A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Risk Assessment of Climate Change Impacts on Runoff in Urmia Lake Basin, Iran
AbstractUrmia Lake, the largest lake in Iran, is an important water body and habitat for a variety of different species. Data of ten climate models among the atmosphere ocean general circulation models (AOGCMs) were assembled for the 2021–2050 period under A2 and B1 emission scenarios. First, both the 30-year historic (1961–1990) and 30-year future monthly temperature and precipitation in the region were generated and weighted by the beta function (β) that assigns weights to AOGCMs based on the model evaluations. Then, the cumulative density function (CDF) in each month was determined. Precipitation and temperature values at 25, 50, and 75% probability were extracted from the CDFs. Subsequently these values were introduced to the Long Ashton Research Station Weather Generator model (LARS-WG) to downscale and produce time series of temperature and precipitation in the future, subject to the uncertainty of climate models. Then, these monthly time series at different risk levels were introduced into the artificial neural network (ANN) model and future monthly runoff time series were generated. The findings indicated a decrease in the flow into the lake of the order of 21, 13, and 0.3% under the A2 scenario and an increase of 4.7, 13.8, and 18.9% under the B2 scenario with respect to the observation period at 25, 50, and 75% risk levels, respectively. The results of this study highlight the necessity to immediately and effectively cut back on the water use in the basin not only to remedy the projected climate change effects in the future, but also to ensure gradual reclamation of the ongoing environmental crisis.
Risk Assessment of Climate Change Impacts on Runoff in Urmia Lake Basin, Iran
AbstractUrmia Lake, the largest lake in Iran, is an important water body and habitat for a variety of different species. Data of ten climate models among the atmosphere ocean general circulation models (AOGCMs) were assembled for the 2021–2050 period under A2 and B1 emission scenarios. First, both the 30-year historic (1961–1990) and 30-year future monthly temperature and precipitation in the region were generated and weighted by the beta function (β) that assigns weights to AOGCMs based on the model evaluations. Then, the cumulative density function (CDF) in each month was determined. Precipitation and temperature values at 25, 50, and 75% probability were extracted from the CDFs. Subsequently these values were introduced to the Long Ashton Research Station Weather Generator model (LARS-WG) to downscale and produce time series of temperature and precipitation in the future, subject to the uncertainty of climate models. Then, these monthly time series at different risk levels were introduced into the artificial neural network (ANN) model and future monthly runoff time series were generated. The findings indicated a decrease in the flow into the lake of the order of 21, 13, and 0.3% under the A2 scenario and an increase of 4.7, 13.8, and 18.9% under the B2 scenario with respect to the observation period at 25, 50, and 75% risk levels, respectively. The results of this study highlight the necessity to immediately and effectively cut back on the water use in the basin not only to remedy the projected climate change effects in the future, but also to ensure gradual reclamation of the ongoing environmental crisis.
Risk Assessment of Climate Change Impacts on Runoff in Urmia Lake Basin, Iran
2016
Article (Journal)
English
Risk Assessment of Climate Change Impacts on Runoff in Urmia Lake Basin, Iran
| Online Contents | 2016