The impacts of a Laki-like eruption on the present Swedish society

In this study, we analyse and discuss the possible impacts on the Swedish society of a volcanic eruption on Iceland, emitting ash particles and large quantities of sulphur dioxide. A scenario was developed, based on the historical Laki eruption of 1783–1784, to describe the content of a potential sulphur fog over time in Sweden. Due to its high complexity and the many uncertainties in the underpinning scientific data, the scenario was developed using a cross-disciplinary approach incorporating experts from different scientific fields. An analysis of the impacts of the hazard on human health, environment and technical equipment was then performed and, finally, representatives from national authorities assessed the overall societal challenges in responding to the consequences of a massive volcanic eruption. The analysis shows that it is the peak concentrations of sulphur dioxide and sulphuric acid rather than the longer periods of moderate concentrations that contribute most to the negative consequences for human health and environment. Altogether, three societal challenges were identified: the ability to compile and disseminate relevant information fast enough, to perform continuous measurements of concentrations of different substances in affected areas and to meet the large demand for medical care.     

Simplified SMA-inspired 1-parameter SCS-CN model for runoff estimation

This study proposes a simplified 1-parameter SCS-CN model (M5) based on Mishra-Singh (2002) model and soil moisture accounting (SMA) procedure for surface runoff estimation and compares its performance with the existing SCS-CN method (SCS, 1956) (M1), Michel 1-P model (Water Resour Res 41:1-6, 2005) (M2), Sahu 1-P model (Hydrol Process 21:2872-2881, 2007) (M3), and Ajmal et al. model (J Hydrol 530:623-633, 2015) (M4) using large rainfall–runoff dataset of 48,763 events from123 USDA-ARS watersheds. The performance of models was evaluated using three statistical error indices such as Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), percentage bias (PBIAS), and rank and grading system (RGS). Based on the results obtained, the models can be ranked as follows: M5?>?M4?>?M3?>?M1?>?M2, i.e., model M5 outperformed all the remaining four models M1–M4 and hence is recommended for field applications.