Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges

Large-sample hydrology (LSH) relies on data from large sets (tens to thousands) of catchments to go beyond individual case studies and derive robust conclusions on hydrological processes and models. Numerous LSH datasets have recently been released, covering a wide range of regions and relying on increasingly diverse data sources to characterize catchment behaviour. These datasets offer novel opportunities, yet they are also limited by their lack of comparability, uncertainty estimates and characterization of human impacts. This article (i) underscores the key role of LSH datasets in hydrological studies, (ii) provides a review of currently available LSH datasets, (iii) highlights current limitations of LSH datasets and (iv) proposes guidelines and coordinated actions to overcome these limitations. These guidelines and actions aim to standardize and automatize the creation of LSH datasets worldwide, and to enhance the reproducibility and comparability of hydrological studies

Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) v1.2:an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations

This paper presents the Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT): A modular open-source toolbox containing documentation and model code based on 46 existing conceptual hydrologic models. The toolbox is developed in MATLAB and works with Octave. MARRMoT models are based solely on traceable published material and model documentation, not on already-existing computer code. Models are implemented following several good practices of model development: The definition of model equations (the mathematical model) is kept separate from the numerical methods used to solve these equations (the numerical model) to generate clean code that is easy to adjust and debug; the implicit Euler time-stepping scheme is provided as the default option to numerically approximate each model's ordinary differential equations in a more robust way than (common) explicit schemes would; threshold equations are smoothed to avoid discontinuities in the model's objective function space; and the model equations are solved simultaneously, avoiding the physically unrealistic sequential solving of fluxes. Generalized parameter ranges are provided to assist with model inter-comparison studies. In addition to this paper and its Supplement, a user manual is provided together with several workflow scripts that show basic example applications of the toolbox. The toolbox and user manual are available from span classCombining double low line"uri"https://github.com/wknoben/MARRMoT/span (last access: 30 May 2019; a hrefCombining double low line"https://doi.org/10.5281/zenodo.3235664"https://doi.org/10.5281/zenodo.3235664). Our main scientific objective in developing this toolbox is to facilitate the inter-comparison of conceptual hydrological model structures which are in widespread use in order to ultimately reduce the uncertainty in model structure selection

Simulating Runoff Under Changing Climatic Conditions:A Framework for Model Improvement

Rainfall-runoff models are often deficient under changing climatic conditions, yet almost no recent studies propose new or improved model structures, instead focusing on model intercomparison, input sensitivity, and/or quantification of uncertainty. This paucity of progress in model development is (in part) due to the difficulty of distinguishing which cases of model failure are truly caused by structural inadequacy. Here we propose a new framework to diagnose the salient cause of poor model performance in changing climate conditions, be it structural inadequacy, poor parameterization, or data errors. The framework can be applied to a single catchment, although larger samples of catchments are helpful to generalize and/or cross-check results. To generate a diagnosis, multiple historic periods with contrasting climate are defined, and the limits of model robustness and flexibility are explored over each period separately and for all periods together. Numerous data-based checks also supplement the results. Using a case study catchment from Australia, improved inference of structural failure and clearer evaluation of model structural improvements are demonstrated. This framework enables future studies to (i) identify cases where poor simulations are due to poor calibration methods or data errors, remediating these cases without recourse to structural changes; and (ii) use the remaining cases to gain greater clarity into what structural changes are needed to improve model performance in changing climate

Australian non-perennial rivers: Global lessons and research opportunities

Non-perennial rivers are valuable water resources that support millions of humans globally, as well as unique riparian ecosystems. In Australia, the Earth’s driest inhabited continent, over 70% of rivers are non-perennial due to a combination of ancient landscape, dry climates, highly variable rainfall regimes, and human interventions that have altered riverine environments. Here, we review Australian non-perennial river research incorporating geomorphology, hydrology, biogeochemistry, ecology, and Indigenous knowledges. The dominant research themes in Australia were drought, floods, salinity, dryland ecology, and water management. Future research will likely follow these themes but must address emerging threats to river systems due to climate change and other anthropogenic impacts. Four high level opportunities for future research are identified, namely: (1) integrating Indigenous and western scientific knowledge; (2) quantifying climate change impacts on hydrological and biological function; (3) clarifying the meaning and measurement of “restoration” of non-perennial systems; and (4) understanding the role of groundwater. These challenges will require inter- and multi-disciplinary efforts supported by technological advances. The evolving body of knowledge about Australian rivers provides a foundation for comparison with other dryland areas globally where recognition of the importance of non-perennial rivers is expanding

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

Towards improved rainfall-runoff modelling in changing climatic conditions

© 2017 Dr. Keirnan James Andrew FowlerRainfall-runoff models are useful tools in water resource planning under climate change. They are commonly used to quantify the impact of changes in climatic variables, such as rainfall, on water availability for human consumption or environmental needs. Many parts of the world are likely to see changes in future climate, and some regions are projected to be substantially drier, possibly with threatened water resources. Given the importance of water to the economy, environment, geopolitical stability and social wellbeing, reliable tools for understanding future water availability are vital. However, literature would suggest that the current generation of rainfall-runoff models are not reliable when applied in changing climatic conditions. Simulations of historic case studies such as the Millennium Drought in South East Australia indicate that models often perform poorly, underestimating the sensitivity of runoff to a given change in precipitation. Many hydrologists have assumed that these deficiencies are due to the model structures themselves - that is, the underlying model equations. However, it is possible that the explanation is broader, and can only be understood via holistic approaches that examine the entire modelling process. This research, presented in four parts, aims to understand and improve various elements of this process. Part 1 investigates whether poor model performance is due to insufficient model calibration and evaluation techniques. An approach based on Pareto optimality is used to explore trade-offs between model performance in different climatic conditions. Five conceptual rainfall-runoff model structures are tested in 86 catchments in Australia. Comparison of Pareto results with a commonly used calibration method reveals that the latter often misses potentially promising parameter sets within a given model structure, giving a false negative impression of the capabilities of the model. This suggests that existing model structures may be more capable under changing climatic conditions than previously thought. The aim of Part 1 is to critically assess commonly used methods of model calibration and evaluation, rather than to develop an alternative calibration strategy. The results indicate that caution is needed when interpreting the results of differential split sample tests. Having demonstrated deficiencies in commonly used calibration methods, Parts 2 and 3 examine alternative calibration strategies. The aim is to identify calibration metrics capable of finding parameter sets with robust performance, even if climatic conditions change compared to the calibration period. Part 2 follows a three-part process to identify which metrics (if any) can identify the robust parameter sets using pre-change data only. The three parts are: randomly generating a large ensemble of parameter sets; identifying parameter sets in the ensemble that provide robust simulations both before and after a change (drying) in climatic conditions; and calculating multiple performance metrics for each ensemble member. Traditional objective functions are trialled, along with less common indices such as the degree of replication of observed hydrologic signatures. The most promising metrics are then tested more rigourously in Part 3, which uses guided search algorithms selected in accordance with metric type (objective function or hydrologic signature), including: calibration by matching of hydrologic signatures (using the DREAM-ABC algorithm), optimisation of global objective functions (using the CMA-ES algorithm), and hybrid approaches blending global objective functions with signatures (using the Pareto approach AMALGAM). The results indicate considerable scope for improved calibration, relative to commonly used approaches. Metrics that consider dynamics over a variety of timescales (eg. annual, not just daily) are more promising, as are objective functions using the sum of absolute errors rather than the sum of squared errors. The key recommendations of Part 2 and 3 are to avoid `least squares' approaches (such as optimising the NSE, RMSE and similar approaches like the KGE) and adopt sum of absolute error and/or metrics considering a variety of timescales, wherever simulations of a drying climate are required. Parts 1-3 confirm the importance of calibration methods when modelling under changing climates. This raises the question: in what circumstances should the focus be on improving calibration methods versus improving model structures, or alternatively on other issues such as poor data quality? Although recent literature has presented various tools for model evaluation - usually using variants of the Differential Split Sample Test (DSST) - there is less focus on such questions. Thus, a modeller whose model has failed the DSST is largely without guidance as to next steps. Part 4 provides guidance for this question within a framework based on Pareto optimality. Similarly to Part 1, modelling objectives are set over multiple historic periods with contrasting climatic conditions. The framework allows cases of DSST failure to be categorised as either: (a) cases of model structural failure, where no parameter set in a model structure can meet all modelling objectives in all periods, indicating the need for structural changes or improved data; or (b) cases where modelling objectives are attainable by the model structure, but the DSST calibration method failed to find the right parameter set(s). The framework outlines separate steps to follow for each of the above categories. Many steps in the framework can be populated by existing sensitivity analysis techniques, but new techniques are designed for some steps, such as the diagnosis of structural inadequacies by analysis of `drift' in hydrologic signature error as climatic conditions change. The framework is demonstrated using a case study from Australia and the IHACRES model structure. Limitations of inferring future hydrologic processes from historic data are also discussed. This research underscores the joint importance of model structures and calibration methods when modelling changing climatic conditions, providing practical guidance for holistic improvement of the modelling process. By prompting more credible runoff projections, it is hoped that this research leads to more robust decisions that safeguard the future of water resources for people and our planet

CAMELS-AUS v1: Hydrometeorological time series and landscape attributes for 222 catchments in Australia

This is the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 unregulated catchments, combining hydrometeorological timeseries (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence, and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85% have streamflow records longer than 40 years) and are relatively free of large scale changes, such as significant changes in landuse. Rating curve uncertainty estimates are provided for most (75%) of the catchments and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. --- To download the dataset, please click the link below "View dataset as HTML"

Data from "A brief analysis of conceptual model structure uncertainty using 36 models and 559 catchments"

Data generated for the paper "A brief analysis of conceptual model structure uncertainty using 36 models and 559 catchments". Briefly, we simulate streamflow in 559 non-human impacted catchments across the United States, using 36 unique lumped (i.e. catchment-averaged) hydrological model structures. We calibrate the models and quantify the accuracy of the simulations using three different objective functions. This gives a total sample of 60732 model application test cases. This data set contains for each combination of model, catchment and objective function the (i) calibrated parameter values, (ii) objective function values during the calibration and evaluation period, (iii) time series of simulated fluxes and model states, and (iv) duration of model warm-up period. General settings and calibration choices are given in the metadata of each file. The input data and models used to generate this data set are freely available from their respective references (given in the readme file)

Data from "A brief analysis of conceptual model structure uncertainty using 36 models and 559 catchments"

Data generated for the paper "A brief analysis of conceptual model structure uncertainty using 36 models and 559 catchments". Briefly, we simulate streamflow in 559 non-human impacted catchments across the United States, using 36 unique lumped (i.e. catchment-averaged) hydrological model structures. We calibrate the models and quantify the accuracy of the simulations using three different objective functions. This gives a total sample of 60732 model application test cases. This data set contains for each combination of model, catchment and objective function the (i) calibrated parameter values, (ii) objective function values during the calibration and evaluation period, (iii) time series of simulated fluxes and model states, and (iv) duration of model warm-up period. General settings and calibration choices are given in the metadata of each file. The input data and models used to generate this data set are freely available from their respective references (given in the readme file)

Many commonly used rainfall‐runoff models lack long, slow dynamics:implications for runoff projections

Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic conditions in past years or even decades. Here we demonstrate that five commonly used conceptual “bucket” rainfall‐runoff models are unable to replicate multiyear trends exhibited by natural systems during the “Millennium Drought” in south‐east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13‐year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model “buckets” are already “emptying” on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall‐runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall‐runoff relationships in the context of a changing climate