A robust multi-purpose hydrological model for Great Britain

PhD ThesisRobust numerical models are an essential tool for informing ood and water management and policy around the world. Physically-based hydrological models have traditionally not been used for such applications due to prohibitively large data, time and computational resource requirements. Given recent advances in computing power and data availability, this study creates, for the rst time, a robust, physically-based hydrological modelling system for Great Britain using the SHETRAN model and national datasets. Such a model has several advantages over less complex systems. Firstly, compared with conceptual models, a national physically-based model is more readily applicable to ungauged catchments, in which hydrological predictions are also required. Secondly, the results of a physically-based system may be more robust under changing conditions such as climate and land cover, as physical processes and relationships are explicitly accounted for. Finally, a fully integrated surface and subsurface model such as SHETRAN o ers a wider range of applications compared with simpler schemes, such as assessments of groundwater resources, sediment transport and ooding from multiple sources. In order to develop a national modelling system based on SHETRAN, a large array of data for the whole of Great Britain and the period 1960-2006 has been integrated into a framework that features a new, user-friendly graphical interface, which extracts and prepares the data required for a SHETRAN simulation of any catchment in Great Britain. This has vastly reduced the time it takes to set up and run a model from months to seconds. Structural changes have also been incorporated into SHETRAN to better represent lakes, handle pits in elevation data and accept gridded meteorological inputs. 306 catchments spanning Great Britain were then modelled using this system. The standard con guration of this system performs satisfactorily (NSE > 0.5) for 72% of catchments and well (NSE > 0.7) for 48%. Many of the remaining 28% of catchments that performed relatively poorly (NSE < 0.5) are located in the chalk in the south east of England. As such, the British Geological Survey 3D geology model for Great Britain (GB3D) has been incorporated for the rst time in any hydrological model to pave the way for improvements to be made to simulations of catchments with important groundwater regimes. This coupling has involved development i of software to allow for easy incorporation of geological information into SHETRAN for any model setup. The addition of more realistic subsurface representation following this approach is shown to greatly improve model performance in areas dominated by groundwater processes. The sensitivity of the modelling system to key inputs and parameters was tested, particularly with respect to the distribution and rates of rainfall and potential evapotranspiration. As part of this, a new national dataset of gridded hourly rainfall was created by disaggregating the 5km UK Climate Projections 2009 (UKCP09) gridded daily rainfall product with partially quality controlled hourly rain gauge data from over 1300 observation stations across the country. Of the sensitivity tests undertaken, the largest improvements in model performance were seen when this hourly gridded rainfall dataset was combined with potential evapotranspiration disaggregated to hourly intervals, with 61% of catchments showing an increase in NSE as a result of more realistic sub-daily meteorological forcing. Additional sensitivity analysis revealed that the slight over-estimation of runo using the initial model con guration which has a median water balance bias of 5% was reduced in 62% of catchments by increasing daily potential evapotranspiration rates by 5%. Similarly, model performance was also found to improve by universally decreasing rainfall rates slightly, which together indicate the possibility of slight under-estimation of potential evapotranspiration derived from available data. In addition to extensive sensitivity testing, the national modelling system for Great Britain has also been coupled with the UKCP09 spatial weather generator to demonstrate the capability of the system to conduct climate change impact assessments. A set of 100 simulations for each of 20 representative catchments across the country were processed for a medium emissions scenario in the 2050s, in order to establish and demonstrate the methodology for conducting such an assessment. The results of these initial simulations suggest that higher potential evapotranspiration rates, combined with modest increases in rainfall under this climate change projection, lead to a general decrease in mean annual river ows. Changes in mean annual ow across the country vary between -26% to +8%, with the biggest reductions in ow found in the south of England and modest increases in runo across Scotland. This work represents a step-change in how the physically-based hydrological model SHETRAN can be used. Not only has this project made SHETRAN much easier to use on its own, but the model can now also be used in conjunction with external applications such as the UKCP09 spatial weather generator and GB3D. This means that the modelling system has great potential to be used as a resource at national, regional and local scales in an array of di erent applications, including climate change impact assessments, land cover change studies and integrated assessments of groundwater and surface water resources

A methodology for assessing flood risk from multiple sources

Ph. D. Thesis.Antecedent catchment conditions can affect the severity of flooding, and floods are typically worse when multiple flood sources superimpose. Over one million properties in the UK are at risk of flooding from multiple sources, however, groundwater, fluvial and pluvial flood sources are usually considered separately due to their differing characteristics. This PhD study was composed of two parts: (1) developing a methodology for assessing the risk of flooding from multiple sources, including the creation of a groundwater-surface water modelling system and (2) conducting a national assessment identifying catchments with potential for flooding from multiple sources. The modelling system used 1000 years of synthetic weather data to create realistic meteorological inputs for a physically-based, spatially-distributed hydrological catchment model (SHETRAN-GB). The hydrological model then simulated 1/30, 1/100 and 1/1000 year catchment conditions, which were used as inputs for a high resolution hydraulic model (HiPIMS). The hydraulic model then routed rainfall, stream flow and groundwater emergence to generate a detailed and comprehensive assessment of flood risk. Sensitivity tests compared the flood extents and depths from different methods of integrating groundwater and surface water conditions from the hydrological model into the hydraulic model to find the best method for linking the models. The capability of a national automated hydrological model to simulate groundwater levels was tested at five case study catchments using open source hydrogeological datasets. Automated model configurations were unable to reproduce historical groundwater levels, however simple automated improvements did increase performance. Improved parameterisation of a basic subsurface increased model performance more than the introduction of more complex geology, although the latter was found to be erroneous in places. Correlations between observed and simulated groundwater levels ranged significantly but were as high as 0.9 at some locations. At one case study site, the model domain was given subsurface boundary conditions and increased from its topographic watershed to the estimated groundwater catchment. This dramatically increased the model’s performance and its sensitivity to parameters. The automated setups provided a useful modelling base, but local calibration, improved hydrogeological parameters, subsurface boundary conditions and the use of groundwater domains are necessary for producing good simulations in catchments containing groundwater. New indexes were derived for classifying flow regimes to aid the identification of catchments likely to benefit from the developed methodology, and an initial 29 multisource catchments were identified out of a total of 435 analysed. Multisource catchments are distributed around the UK but are typically confined to areas with permeable bedrock, thus are most commonly found in the South of England. This research demonstrated that the inclusion of groundwater in the flood risk assessment increased the flood hazard by prolonging the flood duration from hours to days but did not notably increase flood depths. Furthermore, the patterns of flood extent changed depending on the proportion of the flood waters that were derived from the subsurface. In summary, this study provides a methodology for the better quantification, mapping and understanding of multisource flood risk, and identifies catchments that are likely to benefit from the approach

Global-scale regionalization of hydrologic model parameters

Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments ( 10-10,000 km(2)) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5 degrees grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Koppen-Geiger climate types and even for evaluation catchments>5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via

Deep learning for hydrological modelling: from benchmarking to concept formation

Hydrological modelling seeks to address the question: what happens to water once it falls on the land surface? Water can flow into river systems, it can pass through soils into the subsurface, it can be absorbed by the biosphere, or it can be released back into the atmosphere as evaporation. The ultimate purpose of hydrological modelling is twofold, to improve our predictions about the system of interest, and to understand how the system works. In recent decades, advances in science and technology have been made by using techniques from the field of Deep Learning, whereby flexible models are calibrated on large datasets to deduce relationships and make predictions. These techniques have begun to be applied across the environmental sciences. In this thesis I will explore a particular model architecture for deriving relationships between inputs and outputs from data, to provide accurate simulations of hydrological systems as well as to improve our understanding of the hydrological processes themselves. The Long Short-Term Memory (LSTM) is a neural network architecture from the field of Deep Learning which has shown promise for time-series modelling. This model architecture was chosen for its correspondence with our perceptual model of hydrology, whereby we consider the hydrological system to be characterised by a description of its state, and processes that govern the transfer of energy and materials from that state. This input-state-output architecture is similar in many ways to traditional process-based and conceptual models. However, unlike these models the LSTM is capable of searching a much wider range of possible functions that map inputs to outputs, capable of learning any process that can be deduced from the data, as opposed to being limited by the encoding in the traditional models. The chapters that make up this thesis first demonstrate that the LSTM is an appropriate architecture for rainfall-runoff modelling on the island of Great Britain. I trained the model using meteorological and catchment averaged attributes as input, and river discharge as outputs over a large sample of catchments. In comparison with often used conceptual model architectures, I show that the LSTM demonstrates state-of-the-art performance and justify further interrogation of what the model has learned. In a follow up study, I explore what the LSTM has learned about the hydrological system by taking the trained model weights and interpreting them with reference to intermediate stores of water that relate the meteorological inputs to the outputs of discharge. Despite the complexity of translating rainfall to discharge, hydrology is not limited to rainfall-runoff modelling. The final chapter in this thesis turns to the problem of forecasting a satellite-derived vegetation health metric which is used operationally as a proxy for drought conditions. Like rainfall-runoff modelling, the system being simulated is driven by the complex interaction of meteorological and land surface attributes, however, the target variable is now a store of water (vegetation) rather than a flux (discharge). This dissertation provides the hydrological community with three important outcomes. Firstly, the LSTM model results are provided as a benchmark for future work looking to develop a national rainfall runoff model for Great Britain. Secondly, this dissertation demonstrates a method used elsewhere in machine learning research that allows a scientist to diagnose what the LSTM has learned about the hydrological system. Finally, this dissertation demonstrates the utility of the LSTM in a drought monitoring context, forecasting a satellite derived vegetation health metric with the potential to improve the ability of national agencies to respond to drought events. Ultimately, this dissertation offers a demonstration of the power of Deep Learning models in hydrology, and calls on the community to interrogate these tools further to not only advance our predictive goals, but also our scientific ones

Recommendations for improving integration in national end-to-end flood forecasting systems: an overview of the FFIR (flooding from intense rainfall) programme

Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action

A climate-conditioned catastrophe risk model for UK flooding

We present a transparent and validated climate-conditioned catastrophe flood model for the UK, that simulates pluvial, fluvial and coastal flood risks at 1 arcsec spatial resolution (∼ 20–25 m). Hazard layers for 10 different return periods are produced over the whole UK for historic, 2020, 2030, 2050 and 2070 conditions using the UK Climate Projections 2018 (UKCP18) climate simulations. From these, monetary losses are computed for five specific global warming levels above pre-industrial values (0.6, 1.1, 1.8, 2.5 and 3.3 ∘C). The analysis contains a greater level of detail and nuance compared to previous work, and represents our current best understanding of the UK's changing flood risk landscape. Validation against historical national return period flood maps yielded critical success index values of 0.65 and 0.76 for England and Wales, respectively, and maximum water levels for the Carlisle 2005 flood were replicated to a root mean square error (RMSE) of 0.41 m without calibration. This level of skill is similar to local modelling with site-specific data. Expected annual damage in 2020 was GBP 730 million, which compares favourably to the observed value of GBP 714 million reported by the Association of British Insurers. Previous UK flood loss estimates based on government data are ∼ 3× higher, and lie well outside our modelled loss distribution, which is plausibly centred on the observations. We estimate that UK 1 % annual probability flood losses were ∼ 6 % greater for the average climate conditions of 2020 (∼ 1.1 ∘C of warming) compared to those of 1990 (∼ 0.6 ∘C of warming), and this increase can be kept to around ∼ 8 % if all countries' COP26 2030 carbon emission reduction pledges and “net zero” commitments are implemented in full. Implementing only the COP26 pledges increases UK 1 % annual probability flood losses by 23 % above average 1990 values, and potentially 37 % in a “worst case” scenario where carbon reduction targets are missed and climate sensitivity is high.</p