Out with the old and in with the new? Investigating competition between Barred Owls (Strix varia) and Northern Spotted Owls (Strix occidentalis caurina) in northwestern California with a playback experiment

Department Head: Kenneth Ray Wilson.Includes bibliographical references (pages 125-131).The Northern Spotted Owl (Strix occidentalis caurina) is a controversial species in the Pacific Northwest that is listed as threatened under the Endangered Species Act. The Barred Owl (Strix varia), a species historically restricted to eastern North America, has recently expanded its range to completely overlap that of the Northern Spotted Owl. Recent evidence suggests that Barred Owls may displace Northern Spotted Owls from their territories. The focus of my study was to determine whether Barred Owls have the potential to competitively exclude Northern Spotted Owls from their territories. I used a playback experiment to observe and quantify aggressive vocal and physical behavior of Barred and Northern Spotted Owls during territorial defense. Trials consisted of displaying a Northern Spotted or Barred Owl taxidermy mount, and broadcasting recorded vocalizations of the corresponding species, in both Barred and Northern Spotted Owl territories. The frequency and intensity of residents' responses to playbacks were digitally recorded as was the acceleration experienced by the mount's head during physical attacks by the residents. When agonistic interspecific interactions occurred in this study I found that Barred Owls responded with higher levels of vocal and physical aggression than Northern Spotted Owls. However, the frequency of interspecific interactions was lower compared to intraspecific interactions among Northern Spotted Owls alone. This study suggests that Barred Owls are likely to assume the dominant role during interspecific interactions with Northern Spotted Owls and indicates that competitive exclusion is a plausible mechanism by which Barred Owls could contribute to the observed population declines of Northern Spotted Owls in areas of co-occurrence.2009 Mewaldt-King Student Research Award by the Cooper Ornithological Society

Identification of changes in hydrological drought characteristics from a multi-GCM driven ensemble constrained by observed discharge

Peer reviewedPublisher PD

Validation of a strand-level CICC-joint coupling loss model

Calculating the coupling losses in cable-in-conduit conductor (CICC) joints requires a large amount of numerical effort, which is why the numerical system is often reduced by grouping strands together. However, to better understand the loss behaviour, and eventually the stability mechanism in such joints, a full-sized model working on the level of individual strands is more desirable. For this reason, the numerical cable model JackPot-AC has been expanded to also simulate the coupling losses in a CICC joint. This model has been verified with AC loss measurements on a mock-up joint, which was subjected to an applied harmonic field at different angles. The mock-up joint consisted of two sub-sized CICCs connected by a copper sole. For additional verification the AC loss of one of these conductors and the copper sole was also measured separately. The results of the simulation agree with the measurements, and the model therefore proves to be a useful analytical tool for examining the coupling loss in CICC joint

Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

Hydrological drought is increasingly studied using large-scale models. It is, however, not sure whether large-scale models reproduce the development of hydrological drought correctly. The pressing question is how well do large-scale models simulate the propagation from meteorological to hydrological drought? To answer this question, we evaluated the simulation of drought propagation in an ensemble mean of ten large-scale models, both land-surface models and global hydrological models, that participated in the model intercomparison project of WATCH (WaterMIP). For a selection of case study areas, we studied drought characteristics (number of droughts, duration, severity), drought propagation features (pooling, attenuation, lag, lengthening), and hydrological drought typology (<i>classical rainfall deficit drought, rain-to-snow-season drought, wet-to-dry-season drought, cold snow season drought, warm snow season drought, composite drought</i>). <br><br> Drought characteristics simulated by large-scale models clearly reflected drought propagation; i.e. drought events became fewer and longer when moving through the hydrological cycle. However, more differentiation was expected between fast and slowly responding systems, with slowly responding systems having fewer and longer droughts in runoff than fast responding systems. This was not found using large-scale models. Drought propagation features were poorly reproduced by the large-scale models, because runoff reacted immediately to precipitation, in all case study areas. This fast reaction to precipitation, even in cold climates in winter and in semi-arid climates in summer, also greatly influenced the hydrological drought typology as identified by the large-scale models. In general, the large-scale models had the correct representation of drought types, but the percentages of occurrence had some important mismatches, e.g. an overestimation of <i>classical rainfall deficit droughts</i>, and an underestimation of <i>wet-to-dry-season droughts</i> and snow-related droughts. Furthermore, almost no <i>composite droughts</i> were simulated for slowly responding areas, while many multi-year drought events were expected in these systems. <br><br> We conclude that most drought propagation processes are reasonably well reproduced by the ensemble mean of large-scale models in contrasting catchments in Europe. Challenges, however, remain in catchments with cold and semi-arid climates and catchments with large storage in aquifers or lakes. This leads to a high uncertainty in hydrological drought simulation at large scales. Improvement of drought simulation in large-scale models should focus on a better representation of hydrological processes that are important for drought development, such as evapotranspiration, snow accumulation and melt, and especially storage. Besides the more explicit inclusion of storage in large-scale models, also parametrisation of storage processes requires attention, for example through a global-scale dataset on aquifer characteristics, improved large-scale datasets on other land characteristics (e.g. soils, land cover), and calibration/evaluation of the models against observations of storage (e.g. in snow, groundwater)

A generic method for hydrological drought identification across different climate regions

Peer reviewedPublisher PD

Route persistence. Modelling and quantifying historical route-network stability from the Roman period to early-modern times (AD 100–1600):a case study from the Netherlands

Research on route-network stability is rare. In time, due to cultural and/or natural causes, settlement locations and route orientation shift. The nature of these spatial changes sheds light on the complex interaction between settlements and surrounding natural landscape conditions. This study investigates the stability of route networks in the Netherlands during the past two millennia by determining their persistence through time. Environmental, archaeological and historical data are used to reconstruct and compare route networks. By using network friction, archaeological data on settlement patterns and route networks in combination with historical data (e.g. old maps), we were able to model route-network persistence (not necessarily continuity) from the Roman to early medieval periods (AD 100–800) and from the Early Middle Ages to the Early Modern Times (AD 800–1600). Results show that around 67.6% of the modelled early-mediëval routes in the Netherlands are persistent with routes in the Roman period. Covering a much larger surface area of the Netherlands, 24.5% of the early-modern routes show a clear persistence with their early-medieval counterparts. Besides the differences in surface area, this downfall can largely be explained by cultural dynamics, with 71.4% of the earlymodern route network following modelled movement corridors. already in existence during the Early Middle Ages

The future for global water assessment

The global water cycle is a fundamental component of our climate and Earth system. Many, if not the majority, of the impacts of climate change are water related. We have an imperfect description and understanding of components of the water cycle. This arises from an incomplete observation of some of the stores and fluxes in the water cycle (in particular: precipitation, evaporation, soil moisture and groundwater), problems with the simulation of precipitation by global climate models and the wide diversity of global hydrological models currently in use. This paper discusses these sources of errors and, in particular, explores the errors and advantages of bias correcting climate model outputs for hydrological models using a single large catchment as an example (the Rhine). One conclusion from this analysis is that bias correction is necessary and has an impact on the mean flows and their seasonal cycle. However choice of hydrological model has an equal, if not larger effect on the quality of the simulation. The paper highlights the importance of improving hydrological models, which run at a continental and global scale, and the importance of quantifying uncertainties in impact studies

Drought at the global scale in the 2nd part of the 20th century (1963-2001)

The large impacts of drought on society, economy and environment urge for a thorough investigation. A good knowledge of past drought events is important for both understanding of the processes causing drought, as well as to provide reliability assessments for drought projections for the future. Preferably, the investigation of historic drought events should rely on observations. Unfortunately, for a global scale these detailed observations are often not available. Therefore, the outcome of global hydrological models (GHMs) and off-line land surface models (LSMs) is used to assess droughts. In this study we have investigated to what extent simulated gridded time series from these large-scale models capture historic hydrological drought events. Results of ten different models, both GHMs and LSMs, made available by the WATCH project, were compared. All models are run on a global 0.5 degree grid for the period 1963-2000 with the same meteorological forcing data (WATCH forcing data). To identify hydrological drought events, the monthly aggregated total runoff values were used. Different methods were developed to identify spatio-temporal drought characteristics. General drought characteristics for each grid cell, as for example the average drought duration, were compared. These characteristics show that when comparing absolute values the models give substantially different results, whereas relative values lead to more or less the same drought pattern. Next to the general drought characteristics, some documented major historical drought events (one for each continent) were selected and described in more detail. For each drought event, the simulated drought clusters (spatial events) and their characteristics are given for one month during the event. It can be concluded that most major drought events are captured by all models. However, the spatial extent of the drought events differ substantially between the models. In general the models show a fast reaction to rainfall and therefore also capture drought events caused by large rainfall anomalies. More research is still needed, since here we only looked at a few selected number of documented drought events spread over the globe. To assess more in detail if these large-scale models are able to capture drought, additional quantitative analyses are needed together with a more elaborated comparison against observed drought events

Skill of large-scale seasonal drought impact forecasts

Forecasting of drought impacts is still lacking in drought early-warning systems (DEWSs), which presently do not go beyond hazard forecasting. Therefore, we developed drought impact functions using machine learning approaches (logistic regression and random forest) to predict drought impacts with lead times up to 7 months ahead. The observed and forecasted hydrometeorological drought hazards – such as the standardized precipitation index (SPI), standardized precipitation evaporation index (SPEI), and standardized runoff index (SRI) – were obtained from the The EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) DEWS. Reported drought impact data, taken from the European Drought Impact Report Inventory (EDII), were used to develop and validate drought impact functions. The skill of the drought impact functions in forecasting drought impacts was evaluated using the Brier skill score and relative operating characteristic metrics for five cases representing different spatial aggregation and lumping of impacted sectors. Results show that hydrological drought hazard represented by SRI has higher skill than meteorological drought represented by SPI and SPEI. For German regions, impact functions developed using random forests indicate a higher discriminative ability to forecast drought impacts than logistic regression. Moreover, skill is higher for cases with higher spatial resolution and less lumped impacted sectors (cases 4 and 5), with considerable skill up to 3–4 months ahead. The forecasting skill of drought impacts using machine learning greatly depends on the availability of impact data. This study demonstrates that the drought impact functions could not be developed for certain regions and impacted sectors, owing to the lack of reported impacts