Improving global and catchment estimates of runoff through computationally-intelligent ensemble approaches : applications of intelligent multi-model combination, cross-scale model comparisons, ensemble analyses, and new model parameterisations

Water related problems (scarcity, availability and hazards) together form one of the three major crises (the two other are food and energy) for today and the future across the globe (World Economic Forum, 2016, Schewe et al., 2014a, Hanasaki et al., 2013, Rockstrom et al., 2009). Water crises are widespread and heterogeneous around the world and climate change and socioeconomic drivers are expected to accelerate these problems (Veldkamp, 2017). To deal with the above concerns, mitigation and adaptation strategies are developed at different scales (global, regional and local). Developing these strategies, as well as selecting the most appropriate one to the problem of interest, should ideally benefit from the highest possible accuracy in estimates of the hydrological cycle and water resources. More reliable decisions can in turn be made by applying tools and techniques that enhance decision makers' perception of the hydrological cycle, particularly extreme events i.e. droughts and floods. These tools should also facilitate insights into the cycle: ideally by reference to hydrological indicators, spatially (globally and locally) and temporally (present day and future). Global and catchment scale hydrological models (GHMs and CHMs) have been used as such tools that along with advances in data acquisition, analytical techniques and computation power offer powerful tools for modelling natural processes and provide useful insights into the hydrological cycle. GHMs have a shorter history of emergence and application than CHMs. GHMs have been developed and applied from 1986 in recognition of the fact that hydrological processes and water resources are global phenomena and should be treated at global scale (Bierkens, 2015). A GHM is a pragmatic trade-off between a faithful representation of the diversity of hydrological processes found across the world's catchments, and a generalised and simplified representation of hydrological processes that can support multi-decadal, generalised hydrological simulations at global scales. Compared to hydrological models designed for catchment-scale simulations (Arnold et al., 1993; Krysanova et al., 1998; Lindstrom et al., 2010), GHMs employ coarser spatial discretisation and model the global land surface in a single instantiation. The global scope of GHMs, limited availability and quality of observed discharge data across the global domain and their use of spatially generalised parameters make them more difficult to calibrate than catchment hydrological models. Whilst examples of calibrated GHMs do exist (Müller Schmied et al., 2016), the majority of GHMs are uncalibrated (Gosling et al., 2016; Hattermann et al., 2017). This lack of calibration, coupled with the diversity of simplifications employed in the hydrological process representations, means that there can be large inconsistency in the skill, bias and uncertainty of an individual GHM at different locations, as well as large inconsistencies between different GHMs at any given location (van Huijgevoort et al., 2013). This spatial inconsistency means that GHMs risk becoming a "jungle of models" (Kundzewicz, 1986) in which it can be difficult to determine where a particular GHM output is likely to be capable of delivering optimal hydrological simulations. It also makes it dangerous to assume that any individual GHM will be an adequate basis for making projections at any given location, even if the model's ability to replicate observed data in particular catchments is enhanced through the acquisition of higher quality input data or efforts to improve process representations (Liu et al., 2007). To an extent, these arguments are also applicable to CHMs because whilst they have been shown to generally perform better than GHMs in model evaluation studies, ensembles of such models still result in an uncertainty range when the models are run with identical inputs (Hattermann et al., 2017; Hattermann et al., 2018). To minimise the challenge of varying outputs from different models, several model inter-comparison projects (MIPs) have been undertaken around the world (Henderson-Sellers et al., 1995, Entin et al., 1999, Guo and Dirmeyer, 2006, Koster et al., 2006, Harding et al., 2011). These projects usually use standard modelling baselines to deal with discrepancies between model outputs. This results in higher consistency in the climate forcings input to the models (where applicable), their process representations (e.g. the simulation of human impacts such as water abstractions), and the temporal and spatial resolutions of their simulations. This way, model outputs are directly comparable to each other, which supports diagnostic inter-comparisons between them (Bierkens, 2015). One of the largest, ongoing MIPs (whose data are used in this thesis) is the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP) (Schellnhuber et al., 2014, Warszawski et al., 2014). ISIMIP is a community-driven effort by more than 130 modelling groups, that covers different sectors including water (both global and catchment hydrological modelling communities). Outputs from ISIMIP are widely used in different projects, such as reports of the International Panel on Climate Change (IPCC) (http://www.ipcc.ch/). MIPs including ISIMIP provide a unique opportunity to access data from different models and to assess their relative performance. It also facilitates continuous model improvement via the inclusion of new schemes (e.g. human impacts such as dams, reservoirs and water abstractions) accompanied by dozens of models, as well as communication between modelling groups working in the same or different sectors. Nonetheless, they do not fully address the challenge of spatial inconsistencies between models, as well as the question of what ensemble representative to select for use when trying to improve the reliability of decision-making. There remain other shortcomings or unexplored aspects within MIPs (particularly ISIMIP as this research's focus MIP), hence areas of further research and potential improvement in model evaluation and application which will be addressed later in this introduction. The question of how to address the challenges of spatial inconsistency in hydrological models has been a feature of catchment-scale model research for several decades. In answering it, catchment modellers have recognised that reliance on a single, inconsistent model is inherently risky and should be avoided (Marshall et al., 2006; Shamseldin et al., 1997). Instead, they have developed ways to take advantage of the diversity of outputs (Clemen, 1989) generated by different models by using optimised mathematical combination methods to deliver a combined output that performs better than the individual models from which it was created (Hagedorn et al., 2005). This general approach-known as multi-model combination (MMC)-has been an important focus of catchment hydrological modelling studies, especially over the last two decades (Abrahart and See, 2002; Ajami et al., 2006; Arsenault et al., 2015; Azmi et al., 2010; de Menezes et al., 2000; Fernando et al., 2012; Jeong and Kim, 2009; Marshall et al., 2007; Marshall et al., 2006; Moges et al., 2016; Nasseri et al., 2014; Sanderson and Knutti, 2012; Shamseldin et al., 1997). Given its demonstrable potential in catchment studies, it is perhaps surprising that the potential of applying MMC to GHMs has yet to be explored. A wide range of techniques can be used to generate an MMC solution. The simplest example is the calculation of the arithmetic mean of the input models (commonly referred to as an Ensemble Mean (EM)). More sophisticated techniques employ weighted schemes (Arsenault et al., 2015), with the differential weightings applied to each input model reflecting their relative strengths or weaknesses. The mathematical approach taken to determining the weights depends on the objective of the MMC. Where the primary objective is to minimise the difference between the MMC solution and observed data (i.e. maximise the predictive performance), without explicitly accounting for model or parameter uncertainty, the use of multiple linear regression (Doblas-Reyes et al., 2005) or machine learning algorithms to 'learn' the optimal set weights to apply to each MMC input model is a popular approach (Marshall et al., 2007).