Contrasting trends in floods for two sub-arctic catchments in northern Sweden – does glacier presence matter?

Our understanding is limited to how transient changes in glacier response to climate warming will influence the catchment hydrology in the Arctic and Sub-Arctic. This understanding is particularly incomplete for flooding extremes because understanding the frequency of such unusual events requires long records of observation not often available for the Arctic and Sub-Arctic. This study presents a statistical analysis of trends in the magnitude and timing of flood extremes and the mean summer discharge in two sub-arctic catchments, Tarfala and Abisko, in northern Sweden. The catchments have different glacier covers (30% and 1%, respectively). Statistically significant trends (at the 5% level) were identified for both catchments on an annual and on a seasonal scale (3-months averages) using the Mann-Kendall trend test. Stationarity of flood records was tested by analyzing trends in the flood quantiles, using generalized least squares regression. Hydrologic trends were related to observed changes in the precipitation and air temperature, and were correlated with 3-months averaged climate pattern indices (e.g. North Atlantic oscillation). Both catchments showed a statistically significant increase in the annual mean air temperature over the comparison time period of 1985–2009 (Tarfala and Abisko <i>p</i><0.01), but did not show significant trends in the total precipitation (Tarfala <i>p</i> = 0.91, Abisko <i>p</i> = 0.44). Despite the similar climate evolution over the studied period in the two catchments, data showed contrasting trends in the magnitude and timing of flood peaks and the mean summer discharge. Hydrologic trends indicated an amplification of the streamflow and flood response in the highly glacierized catchment and a dampening of the response in the non-glacierized catchment. The glacierized mountain catchment showed a statistically significant increasing trend in the flood magnitudes (<i>p</i> = 0.04) that is clearly correlated to the occurrence of extreme precipitation events. It also showed a significant increase in mean summer discharge (<i>p</i> = 0.0002), which is significantly correlated to the decrease in glacier mass balance and the increase in air temperature (<i>p</i> = 0.08). Conversely, the non-glacierized catchment showed a significant decrease in the mean summer discharge (<i>p</i> = 0.01), the flood magnitudes (<i>p</i> = 0.07) and an insignificant trend towards earlier flood occurrences (<i>p</i> = 0.53). These trends are explained by a reduction of the winter snow pack due to higher temperatures in the winter and spring and an increasing soil water storage capacity or catchment storage due to progressively thawing permafrost

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank

DESIGNING PORT INFRASTRUCTURE FOR SEA LEVEL CHANGE: A SURVEY OF U.S. ENGINEERS

Seaports are particularly vulnerable to the impacts of climate change due to their coastal location. With the potential threat of up to 2.5m in sea level rise by 2100, resilient port infrastructure is vital for the continued operation of ports. There are strong economic and social incentives for seaports to provide long-term resilience against climate conditions. For example, service disruptions can cost billions of dollars and impact the livelihoods of those who depend on the port. Engineers play a pivotal role in improving the resilience of ports, as they are responsible for designing port infrastructure that will be adequately prepared for future sea level change (SLC). However, incorporating SLC is a challenging task due to the uncertainty of SLC projections, the long service lives of port infrastructure, and the differing guidelines and recommendations for managing SLC. Through an online survey of 85 U.S. port and marine infrastructure engineers, this research explores the engineering community’s attitude and approach to planning for SLC for large-scale maritime infrastructure projects. Survey findings highlight the extent that projects incorporate SLC, the wide range of factors that drive the inclusion of SLC, and the numerous barriers that prevent engineers from incorporating SLC into design. This research emphasizes that traditional engineering practices may no longer be appropriate for dealing with climate change design variables and their associated uncertainties. Furthermore, results call for collaboration among engineers, port authorities, and policy makers to develop design standards and practical design methods for designing resilient port infrastructure

The danger of mapping risk from multiple natural hazards

In recent decades, society has been greatly affected by natural disasters (e.g. floods, droughts, earthquakes), losses and effects caused by these disasters have been increasing. Conventionally, risk assessment focuses on individual hazards, but the importance of addressing multiple hazards is now recognised. Two approaches exist to assess risk from multiple-hazards; the risk index (addressing hazards, and the exposure and vulnerability of people or property at risk) and the mathematical statistics method (which integrates observations of past losses attributed to each hazard type). These approaches have not previously been compared. Our application of both to China clearly illustrates their inconsistency. For example, from 31 Chinese provinces assessed for multi-hazard risk, Gansu and Sichuan provinces are at low risk of life loss with the risk index approach, but high risk using the mathematical statistics approach. Similarly, Tibet is identified as being at almost the highest risk of economic loss using the risk index, but lowest risk under the mathematical statistics approach. Such inconsistency should be recognised if risk is to be managed effectively, whilst the practice of multi-hazard risk assessment needs to incorporate the relative advantages of both approaches

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data