Instream wood increases riverbed temperature variability in a lowland sandy stream

The (re)introduction of wood into rivers is becoming increasingly popular in river restoration and natural flood management schemes. While instream wood is known to promote geomorphic and hydraulic diversity, the impact of wood in driving surface water‐streambed exchange and subsequent streambed temperatures remains under‐researched, particularly in lowland rivers. We make use of the occurrence of three naturally occurring wood structures in a small, lowland sandy stream to determine how the presence of wood alters the geomorphic, hydraulic and thermal properties of the streambed. Our results show that instream wood plays an important role in promoting localized geomorphic complexity and thermal variation in the streambed. Locations within and immediately downstream of wood structures displayed the highest temperature range and daily variation. Locations upstream of wood structures were characterized by weaker daily temperature variation, while areas without wood displayed relatively stable streambed temperatures, with little diurnal fluctuation. Our study indicates that at this lowland site, instream wood increased seasonal temperature extremes (increased summer and decreased winter temperatures) at shallow depths by enhancing infiltration of warmer (summer) and colder (winter) surface water. This reduction in thermal buffering is likely to have significant implications to streambed‐dwelling communities and highlights that the thermal impacts of wood reintroduction in lowland rivers should be considered prior to river restoration

Repeated high flows drive morphological change in rivers in recently deglaciated catchments

Climate change is decreasing glacier cover and increasing the frequency and magnitude of precipitation-driven high flows and floods in many regions of the world. Precipitation may become the dominant water source for river systems in recently deglaciated catchments, with major rainfall events driving significant changes in river channel morphology. Few studies, however, have examined river channel response to repeated precipitation-driven high flows. In this study, we measured the geomorphological condition of four low-order rivers in recently deglaciated catchments (70–210 years ice free) before and after a series of repeated precipitation-driven high flows during summer 2014. High flows drove substantial initial morphological change, with up to 75% change in baseflow channel planform position and active channel form change from pre- to post-high flow. Post-high flow years were associated with increased instream wood and geomorphological complexity at all but the youngest river. Channel changes were part of an active relaxation stage at all rivers, where channels continued to migrate, and complexity varied through time. Overall, these measurements permit us to propose a conceptual model of the role of geomorphologically effective high flows in the context of paraglacial adjustment theory. Specifically, we suggest that older rivers in recently deglaciated catchments can undergo a short-term (&lt;10 years) increase in the rate of geomorphological development as a result of the recruitment of instream wood and channel migration during and following repeated precipitation-driven high flows. Enhancing our knowledge of these geomorphological and paraglacial processes in response to high flows is important for the effective management of riverine water and ecosystem resources in rapidly changing environments.</p

A river classification scheme to assess macroinvertebrate sensitivity to water abstraction pressures

The concept of environmental flows has been developed to manage human alteration of river flow regimes, as effective management requires an understanding of the ecological consequences of flow alteration. This study explores the concept of macroinvertebrate sensitivity to river flow alteration to establish robust quantitative relationships between biological indicators and hydrological pressures. Existing environmental flow classifications used by the environmental regulator for English rivers were tested using multilevel regression modelling. Results showed a weak relationship between the current abstraction sensitivity classification and macroinvertebrate response to flow pressure. An alternative approach, based on physically‐derived river types, was a better predictor of macroinvertebrate response. Intermediate sized lowland streams displayed the best model fit, while upland rivers exhibited poor model performance. A better understanding of the ecological response to flow variation in different river types could help water resource managers develop improved ecologically appropriate flow regimes, which support the integrity of river ecosystems

Evaluating the effectiveness of land use management as a natural flood management intervention in reducing the impact of flooding for an upland catchment

Natural flood management (NFM) is a method for reducing flooding by using a catchment-based approach to managing flood risk. Understanding and quantifying the impact of implementing NFM at the catchment scale remains ambiguous with a clear need for robust empirical evidence. A combination of fieldwork, laboratory analysis and modelling was applied to quantify the impacts of land use management changes on catchment flood hazard. Soil hydraulic conductivity was measured under varying land management regimes and used to parameterize a physically based spatially distributed hydrological model (SD-TOPMODEL). A suite of stakeholder informed land management scenarios was modelled, permitting the quantification of the impact of NFM interventions on the timing and the intensity of the peak discharge at the catchment outlet. The findings support the implementation of NFM interventions as a means of reducing flood hazard within a rural upland catchment. Improved soil infiltration provided the greatest reduction in the intensity and delayed timing of the flood peak for a 10-year occurrence storm event (7% reduction in peak runoff and 8% increase in lag time) with similar reductions observed for a 100-year storm event. Catchment wide woodland planting reduced peak flow by 11% during the 100-year event but was not effective during the 10-year event. Riparian buffer strips provided consistent reductions in peak flow and in the timing of the peak across both storm events with no significant differences relating to vegetation age. Critically, we observed that the effect of implementing multiple NFM interventions was not additive and that efficiencies can be made in using this modelling approach to prioritize the most effective outcomes

Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation

A unified conceptual framework for river corridors requires synthesis of diverse site-, method- and discipline-specific findings. The river research community has developed a substantial body of observations and process-specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site-, scale-, and mechanism-specific observations begins the process of synthesis. Machine learning is particularly well-suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively-generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery

Intrinsic molecular signature of breast cancer in a population-based cohort of 412 patients

BACKGROUND: Molecular markers and the rich biological information they contain have great potential for cancer diagnosis, prognostication and therapy prediction. So far, however, they have not superseded routine histopathology and staging criteria, partly because the few studies performed on molecular subtyping have had little validation and limited clinical characterization. METHODS: We obtained gene expression and clinical data for 412 breast cancers obtained from population-based cohorts of patients from Stockholm and Uppsala, Sweden. Using the intrinsic set of approximately 500 genes derived in the Norway/Stanford breast cancer data, we validated the existence of five molecular subtypes – basal-like, ERBB2, luminal A/B and normal-like – and characterized these subtypes extensively with the use of conventional clinical variables. RESULTS: We found an overall 77.5% concordance between the centroid prediction of the Swedish cohort by using the Norway/Stanford signature and the k-means clustering performed internally within the Swedish cohort. The highest rate of discordant assignments occurred between the luminal A and luminal B subtypes and between the luminal B and ERBB2 subtypes. The subtypes varied significantly in terms of grade (p < 0.001), p53 mutation (p < 0.001) and genomic instability (p = 0.01), but surprisingly there was little difference in lymph-node metastasis (p = 0.31). Furthermore, current users of hormone-replacement therapy were strikingly over-represented in the normal-like subgroup (p < 0.001). Separate analyses of the patients who received endocrine therapy and those who did not receive any adjuvant therapy supported the previous hypothesis that the basal-like subtype responded to adjuvant treatment, whereas the ERBB2 and luminal B subtypes were poor responders. CONCLUSION: We found that the intrinsic molecular subtypes of breast cancer are broadly present in a diverse collection of patients from a population-based cohort in Sweden. The intrinsic gene set, originally selected to reveal stable tumor characteristics, was shown to have a strong correlation with progression-related properties such as grade, p53 mutation and genomic instability