Sarah M. Dunn

Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology

Field studies in watershed hydrology continue to characterize and catalogue the enormous heterogeneity and complexity of rainfall runoff processes in more and more watersheds, in different hydroclimatic regimes, and at different scales. Nevertheless, the ability to generalize these findings to ungauged regions remains out of reach. In spite of their apparent physical basis and complexity, the current generation of detailed models is process weak. Their representations of the internal states and process dynamics are still at odds with many experimental findings. In order to make continued progress in watershed hydrology and to bring greater coherence to the science, we need to move beyond the status quo of having to explicitly characterize or prescribe landscape heterogeneity in our (highly calibrated) models and in this way reproduce process complexity and instead explore the set of organizing principles that might underlie the heterogeneity and complexity. This commentary addresses a number of related new avenues for research in watershed science, including the use of comparative analysis, classification, optimality principles, and network theory, all with the intent of defining, understanding, and predicting watershed function and enunciating important watershed functional traits.

Factors influencing the residence time of catchment waters : A virtual experiment approach

[1] Estimates of mean residence time (MRT) are increasingly used as simple summary descriptors of the hydrological processes involving storage and mixing of water within catchment systems. Current understanding of the physical controls on MRT remains limited, and various hypotheses have been proposed to explain its variability between catchments. We present a series of virtual experiments to investigate different hypotheses regarding the significance of different hydrological processes and geographical controls in determining the MRT of catchment waters. The experiments were undertaken using a semidistributed conceptual hydrological model, applied to the Maimai experimental catchment in New Zealand. Our results show that in this small steep catchment, with largely impermeable bedrock, the primary control on the stream water mean residence time is storage within the unsaturated zone. The physical location on the hillslope had only a small influence on soil water residence time. Stream water mean residence time was very sensitive to small additional amounts of deep groundwater in the model. Overall, our results suggest that stream water MRT is additive. The component residence times of stream water MRT appear relatable to characteristic properties of the catchment. Through this mechanism there is future potential for extrapolating MRT data from experimental catchments to other areas.

Investigating the relationship between a soils classification and the spatial parameters of a conceptual catchment-scale hydrological model

Abstract There are now many examples of hydrological models that utilise the capabilities of Geographic Information Systems to generate spatially distributed predictions of behaviour. However, the spatial variability of hydrological parameters relating to distributions of soils and vegetation can be hard to establish. In this paper, the relationship between a soil hydrological classification Hydrology of Soil Types (HOST) and the spatial parameters of a conceptual catchment-scale model is investigated. A procedure involving inverse modelling using Monte-Carlo simulations on two catchments is developed to identify relative values for soil related parameters of the DIY model. The relative values determine the internal variability of hydrological processes as a function of the soil type. For three out of the four soil parameters studied, the variability between HOST classes was found to be consistent across two catchments when tested independently. Problems in identifying values for the fourth ‘fast response distance’ parameter have highlighted a potential limitation with the present structure of the model. The present assumption that this parameter can be related simply to soil type rather than topography appears to be inadequate. With the exclusion of this parameter, calibrated parameter sets from one catchment can be converted into equivalent parameter sets for the alternate catchment on the basis of their HOST distributions, to give a reasonable simulation of flow. Following further testing on different catchments, and modifications to the definition of the fast response distance parameter, the technique provides a methodology whereby it is possible to directly derive spatial soil parameters for new catchments.

Developing the snow component of a distributed hydrological model: a step-wise approach based on multi-objective analysis

Abstract A snow component has been developed for the distributed hydrological model, DIY, using an approach that sequentially evaluates the behaviour of different functions as they are implemented in the model. The evaluation is performed using multi-objective functions to ensure that the internal structure of the model is correct. The development of the model, using a sub-catchment in the Cairngorm Mountains in Scotland, demonstrated that the degree–day model can be enhanced for hydroclimatic conditions typical of those found in Scotland, without increasing meteorological data requirements. An important element of the snow model is a function to account for wind re-distribution. This causes large accumulations of snow in small pockets, which are shown to be important in sustaining baseflows in the rivers during the late spring and early summer, long after the snowpack has melted from the bulk of the catchment. The importance of the wind function would not have been identified using a single objective function of total streamflow to evaluate the model behaviour.

The impact of variable snow pack accumulation on a major Scottish water resource.

In regions such as northern Scotland, where winter temperatures are such that the occurrence of snow is borderline under the present climate, potential changes affecting precipitation and temperature regimes may have a disproportionately large impact on snow processes and hydrological behaviour. The physical characteristics of mountainous areas in Scotland mean that the spatial variability of snowpack accumulation is high, as well as the temporal variability caused by the climate. There have been few modelling studies aimed at assessing the significance of snow resources in these areas and none that have adopted a spatially distributed approach. This paper describes the approach taken in applying a new distributed model to a headwater catchment in the Cairngorm Mountains. The results demonstrate the importance of wind on re-distributing snow to create deep accumulations in small sheltered pockets. These accumulations are shown to be important in sustaining baseflows in the rivers, long after snow has melted from the rest of the catchment. The model has also produced a first set of maps showing how predicted snow depths vary across the catchment through the winter.

Development and application of a distributed catchment-scale hydrological model for the River Ythan, NE Scotland

Mathematical models are being used to develop a decision support system for integrated management of the Ythan catchment in NE Scotland. One component of this has involved the development of a distributed catchment-scale hydrological model. The model is based on subsurface flow routing and calculates the contribution to stream flow from each 50 m×50 m cell in the 548 km2 catchment. It uses two topographic parameters, slope and distance to stream following the main line of flow, and five physical parameters. The topographic analysis and distributed flow accumulation are performed by linking the single cell model with a geographic information system. Preliminary results from a three-year simulation of daily flows indicate that the model successfully predicts the main characteristics of the catchment flow.

Long-term trends in hydro-climatology of a major Scottish mountain river.

The River Dee, in North East Scotland, is a mountainous river strongly influenced by patterns of snow accumulation and melt from the Cairngorm Mountains. Analysis of this rivers flow record from 1929-2004, the longest in Scotland, supports anecdotal evidence that river extreme flows are increasing. There was no detectable change in the overall annual flow patterns. However, an analysis of seasonal data suggested a shift towards increased flows in spring (March-May) and decreased flows in summer (June-August) over the 75 years of the record. Flows in spring exceeded 29 m(3) s(-1) for 50% of the time over the earliest part of the record (1930 to 1954), whereas in the last 25 years of the record (1979 to 2004) 50% of the flows exceeded 35 m(3) s(-1). Precipitation is increasing in the spring and decreasing in July and August. If these trends continue they have important implications for water management in the Dee, with a potential increase in flood risk in spring and the increased possibility of drought in summer. Combined with this increase in flows the river appears to be more responsive to precipitation events in the catchment. In large heterogeneous catchments with a marginal alpine/high latitude climate it is difficult to assess the amount of precipitation falling as snow and its relative accumulation and ablation dynamics on daily to seasonal time scales. Changes in the temporal pattern of coherence between flow and precipitation are thought to be linked to changing snow patterns in the upland part of the catchment. A decreased amount of precipitation occurring as snow has led to higher coherence. We also show that in responsive systems it is important to record river flows at an hourly rather than daily time step in order to characterise peak flow events.

How well can we model stream phosphorus concentrations in agricultural catchments

Mechanistic catchment-scale phosphorus models appear to perform poorly where diffuse sources dominate. We investigate the reasons for this for one model, INCA-P, testing model output against 18 months of daily data in a small Scottish catchment. We examine key model processes and provide recommendations for model improvement and simplification. Improvements to the particulate phosphorus simulation are especially needed. The model evaluation procedure is then generalised to provide a checklist for identifying why model performance may be poor or unreliable, incorporating calibration, data, structural and conceptual challenges. There needs to be greater recognition that current models struggle to produce positive Nash-Sutcliffe statistics in agricultural catchments when evaluated against daily data. Phosphorus modelling is difficult, but models are not as useless as this might suggest. We found a combination of correlation coefficients, bias, a comparison of distributions and a visual assessment of time series a better means of identifying realistic simulations. We use daily data to test a mechanistic phosphorus model in an agricultural area.The model reproduces dissolved phosphorus dynamics but struggles with particulates.A number of potential model simplifications and improvements are highlighted.Nash-Sutcliffe is of limited use for measuring phosphorus model performance.We present a checklist for assessing why environmental models may underperform.

A Hierarchical Model for Compositional Data Analysis

This article introduces a hierarchical model for compositional analysis. Our approach models both source and mixture data simultaneously, and accounts for several different types of variation: these include measurement error on both the mixture and source data; variability in the sample from the source distributions; and variability in the mixing proportions themselves, generally of main interest. The method is an improvement on some existing methods in that estimates of mixing proportions (including their interval estimates) are sure to lie in the range [0, 1]; in addition, it is shown that our model can help in situations where identification of appropriate source data is difficult, especially when we extend our model to include a covariate. We first study the likelihood surface of a base model for a simple example, and then include prior distributions to create a Bayesian model that allows analysis of more complex situations via Markov chain Monte Carlo sampling from the likelihood. Application of the model is illustrated with two examples using real data: one concerning chemical markers in plants, and another on water chemistry.

Adjusting irrigation abstraction to minimise the impact on stream flow in the East of Scotland.

Abstractions of surface and groundwater for irrigation in Scotland are currently subject to control in only two small catchments. Under the terms of the EU Water Framework Directive, it will be necessary to introduce new legislation to control abstractions elsewhere. To help in the development of appropriate policy for Scotland a study has been carried out to examine the significance of irrigation and the effectiveness of different types of control strategies in terms of the economics of potato cropping and stream hydrology in Scotland. This paper presents the findings of the hydrological study and highlights some of the spatial and temporal issues that need to be considered in the selection of control mechanisms, if they are to be successful in achieving objectives for environmental improvement. The study was focussed on two catchments in the east of Scotland, the Tyne and West Peffer. The effectiveness of several different abstraction control strategies was examined to see how stream flows in the catchment would be modified by their implementation. The results of the study demonstrated that the West Peffer catchment in particular is significantly affected by irrigation abstractions. Control mechanisms based on allowable monthly abstraction volumes and flow-based abstraction bans would be of considerable help in restoring stream flows to their natural levels, but would modify the hydrological regime in slightly different ways. A spatial analysis of stream flows demonstrated that implementation of controls based on a single monitoring point may be ineffective at maintaining acceptable levels of flow throughout the catchment and that this may require a tighter control at the monitoring point.