Climate Impact Assessment as an Emerging Obligation Under Customary International Law

2018 ◽  
Author(s):  
Benoit Mayer
2018 ◽  
Vol 24 (1) ◽  
pp. 160-176 ◽  
Author(s):  
Diego Peñaloza ◽  
Frida Røyne ◽  
Gustav Sandin ◽  
Magdalena Svanström ◽  
Martin Erlandsson

2020 ◽  
Vol 163 (3) ◽  
pp. 1353-1377 ◽  
Author(s):  
Valentina Krysanova ◽  
Jamal Zaherpour ◽  
Iulii Didovets ◽  
Simon N. Gosling ◽  
Dieter Gerten ◽  
...  

AbstractImportance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections.


2014 ◽  
Vol 18 (1) ◽  
pp. 67-84 ◽  
Author(s):  
A. A. Oubeidillah ◽  
S.-C. Kao ◽  
M. Ashfaq ◽  
B. S. Naz ◽  
G. Tootle

Abstract. To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation- and data-intensive effort was conducted to organize a comprehensive hydrologic data set with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation – including meteorologic forcings, soil, land class, vegetation, and elevation – were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous US at refined 1/24° (~4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter data set was prepared for the macro-scale variable infiltration capacity (VIC) hydrologic model. The VIC simulation was driven by Daymet daily meteorological forcing and was calibrated against US Geological Survey (USGS) WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter data set may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous US. We anticipate that through this hydrologic parameter data set, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter data set will be provided to interested parties to support further hydro-climate impact assessment.


1987 ◽  
Vol 18 (1) ◽  
pp. 127
Author(s):  
Sherry D. Oaks ◽  
Robert W. Kates ◽  
Jesse H. Ausubel ◽  
Mimi Berberian

2020 ◽  
Vol 20 (0) ◽  
pp. 1-8
Author(s):  
Motoki NISHIMORI ◽  
Yasushi ISHIGOOKA ◽  
Hitomi WAKATSUKI ◽  
Tsuneo KUWAGATA ◽  
Toshihiro HASEGAWA ◽  
...  

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