David G. Milledge

Overland flow velocity and roughness properties in peatlands

Overland flow is an important component of peatland hydrology. Hydrological models of peatlands are being developed that require estimates of flow velocity and its controls. However, surprisingly little is known about overland flow velocities in peatlands. Some peatlands have also been drained using open ditches, and these need to be incorporated into flow models. This paper presents field data on the velocity of overland flow and drain flow in upland peatlands. The relationships between flow velocity, vegetation cover, slope, and water depth are explored. Sphagnum provided a significantly greater effective hydraulic roughness to overland flow than peatland grasses. In all cases, a significant break in process occurred for flows with water depths of around 1 cm so that there were two components of the roughness curve. This is consistent with partial submergence theory for very shallow flows where resistance increases with depth as the soil surface first becomes fully submerged. While each surface cover type should be considered separately, the results also suggest that a first-order estimate of Darcy-Weisbach roughness and mean velocity can be based on a single parameter for each surface cover. This paper presents an empirical overland flow velocity forecasting model that can be applied to peatlands. The model combines the partially submerged component for flows with water depths below 1 cm with the fully submerged component for flows with depths up to 5 cm, which are representative of the depths of flows that occur across peatlands.

A multidimensional stability model for predicting shallow landslide size and shape across landscapes

The size of a shallow landslide is a fundamental control on both its hazard and geomorphic importance. Existing models are either unable to predict landslide size or are computationally intensive such that they cannot practically be applied across landscapes. We derive a model appropriate for natural slopes that is capable of predicting shallow landslide size but simple enough to be applied over entire watersheds. It accounts for lateral resistance by representing the forces acting on each margin of potential landslides using earth pressure theory and by representing root reinforcement as an exponential function of soil depth. We test our models ability to predict failure of an observed landslide where the relevant parameters are well constrained by field data. The model predicts failure for the observed scar geometry and finds that larger or smaller conformal shapes are more stable. Numerical experiments demonstrate that friction on the boundaries of a potential landslide increases considerably the magnitude of lateral reinforcement, relative to that due to root cohesion alone. We find that there is a critical depth in both cohesive and cohesionless soils, resulting in a minimum size for failure, which is consistent with observed size-frequency distributions. Furthermore, the differential resistance on the boundaries of a potential landslide is responsible for a critical landslide shape which is longer than it is wide, consistent with observed aspect ratios. Finally, our results show that minimum size increases as approximately the square of failure surface depth, consistent with observed landslide depth-area data.

Hørbyebreen polythermal glacial landsystem, Svalbard

A contoured surficial geology and geomorphology map of the forelands of the Hørbyebreen, Svenbreen and Ferdinandbreen valley glaciers in Petuniabukta, Svalbard was compiled from an orthophotograph based upon aerial photographs taken in 2009. The map reveals typical polythermal glacial landsystems, comprising ice-cored latero-frontal moraine arcs grading up valley into fluted till surfaces draped by supraglacially-derived longitudinal debris stripes. The additional occurrence on the Hørbyebreen foreland of linear esker and debris ridges arranged in a geometric ridge network is thought to be related to the infilling of densely spaced crevasses, created during a period of elevated meltwater pressures and ice hydrofracturing. These landforms were associated either with a jökulhlaup that was blocked by the frozen snout or an historical surge. The Hørbyebreen landform assemblage therefore constitutes an analogue for either: (1) spatial and temporal landsystem overprinting (polythermal and surging activity); or (2) a more refined polythermal landsystem in which the build up and release of meltwater reservoirs in warm-based interiors of polythermal glaciers give rise to a particularly diagnostic landform at the up-ice junction with the cold-based snout.

Predicting microbial water quality with models : over-arching questions for managing risk in agricultural catchments.

The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to other agricultural pollutants (e.g. nutrients) it is imperative that the end-user expectations of FIO models are appropriately managed. In response, this commentary highlights four over-arching questions associated with: (i) model purpose; (ii) modelling approach; (iii) data availability; and (iv) model application, that must be considered as part of good practice prior to the deployment of any modelling approach to predict FIO behaviour in catchment systems. A series of short and longer-term research priorities are proposed in response to these questions in order to promote better model deployment in the field of catchment microbial dynamics.

A spectral clustering search algorithm for predicting shallow landslide size and location

The potential hazard and geomorphic significance of shallow landslides depend on their location and size. Commonly applied one-dimensional stability models do not include lateral resistances and cannot predict landslide size. Multidimensional models must be applied to specific geometries, which are not known a priori, and testing all possible geometries is computationally prohibitive. We present an efficient deterministic search algorithm based on spectral graph theory and couple it with a multidimensional stability model to predict discrete landslides in applications at scales broader than a single hillslope using gridded spatial data. The algorithm is general, assuming only that instability results when driving forces acting on a cluster of cells exceed the resisting forces on its margins and that clusters behave as rigid blocks with a failure plane at the soil-bedrock interface. This algorithm recovers predefined clusters of unstable cells of varying shape and size on a synthetic landscape, predicts the size, location, and shape of an observed shallow landslide using field-measured physical parameters, and is robust to modest changes in input parameters. The search algorithm identifies patches of potential instability within large areas of stable landscape. Within these patches will be many different combinations of cells with a Factor of Safety less than one, suggesting that subtle variations in local conditions (e.g., pore pressure and root strength) may determine the ultimate form and exact location at a specific site. Nonetheless, the tests presented here suggest that the search algorithm enables the prediction of shallow landslide size as well as location across landscapes.

A Monte Carlo approach to the inverse problem of diffuse pollution risk in agricultural catchments

The hydrological and biogeochemical processes that operate in catchments influence the ecological quality of freshwater systems through delivery of fine sediment, nutrients and organic matter. Most models that seek to characterise the delivery of diffuse pollutants from land to water are reductionist. The multitude of processes that are parameterised in such models to ensure generic applicability make them complex and difficult to test on available data. Here, we outline an alternative--data-driven--inverse approach. We apply SCIMAP, a parsimonious risk based model that has an explicit treatment of hydrological connectivity. We take a bayesian approach to the inverse problem of determining the risk that must be assigned to different land uses in a catchment in order to explain the spatial patterns of measured in-stream nutrient concentrations. We apply the model to identify the key sources of nitrogen (N) and phosphorus (P) diffuse pollution risk in eleven UK catchments covering a range of landscapes. The model results show that: 1) some land use generates a consistently high or low risk of diffuse nutrient pollution; but 2) the risks associated with different land uses vary both between catchments and between nutrients; and 3) that the dominant sources of P and N risk in the catchment are often a function of the spatial configuration of land uses. Taken on a case-by-case basis, this type of inverse approach may be used to help prioritise the focus of interventions to reduce diffuse pollution risk for freshwater ecosystems.

Going with the flow? Using participatory action research in physical geography

This paper critically appraises the idea and practice of ‘participation’ in scientific environmental research, arguing for the wider uptake by physical geographers of a more radical participatory approach. It proposes participatory action research (PAR), which offers an alternative mode of science, involving collaboration and co-production of research from question definition through to outcomes. We begin with a critical view of public participation in environmental research and policy-making to date. We argue that much rhetoric and practice of participation is shallow, focusing simply on including relevant publics and stakeholders, or having an underlying agenda of building trust in science or policy-making. Both orientations diverge drastically from the radical traditions in which participatory research and planning originate. In the rest of the paper, we illustrate an alternative process of knowledge co-production, reporting on a PAR project on farm slurry pollution conducted with a UK Rivers Trust. We evaluate the knowledge co-produced, the responses of participants and the scientific process. Suggesting that we reframe co-production as the circulation of expertise, we argue that PAR can enrich the learning, knowledge and skills of all those involved and lead to innovation and positive environmental outcomes. A number of structural and institutional barriers to deep participatory processes need to be addressed.

Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm

Predicting shallow landslide size and location across landscapes is important for understanding landscape form and evolution and for hazard identification. We test a recently developed model that couples a search algorithm with 3-D slope stability analysis that predicts these two key attributes in an intensively studied landscape with a 10 year landslide inventory. We use process-based submodels to estimate soil depth, root strength, and pore pressure for a sequence of landslide-triggering rainstorms. We parameterize submodels with field measurements independently of the slope stability model, without calibrating predictions to observations. The model generally reproduces observed landslide size and location distributions, overlaps 65% of observed landslides, and of these predicts size to within factors of 2 and 1.5 in 55% and 28% of cases, respectively. Five percent of the landscape is predicted unstable, compared to 2% recorded landslide area. Missed landslides are not due to the search algorithm but to the formulation and parameterization of the slope stability model and inaccuracy of observed landslide maps. Our model does not improve location prediction relative to infinite-slope methods but predicts landslide size, improves process representation, and reduces reliance on effective parameters. Increasing rainfall intensity or root cohesion generally increases landslide size and shifts locations down hollow axes, while increasing cohesion restricts unstable locations to areas with deepest soils. Our findings suggest that shallow landslide abundance, location, and size are ultimately controlled by covarying topographic, material, and hydrologic properties. Estimating the spatiotemporal patterns of root strength, pore pressure, and soil depth across a landscape may be the greatest remaining challenge.

Population density controls on microbial pollution across the Ganga catchment.

For millions of people worldwide, sewage-polluted surface waters threaten water security, food security and human health. Yet the extent of the problem and its causes are poorly understood. Given rapid widespread global urbanisation, the impact of urban versus rural populations is particularly important but unknown. Exploiting previously unpublished archival data for the Ganga (Ganges) catchment, we find a strong non-linear relationship between upstream population density and microbial pollution, and predict that these river systems would fail faecal coliform standards for irrigation waters available to 79% of the catchments 500 million inhabitants. Overall, this work shows that microbial pollution is conditioned by the continental-scale network structure of rivers, compounded by the location of cities whose growing populations contribute c. 100 times more microbial pollutants per capita than their rural counterparts.