CHARACTERIZATION OF UNCERTAINTY IN MODEL PARAMETER AND PRECIPITATION DATA ACROSS SEVERAL HEADWATER CATCHMENTS IN THE CANADIAN ROCKIES: A LARGE-SAMPLE HYDROLOGY APPROACH

Hydrologic modelling and prediction in the Canadian Rookies are hampered by the sparsity of hydro-climatic data, limited accessibility, and the complexity of the cold regions hydrologic processes. Previous studies in this region have mainly focused on very few heavily instrumented catchments, typically with limited generalizability to other catchments in the region. In this thesis, I adopt a “large-sample hydrology” approach to address some of the outstanding issues pertaining to data uncertainty, model parameter identifiability, and predictive power of hydrologic modelling in this region. My analyses cover 25 catchments with a range of physiographic and hydrologic properties located across the Canadian Rockies. To address forcing data uncertainty, which is commonly considered as the most dominant source of uncertainty in the hydrology of this region, I processed and utilized three different gridded-data products, namely ANUSPLIN, CaPA, and WFDEI. To make the problem tractable, I applied an efficient-to-run conceptual hydrologic model to simulate the hydrologic processes in this region under a variety of parameter and input data configurations. My analyses showed significant discrepancies in precipitation amounts between the different climate data products with varying degrees across the different catchments. Runoff ratios were quite variable under the different products and across the catchments, ranging from 0.25 to 2, highlighting the significant uncertainty in precipitation amounts. To handle precipitation uncertainty in hydrologic modelling, I developed and tested two strategies: (1) implementing a correction parameter for each data product separately, and (2) developing and parameterizing a linear combination of the different data products to have a unified, presumably more accurate data product. These new precipitation-correcting parameters along with a selected set of the hydrologic model parameters were analyzed and identified via Monte-Carlo simulation, considering three model performance criteria on streamflow simulation, namely Nash-Sutcliffe Efficiency (NSE), NSE on log-transformed streamflow (NSE-Log), and Percent Bias (PBias). Overall, the hydrologic model showed adequate performance in reproducing observed streamflows in most of the catchments, with NSE, NSE-Log, and PBIAS ranging in 0.36-0.87, 0.43-88, and 0.001%-34%, respectively. However, most of the model parameters showed limited identifiability, limiting the power of the model for the assessment of climate and land cover changes. Overall, WFDEI climate data provided the best performance in parameter identification, while demonstrating a superior performance in reproducing observed streamflows

Catering for Uncertainty in a Conceptual Rainfall Runoff Model: Model Preparation for Climate Change Impact Assessment and the Application of GLUE using Latin Hypercube Sampling

Changes in Irish climate may pose a number of obstacles for water resource management. There is a need to approach this problem using the catchment as the basic unit of analysis. The application of a lumped conceptual rainfall-runoff model for simulating beyond a baseline calibration set is a major challenge for climate change impact assessment. This is due in no small part to the limitations associated with the use of these models, with uncertainty in model output being associated with model structure and the non-uniqueness of optimised parameter sets. In this paper, HYSIM, an “off-the-shelf” conceptual rainfall runoff model using data on a daily time-step is applied to a suite of catchments throughout Ireland in preparation for use with downscaled climate data. Uncertainties relating to process parameter calibration due to parameter interaction and equifinality are highlighted. In an attempt to improve the reliability of model output the generalised likelihood uncertainty estimation (GLUE) framework is adopted to analyse the uncertainty in model output derived from parametric sources. Traditionally this approach has been applied using Monte Carlo random sampling (MCRS). However, when using an “off-the-shelf” type model, source code may not be available and it may not be feasible to run the model for large MCRS samples without user intervention. In order to make the propagation of uncertainty through the model more efficient, input parameter sets are generated using Latin Hypercube sampling (LHS). A number of acceptable parameter sets are generated and uncertainty bounds are constructed for each time step using the 5th and 95th percentile at each temporal interval. These uncertainty bounds will be used to quantify the uncertainty in simulations carried out beyond the baseline calibration period as they include the error derived from data measurement, model structure, and parameterisation

Bayesian calibration of fluvial flood models for risk analysis

Flood risk analysis is now fundamental to ood management decision making. It relies on the use of computer models to estimate ood depths for given hydrological conditions. The correct calculation of risks associated with di erent management options requires that the uncertainty in the computer model output is carefully estimated. There are several sources of uncertainty in flood models, including structural uncertainties in the model representation of reality, uncertainty in model parameters, and observation errors. We refer to the rst of these as "model inadequacy". The work described in this thesis concerns the calibration of computer models to describe fluvial flooding, taking into account model inadequacy and paying particular attention to the requirements of risk analysis calculations. A methodology which has had some success in other application areas is Bayesian model calibration, using Gaussian process representation both for the error arising from model inadequacy, and to emulate the computer model output. The e ectiveness of this methodology is demonstrated for steady state flood models, both of a series of laboratory experiments, and of a historical ood using a satellite image of flood outline for calibration. Extension of the methodology to calibration of dynamic models using gauged data is not straightforward, but is achieved for flood models by means of an emulator, which replaces the computationally expensive hydrodynamic model with a time-dependent transfer function. This permits calibrated prediction of floods using historical gauged data, both in the existing channel and after modelling potential modi cations to the channel. It is shown that calibration without inclusion of a model inadequacy function cannot match measured data. Finally, application of the methodology is demonstrated in the context of a calculation of probability of inundation in the channel, both with and without modi cation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modeling hydrological consequences of climate and land use change - Progress and Challenges

RESUMEN Este artículo resume el estado del arte en relación a la modelación hidrológica y su capacidad de predecir los efectos del cambio climático y del uso de la tierra sobre el ciclo hidrológico. En conclusión, se abordan los retos investigativos que la comunidad científica debería considerar para poder observar y modelar, a escala regional, el ciclo hidrometeorológico en respuesta a los cambios de origen antropogénico sobre el clima y el uso de la tierra. Los resultados presentados en este documento son el producto de una extensa revisión de literatura científica y de la experiencia personal de los autores. Dado que el enfoque principal de este artículo es el de modelación hidrológica, muchos otros aspectos tales como las buenas prácticas de la gestión integrada de los recursos hídricos, la gobernabilidad transfronteriza de las aguas, el establecimiento de marcos de trabajo institucionales, la potenciación de las comunidades locales para su participación efectiva en la gestión del agua y en la formulación de políticas, entre muchos otros aspectos muy relevantes, no se consideran en este artículo para evitar que el enfoque del mismo se diluya de manera innecesaria. Palabras clave: Modelación hidrológica, cambio climático, cambio del uso de la tierra, retos de investigación, revisión extensa de literatura. ABSTRACT This paper provides the state-of-the-art in hydrologic modeling regarding its capability of predicting the effects of climate and land use change on the hydrological cycle. In conclusion, the research challenges are pinpointed, which the community should address as to be able to observe and model, at a regional-scale, the coupled climate-water cycle in response to the human induced changes in climate and land use. The findings presented herein are a compilation resulting from an extensive literature review and the personal expertise of the authors. Given the focus on hydrologic modeling, many other aspects such as the best practices of integrated water resources management, cross-boundary water governance, the establishment of institutional frameworks, empowerment of local communities to participate effectively in water management and policy making, among many other very relevant aspects, were intentionally not considered as not to dilute the focus of the paper

A snow and glacier hydrological model for large catchments – case study for the Naryn River, central Asia

In this paper we implement a degree day snowmelt and glacier melt model in the Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) model. The purpose is to develop a hydrological model that can be applied to large glaciated and snow-fed catchments yet is computationally efficient enough to include model uncertainty in streamflow predictions. The model is evaluated by simulating monthly discharge at six gauging stations in the Naryn River catchment (57 833 km2) in central Asia over the period 1951 to a variable end date between 1980 and 1995 depending on the availability of discharge observations. The spatial distribution of simulated snow cover is validated against MODIS weekly snow extent for the years 2001–2007. Discharge is calibrated by selecting parameter sets using Latin hypercube sampling and assessing the model performance using six evaluation metrics. The model shows good performance in simulating monthly discharge for the calibration period (NSE is 0.74&lt;NSE&lt;0.87) and validation period (0.7&lt;NSE&lt;0.9), where the range of NSE values represents the 5th–95th percentile prediction limits across the gauging stations. The exception is the Uch-Kurgan station, which exhibits a reduction in model performance during the validation period attributed to commissioning of the Toktogul reservoir in 1975 which impacted the observations. The model reproduces the spatial extent in seasonal snow cover well when evaluated against MODIS snow extent; 86 % of the snow extent is captured (mean 2001–2007) for the median ensemble member of the best 0.5 % calibration simulations. We establish the present-day contributions of glacier melt, snowmelt and rainfall to the total annual runoff and the timing of when these components dominate river flow. The model predicts well the observed increase in discharge during the spring (April–May) associated with the onset of snow melting and peak discharge during the summer (June, July and August) associated with glacier melting. Snow melting is the largest component of the annual runoff (89 %), followed by the rainfall (9 %) and the glacier melt component (2 %), where the values refer to the 50th percentile estimates at the catchment outlet gauging station Uch-Kurgan. In August, glacier melting can contribute up to 66 % of the total runoff at the highly glacierized Naryn headwater sub-catchment. The glaciated area predicted by the best 0.5 % calibration simulations overlaps the Landsat observations for the late 1990s and mid-2000s. Despite good predictions for discharge, the model produces a large range of estimates for the glaciated area (680–1196 km2) (5th–95th percentile limits) at the end of the simulation period. To constrain these estimates further, additional observations such as glacier mass balance, snow depth or snow extent should be used directly to constrain model simulations.</p

Operational progression of digital soil assessment for agricultural growth in Tasmania, Australia

Tasmania, Australia, is currently undergoing a period of agricultural expansion through the development of new irrigation schemes across the State, primarily to stimulate the economy and ensure future food security. ‘Operational Progression of Digital Soil Assessment (DSA) for Agricultural Growth in Tasmania, Australia’ presents the adaptation and operationalisation of quantitative approaches for regional land evaluation within these schemes, specifically applied Digital Soil Mapping (DSM) to inform a land suitability evaluation for 20 different agricultural crops, and ultimately a spatial indication of the State’s agricultural versatility and capital. DSM had not previously been applied or tested in Tasmania; the research examines and validates DSM approaches with respect to the State’s unique and complex soils and biophysical interactions with climate and terrain, and how these apply to various agricultural land uses. The thesis is a major contribution to the methodology and development of one of the first major operational DSA programs in Australia, and forms a framework for this type of DSM approach to be used in future operational land evaluation elsewhere

Developing Efficient Strategies For Global Sensitivity Analysis Of Complex Environmental Systems Models

Complex Environmental Systems Models (CESMs) have been developed and applied as vital tools to tackle the ecological, water, food, and energy crises that humanity faces, and have been used widely to support decision-making about management of the quality and quantity of Earth’s resources. CESMs are often controlled by many interacting and uncertain parameters, and typically integrate data from multiple sources at different spatio-temporal scales, which make them highly complex. Global Sensitivity Analysis (GSA) techniques have proven to be promising for deepening our understanding of the model complexity and interactions between various parameters and providing helpful recommendations for further model development and data acquisition. Aside from the complexity issue, the computationally expensive nature of the CESMs precludes effective application of the existing GSA techniques in quantifying the global influence of each parameter on variability of the CESMs’ outputs. This is because a comprehensive sensitivity analysis often requires performing a very large number of model runs. Therefore, there is a need to break down this barrier by the development of more efficient strategies for sensitivity analysis. The research undertaken in this dissertation is mainly focused on alleviating the computational burden associated with GSA of the computationally expensive CESMs through developing efficiency-increasing strategies for robust sensitivity analysis. This is accomplished by: (1) proposing an efficient sequential sampling strategy for robust sampling-based analysis of CESMs; (2) developing an automated parameter grouping strategy of high-dimensional CESMs, (3) introducing a new robustness measure for convergence assessment of the GSA methods; and (4) investigating time-saving strategies for handling simulation failures/crashes during the sensitivity analysis of computationally expensive CESMs. This dissertation provides a set of innovative numerical techniques that can be used in conjunction with any GSA algorithm and be integrated in model building and systems analysis procedures in any field where models are used. A range of analytical test functions and environmental models with varying complexity and dimensionality are utilized across this research to test the performance of the proposed methods. These methods, which are embedded in the VARS–TOOL software package, can also provide information useful for diagnostic testing, parameter identifiability analysis, model simplification, model calibration, and experimental design. They can be further applied to address a range of decision making-related problems such as characterizing the main causes of risk in the context of probabilistic risk assessment and exploring the CESMs’ sensitivity to a wide range of plausible future changes (e.g., hydrometeorological conditions) in the context of scenario analysis