In this study, we developed a modeling framework using a machine learning approach to predict hydrologic ratios for watersheds located in contiguous United States (CONUS) by utilizing a set of climate, soil, vegetation, and topographic variables.
Sensitivity tests of the Iberá basin hydrology were performed by varying regional temperature and rainfall conditions according to the outputs of a set of climate models for three different time slices during the 21st century.
To address this issue, an ensemble modelling approach – which considers a set of climate models – is applied in this study with a monthly, conceptual hydrological model for assessing future runoff conditions in the upper Danube basin (101,810 km2).
Finally, simulation results for a set of climate change scenarios suggested a 17% decrease in precipitation, 8% decrease in ET and 22% decrease in flow in 2030 2050 compared to 2010 2029.
The study quantifies the effect of high ambient temperatures on electricity generation, the capacity of substations and transmission lines, and the demand for peak power for a set of climate scenarios.
Spatially distributed information on ventilation, air quality and thermal situation in the study area was generated using the CAMPUS framework, which is a set of climate analysis and evaluation tools suitable for planning purposes.
The Climate Fiscal Framework recommends a set of climate codes for Bangladesh to track climate change expenditures for policy analysis and reporting, and estimate long term climate finance needs of Bangladesh through identifying potential climate related public expenditures across Government Ministries.
If a niche model can predict where a species occurs at present, we then take a set of climate variables from the past and use the model to predict where the species could have occurred at that time (Peterson 2001, Peterson et al. 2012, Waltari et al. 2007).
The Plan contained a set of climate and energy-related targets aimed at reducing its dependence on fossil fuels (like coal) while building a healthy alternative fuels industry.
Using a set of climate, agricultural [17], water supply and use [18], [19], and LULC change models [20], [21], the MA team translated these expected regional change and demand trajectories into global grid cell-level LULC maps for the years 2050 and 2100 [22].
