is of high importance for water management and biodiversity conservation. Landsat-based assessments of surface water, such as the Global Surface Water (GSW) dataset developed by the European Commission Joint Research Centre (JRC), may not capture important changes in surface water during months with considerable cloud cover. This results in large temporal gaps in the Landsat record that prevent the accurate assessment of surface water dynamics. Here we show that the frequent global acquisitions by the Moderate Resolution Imaging Spectrometer (MODIS) sensors can compensate for this shortcoming, and in addition allow for the examination of surface water changes at fine temporal resolution. To account for water bodies smaller than a MODIS cell, we developed a global rule-based regression model for estimating the surface water fraction from a 500 m nadir reflectance product from MODIS (MCD43A4). The model was trained and evaluated with the GSW monthly water history dataset. A high estimation accuracy (R2=0.91, RMSE =11.41 %, and MAE =6.39 %) was achieved. We then applied the algorithm to 18 years of MODIS data (2000–2017) to generate a time series of surface water fraction maps at an 8 d interval for the Mediterranean. From these maps we derived metrics including the mean annual maximum, the standard deviation, and the seasonality of surface water. The dynamic surface water extent estimates from MODIS were compared with the results from GSW and water level data measured in situ or by satellite altimetry, yielding similar temporal patterns. Our dataset complements surface water products at a fine spatial resolution by adding more temporal detail, which permits the effective monitoring and assessment of the seasonal, inter-annual, and long-term variability of water resources, inclusive of small water bodies.

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“…Our MODIS surface water fraction dataset may benefit a large number of applications. (Degu et al 2011;Ekhtiari et al 2017;Hossain et al 2009). Similarly, our long-term records and derived metrics have the potential to contribute to the management and conservation of biodiversity and other ecosystem services associated with terrestrial surface water and wetlands.…”

Section: Discussionmentioning

confidence: 94%

“…Surface water extent can exhibit large variability due to natural processes and human interventions (Pekel et al 2016). The changes can affect ecosystem functioning, species distributions and composition (Koning 2005;Robledano et al 2010), and influence climate change (Degu et al 2011;Ekhtiari et al 2017). Waterbodies show a wide range of dynamic patterns in relation to the frequency of inundation and duration of standing water, such as intermittently and temporary dynamic water, seasonal water, semipermanent and permanent water, and artificially flooded water (Cowardin et al 1979).…”

Section: A New Long and Dense Time Series Of Water Extent For Monitormentioning

confidence: 99%

“…The long-term recording of the temporal changes in the number and distribution of surface water bodies, as well as the extent and duration of surface water presence may provide new insights for understanding their effect on climate. For instance, newly built large artificial reservoirs and dams can significantly alter local and regional natural precipitation (Degu et al 2011;Ekhtiari et al 2017;Hossain et al 2009). The lake area together with other lake physical properties can be utilised in general circulation models for climate simulation (Subin et al 2012).…”

Section: )mentioning

confidence: 99%

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Satellite-based monitoring of surface water dynamics

Li¹

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PhD journey is truly a marathon event full of challenges. Alongside the challenges, there is also the joy of the journey itself and the many inspiring people encountered along the way. I would like to thank everyone who was involved in this journey. First and foremost, I would like to express my sincere thanks to my promoter, Prof. dr. Andrew K. Skidmore, who provided me the opportunity to undertake this PhD research at ITC and led me to the scientific world in the first place. It has been an honour to be his PhD student. He always encouraged me to keep learning new things and seeking answers to "why". He has taught me, both consciously and unconsciously, how to think critically and defend my views, and how to work smarter instead of harder. I am very grateful for his continuous guidance and support. This thesis would not have been possible without the support and guidance of my daily supervisor A/Prof. dr. Anton Vrieling. I am so lucky to have him as my supervisor for this PhD research. During all these years, he guided me with infinite patience and full support, providing useful discussions, constructive criticisms, enlightened ideas, and excellent English editing. He was always the first person helping me out whenever I got in the coming years (Prigent et al. 2012; Vörösmarty et al. 2000). Natural factors affecting water bodies include anomalous high-rainfall-driven flood events (Cian et al. 2018), drought events due to rainfall deficits (van Dijk et al. 2013), seasonal thawing and snowmelt in spring (Watts et al. 2012), and longer-term environmental changes (Lutz et al. 2014; Street and Grove 1976). Many human activities directly affect the availability of water resources. Examples are groundwater pumping, drainage of wetlands, irrigation schemes, and construction of new dams. Anthropogenic changes in land surfaces such as urbanization, agriculture and deforestation also lead to changes in surface water. These changes strongly affect ecosystem functioning, which further results in shifting species distributions and composition (Koning 2005; Robledano et al. 2010), especially for species that are sensitive to hydroperiod variability (Baldwin et al. 2006; Roshier et al. 2002). It may also affect other ecosystem functions including ground water recharge and nutrient cycling (Leibowitz 2003). Globally, the biodiversity of water-related ecosystems continues to decline at an alarming rate (Collen et al. 2014). In addition to these direct threats, the changes of surface water further influence climate change (Degu

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Effects of the Lake Sobradinho Reservoir (Northeastern Brazil) on the Regional Climate

Cited by 15 publications

References 20 publications

Breeze effects at a large artificial lake: summer case study

Breeze effects at a large artificial lake: summer case study

A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region

Satellite-based monitoring of surface water dynamics

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