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description Publicationkeyboard_double_arrow_right Article , Other literature type 2022 Italy, GermanyPublisher:Springer Science and Business Media LLC Behling, R.; Roessner, S.; Förster, S.; Saemian, P.; Tourian, M.; Portele, T.; Lorenz, C.;pmid: 36456635
pmc: PMC9715656
AbstractIran has experienced a drastic increase in water scarcity in the last decades. The main driver has been the substantial unsustainable water consumption of the agricultural sector. This study quantifies the spatiotemporal dynamics of Iran’s hydrometeorological water availability, land cover, and vegetation growth and evaluates their interrelations with a special focus on agricultural vegetation developments. It analyzes globally available reanalysis climate data and satellite time series data and products, allowing a country-wide investigation of recent 20+ years at detailed spatial and temporal scales. The results reveal a wide-spread agricultural expansion (27,000 km$$^2$$ 2 ) and a significant cultivation intensification (48,000 km$$^2$$ 2 ). At the same time, we observe a substantial decline in total water storage that is not represented by a decrease of meteorological water input, confirming an unsustainable use of groundwater mainly for agricultural irrigation. As consequence of water scarcity, we identify agricultural areas with a loss or reduction of vegetation growth (10,000 km$$^2$$ 2 ), especially in irrigated agricultural areas under (hyper-)arid conditions. In Iran’s natural biomes, the results show declining trends in vegetation growth and land cover degradation from sparse vegetation to barren land in 40,000 km$$^2$$ 2 , mainly along the western plains and foothills of the Zagros Mountains, and at the same time wide-spread greening trends, particularly in regions of higher altitudes. Overall, the findings provide detailed insights in vegetation-related causes and consequences of Iran’s anthropogenic drought and can support sustainable management plans for Iran or other semi-arid regions worldwide, often facing similar conditions.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZ German Research Centre for GeosciencesArticle . 2022License: CC BYData sources: GFZ German Research Centre for GeosciencesOnline Publikationen der Universität StuttgartArticle . 2022License: CC BYData sources: Online Publikationen der Universität Stuttgartadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZ German Research Centre for GeosciencesArticle . 2022License: CC BYData sources: GFZ German Research Centre for GeosciencesOnline Publikationen der Universität StuttgartArticle . 2022License: CC BYData sources: Online Publikationen der Universität Stuttgartadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.description Publicationkeyboard_double_arrow_right Article , Conference object , Other literature type 2022Embargo end date: 12 Dec 2022 Italy, AustraliaPublisher:MDPI AG Authors: Mahmoud Pirooznia; Mehdi Raoofian Naeeni; Alireza Atabati; Mohammad J. Tourian;handle: 1959.13/1489078
Sea surface currents are often modeled using numerical models without adequately addressing the issue of model calibration at the regional scale. The aim of this study is to calibrate the MIKE 21 numerical ocean model for the Persian Gulf and the Oman Sea to improve the sea surface currents obtained from the model. The calibration was performed through data assimilation of the model with altimetry and hydrographic observations using variational data assimilation, where the weights of the objective functions were defined based on the type of observations and optimized using metaheuristic optimization methods. According to the results, the calibration of the model generally led the model results closer to the observations. This was reflected in an improvement of about 0.09 m/s in the obtained sea surface currents. It also allowed for more accurate evaluations of model parameters, such as Smagorinsky and Manning coefficients. Moreover, the root mean square error values between the satellite altimetry observations at control stations and the assimilated model varied between 0.058 and 0.085 m. We further showed that the kinetic energy produced by sea surface currents could be used for generating electricity in the Oman Sea and near Jask harbor.
Remote Sensing arrow_drop_down Remote SensingOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2072-4292/14/19/4901/pdfData sources: Multidisciplinary Digital Publishing InstituteOnline Publikationen der Universität StuttgartArticle . 2022License: CC BYData sources: Online Publikationen der Universität StuttgartOPUS - Publication Server of the University of StuttgartArticle . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.more_vert Remote Sensing arrow_drop_down Remote SensingOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2072-4292/14/19/4901/pdfData sources: Multidisciplinary Digital Publishing InstituteOnline Publikationen der Universität StuttgartArticle . 2022License: CC BYData sources: Online Publikationen der Universität StuttgartOPUS - Publication Server of the University of StuttgartArticle . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.
description Publicationkeyboard_double_arrow_right Article , Other literature type 2022 Italy, GermanyPublisher:Springer Science and Business Media LLC Behling, R.; Roessner, S.; Förster, S.; Saemian, P.; Tourian, M.; Portele, T.; Lorenz, C.;pmid: 36456635
pmc: PMC9715656
AbstractIran has experienced a drastic increase in water scarcity in the last decades. The main driver has been the substantial unsustainable water consumption of the agricultural sector. This study quantifies the spatiotemporal dynamics of Iran’s hydrometeorological water availability, land cover, and vegetation growth and evaluates their interrelations with a special focus on agricultural vegetation developments. It analyzes globally available reanalysis climate data and satellite time series data and products, allowing a country-wide investigation of recent 20+ years at detailed spatial and temporal scales. The results reveal a wide-spread agricultural expansion (27,000 km$$^2$$ 2 ) and a significant cultivation intensification (48,000 km$$^2$$ 2 ). At the same time, we observe a substantial decline in total water storage that is not represented by a decrease of meteorological water input, confirming an unsustainable use of groundwater mainly for agricultural irrigation. As consequence of water scarcity, we identify agricultural areas with a loss or reduction of vegetation growth (10,000 km$$^2$$ 2 ), especially in irrigated agricultural areas under (hyper-)arid conditions. In Iran’s natural biomes, the results show declining trends in vegetation growth and land cover degradation from sparse vegetation to barren land in 40,000 km$$^2$$ 2 , mainly along the western plains and foothills of the Zagros Mountains, and at the same time wide-spread greening trends, particularly in regions of higher altitudes. Overall, the findings provide detailed insights in vegetation-related causes and consequences of Iran’s anthropogenic drought and can support sustainable management plans for Iran or other semi-arid regions worldwide, often facing similar conditions.
KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZ German Research Centre for GeosciencesArticle . 2022License: CC BYData sources: GFZ German Research Centre for GeosciencesOnline Publikationen der Universität StuttgartArticle . 2022License: CC BYData sources: Online Publikationen der Universität Stuttgartadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.more_vert KITopen (Karlsruhe I... arrow_drop_down KITopen (Karlsruhe Institute of Technologie)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)GFZ German Research Centre for GeosciencesArticle . 2022License: CC BYData sources: GFZ German Research Centre for GeosciencesOnline Publikationen der Universität StuttgartArticle . 2022License: CC BYData sources: Online Publikationen der Universität Stuttgartadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.description Publicationkeyboard_double_arrow_right Article , Conference object , Other literature type 2022Embargo end date: 12 Dec 2022 Italy, AustraliaPublisher:MDPI AG Authors: Mahmoud Pirooznia; Mehdi Raoofian Naeeni; Alireza Atabati; Mohammad J. Tourian;handle: 1959.13/1489078
Sea surface currents are often modeled using numerical models without adequately addressing the issue of model calibration at the regional scale. The aim of this study is to calibrate the MIKE 21 numerical ocean model for the Persian Gulf and the Oman Sea to improve the sea surface currents obtained from the model. The calibration was performed through data assimilation of the model with altimetry and hydrographic observations using variational data assimilation, where the weights of the objective functions were defined based on the type of observations and optimized using metaheuristic optimization methods. According to the results, the calibration of the model generally led the model results closer to the observations. This was reflected in an improvement of about 0.09 m/s in the obtained sea surface currents. It also allowed for more accurate evaluations of model parameters, such as Smagorinsky and Manning coefficients. Moreover, the root mean square error values between the satellite altimetry observations at control stations and the assimilated model varied between 0.058 and 0.085 m. We further showed that the kinetic energy produced by sea surface currents could be used for generating electricity in the Oman Sea and near Jask harbor.
Remote Sensing arrow_drop_down Remote SensingOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2072-4292/14/19/4901/pdfData sources: Multidisciplinary Digital Publishing InstituteOnline Publikationen der Universität StuttgartArticle . 2022License: CC BYData sources: Online Publikationen der Universität StuttgartOPUS - Publication Server of the University of StuttgartArticle . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.more_vert Remote Sensing arrow_drop_down Remote SensingOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2072-4292/14/19/4901/pdfData sources: Multidisciplinary Digital Publishing InstituteOnline Publikationen der Universität StuttgartArticle . 2022License: CC BYData sources: Online Publikationen der Universität StuttgartOPUS - Publication Server of the University of StuttgartArticle . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.
