Articles | Volume 14, issue 11
Model description paper
19 Nov 2021
Model description paper | 19 Nov 2021
SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models
Marco Dal Molin et al.
Marco Dal Molin, Mario Schirmer, Massimiliano Zappa, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 24, 1319–1345,
Richard Laugesen, Mark Thyer, David McInerney, and Dmitri Kavetski
Hydrol. Earth Syst. Sci. Discuss.,
Preprint under review for HESSShort summary
Forecasts may be valuable for user decisions but current practice to quantify it has critical limitations. This study presents a new metric that can be tailored to specific decisions and decision-makers, and shows that streamflow forecasts out to 30 days provide high value for almost all users, but not always. This study paves the way for agencies to tailor the evaluation of their services to customer decisions, and researchers to study model improvements through the lens of user impact.
Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript under review for HESSShort summary
For stream flow predictions in hydrology, commonly two types of models are used: deep learning models (high predictive performance) and ODE-based conceptual hydrologic models (fully interpretable, encoding scientific assumptions). We introduce hydrologic Neural ODE models that fuse both approaches and have their benefits: We obtain state-of the-art predictive performance and gain insights into dynamics of model processes and states. We demonstrate the approach on a large real-world data set.
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci. Discuss.,
Preprint under review for HESSShort summary
Forecasts of streamflow a day to a month ahead are highly valuable for water management. Current practice often employs models developed for specific lead times. In contrast, a "seamless" forecast model is intended to serve multiple lead times. This study shows that the seamless model matches the performance of a model tuned specifically for monthly predictions – while providing forecasts at other lead times. This finding paves the way for wider practical adoption of seamless forecast models.
Marco Dal Molin, Mario Schirmer, Massimiliano Zappa, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 24, 1319–1345,
Lorenz Ammann, Fabrizio Fenicia, and Peter Reichert
Hydrol. Earth Syst. Sci., 23, 2147–2172,Short summary
The uncertainty of hydrological models can be substantial, and its quantification and realistic description are often difficult. We propose a new flexible probabilistic framework to describe and quantify this uncertainty. It is show that the correlation of the errors can be non-stationary, and that accounting for temporal changes in correlation can lead to strongly improved probabilistic predictions. This is a promising avenue for improving uncertainty estimation in hydrological modelling.
Andreas Moser, Devon Wemyss, Ruth Scheidegger, Fabrizio Fenicia, Mark Honti, and Christian Stamm
Hydrol. Earth Syst. Sci., 22, 4229–4249,Short summary
Many chemicals such as pesticides, pharmaceuticals or household chemicals impair water quality in many areas worldwide. Measuring pollution everywhere is too costly. Models can be used instead to predict where high pollution levels are expected. We tested a model that can be used across large river basins. We find that for the selected chemicals predictions are generally within a factor of 2 to 4 from observed concentrations. Often, knowledge about the chemical use limits the predictions.
Tanja de Boer-Euser, Laurène Bouaziz, Jan De Niel, Claudia Brauer, Benjamin Dewals, Gilles Drogue, Fabrizio Fenicia, Benjamin Grelier, Jiri Nossent, Fernando Pereira, Hubert Savenije, Guillaume Thirel, and Patrick Willems
Hydrol. Earth Syst. Sci., 21, 423–440,Short summary
In this study, the rainfall–runoff models of eight international research groups were compared for a set of subcatchments of the Meuse basin to investigate the influence of certain model components on the modelled discharge. Although the models showed similar performances based on general metrics, clear differences could be observed for specific events. The differences during drier conditions could indeed be linked to differences in model structures.
Related subject area
HydrologyA framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake SectorCLIMFILL v0.9: a framework for intelligently gap filling Earth observationsModeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake modelEvaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model couplingImproved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5GSTools v1.3: a toolbox for geostatistical modelling in PythonAI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methodsModeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.xTree hydrodynamic modelling of the soil–plant–atmosphere continuum using FETCH3Effects of dimensionality on the performance of hydrodynamic models for stratified lakes and reservoirsComputation of backwater effects in surface waters of lowland catchments including control structures – an efficient and re-usable method implemented in the hydrological open-source model Kalypso-NA (4.0)Inishell 2.0: semantically driven automatic GUI generation for scientific modelsIrrigation quality and management determine salinization in Israeli olive orchardsImplementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deploymentHydroPy (v1.0): a new global hydrology model written in PythonGMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale modelsSELF v1.0: a minimal physical model for predicting time of freeze-up in lakesPOET (v0.1): speedup of many-core parallel reactive transport simulations with fast DHT lookupsAssessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United StatesCosmic-Ray neutron Sensor PYthon tool (crspy 1.2.1): an open-source tool for the processing of cosmic-ray neutron and soil moisture dataThe eWaterCycle platform for Open and FAIR Hydrological collaborationDRYP 1.0: a parsimonious hydrological model of DRYland Partitioning of the water balanceHydroBlocks v0.2: enabling a field-scale two-way coupling between the land surface and river networks in Earth system modelsGP-SWAT (v1.0): a two-level graph-based parallel simulation tool for the SWAT modelDevelopment of a coupled simulation framework representing the lake and river continuum of mass and energy (TCHOIR v1.0)Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff productsEvaluating the Atibaia River Hydrology using JULES6.1Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0DecTree v1.0 – chemistry speedup in reactive transport simulations: purely data-driven and physics-based surrogatesUnderstanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communicationLISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUsInundatEd-v1.0: a height above nearest drainage (HAND)-based flood risk modeling system using a discrete global grid systemFluxes from soil moisture measurements (FluSM v1.0): a data-driven water balance framework for permeable pavementsParametrization of a lake water dynamics model MLake in the ISBA-CTRIP land surface system (SURFEX v8.1)The global water resources and use model WaterGAP v2.2d: model description and evaluationShyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrologyA distributed simple dynamical systems approach (dS2 v1.0) for computationally efficient hydrological modelling at high spatio-temporal resolutionSimulating second-generation herbaceous bioenergy crop yield using the global hydrological model H08 (v.bio1)KLT-IV v1.0: image velocimetry software for use with fixed and mobile platformsSimulating human impacts on global water resources using VIC-5The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case studyML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrientsThe latest improvements with SURFEX v8.0 of the Safran–Isba–Modcou hydrometeorological model for FranceA multirate mass transfer model to represent the interaction of multicomponent biogeochemical processes between surface water and hyporheic zones (SWAT-MRMT-R 1.0)MFIT 1.0.0: Multi-Flow Inversion of Tracer breakthrough curves in fractured and karst aquifersSimulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume methodHydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sourcesTIER version 1.0: an open-source Topographically InformEd Regression (TIER) model to estimate spatial meteorological fieldsAutomated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)glmGUI v1.0: an R-based graphical user interface and toolbox for GLM (General Lake Model) simulations
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623,Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596,Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295,Short summary
The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192,Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416,Short summary
This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182,Short summary
The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039,Short summary
The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947,Short summary
Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Marcela Silva, Ashley M. Matheny, Valentijn R. N. Pauwels, Dimetre Triadis, Justine E. Missik, Gil Bohrer, and Edoardo Daly
Geosci. Model Dev., 15, 2619–2634,Short summary
Our study introduces FETCH3, a ready-to-use, open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that FETCH3 can correctly simulate above- and below-ground water transport.
Mayra Ishikawa, Wendy Gonzalez, Orides Golyjeswski, Gabriela Sales, J. Andreza Rigotti, Tobias Bleninger, Michael Mannich, and Andreas Lorke
Geosci. Model Dev., 15, 2197–2220,Short summary
Reservoir hydrodynamics is often described in numerical models differing in dimensionality. 1D and 2D models assume homogeneity along the unresolved dimension. We compare the performance of models with 1 to 3 dimensions. All models presented reasonable results for seasonal temperature dynamics. Neglecting longitudinal transport resulted in the largest deviations in temperature. Flow velocity could only be reproduced by the 3D model. Results support the selection of models and their assessment.
Sandra Hellmers and Peter Fröhle
Geosci. Model Dev., 15, 1061–1077,Short summary
A hydrological method to compute backwater effects in surface water streams and on adjacent lowlands caused by mostly complex flow control systems is presented. It enables transfer of discharges to water levels and calculation of backwater volume routing along streams and lowland areas by balancing water level slopes. The developed, implemented and evaluated method extends the application range of hydrological models significantly for flood-routing simulation in backwater-affected catchments.
Mathias Bavay, Michael Reisecker, Thomas Egger, and Daniela Korhammer
Geosci. Model Dev., 15, 365–378,Short summary
Most users struggle with the configuration of numerical models. This can be improved by relying on a GUI, but this requires a significant investment and a specific skill set and does not fit with the daily duties of model developers, leading to major maintenance burdens. Inishell generates a GUI on the fly based on an XML description of the required configuration elements, making maintenance very simple. This concept has been shown to work very well in our context.
Vladimir Mirlas, Yaakov Anker, Asher Aizenkod, and Naftali Goldshleger
Geosci. Model Dev., 15, 129–143,Short summary
Salinization owing to irrigation water quality causes soil degradation and soil fertility reduction that with poor drainage conditions impede plant development and manifest in economic damage. This study provided a soil salting process evaluation procedure through a combination of soil salinity monitoring, field experiments, remote sensing, and unsaturated soil profile saline water movement modeling. The modeling results validated the soil salinization danger from using brackish irrigation.
Niccolò Tubini and Riccardo Rigon
Geosci. Model Dev., 15, 75–104,Short summary
This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the water and energy budgets in a soil column. WHETGEO-1D is intended to be the first building block of a new customisable land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code and is fully integrated into the GEOframe/OMS3 system, allowing the use of the many ancillary tools it provides.
Tobias Stacke and Stefan Hagemann
Geosci. Model Dev., 14, 7795–7816,Short summary
HydroPy is a new version of an established global hydrology model. It was rewritten from scratch and adapted to a modern object-oriented infrastructure to facilitate its future development and application. With this study, we provide a thorough documentation and evaluation of our new model. At the same time, we open our code base and publish the model's source code in a public software repository. In this way, we aim to contribute to increasing transparency and reproducibility in science.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571,Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
Marco Toffolon, Luca Cortese, and Damien Bouffard
Geosci. Model Dev., 14, 7527–7543,Short summary
The time when lakes freeze varies considerably from year to year. A common way to predict it is to use negative degree days, i.e., the sum of air temperatures below 0 °C, a proxy for the heat lost to the atmosphere. Here, we show that this is insufficient as the mixing of the surface layer induced by wind tends to delay the formation of ice. To do so, we developed a minimal model based on a simplified energy balance, which can be used both for large-scale analyses and short-term predictions.
Marco De Lucia, Michael Kühn, Alexander Lindemann, Max Lübke, and Bettina Schnor
Geosci. Model Dev., 14, 7391–7409,Short summary
POET is a parallel reactive transport simulator which implements a mechanism to store and reuse previous results of geochemical simulations through distributed hash tables. POET parallelizes chemistry using a master/worker design with noncontiguous grid partitions to maximize its efficiency and load balance on shared-memory machines and compute clusters.
Mary M. F. O'Neill, Danielle T. Tijerina, Laura E. Condon, and Reed M. Maxwell
Geosci. Model Dev., 14, 7223–7254,Short summary
Modeling the hydrologic cycle at high resolution and at large spatial scales is an incredible opportunity and challenge for hydrologists. In this paper, we present the results of a high-resolution hydrologic simulation configured over the contiguous United States. We discuss simulated water fluxes through groundwater, soil, plants, and over land, and we compare model results to in situ observations and satellite products in order to build confidence and guide future model development.
Daniel Power, Miguel Angel Rico-Ramirez, Sharon Desilets, Darin Desilets, and Rafael Rosolem
Geosci. Model Dev., 14, 7287–7307,Short summary
Cosmic-ray neutron sensors estimate root-zone soil moisture at sub-kilometre scales. There are national-scale networks of these sensors across the globe; however, methods for converting neutron signals to soil moisture values are inconsistent. This paper describes our open-source Python tool that processes raw sensor data into soil moisture estimates. The aim is to allow a user to ensure they have a harmonized data set, along with informative metadata, to facilitate both research and teaching.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research fully compatible with the principles of Open Science as well as FAIR science. eWatercyle gives easy access to well known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
E. Andrés Quichimbo, Michael Bliss Singer, Katerina Michaelides, Daniel E. J. Hobley, Rafael Rosolem, and Mark O. Cuthbert
Geosci. Model Dev., 14, 6893–6917,Short summary
Understanding and quantifying water partitioning in dryland regions are of key importance to anticipate the future impacts of climate change in water resources and dryland ecosystems. Here, we have developed a simple hydrological model (DRYP) that incorporates the key processes of water partitioning in drylands. DRYP is a modular, versatile, and parsimonious model that can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions.
Nathaniel W. Chaney, Laura Torres-Rojas, Noemi Vergopolan, and Colby K. Fisher
Geosci. Model Dev., 14, 6813–6832,Short summary
Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decades, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between tiling schemes and river networks in the HydroBlocks land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.
Dejian Zhang, Bingqing Lin, Jiefeng Wu, and Qiaoying Lin
Geosci. Model Dev., 14, 5915–5925,Short summary
GP-SWAT is a two-layer model parallelization tool for a SWAT model based on the graph-parallel Pregel algorithm. It can be employed to perform both individual and iterative model parallelization, endowing it with a range of possible applications and great flexibility in maximizing performance. As a flexible and scalable tool, it can run in diverse environments, ranging from a commodity computer with a Microsoft Windows, Mac or Linux OS to a Spark cluster consisting of a large number of nodes.
Daisuke Tokuda, Hyungjun Kim, Dai Yamazaki, and Taikan Oki
Geosci. Model Dev., 14, 5669–5693,Short summary
We developed TCHOIR, a hydrologic simulation framework, to solve fluvial- and thermodynamics of the river–lake continuum. This provides an algorithm for upscaling high-resolution topography as well, which enables the representation of those interactions at the global scale. Validation against in situ and satellite observations shows that the coupled mode outperforms river- or lake-only modes. TCHOIR will contribute to elucidating the role of surface hydrology in Earth’s energy and water cycle.
Marko Kallio, Joseph H. A. Guillaume, Vili Virkki, Matti Kummu, and Kirsi Virrantaus
Geosci. Model Dev., 14, 5155–5181,Short summary
Different runoff and streamflow products are freely available but may come with unsuitable spatial units. On the other hand, starting a new modelling exercise may require considerable resources. Hydrostreamer improves the usability of existing runoff products, allowing runoff and streamflow estimates at the desired spatial units with minimal data requirements and intuitive workflow. The case study shows that Hydrostreamer performs well compared to benchmark products and observation data.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
Land surface models allowing us to understand and investigate the cause and effect of environmental processes changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia river basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to predict the basin hydrology.
Peter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, and Hylke E. Beck
Geosci. Model Dev., 14, 4865–4890,Short summary
We present a cascade of models to compute high-resolution river flooding. This takes meteorological inputs, e.g., rainfall and temperature from observations or climate models, and takes them through a series of modeling steps. This is relevant to evaluating current day and future flood risk and impacts. The model framework uses global data sets, allowing it to be applied anywhere in the world.
Marco De Lucia and Michael Kühn
Geosci. Model Dev., 14, 4713–4730,Short summary
DecTree evaluates a hierarchical coupling method for reactive transport simulations in which pre-trained surrogate models are used to speed up the geochemical subprocess, and equation-based
full-physicssimulations are called only if the surrogate predictions are implausible. Furthermore, we devise and evaluate a decision tree surrogate approach designed to inject domain knowledge of the surrogate by defining engineered features based on law of mass action or stoichiometric reaction equations.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878,Short summary
We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
James Shaw, Georges Kesserwani, Jeffrey Neal, Paul Bates, and Mohammad Kazem Sharifian
Geosci. Model Dev., 14, 3577–3602,Short summary
LISFLOOD-FP has been extended with new shallow-water solvers – DG2 and FV1 – for modelling all types of slow- or fast-moving waves over any smooth or rough surface. Using GPU parallelisation, FV1 is faster than the simpler ACC solver on grids with millions of elements. The DG2 solver is notably effective on coarse grids where river channels are hard to capture, improving predicted river levels and flood water depths. This marks a new step towards real-world DG2 flood inundation modelling.
Chiranjib Chaudhuri, Annie Gray, and Colin Robertson
Geosci. Model Dev., 14, 3295–3315,Short summary
A flood risk estimation model for two study watersheds in Canada and an interactive visualization platform using publicly available hydrometric data are presented. The risk model uses a height above nearest drainage (HAND)-based solution for Manning’s formula and is implemented on a big-data discrete global grid system framework. Overall, the novel data model decreases processing time and provides easy parallelization, resulting in performance gains in online flood analytics.
Axel Schaffitel, Tobias Schuetz, and Markus Weiler
Geosci. Model Dev., 14, 2127–2142,Short summary
This paper presents FluSM, an algorithm to derive the water balance from soil moisture and metrological measurements. This data-driven water balance framework uses soil moisture as an input and therefore is applicable for cases with unclear processes and lacking parameters. In a case study, we apply FluSM to derive the water balance of 15 different permeable pavements under field conditions. These findings are of special interest for urban hydrology.
Thibault Guinaldo, Simon Munier, Patrick Le Moigne, Aaron Boone, Bertrand Decharme, Margarita Choulga, and Delphine J. Leroux
Geosci. Model Dev., 14, 1309–1344,Short summary
Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services such as freshwater supply. Despite the impact of lakes on the water cycle, they are generally not considered in global hydrological studies. Based on a model called MLake, we assessed both the importance of lakes in simulating river flows at global scale and the value of their level variations for water resource management.
Hannes Müller Schmied, Denise Cáceres, Stephanie Eisner, Martina Flörke, Claudia Herbert, Christoph Niemann, Thedini Asali Peiris, Eklavyya Popat, Felix Theodor Portmann, Robert Reinecke, Maike Schumacher, Somayeh Shadkam, Camelia-Eliza Telteu, Tim Trautmann, and Petra Döll
Geosci. Model Dev., 14, 1037–1079,Short summary
In a globalized world with large flows of virtual water between river basins and international responsibilities for the sustainable development of the Earth system and its inhabitants, quantitative estimates of water flows and storages and of water demand by humans are required. Global hydrological models such as WaterGAP are developed to provide this information. Here we present a thorough description, evaluation and application examples of the most recent model version, WaterGAP v2.2d.
John F. Burkhart, Felix N. Matt, Sigbjørn Helset, Yisak Sultan Abdella, Ola Skavhaug, and Olga Silantyeva
Geosci. Model Dev., 14, 821–842,Short summary
We present a new hydrologic modeling framework for interactive development of inflow forecasts for hydropower production planning and other operational environments (e.g., flood forecasting). The software provides a Python user interface with an application programming interface (API) for a computationally optimized C++ model engine, giving end users extensive control over the model configuration in real time during a simulation. This provides for extensive experimentation with configuration.
Joost Buitink, Lieke A. Melsen, James W. Kirchner, and Adriaan J. Teuling
Geosci. Model Dev., 13, 6093–6110,Short summary
This paper presents a new distributed hydrological model: the distributed simple dynamical systems (dS2) model. The model is built with a focus on computational efficiency and is therefore able to simulate basins at high spatial and temporal resolution at a low computational cost. Despite the simplicity of the model concept, it is able to correctly simulate discharge in both small and mesoscale basins.
Zhipin Ai, Naota Hanasaki, Vera Heck, Tomoko Hasegawa, and Shinichiro Fujimori
Geosci. Model Dev., 13, 6077–6092,Short summary
Incorporating bioenergy crops into the well-established global hydrological models is seldom seen today. Here, we successfully enhance a state-of-the-art global hydrological model H08 to simulate bioenergy crop yield. We found that unconstrained irrigation more than doubled the yield under rainfed conditions while simultaneously reducing the water use efficiency by 32 % globally. Our enhanced model provides a new tool for the future assessment of bioenergy–water tradeoffs.
Matthew T. Perks
Geosci. Model Dev., 13, 6111–6130,Short summary
KLT-IV v1.0 offers a user-friendly graphical interface for the determination of river flow velocity and river discharge using videos acquired from both fixed and mobile remote sensing platforms. Platform motion can be accounted for using ground control points and/or stable features or a GPS device and inertial measurement unit sensor. Examples of the KLT-IV workflow are provided for two case studies where footage is acquired using unmanned aerial systems and fixed cameras.
Bram Droppers, Wietse H. P. Franssen, Michelle T. H. van Vliet, Bart Nijssen, and Fulco Ludwig
Geosci. Model Dev., 13, 5029–5052,Short summary
Our study aims to include both both societal and natural water requirements and uses into a hydrological model in order to enable worldwide assessments of sustainable water use. The model was extended to include irrigation, domestic, industrial, energy, and livestock water uses as well as minimum flow requirements for natural systems. Initial results showed competition for water resources between society and nature, especially with respect to groundwater withdrawals.
Zachary L. Flamig, Humberto Vergara, and Jonathan J. Gourley
Geosci. Model Dev., 13, 4943–4958,Short summary
The Ensemble Framework For Flash Flood Forecasting (EF5) is used in the US National Weather Service for operational monitoring and short-term forecasting of flash floods. This article describes the hydrologic models supported by the framework and evaluates their accuracy by comparing simulations of streamflow from 2001 to 2011 at 4 366 observation sites with catchments less than 1000 km2. Overall, the uncalibrated models reasonably simulate flash flooding events.
Benya Wang, Matthew R. Hipsey, and Carolyn Oldham
Geosci. Model Dev., 13, 4253–4270,Short summary
Surface water nutrients are essential to manage water quality, but it is hard to analyse trends. We developed a hybrid model and compared with other models for the prediction of six different nutrients. Our results showed that the hybrid model had significantly higher accuracy and lower prediction uncertainty for almost all nutrient species. The hybrid model provides a flexible method to combine data of varied resolution and quality and is accurate for the prediction of nutrient concentrations.
Patrick Le Moigne, François Besson, Eric Martin, Julien Boé, Aaron Boone, Bertrand Decharme, Pierre Etchevers, Stéphanie Faroux, Florence Habets, Matthieu Lafaysse, Delphine Leroux, and Fabienne Rousset-Regimbeau
Geosci. Model Dev., 13, 3925–3946,Short summary
The study describes how a hydrometeorological model, operational at Météo-France, has been improved. Particular emphasis is placed on the impact of climatic data, surface, and soil parametrizations on the model results. Model simulations and evaluations carried out on a variety of measurements of river flows and snow depths are presented. All improvements in climate, surface data, and model physics have a positive impact on system performance.
Yilin Fang, Xingyuan Chen, Jesus Gomez Velez, Xuesong Zhang, Zhuoran Duan, Glenn E. Hammond, Amy E. Goldman, Vanessa A. Garayburu-Caruso, and Emily B. Graham
Geosci. Model Dev., 13, 3553–3569,Short summary
Surface water quality along river corridors can be improved by the area of the stream bed and stream bank in which stream water mixes with shallow groundwater or hyporheic zones (HZs). These zones are ubiquitous and dominated by microorganisms that can process the dissolved nutrients exchanged at this interface of these zones. The modulation of surface water quality can be simulated by connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer representation.
Geosci. Model Dev., 13, 2905–2924,Short summary
Fractured and karst aquifers constitute important groundwater reservoirs worldwide but are particularly vulnerable to anthropogenic pollution. MFIT is a new GUI-based software for the analytical modeling of artificial tracer tests in such media. It integrates four transport models that are all capable of simulating complex (multimodal and/or heavy-tailed) tracer breakthrough curve responses and includes advanced tools for the automatic calibration and uncertainty analysis of model parameters.
Lele Shu, Paul A. Ullrich, and Christopher J. Duffy
Geosci. Model Dev., 13, 2743–2762,Short summary
Hydrologic modeling is an essential strategy for understanding and predicting natural flows. The paper introduces the design of Simulator for Hydrologic Unstructured Domains (SHUD), from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-Catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed.
Harsh Beria, Joshua R. Larsen, Anthony Michelon, Natalie C. Ceperley, and Bettina Schaefli
Geosci. Model Dev., 13, 2433–2450,Short summary
We develop a Bayesian mixing model to address the issue of small sample sizes to describe different sources in hydrological mixing applications. Using composite likelihood functions, the model accounts for an often overlooked bias arising due to unweighted mixing. We test the model efficacy using a series of statistical benchmarking tests and demonstrate its real-life applicability by applying it to a Swiss Alpine catchment to obtain the proportion of groundwater recharged from rain vs. snow.
Andrew J. Newman and Martyn P. Clark
Geosci. Model Dev., 13, 1827–1843,Short summary
This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain attributes to turn observations of precipitation and temperature into spatial maps. TIER allows our understanding of complex atmospheric processes such as terrain-enhanced precipitation to be modeled in a very simple way. TIER lets users change the model so they can experiment with different ways of making maps. A key conclusion is that small changes in TIER will change the final map.
Zhen Yin, Sebastien Strebelle, and Jef Caers
Geosci. Model Dev., 13, 651–672,Short summary
We provide completely automated Bayesian evidential learning (AutoBEL) for geological uncertainty quantification. AutoBEL focuses on model falsification, global sensitivity analysis, and statistical learning for joint model uncertainty reduction by borehole data. Application shows fast and robust uncertainty reduction in geological models and predictions for large field cases, showing its applicability in subsurface applications, e.g., groundwater, oil, gas, and geothermal or mineral resources.
Thomas Bueche, Marko Wenk, Benjamin Poschlod, Filippo Giadrossich, Mario Pirastru, and Mark Vetter
Geosci. Model Dev., 13, 565–580,Short summary
The R-based graphical user interface glmGUI provides tools for pre- and postprocessing of General Lake Model (GLM) simulations. This includes an autocalibration, parameter sensitivity analysis, and several plot options. The model parameters can be analyzed and calibrated for the simulation output variables water temperature and lake level. The toolbox is tested for two sites (lake Ammersee, Germany, and lake Baratz, Italy).
Ammann, L., Doppler, T., Stamm, C., Reichert, P., and Fenicia, F.: Characterizing fast herbicide transport in a small agricultural catchment with conceptual models, J. Hydrol., 586, 124812, https://doi.org/10.1016/j.jhydrol.2020.124812, 2020.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling and assessment, Part I: model development, J. Am. Water Res. Assoc., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., van Griensven, A., Van Liew, M. W., Kannan, N., and Jha, M. K.: SWAT: Model Use, Calibration, and Validation, Transactions of the ASABE, 55, 1491–1508, https://doi.org/10.13031/2013.42256, 2012.
Bancheri, M., Serafin, F., and Rigon, R.: The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN), Water Resour. Res., 55, 8895–8921, https://doi.org/10.1029/2019WR025099, 2019.
Bertuzzo, E., Thomet, M., Botter, G., and Rinaldo, A.: Catchment-scale herbicides transport: Theory and application, Adv. Water Resour., 52, 232–242, https://doi.org/10.1016/j.advwatres.2012.11.007, 2013.
Beven, K.: Changing ideas in hydrology – The case of physically-based models, J. Hydrol., 105, 157–172, https://doi.org/10.1016/0022-1694(89)90101-7, 1989.
Beven, K. J.: Uniqueness of place and process representations in hydrological modelling, Hydrol. Earth Syst. Sci., 4, 203–213, https://doi.org/10.5194/hess-4-203-2000, 2000.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci. Bull., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979.
Boyle, D. P.: Multicriteria calibration of hydrologic models, The University of Arizona, 2001.
Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z., and Smith, M.: Toward improved streamflow forecasts: value of semidistributed modeling, Water Resour. Res., 37, 2749–2759, https://doi.org/10.1029/2000wr000207, 2001.
Butcher, J. C. and Goodwin, N.: Numerical methods for ordinary differential equations, Wiley Online Library, 2008.
Clark, M. P. and Kavetski, D.: Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes, Water Resour. Res., 46, 10, https://doi.org/10.1029/2009WR008894, 2010.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00b02 https://doi.org/10.1029/2007wr006735, 2008.
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301, https://doi.org/10.1029/2010wr009827, 2011a.
Clark, M. P., McMillan, H. K., Collins, D. B. G., Kavetski, D., and Woods, R. A.: Hydrological field data from a modeller's perspective: Part 2: process-based evaluation of model hypotheses, Hydrol. Process., 25, 523–543, https://doi.org/10.1002/hyp.7902, 2011b.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015wr017198, 2015.
Craig, J. R., Brown, G., Chlumsky, R., Jenkinson, R. W., Jost, G., Lee, K., Mai, J., Serrer, M., Sgro, N., Shafii, M., Snowdon, A. P., and Tolson, B. A.: Flexible watershed simulation with the Raven hydrological modelling framework, Environ. Modell. Softw., 129, 104728, https://doi.org/10.1016/j.envsoft.2020.104728, 2020.
Dal Molin, M., Schirmer, M., Zappa, M., and Fenicia, F.: Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment, Hydrol. Earth Syst. Sci., 24, 1319–1345, https://doi.org/10.5194/hess-24-1319-2020, 2020.
Dal Molin, M., Kavetski, D., and Fenicia, F.: SuperflexPy: The flexible language of hydrological modelling, SuperflexPy [code], available at: https://pypi.org/project/superflexpy and https://github.com/dalmo1991/superflexPy, last access: 18 October 2021a.
Dal Molin, M., Kavetski, D., and Fenicia, F.: SuperflexPy 1.3.0, Zenodo [code], https://doi.org/10.5281/zenodo.5235158, 2021b.
Dal Molin, M., Kavetski, D., and Fenicia, F.: SuperflexPy, SuperflexPy, available at: https://superflexpy.readthedocs.io, last access: 18 October 2021c.
David, P. C., Oliveira, D. Y., Grison, F., Kobiyama, M., and Chaffe, P. L. B.: Systematic increase in model complexity helps to identify dominant streamflow mechanisms in two small forested basins, Hydrol. Sci. J., 64, 455–472, https://doi.org/10.1080/02626667.2019.1585858, 2019.
Dowell, M. and Jarratt, P.: The “Pegasus” method for computing the root of an equation, BIT, 12, 503–508, https://doi.org/10.1007/BF01932959, 1972.
Eckhardt, K. and Ulbrich, U.: Potential impacts of climate change on groundwater recharge and streamflow in a central European low mountain range, J. Hydrol., 284, 244–252, https://doi.org/10.1016/j.jhydrol.2003.08.005, 2003.
Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Is the groundwater reservoir linear? Learning from data in hydrological modelling, Hydrol. Earth Syst. Sci., 10, 139–150, https://doi.org/10.5194/hess-10-139-2006, 2006.
Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Understanding catchment behavior through stepwise model concept improvement, Water Resour. Res., 44, 1, https://doi.org/10.1029/2006WR005563, 2008.
Fenicia, F., Wrede, S., Kavetski, D., Pfister, L., Hoffmann, L., Savenije, H. H. G., and McDonnell, J. J.: Assessing the impact of mixing assumptions on the estimation of streamwater mean residence time, Hydrol. Process., 24, 1730–1741, https://doi.org/10.1002/hyp.7595, 2010.
Fenicia, F., Kavetski, D., and Savenije, H. H. G.: Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development, Water Resour. Res., 47, W11510, https://doi.org/10.1029/2010wr010174, 2011.
Fenicia, F., Kavetski, D., Savenije, H. H. G., Clark, M. P., Schoups, G., Pfister, L., and Freer, J.: Catchment properties, function, and conceptual model representation: is there a correspondence?, Hydrol. Process., 28, 2451–2467, https://doi.org/10.1002/hyp.9726, 2014.
Fenicia, F., Kavetski, D., Savenije, H. H. G., and Pfister, L.: From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions, Water Resour. Res., 52, 954–989, https://doi.org/10.1002/2015wr017398, 2016.
Feyen, L., Kalas, M., and Vrugt, J. A.: Semi-distributed parameter optimization and uncertainty assessment for large-scale streamflow simulation using global optimization/Optimisation de paramètres semi-distribués et évaluation de l'incertitude pour la simulation de débits à grande échelle par l'utilisation d'une optimisation globale, Hydrol. Sci. J., 53, 293–308, 2008.
Formetta, G., Antonello, A., Franceschi, S., David, O., and Rigon, R.: Hydrological modelling with components: A GIS-based open-source framework, Environ. Modell. Softw., 55, 190–200, https://doi.org/10.1016/j.envsoft.2014.01.019, 2014.
Futter, M. N., Erlandsson, M. A., Butterfield, D., Whitehead, P. G., Oni, S. K., and Wade, A. J.: PERSiST: a flexible rainfall-runoff modelling toolkit for use with the INCA family of models, Hydrol. Earth Syst. Sci., 18, 855–873, https://doi.org/10.5194/hess-18-855-2014, 2014.
Gao, H., Hrachowitz, M., Fenicia, F., Gharari, S., and Savenije, H. H. G.: Testing the realism of a topography-driven model (FLEX-Topo) in the nested catchments of the Upper Heihe, China, Hydrol. Earth Syst. Sci., 18, 1895–1915, https://doi.org/10.5194/hess-18-1895-2014, 2014.
Henn, B., Clark, M. P., Kavetski, D., Newman, A. J., Hughes, M., McGurk, B., and Lundquist, J. D.: Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range, J. Hydrol., 556, 993–1012, https://doi.org/10.1016/j.jhydrol.2016.08.009, 2018.
Houska, T., Kraft, P., Chamorro-Chavez, A., and Breuer, L.: SPOTting Model Parameters Using a Ready-Made Python Package, PLoS One, 10, e0145180, https://doi.org/10.1371/journal.pone.0145180, 2015.
Hrachowitz, M., Fovet, O., Ruiz, L., Euser, T., Gharari, S., Nijzink, R., Freer, J., Savenije, H. H. G., and Gascuel-Odoux, C.: Process consistency in models: The importance of system signatures, expert knowledge, and process complexity, Water Resour. Res., 50, 7445–7469, https://doi.org/10.1002/2014wr015484, 2014.
Ibbitt, R. P. and O'Donnell, T.: Designing conceptual catchment models for automatic fitting methods, IAHS Publication, 101, 462–475, 1971.
Jakeman, A. J. and Hornberger, G. M.: How Much Complexity Is Warranted in a Rainfall-Runoff Model, Water Resour. Res., 29, 2637–2649, https://doi.org/10.1029/93wr00877, 1993.
Jansen, K. F., Teuling, A. J., Craig, J. R., Dal Molin, M., Knoben, W. J. M., Parajka, J., Vis, M., and Melsen, L. A.: Mimicry of a conceptual hydrological model (HBV): What's in a name?, Water Resour. Res., 57, e2020WR029143, https://doi.org/10.1029/2020WR029143, 2021.
Kavetski, D. and Clark, M. P.: Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction, Water Resour. Res., 46, 10, https://doi.org/10.1029/2009wr008896, 2010.
Kavetski, D. and Fenicia, F.: Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights, Water Resour. Res., 47, W11511, https://doi.org/10.1029/2011wr010748, 2011.
Kavetski, D. and Kuczera, G.: Model smoothing strategies to remove microscale discontinuities and spurious secondary optima in objective functions in hydrological calibration, Water Resour. Res., 43, W03411, https://doi.org/10.1029/2006wr005195, 2007.
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment characterization, rainfall-runoff modeling, and doing hydrology backward, Water Resour. Res., 45, W02429, https://doi.org/10.1029/2008wr006912, 2009.
Kneis, D.: A lightweight framework for rapid development of object-based hydrological model engines, Environ. Modell. Softw., 68, 110–121, https://doi.org/10.1016/j.envsoft.2015.02.009, 2015.
Knoben, W. J. M., Freer, J. E., Fowler, K. J. A., Peel, M. C., and Woods, R. A.: 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, Geosci. Model. Dev., 12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019, 2019.
Kraft, P., Vaché, K. B., Frede, H.-G., and Breuer, L.: CMF: A Hydrological Programming Language Extension For Integrated Catchment Models, Environ. Modell. Softw., 26, 828–830, https://doi.org/10.1016/j.envsoft.2010.12.009, 2011.
Kuczera, G., Kavetski, D., Franks, S., and Thyer, M.: Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters, J. Hydrol., 331, 161–177, https://doi.org/10.1016/j.jhydrol.2006.05.010, 2006.
Lam, S. K., Pitrou, A., and Seibert, S.: Numba: a LLVM-based Python JIT compiler, Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, Association for Computing Machinery, Austin, Texas, 7 pp., 2015.
Leavesley, G. H.: Precipitation-runoff modeling system: User's manual, 4238, US Department of the Interior, U.S. Geological Survey, Water Resources Division, 1984.
Lerat, J., Andreassian, V., Perrin, C., Vaze, J., Perraud, J.-M., Ribstein, P., and Loumagne, C.: Do internal flow measurements improve the calibration of rainfall-runoff models?, Water Resour. Res., 48, https://doi.org/10.1029/2010WR010179, 2012.
Lindstrom, G., Johansson, B., Persson, M., Gardelin, M., and Bergstrom, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997.
Marsh, C. B., Pomeroy, J. W., and Wheater, H. S.: The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview, Geosci. Model Dev., 13, 225–247, https://doi.org/10.5194/gmd-13-225-2020, 2020.
Matgen, P., Fenicia, F., Heitz, S., Plaza, D., de Keyser, R., Pauwels, V. R. N., Wagner, W., and Savenije, H.: Can ASCAT-derived soil wetness indices reduce predictive uncertainty in well-gauged areas? A comparison with in situ observed soil moisture in an assimilation application, Adv. Water Resour., 44, 49–65, https://doi.org/10.1016/j.advwatres.2012.03.022, 2012.
Maxwell, R. M.: A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling, Adv. Water Resour., 53, 109–117, https://doi.org/10.1016/j.advwatres.2012.10.001, 2013.
McInerney, D., Thyer, M., Kavetski, D., Githui, F., Thayalakumaran, T., Liu, M., and Kuczera, G.: The Importance of Spatiotemporal Variability in Irrigation Inputs for Hydrological Modeling of Irrigated Catchments, Water Resour. Res., 54, 6792–6821, https://doi.org/10.1029/2017wr022049, 2018.
Meyer, B.: Object-oriented software construction, Prentice Hall, New York, 1988.
Moore, R. J. and Clarke, R. T.: A distribution function approach to rainfall runoff modeling, Water Resour. Res., 17, 1367–1382, https://doi.org/10.1029/WR017i005p01367, 1981.
Moradkhani, H. and Sorooshian, S.: General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis, in: Hydrological modelling and the water cycle, Springer, 1–24, 2009.
Moser, A., Wemyss, D., Scheidegger, R., Fenicia, F., Honti, M., and Stamm, C.: Modelling biocide and herbicide concentrations in catchments of the Rhine basin, Hydrol. Earth Syst. Sci., 22, 4229–4249, https://doi.org/10.5194/hess-22-4229-2018, 2018.
Nash, J.: The form of the instantaneous unit hydrograph, Int. Assoc. Sci. Hydrol., 3, 114–121, 1957.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models, Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, 2016.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L.: PyTorch: An imperative style, high-performance deep learning library, Adv. Neur. In., 8024–8035, 2019.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., and Dubourg, V.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
Press, W. H., Teukolsky, S. A., Flannery, B. P., and Vetterling, W. T.: Numerical recipes in Fortran 77, Vol. 1, Volume 1 of Fortran numerical recipes: the art of scientific computing, Cambridge University Press, 1992.
Refsgaard, J.: Terminology, Modelling Protocol And Classification of Hydrological Model Codes, in: Distributed Hydrological Modelling, 22, p. 17, 1996.
Refsgaard, J. C. and Storm, B.: MIKE SHE, in: Computer Models of Watershed Hydrology, edited by: Singh, V. P., Water Resources Publications, Colorado, 809–846, 1995.
Reichert, P. and Mieleitner, J.: Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters, Water Resour. Res., 45, 10, https://doi.org/10.1029/2009wr007814, 2009.
Renard, B., Kavetski, D., Leblois, E., Thyer, M., Kuczera, G., and Franks, S. W.: Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation, Water Resou. Res., 47, 11, https://doi.org/10.1029/2011WR010643, 2011.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, 5, https://doi.org/10.1029/2008wr007327, 2010.
Seibert, J. and McDonnell, J. J.: On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration, Water Resour. Res., 38, 23-21–23-14, https://doi.org/10.1029/2001wr000978, 2002.
Seibert, J., Rodhe, A., and Bishop, K.: Simulating interactions between saturated and unsaturated storage in a conceptual runoff model, Hydrol. Process., 17, 379–390, 2003.
Sivapalan, M., Beven, K., and Wood, E. F.: On hydrologic similarity: 2. A scaled model of storm runoff production, Water Resour. Res., 23, 2266–2278, https://doi.org/10.1029/WR023i012p02266, 1987.
Sivapalan, M., Blöschl, G., Zhang, L., and Vertessy, R.: Downward approach to hydrological prediction, Hydrol. Process., 17, 2101–2111, https://doi.org/10.1002/hyp.1425, 2003.
van Esse, W. R., Perrin, C., Booij, M. J., Augustijn, D. C. M., Fenicia, F., Kavetski, D., and Lobligeois, F.: The influence of conceptual model structure on model performance: a comparative study for 237 French catchments, Hydrol. Earth Syst. Sci., 17, 4227–4239, https://doi.org/10.5194/hess-17-4227-2013, 2013.
Vitolo, C., Wells, P., Dobias, M., and Buytaert, W.: fuse: An R package for ensemble Hydrological Modelling, Journal of Open Source Software, 1, 52, https://doi.org/10.21105/joss.00052, 2016.
Wagener, T., Sivapalan, M., Troch, P., and Woods, R.: Catchment Classification and Hydrologic Similarity, Geography Compass, 1, 901–931, https://doi.org/10.1111/j.1749-8198.2007.00039.x, 2007.
Walt, S. V. D., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/mcse.2011.37, 2011.
Westra, S., Thyer, M., Leonard, M., Kavetski, D., and Lambert, M.: A strategy for diagnosing and interpreting hydrological model nonstationarity, Water Resour. Res., 50, 5090–5113, https://doi.org/10.1002/2013wr014719, 2014.
Wrede, S., Fenicia, F., Martínez-Carreras, N., Juilleret, J., Hissler, C., Krein, A., Savenije, H. H. G., Uhlenbrook, S., Kavetski, D., and Pfister, L.: Towards more systematic perceptual model development: a case study using 3 Luxembourgish catchments, Hydrol. Process., 29, 2731–2750, https://doi.org/10.1002/hyp.10393, 2015.
Young, P.: Data-based mechanistic modelling of environmental, ecological, economic and engineering systems, Environ. Modell. Softw., 13, 105–122, https://doi.org/10.1016/S1364-8152(98)00011-5, 1998.
Young, P. C.: Stochastic, dynamic modelling and signal processing: time variable and state dependent parameter estimation, Nonlinear and nonstationary signal processing, in: Nonstationary and Nonlinear Time Series Analysis, 74–114, 2000.
Young, P. C., Tych, W., and Taylor, C. J.: The Captain Toolbox for Matlab, IFAC Proceedings Volumes, 42, 758–763, https://doi.org/10.3182/20090706-3-FR-2004.00126, 2009.
This paper introduces SuperflexPy, an open-source Python framework for building flexible conceptual hydrological models. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work.
This paper introduces SuperflexPy, an open-source Python framework for building flexible...