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Hydrologic models are essential tools to understand and plan for the effect of changing climates; however, they underperform in transitory climate conditions. Existing research identifies models' inadequacy to perform during prolonged drought, but falls short on pinpointing which specific aspects of model performance are affected. We study five conceptual rainfall‐runoff models and their performance in 155 Australian catchments which recently experienced a 13‐year long drought. We use a wide range of performance metrics and a methodology based on ranked differences to a benchmark to fairly compare levels of degradation across metrics and periods. We show model performance degrading extensively during and after the drought, largely driven by overestimation of flow. Representation of shape and variability of hydrograph and flow‐duration curve are more resilient to the prolonged dry climate and rarely more degraded during the multi‐annual drought that on isolated dry years in the pre‐drought record. Conversely, volumetric error suffers from significant exacerbation over the multiple subsequent dry years. This indicates that catchment retention times and rates of storage depletion storage are significantly less affected by the drought than amounts of streamflow produced, pointing to a mismatch between reduction of influxes and out‐fluxes during the drought. We also identify a deficiency of models to delay and remove flow before it reaches the stream and keep track of moisture deficits over multiple dry seasons. By promoting rigorous investigation of models' shortcomings, we hope to foster the development of more robust model structures and/or calibration frameworks to improve applicability within climate change scenarios. Key Points: We compare aspects of model performance during and after multi‐annual drought against pre‐drought performancePerformance degradation is driven by bias in water balance estimates rather than errors in hydrograph shapeAccumulation and aggravation of errors over multiple dry years exacerbates performance degradation [ABSTRACT FROM AUTHOR]

The Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) is a flexible modelling framework reproducing the behaviour of 47 established hydrological models. This toolbox can be used to calibrate and run models in a user-friendly and consistent way and is designed to facilitate the sharing of model code for reproducibility and to support intercomparison between hydrological models. Additionally, it allows users to create or modify models using components of existing ones. We present a new MARRMoT release (v2.1) designed for improved speed and ease of use. While improved computational efficiency was the main driver for this redevelopment, MARRMoT v2.1 also succeeds in drastically reducing the verbosity and repetitiveness of the code, which improves readability and facilitates debugging. The process to create new models or modify existing ones within the toolbox is also simplified in this version, making MARRMoT v2.1 accessible for researchers and practitioners at all levels of expertise. These improvements were achieved by implementing an object-oriented structure and aggregating all common model operations into a single class definition from which all models inherit. The new modelling framework maintains and improves on several good practices built into the original MARRMoT and includes a number of new features such as the possibility of retrieving more output in different formats that simplifies troubleshooting, and a new functionality that simplifies the calibration process. We compare outputs of 36 of the models in the framework to an earlier published analysis and demonstrate that MARRMoT v2.1 is highly consistent with the previous version of MARRMoT (v1.4), while achieving a 3.6-fold improvement in runtime on average. The new version of the toolbox and user manual, including several workflow examples for common application, are available from GitHub (https://github.com/wknoben/MARRMoT , last access: 12 May 2022; 10.5281/zenodo.6484372, Trotter and Knoben, 2022b). [ABSTRACT FROM AUTHOR]

The Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) is a flexible modelling framework reproducing the behaviour of 47 established hydrological models. MARRMoT can be used to calibrate and run models in a user-friendly and consistent way and is designed to facilitate the sharing of model code for reproducibility and to support intercomparison between hydrological models. Additionally, it allows users to create or modify models using components of existing ones. We present a new MARRMoT release (v2.1) designed for improved speed and ease of use. Whereas improved computational efficiency was the main driver for this redevelopment, MARRMoT v2.1 also succeeds in drastically reducing the verbosity and repetitiveness of the code, which improves readability and facilitates debugging. The process to create new models or modify existing ones within the toolbox is also simplified in this version, making MARRMoT v2.1 accessible for researchers and practitioners at all levels of expertise. These improvements were achieved by implementing an object-oriented structure and aggregating all common model operations into a single class definition that all models inherit from. The new modelling framework maintains and improves on several of the good practices built into the original MARRMoT and includes a number of new features such as the possibility of retrieving more output in different formats, which simplifies troubleshooting, and a new functionality that simplifies the calibration process. We compare outputs of 36 of the models in the framework to an earlier published analysis and demonstrate that MARRMoT v2.1 is highly consistent with the previous version of MARRMoT (v1.4), while achieving a 3.6-fold improvement in runtime on average. The new version of the toolbox and user manual, including several workflow examples for common application, are available from GitHub. [ABSTRACT FROM AUTHOR]

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 https://github.com/wknoben/MARRMoT (last access: 30 May 2019; 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. [ABSTRACT FROM AUTHOR]

Hydrologic models have potential to be useful tools in planning for future climate variability. However, recent literature suggests that the current generation of conceptual rainfall runoff models tend to underestimate the sensitivity of runoff to a given change in rainfall, leading to poor performance when evaluated over multiyear droughts. This research revisited this conclusion, investigating whether the observed poor performance could be due to insufficient model calibration and evaluation techniques. We applied an approach based on Pareto optimality to explore trade-offs between model performance in different climatic conditions. Five conceptual rainfall runoff model structures were tested in 86 catchments in Australia, for a total of 430 Pareto analyses. The Pareto results were then compared with results from a commonly used model calibration and evaluation method, the Differential Split Sample Test. We found that the latter often missed potentially promising parameter sets within a given model structure, giving a false negative impression of the capabilities of the model. This suggests that models may be more capable under changing climatic conditions than previously thought. Of the 282[347] cases of apparent model failure under the split sample test using the lower [higher] of two model performance criteria trialed, 155[120] were false negatives. We discuss potential causes of remaining model failures, including the role of data errors. Although the Pareto approach proved useful, our aim was not to suggest an alternative calibration strategy, but to critically assess existing methods of model calibration and evaluation. We recommend caution when interpreting split sample results. [ABSTRACT FROM AUTHOR]

Previous studies have reported relationships between mean annual climatic variables and mean annual leaf area index (LAI), but the seasonal and spatial variability of this relationship for different vegetation cover types in different climate zones have rarely been explored in Australia. The authors developed simple models using remotely sensed LAI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and gridded climatic data from the Australian Water Availability Project. They were able to relate seasonal and annual LAI of three different land cover types (tree, pasture, and crop) with climatic variables for the period 2000-09 in the Goulburn-Broken catchment, Australia. Strong relationships were obtained between annual LAI of crop, pasture, and tree with annual precipitation ( R2 = 0.70, 0.65, and 0.82, respectively). Monthly LAI of each land cover type also showed a strong relationship ( R2 = 0.92, 0.95, and 0.95) with the difference between precipitation P and reference crop evapotranspiration (PET; P − PET) for crop, pasture, and tree. Independent model calibration and validation showed good agreement with remotely sensed MODIS LAI. The results from the application of the developed model on the future impact of climate change suggest that under all climate scenarios crop, pasture, and tree showed consistent decreases in mean annual LAI. For the future climate change scenarios considered, crop showed a decline of 7%-38%, pasture showed a decline of 5%-24%, and tree showed a decline of 2%-11% from the historical mean annual. These results can be used to assess the impacts of future climatic and land cover changes on water resources by coupling them with hydrological models. [ABSTRACT FROM AUTHOR]

Satellite and gridded meteorological data can be used to estimate evaporation ( E) from land surfaces using simple diagnostic models. Two satellite datasets indicate a positive trend (first time derivative) in global available energy from 1983 to 2006, suggesting that positive trends in evaporation may occur in 'wet' regions where energy supply limits evaporation. However, decadal trends in evaporation estimated from water balances of 110 wet catchments do not match trends in evaporation estimated using three alternative methods: 1) , a model-tree ensemble approach that uses statistical relationships between E measured across the global network of flux stations, meteorological drivers, and remotely sensed fraction of absorbed photosynthetically active radiation; 2) , a Budyko-style hydrometeorological model; and 3) , the Penman-Monteith energy-balance equation coupled with a simple biophysical model for surface conductance. Key model inputs for the estimation of and are remotely sensed radiation and gridded meteorological fields and it is concluded that these data are, as yet, not sufficiently accurate to explain trends in E for wet regions. This provides a significant challenge for satellite-based energy-balance methods. Trends in for 87 'dry' catchments are strongly correlated to trends in precipitation ( R2 == 0.85). These trends were best captured by , which explicitly includes precipitation and available energy as model inputs. [ABSTRACT FROM AUTHOR]