对冲效应弱化了土地利用变化对干流水文情势的影响——以中国金沙江流域为例

Global extreme hydrological events pose considerable challenges to the sustainable development of human society and river ecology.Land use/cover change(LUCC)is a visible manifestation of human activity and has caused substantial alterations in extreme hydrological regimes across rivers worldwide.The Jinsha River lies upstream of the Yangtze River and its hydrological variability has had profound socioeconomic and environmental effects.In this study,we developed Hydrological Simulation Program–FORTRAN(HSPF)and land-use simulation models of the entire watershed to simulate the effects of LUCC on hydrological extremes and quantify the inter-relationships among them.The main land-use changes between 1995 and 2015 were those associated with cropland,forest land,and grassland.Between 2015 and 2030,it is estimated that the coverage of forest land,grassland,construction land,and unused land will increase by 0.64%,0.18%,69.38%,and 45.08%,respectively,whereas that of cropland,water bodies,and snow-and ice-covered areas will decline by 8.02%,2.63%,and 0.89%,respectively.LUCC has had irregular effects on different hydrological regimes and has most severely altered stream flows.The responses of hydrological extremes to historical land-use change were characterized by spatial variation.Extreme low flows increased by 0.54%–0.59%whereas extreme high flows increased by 0%–0.08%at the lowest outlet.Responses to future land-use change will be amplified by a 0.72%–0.90%reduction in extreme low flows and a 0.08%–0.12%increase in extreme high flows.The hedging effect caused by irregular changes in tributary stream flow was found to alleviate the observed flow in mainstream rivers caused by land-use change.The extreme hydrological regimes were affected mainly by the net swap area transferred from ice and snow area to forest(NSAIF)and thereafter to cultivated land(NSAIC).Extreme low flows were found to be positively correlated with NSAIF and NSAIC,whereas extreme high flows were positively correlated with NSAIC and negatively correlated with NSAIF.

The influence of anthropogenic climate change on wet and dry summers in Europe

Understanding the role of anthropogenic forcings in regional hydrological changes can help communities plan their adaptation in an informed manner.Here we apply attribution research methods to investigate the effect of human influence on historical trends in wet and dry summers and changes in the likelihood of extreme events in Europe.We employ an ensemble of new climate models and compare experiments with and without the effect of human influence to assess the anthropogenic contribution.Future changes are also analysed with projections to year 2100.We employ two drought indices defined relative to the pre-industrial climate:one driven by changes in rainfall only and one that also includes the effect of temperature via changes in potential evapotranspiration.Both indices suggest significant changes in European summers have already emerged above variability and are expected to intensify in the future,leading to widespread dryer conditions which are more extreme in the south.When only the effect of rainfall is considered,there is a distinct contrast between a shift towards wetter conditions in the north and dryer in the south of the continent,as well as an overall increase in variability.However,when the effect of warming is also included,it largely masks the wet trends in the north,resulting in increasingly drier summers across most of the continent.Historical index trends are already detected in the observations,while models suggest that what were extremely dry conditions in the pre-industrial climate will become normal in the south by the end of the century.

Application of a Full Hierarchical Bayesian Model in Assessing Streamflow Response to a Climate Change Scenario at the Coweeta Basin, NC, USA

We have applied a full hierarchical Baysian （HB） model to simulate streamffow at the Coweeta Basin that drains western North Carolina, USA under a doubled CO2 climate scenario. The full HB model coherently assimilated multiple data sources and accounted for uncertainties from data, parameters and model structures. Full predictive distributions for streamflow from the Bayesian analysis indicate not only increasing drought, with substantial decrease in fall and summer flows, and soil moisture content, but also increase in the frequency of flood events when they were fit with Generalized Extreme Value （GEV） distribution and Generalized Pareto Distribution （GPD） under this doubled CO2 climate scenario compared to the current climate scenario. Full predictive distributions based on the hierarchical Bayesian model, compared to deterministic point estimates, is capable of providing richer information to facilitate development of adaptation strategy to changing climate for a sustainable water resource management.