K
Karim C. Abbaspour
Researcher at Swiss Federal Institute of Aquatic Science and Technology
Publications - 158
Citations - 15678
Karim C. Abbaspour is an academic researcher from Swiss Federal Institute of Aquatic Science and Technology. The author has contributed to research in topics: Soil and Water Assessment Tool & Water resources. The author has an hindex of 50, co-authored 149 publications receiving 12814 citations. Previous affiliations of Karim C. Abbaspour include Texas A&M University.
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Modelling the effect of riparian vegetation restoration on sediment transport in a human‐impacted Brazilian catchment
José A. F. Monteiro,José A. F. Monteiro,Bahareh Kamali,Raghavan Srinivasan,Karim C. Abbaspour,Björn Gücker +5 more
TL;DR: In this article, the authors used the Soil and Water Assessment Tool to simulate river discharge and sediment exports in a typical human-impacted Brazilian catchment, the Rio das Mortes catchment.
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Modeling Crop Water Productivity Using a Coupled SWAT–MODSIM Model
Saeid Ashraf Vaghefi,Karim C. Abbaspour,Monireh Faramarzi,Raghavan Srinivasan,Jeffrey G. Arnold +4 more
TL;DR: In this article, the authors examined the water productivity of irrigated wheat and maize yields in Karkheh River Basin (KRB) in the semi-arid region of Iran using a coupled modeling approach consisting of the hydrological model (SWAT) and the river basin water allocation model (MODSIM).
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Health Risk Assessment of Fluoride Exposure in Soil, Plants, and Water at Isfahan, Iran
Elham Chavoshi,Majid Afyuni,Mohammad Ali Hajabbasi,A. H. Khoshgoftarmanesh,Karim C. Abbaspour,Hossein Shariatmadari,N Mirghafari +6 more
TL;DR: In this article, 255 topsoil samples (0-20 cm) in an area of 6800 km2 in Isfahan province of central Iran were collected during the spring and summer seasons.
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Hydraulic and transport properties of the plant–soil system estimated by inverse modelling
TL;DR: In this article, the applicability of inverse modeling to a soil-plant system in lysimeter experiments was investigated and the results suggested that inverse modelling could be used to identify important processes.
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Combined analysis of time-varying sensitivity and identifiability indices to diagnose the response of a complex environmental model
TL;DR: It was found that identifiability of a parameter does not necessarily reduce output uncertainty, and it was also found that the information from the main and total effects is required to allow uncertainty reduction in the model output.