ABSTRACTThe net all-wave radiation of the Great Lakes (GL) is a key to understanding the effects of climate change on the GL. There is a high possibility of underestimating the net all-wave radiation of the GL when using existing methodologies with inputs from near-shore and land-based meteorological data. This study provides the first technique to estimate net all-wave radiation over the GL from July 2001 to December 2014 using a combination of data from satellite remote sensing, reanalysis data sets, and direct measurements. The components of the surface radiation budget estimated from the proposed method showed good statistical agreement. The instantaneous net radiation estimated by our methods was compared with the in situ measurements from June 2008 to April 2012 (Stannard Rock Lighthouse: SR) and September 2009–April 2011 (Spectacle Reef Lighthouse: SP). The comparisons from SR and SP also showed strong statistic agreement (R2?=?0.74 and 0.7; RMSE?=?9.26 and 10.60?W?m?2 respectively). Monthly spatial variations of net shortwave radiation varied with cloud cover and surface albedo while net longwave radiation varied with the temperature difference between the water surface and the atmosphere. 相似文献
Abstract A glacier submodel was successfully integrated into the distributed hydrological model WaSiM-ETH to simulate the discharge of a heavily glaciated drainage basin. The glacier submodel comprises a distributed temperature index model including solar radiation to simulate the melt rate of glaciated areas. Meltwater and rainfall are transformed into glacier discharge by using a linear reservoir approach. The model was tested on a high-alpine sub-basin of the Rhone basin (central Switzerland) of which 48% is glaciated. Continuous discharge simulations were performed for the period 1990–1996 and compared with hourly discharge observations. The pronounced daily and annual fluctuations in discharge were simulated well. The obtained efficiency criterion, R2, exceeds 0.89 for all years. The good performance of the glacier submodel is also demonstrated by integrating it into the hydrological model PREVAH. 相似文献
Abstract The catchment-scale groundwater vulnerability assessment that delineates zones representing different levels of groundwater susceptibility to contaminants from diffuse agricultural sources has become an important element in groundwater pollution prevention for the implementation of the EU Water Framework Directive (WFD). This paper evaluates the DRASTIC method using an ArcGIS platform for assessing groundwater vulnerability in the Upper Bann catchment, Northern Ireland. Groundwater vulnerability maps of both general pollutants and pesticides in the study area were generated by using data on the factors depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity, as defined in DRASTIC. The mountain areas in the study area have “high” (in 4.5% of the study area) or “moderate” (in 25.5%) vulnerability for general pollutants due to high rainfall, net recharge and soil permeability. However, by considering the diffuse agricultural sources, the mountain areas are actually at low groundwater pollution risk. The results of overlaying the maps of land use and the groundwater vulnerability are closer to the reality. This study shows that the DRASTIC method is helpful for guiding the prevention practices of groundwater pollution at the catchment scale in the UK. Citation Yang, Y. S. & Wang, L. (2010Yang, Y. S. and Wang, L.2010. A review of modelling tools for implementation of the EU Water Framework Directive in handling diffuse water pollution. Water Resour. Manage., 24: 1819–1843. [Google Scholar]) Catchment scale vulnerability assessment of groundwater pollution from diffuse sources using the DRASTIC method: a case study. Hydrol. Sci. J.55(7), 1206–1216. 相似文献
ABSTRACTConsideration of solar geoengineering as a potential response to climate change will demand complex decisions. These include not only the choice of whether to deploy solar engineering, but decisions regarding how to deploy, and ongoing decision-making throughout deployment. Research on the governance of solar geoengineering to date has primarily engaged only with the question of whether to deploy. We examine the science of solar geoengineering in order to clarify the technical dimensions of decisions about deployment – both strategic and operational – and how these might influence governance considerations, while consciously refraining from making specific recommendations. The focus here is on a hypothetical deployment rather than governance of the research itself. We first consider the complexity surrounding the design of a deployment scheme, in particular the complicated and difficult decision of what its objective(s) would be, given that different choices for how to deploy will lead to different climate outcomes. Next, we discuss the on-going decisions across multiple timescales, from the sub-annual to the multi-decadal. For example, feedback approaches might effectively manage some uncertainties, but would require frequent adjustments to the solar geoengineering deployment in response to observations. Other decisions would be tied to the inherently slow process of detection and attribution of climate effects in the presence of natural variability. Both of these present challenges to decision-making. These considerations point toward particular governance requirements, including an important role for technical experts – with all the challenges that entails.Key policy insights
Decisions about solar geoengineering deployment will be informed not only by political choices, but also by climate science and engineering.
Design decisions will pertain to the spatial and temporal goals of a climate intervention and strategies for achieving those goals.
Some uncertainty can be managed through feedback, but this would require frequent operational decisions.
Some strategic decisions will depend on the detection and attribution of climatic effects from solar geoengineering, which may take decades.
Governance for solar geoengineering deployment will likely need to incorporate technical expertise for making short-term adjustments to the deployment and conducting attribution analysis, while also slowing down decisions made in response to attribution analysis to avoid hasty choices.