The role of physics based models for simulating runoff responses to rural land management scenarios

Recent floods in the UK have focused attention on the effects of rural land use and land management change on flood risk. Over recent decades agricultural intensification has been widespread across the uplands of the UK, with increases in stocking density, ploughing, reseeding and drainage of fields, use of heavy machinery, and the removal of trees from the landscape. A key scientific question is whether or not these changes in land use and land management in the uplands are increasing flood frequency and magnitude. Although land use and land management changes have been observed to change local surface runoff, attempts to isolate these responses at the catchment scale have failed due to limitations of data sets and modelling capability. While hydrological modelling is a well advanced field of science, a key methodological challenge that remains is how to upscale information about local scale changes. This Thesis evaluates the role of physics based hydrological models for upscaling local scale hydrological process knowledge and data to catchment scale flood flow responses. A model upscaling procedure that aims to quantify the changes in peak flows at multiple scales related to localised land use management changes is presented. The procedure divides the catchment into a number of runoff generating elements, which are each classified based on soil types and land management. For each runoff generating element, a physics based model is developed, incorporating understanding of hydrological processes and properties. This permits the investigation of local scale impacts, but cannot be applied at the catchment scale due to excessive computational burden. Therefore, the outputs from these physics based models are used to train simpler “metamodels”, which are then incorporated into a semi-distributed catchment model. In this way, the understanding of local changes in physical properties can be incorporated into a more flexible and computationally efficient catchment scale conceptual model. This procedure has previously been tested to a limited extent on a 12km² experimental catchment in upland Wales, which provided multi-scale hydrological data sets. The applicability of the procedure is now examined for a 25km² upland subcatchment of the Hodder in north-west England for an extended range of land management questions. This catchment is currently undergoing a number of land management changes, including: the blocking of open drains in the peatlands that cover the upper extent of the subcatchment, changes to an existing coniferous plantation and extensive deciduous riparian planting. The catchment does not include supporting multi-scale monitoring; without local data, physics based models are developed a priori using information from the literature, qualitative field observations and a proxy catchment. The significance of the uncertainties due to this lack of data and also uncertainties related to the upscaling procedure itself are explored, particularly examining the identifiability of the predicted effects at multiple scales. Based on the findings, the strengths and limitations of physics based modelling and the upscaling procedure in terms of ability to predict catchment-scale impacts of local land management interventions are assessed. The outputs from the multi scale modelling are also used to increase conceptual understanding of the hydrological processes and their relative importance under different land use and land management scenarios at the local scale, and also to quantify the impacts of land management scenarios at the catchment scale, taking into account the limitations of the modelling procedure

Process-oriented investigation of snow accumulation, snowmelt and runoff generation in forested sites in Finland

This thesis summarises development and application of a hydrological model for simulating forest canopy processes, snow accumulation and melt, soil and ground water interactions, and streamflow routing. A motivation behind the model development is to outline a methodology for predicting the influence of land use changes on catchment hydrological processes. In addition, the development aims at providing linkages from the hydrological model to atmospheric models through implementation of surface energy balance and to water quality models through quantification of runoff components. The work started with comparison of two existing snow energy balance models using meteorological and snow data from Northern Finland. Based on the comparison the more simple of the tested snow parameterisations was modified to improve its performance in terms of snow heat balance simulation. The modified snow model was then coupled with a canopy scheme to account for the influence of forest on snow processes. The combined model was applied to clear-cut and coniferous forest sites in Southern Finland to identify the differences in snow mass and energy fluxes between open and forest. Finally, runoff generation in a forested catchment (Rudbäck, 0.18 km2) was studied by using two different parameterisations. First, the catchment was parameterised as a three-dimensional domain, and secondly, as a vertical two-dimensional hillslope. The models produced similar results in terms of fit against measured daily streamflow, but the computed runoff components were different. Independent calibration of hydrological submodels yielded a more realistic partition of runoff into surface and subsurface components than did calibration merely against streamflow data. It is proposed that the hillslope model can be used to simulate runoff generation in each possibly non-contiguous area that is similar in terms of its land-use. A system where a set of such models is combined together can be used to quantify runoff contributions from pre-classified areas of different land-use, and constitutes a tool for studying hydrological impacts of land use changes.reviewe

Monitoring and modelling hydrological response and sediment yield in a North York Moors catchment : an assessment of predictive uncertainty in a coupled hydrological-sediment yield model

A fully distributed coupled hydrological-sediment yield model was developed. An assessment was made of the predictive uncertainty in the individual model predictions, as well as the uncertainty propagated from the primary hydrological model to the secondary sediment yield model, using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The value of additional data, in the form of additional periods of flow data, as well as deterministic (based on landuse and soil type) and random spatial parameterisation of hydrological parameters in restricting model uncertainty of the spatially lumped model parameterisation were examined, using Bayesian updating.The results revealed significant model uncertainty in both the hydrological and sediment yield models, with uncertainty bounds widest at peak flow and sediment flux, and predictive failure in recession flows, similar to other applications of GLUE methodology. Uncertainty in the sediment yield model was found to be due to uncertainty inherited from the hydrological model, as well as simplifying assumptions made about sediment removal and transport, and resulted in lower model efficiencies and generally poorer qualitative sedigraph fit.The model validation exercise revealed that the calibrated 'optimum' parameter set was not 'optimum' for all validation periods and resulted in inaccurate spatial and temporal hydrological response predictions for the validation periods. This suggested that traditional split-sample model calibration methods may not be effective in capturing the true spatial and temporal variability of the system.Successive periods of flow data were effective in reducing the calibration period uncertainty bounds. Similarly, the use of sediment yield predictions to update hydrological model uncertainty resulted in a reduction in hydrological model uncertainty. Spatially distributed parameterisation was found to also improve model predictions, resulting in a reduction in uncertainty bounds, particularly for soil-distributed parameterisation. However, stochastic parameterisation of spatially variable hydrological parameters provided equally acceptable predictions for both models, suggesting that a deterministic approach might not be required to capture the spatial variability in hydrological and sedimentological response in the study catchment, and that a stochastic approach may be adequate

Monitoring and modelling hydrological response and sediment yield in a North York Moors catchment : an assessment of predictive uncertainty in a coupled hydrological-sediment yield model

A fully distributed coupled hydrological-sediment yield model was developed. An assessment was made of the predictive uncertainty in the individual model predictions, as well as the uncertainty propagated from the primary hydrological model to the secondary sediment yield model, using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The value of additional data, in the form of additional periods of flow data, as well as deterministic (based on landuse and soil type) and random spatial parameterisation of hydrological parameters in restricting model uncertainty of the spatially lumped model parameterisation were examined, using Bayesian updating.The results revealed significant model uncertainty in both the hydrological and sediment yield models, with uncertainty bounds widest at peak flow and sediment flux, and predictive failure in recession flows, similar to other applications of GLUE methodology. Uncertainty in the sediment yield model was found to be due to uncertainty inherited from the hydrological model, as well as simplifying assumptions made about sediment removal and transport, and resulted in lower model efficiencies and generally poorer qualitative sedigraph fit.The model validation exercise revealed that the calibrated 'optimum' parameter set was not 'optimum' for all validation periods and resulted in inaccurate spatial and temporal hydrological response predictions for the validation periods. This suggested that traditional split-sample model calibration methods may not be effective in capturing the true spatial and temporal variability of the system.Successive periods of flow data were effective in reducing the calibration period uncertainty bounds. Similarly, the use of sediment yield predictions to update hydrological model uncertainty resulted in a reduction in hydrological model uncertainty. Spatially distributed parameterisation was found to also improve model predictions, resulting in a reduction in uncertainty bounds, particularly for soil-distributed parameterisation. However, stochastic parameterisation of spatially variable hydrological parameters provided equally acceptable predictions for both models, suggesting that a deterministic approach might not be required to capture the spatial variability in hydrological and sedimentological response in the study catchment, and that a stochastic approach may be adequate

Understanding Structure and Function in Semiarid Ecosystems: Implications for Terrestrial Carbon Dynamics in Drylands

This study advances understanding of how the changes in ecosystem structure and function associated with woody shrub encroachment in semi-arid grasslands alter ecosystem carbon (C) dynamics. In terms of both magnitude and dynamism, dryland ecosystems represent a major component of the global C cycle. Woody shrub encroachment is a widespread phenomenon globally, which is known to substantially alter ecosystem structure and function, with resultant impacts on C dynamics. A series of focal sites were studied at the Sevilleta National Wildlife Refuge in central New Mexico, USA. A space-for-time analogue was used to identify how landscape structure and function change at four stages over a grassland to shrubland transition. The research had three key threads: 1. Soil-associated carbon: Stocks of organic and inorganic C in the near-surface soil, and the redistribution of these C stocks by erosion during high-intensity rainfall events were quantified using hillslope-scale monitoring plots. Coarse (>2 mm) clasts were found to account for a substantial proportion of the organic and inorganic C in these calcareous soils, and the erosional effluxes of both inorganic and organic C increased substantially across the vegetation ecotone. Eroded sediment was found to be significantly enriched in organic C relative to the contributing soil with systematic changes in OC enrichment across the vegetation transition. The OC enrichment dynamics observed were inconsistent with existing understanding (derived largely from reductionist, laboratory-based experiments) that OC enrichment is largely insignificant in the erosional redistribution of C. 2. Plant biomass: Cutting-edge proximal remote sensing approaches, using a remotely piloted lightweight multirotor drone combined with structure-from-motion (SfM) photogrammetry were developed and used to quantify biomass carbon stocks at the focal field sites. In such spatially heterogeneous and temporally dynamic ecosystems existing measurement techniques (e.g. on-the-ground observations or satellite- or aircraft-based remote sensing) struggle to capture the complexity of fine-grained vegetation structure, which is crucial for accurately estimating biomass. The data products available from the novel SfM approach developed for this research quantified plants just 15 mm high, achieving a fidelity nearly two orders of magnitude finer than previous implementations of the method. The approach developed here will revolutionise the study of biomass dynamics in short-sward ecogeomorphic systems. 3. Ecohydrological modelling: Understanding the effects of water-mediated degradation processes on ecosystem carbon dynamics over greater than observable spatio-temporal scales is complicated by significant scale-dependencies and thus requires detailed mechanistic understanding. A process-based, spatially-explicit ecohydrological modelling approach (MAHLERAN - Model for Assessing Hillslope to Landscape Erosion, Runoff and Nutrients) was therefore comprehensively evaluated against a large assemblage of rainfall runoff events. This evaluation highlighted both areas of strength in the current model structure, and also areas of weakness for further development. The research has improved understanding of ecosystem degradation processes in semi-arid rangelands, and demonstrates that woody shrub encroachment may lead to a long-term reduction in ecosystem C storage, which is contrary to the widely promulgated view that woody shrub encroachment increases C storage in terrestrial ecosystems.NERC Doctoral Training Grant (NE/K500902/1)NSF Long Term Ecological Research Program at the Sevilleta National Wildlife Refuge (DEB-1232294