A robust method to develop future rainfall IDF curves under climate change condition in two major basins of Iran

2021 ◽  
Author(s):  
Mohammad Reza Khazaei
Keyword(s):  
Climate Change ◽  
Robust Method ◽  
Change Condition ◽  
Idf Curves

scholarly journals Effect of grazing period management on growth performances of camel in climate change condition

2019 ◽  
Author(s):  
Champak Bhakat
Keyword(s):  
Climate Change ◽  
Body Weight ◽  
Weight Gain ◽  
Body Weight Gain ◽  
The Body ◽  
Maximum Temperature ◽  
Change Condition ◽  
Average Maximum ◽  
Group 2 ◽  
Group 1

In order to decide the optimum time of grazing for camels during hot summer months, 10 growing camel calveswere divided into 2 equal groups. First group was sent for grazing during 10:00 h to 16:00 h daily and second groupallowed for grazing during thermo neutral period. The climatic variables were recorded daily (April 2012 to March2013). The average daily gain and total body weight gain in calves sent for grazing during relatively cool parts ofday (group 2) was significantly higher as compared to group 1 calves sent as per routine farm schedule. Theaverage intake of fodder and water from manger was higher in group 1 calves. The average DMI from manger forgroup 1 calves was higher as compared to group 2 calves. The comparative biometrics of camel calves in differentgrazing management practices revealed that body length, heart girth, height at wither, neck length were significantly(P<0.01) higher in group 2 calves as compared to group 1 calves. After 180 days of experimentation, humpcircumference vertical and hind leg length were significantly (P<0.05) increased in group 2 as compared to group1. Analysis of recorded data of climatic parameters revealed that average maximum temperature was higher duringJune 2012. The values of THI also were higher in monsoon and post monsoon months hence the practice of sendingcamel calves during relatively comfortable part of hot and hot humid months was successful in getting good growth.The relative humidity was significantly higher during morning as compared to evening period for all months. TheTHI was significantly lower during morning as compared to evening hours for all months in different climate forwhole year. Economic analysis reveals that the cost of feed per kg body weight gain was quite less in group 2 ascompared to group 1. So the practice of grazing of camel calves during cool hours of day remain profitable forfarmers by looking at the body weight gain and better body conformation in climate change condition.

scholarly journals Projected Changes of Precipitation IDF Curves for Short Duration under Climate Change in Central Vietnam

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 33 ◽  
Author(s):  
Nguyen Tien Thanh ◽  
Luca Dutto Aldo Remo
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Southern Oscillation ◽  
Regional Climate ◽  
Extreme Weather Events ◽  
Climate Data ◽  
Rcp Scenarios ◽  
Central Vietnam ◽  
Idf Curves ◽  
Projected Changes

In future years, extreme weather events are expected to frequently increase due to climate change, especially in the combination of climate change and events of El Niño–Southern Oscillation. This pays special attention to the construction of intensity–duration–frequency (IDF) curves at a tempo-spatial scale of sub-daily and sub-grid under a context of climate change. The reason for this is that IDF curves represent essential means to study effects on the performance of drainage systems, damps, dikes and reservoirs. Therefore, the objective of this study is to present an approach to construct future IDF curves with high temporo-spatial resolutions under climate change in central Vietnam, using the case of VuGia-ThuBon. The climate data of historical and future from a regional climate model RegCM4 forced by three global models MPI-ESM-MR, IPSL-CM5A-LR and ICHEC-EC-EARTH are used to re-grid the resolution of 10 km × 10 km grid spacing from 25 km × 25 km on the base of bilinear interpolation. A bias correction method is then applied to the finest resolution of a hydrostatic climate model for an ensemble of simulations. Furthermore, the IDF curves for short durations of precipitation are constructed for the historical climate and future climates under two representative concentration pathway (RCP) scenarios, RCP4.5 and RCP8.5, based on terms of correlation factors. The major findings show that the projected precipitation changes are expected to significantly increase by about 10 to 30% under the scenarios of RCP4.5 and RCP8.5. The projected changes of a maximum of 1-, 2-, and 3-days precipitation are expected to increase by about 30–300 mm/day. More importantly, for all return periods (i.e., 10, 20, 50, 100, and 200 years), IDF curves completely constructed for short durations of precipitation at sub-daily show an increase in intensities for the RCP4.5 and RCP8.5 scenarios.

scholarly journals Uncertainties in Riverine and Coastal Flood Impacts under Climate Change

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1774
Author(s):  
Shuyi Wang ◽  
Mohammad Reza Najafi ◽  
Alex J. Cannon ◽  
Amir Ali Khan
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Block Design ◽  
Coastal Flooding ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Storm Events ◽  
Idf Curves

Climate change can affect different drivers of flooding in low-lying coastal areas of the world, challenging the design and planning of communities and infrastructure. The concurrent occurrence of multiple flood drivers such as high river flows and extreme sea levels can aggravate such impacts and result in catastrophic damages. In this study, the individual and compound effects of riverine and coastal flooding are investigated at Stephenville Crossing located in the coastal-estuarine region of Newfoundland and Labrador (NL), Canada. The impacts of climate change on flood extents and depths and the uncertainties associated with temporal patterns of storms, intensity–duration–frequency (IDF) projections, spatial resolution, and emission scenarios are assessed. A hydrologic model and a 2D hydraulic model are set up and calibrated to simulate the flood inundation for the historical (1976–2005) as well as the near future (2041–2070) and far future (2071–2100) periods under Representative Concentration Pathways (RCPs) 4.5 and 8.5. Future storm events are generated based on projected IDF curves from convection-permitting Weather Research and Forecasting (WRF) climate model simulations, using SCS, Huff, and alternating block design storm methods. The results are compared with simulations based on projected IDF curves derived from statistically downscaled Global Climate Models (GCMs). Both drivers of flooding are projected to intensify in the future, resulting in higher risks of flooding in the study area. Compound riverine and coastal flooding results in more severe inundation, affecting the communities on the coastline and the estuary area. Results show that the uncertainties associated with storm hyetographs are considerable, which indicate the importance of accurate representation of storm patterns. Further, simulations based on projected WRF-IDF curves show higher risks of flooding compared to the ones associated with GCM-IDFs.

scholarly journals Agro Climatic Feasibility Rosa damascena mill Cultivation in Climate Change Condition in northern part of Isfahan Province Zahra sadat Jalali Chiomeh

2021 ◽  
Vol 21 (62) ◽  
pp. 281-298
Author(s):  
zahra sadat Jalali Chimeh ◽  
Amir Gandomkar ◽  
Morteza Khodagholi ◽  
Hossein Battoli ◽  
◽  
...  
Keyword(s):  
Climate Change ◽  
Rosa Damascena ◽  
Change Condition ◽  
Isfahan Province

scholarly journals Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate

Geosciences ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 209
Author(s):  
Huiling Hu ◽  
Bilal M. Ayyub
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Extreme Precipitation ◽  
Circulation Model ◽  
Machine Learning Algorithms ◽  
Precipitation Intensity ◽  
Potential Threat ◽  
Drainage Systems ◽  
Idf Curves ◽  
Intensity Duration Frequency

Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed.

scholarly journals Smart city planning under the climate change condition

2017 ◽  
Vol 81 ◽  
pp. 012091
Author(s):  
Dexiang Deng ◽  
Yue Zhao ◽  
Xi Zhou
Keyword(s):  
Climate Change ◽  
Smart City ◽  
City Planning ◽  
Change Condition

scholarly journals Predicting changes in monthly streamflow to Karaj dam reservoir, Iran, in climate change condition and assessing its uncertainty

2019 ◽  
Vol 10 (4) ◽  
pp. 669-679 ◽  
Author(s):  
Vahid Kimiagar Keteklahijani ◽  
Saeed Alimohammadi ◽  
Ebrahim Fattahi
Keyword(s):  
Climate Change ◽  
Dam Reservoir ◽  
Change Condition ◽  
Monthly Streamflow
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