Articles | Volume 14, issue 2
https://doi.org/10.5194/gmd-14-821-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-821-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
05 Feb 2021
Model description paper |
| 05 Feb 2021
| 05 Feb 2021
Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology
John F. Burkhart
CORRESPONDING AUTHOR
Department of Geosciences, University of Oslo, Oslo, Norway
Felix N. Matt
Department of Geosciences, University of Oslo, Oslo, Norway
Statkraft AS, Lysaker, Norway
Sigbjørn Helset
Statkraft AS, Lysaker, Norway
Yisak Sultan Abdella
Statkraft AS, Lysaker, Norway
Ola Skavhaug
Expert Analytics AS, Oslo, Norway
Olga Silantyeva
Department of Geosciences, University of Oslo, Oslo, Norway
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The magnitude and makeup of error in cryospheric radiation observations due to small sensor misalignment in in situ measurements of solar irradiance is evaluated. It is shown that relatively minor sensor misalignments give significant errors in irradiance and hence albedo measurements. The total measurement error introduced by sensor tilt is dominated by the direct component. Significant measurement error can also persist in integrated daily irradiance and albedo.
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Sauvik Santra, Shubha Verma, Koji Fujita, Indrajit Chakraborty, Olivier Boucher, Toshihiko Takemura, John F. Burkhart, Felix Matt, and Mukesh Sharma
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Aynom T. Teweldebrhan, John F. Burkhart, and Thomas V. Schuler
Hydrol. Earth Syst. Sci., 22, 5021–5039, https://doi.org/10.5194/hess-22-5021-2018, https://doi.org/10.5194/hess-22-5021-2018, 2018
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Hydrol. Earth Syst. Sci., 22, 179–201, https://doi.org/10.5194/hess-22-179-2018, https://doi.org/10.5194/hess-22-179-2018, 2018
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Umed Paliwal, Mukesh Sharma, and John F. Burkhart
Atmos. Chem. Phys., 16, 12457–12476, https://doi.org/10.5194/acp-16-12457-2016, https://doi.org/10.5194/acp-16-12457-2016, 2016
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Wiley Steven Bogren, John Faulkner Burkhart, and Arve Kylling
The Cryosphere, 10, 613–622, https://doi.org/10.5194/tc-10-613-2016, https://doi.org/10.5194/tc-10-613-2016, 2016
Short summary
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The magnitude and makeup of error in cryospheric radiation observations due to small sensor misalignment in in situ measurements of solar irradiance is evaluated. It is shown that relatively minor sensor misalignments give significant errors in irradiance and hence albedo measurements. The total measurement error introduced by sensor tilt is dominated by the direct component. Significant measurement error can also persist in integrated daily irradiance and albedo.
L. J. Kramer, D. Helmig, J. F. Burkhart, A. Stohl, S. Oltmans, and R. E. Honrath
Atmos. Chem. Phys., 15, 6827–6849, https://doi.org/10.5194/acp-15-6827-2015, https://doi.org/10.5194/acp-15-6827-2015, 2015
J. Brioude, D. Arnold, A. Stohl, M. Cassiani, D. Morton, P. Seibert, W. Angevine, S. Evan, A. Dingwell, J. D. Fast, R. C. Easter, I. Pisso, J. Burkhart, and G. Wotawa
Geosci. Model Dev., 6, 1889–1904, https://doi.org/10.5194/gmd-6-1889-2013, https://doi.org/10.5194/gmd-6-1889-2013, 2013
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Revised manuscript accepted for GMD
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-9, https://doi.org/10.5194/gmd-2024-9, 2024
Revised manuscript accepted for GMD
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-151, https://doi.org/10.5194/gmd-2023-151, 2023
Revised manuscript accepted for GMD
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We develop an operational forecast system, COATLINES-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model requires a relatively small computational demand and results compare well with near real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and waves predictions can improve in accuracy.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-190, https://doi.org/10.5194/gmd-2023-190, 2023
Revised manuscript accepted for GMD
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Accurate hydrological modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall-runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Cited articles
Abramowitz, G.: Towards a public, standardized,
diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827,
https://doi.org/10.5194/gmd-5-819-2012, 2012. a
Albrecht, B. A.: Aerosols, Cloud Microphysics, and
Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Andersson, E. and Thépaut, J.:
ECMWF's 4D-Var data assimilation system – the genesis and ten years in operations, ECMWF
Newslett., 115, 8–12, https://doi.org/10.21957/wnmguimihe, 2008. a
Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F.,
Nijssen, B., and Lettenmaier, D. P.: Value of long-term streamflow forecasts to reservoir
operations for water supply in snow-dominated river catchments, Water Resour. Res., 52,
4209–4225, https://doi.org/10.1002/2015WR017864, 2016. a, b
Anghileri, D., Monhart, S., Zhou, C., Bogner, K., Castelletti,
A., Burlando, P., and Zappa, M.: The Value of Subseasonal Hydrometeorological Forecasts to
Hydropower Operations: How Much Does Preprocessing Matter?, Water Resour. Res., 55, 10,
https://doi.org/10.1029/2019WR025280, 2019. a, b, c
Bengtsson,
L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W., Gleeson, E., Hansen-Sass, B.,
Homleid, M., Hortal, M., Ivarsson, K.-I., Lenderink, G., Niemelä, S., Nielsen, K. P., Onvlee,
J., Rontu, L., Samuelsson, P., Muñoz, D. S., Subias, A., Tijm, S., Toll, V., Yang, X., and
Koltzow, M. O.: The HARMONIE-AROME Model Configuration in the ALADIN-HIRLAM NWP System,
Mon. Weather Rev., 145, 1919–1935, https://doi.org/10.1175/MWR-D-16-0417.1, 2017. a
Berg, P., Donnelly,
C., and Gustafsson, D.: Near-real-time adjusted reanalysis forcing data for hydrology,
Hydrol. Earth Syst. Sci., 22, 989–1000, https://doi.org/10.5194/hess-22-989-2018, 2018. a
Beven, K.: A manifesto for the equifinality thesis,
J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a
Beven, K., Smith, P. J., and
Wood, A.: On the colour and spin of epistemic error (and what we might do about it), Hydrol. Earth
Syst. Sci., 15, 3123–3133, https://doi.org/10.5194/hess-15-3123-2011, 2011. a
Bryant, A. C.,
Painter, T. H., Deems, J. S., and Bender, S. M.: Impact of dust radiative forcing in snow on
accuracy of operational runoff prediction in the Upper Colorado River Basin, Geophys. Res. Lett.,
40, 3945–3949, https://doi.org/10.1002/grl.50773, 2013. a
Burkhart, J. F., Matt, F., Sigbjorn, H., Abdella, Y. S., Skavhaug, O., and Silantyeva, O.: Shyft v4.8: A Framework for Uncertainty Assessment and Distributed Hydrologic Modelling for Operational Hydrology, Zenodo, https://doi.org/10.5281/zenodo.3782737, 2020. a
Clark, M. P.,
Kavetski, D., and Fenicia, F.: Pursuing the method of multiple working hypotheses for hydrological
modeling, Water Resour. Res., 47, 1–16, https://doi.org/10.1029/2010WR009827, 2011. a
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, 2015a. a, b
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., Gochis, D. J., Rasmussen, R. M., Tarboton, D. G., Mahat, V.,
Flerchinger, G. N., and Marks, D. G.: A unified approach for process-based hydrologic modeling:
2. Model implementation and case studies, Water Resour. Res., 51, 2515–2542,
https://doi.org/10.1002/2015WR017200, 2015b. a, b
Day, G. N.: Extended Streamflow Forecasting Using NWSRFS, J. Water
Resour. Plan. Manage., 111, 157–170, https://doi.org/10.1061/(ASCE)0733-9496(1985)111:2(157), 1985. a
Demming, R., Duffy,
D. J., and Schling, B.: Introduction to the Boost C++ libraries. Volume I, Foundations, Datasim
Education BV, Amsterdam, The Netherlands, 2010. a
Doherty, S. J., Hegg, D. A., Johnson, J. E., Quinn, P. K.,
Schwarz, J. P., Dang, C., and Warren, S. G.: Causes of variability in light absorption by
particles in snow at sites in Idaho and Utah, J. Geophys. Res.-Atmos., 121, 4751–4768,
https://doi.org/10.1002/2015JD024375, 2016. a
Flanner,
M. G., Zender, C. S., Randerson, J. T., and Rasch, P. J.: Present-day climate forcing and response
from black carbon in snow, J. Geophys. Res.-Atmos., 112, D11202, https://doi.org/10.1029/2006JD008003, 2007. a, b
Fowler, M.: Patterns of Enterprise Application
Architecture, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2002. a
Germann, U., Berenguer, M., Sempere-Torres, D., and Zappa, M.: REAL-Ensemble radar precipitation
estimation for hydrology in a mountainous region, Q. J. Roy. Meteorol. Soc., 135, 445–456,
https://doi.org/10.1002/qj.375, 2009. a
Gochis, D. J., Barlage,
M., Dugger, A., Fitzgerald, K., Karsten, L., McAllister, M., McCreight, J., Mills, J.,
RafieeiNasab, A., Read, L., Sampson, K., Yates, D., and Yu, W.: The WRF-Hydro modeling system
technical description, (Version 5.0), NCAR Technical Note, pp. 1–107, https://doi.org/10.5065/D6J38RBJ,
2018. a
Grewe, V., Moussiopoulos, N., Builtjes, P., Borrego, C.,
Isaksen, I. S. A., and Volz-Thomas, A.: The ACCENT-protocol: a framework for benchmarking and
model evaluation, Geosci. Model Dev., 5, 611–618, https://doi.org/10.5194/gmd-5-611-2012, 2012. a
Grubert, E. and Sanders, K. T.: Water Use
in the United States Energy System: A National Assessment and Unit Process Inventory of Water
Consumption and Withdrawals, Env. Sci. Technol., 52, 6695–6703, https://doi.org/10.1021/acs.est.8b00139,
2018. a
Gupta, H. V., Sorooshian, S.,
and Yapo, P. O.: Toward improved calibration of hydrologic models: Multiple and noncommensurable
measures of information, Water Resour. Res., 34, 751–763, https://doi.org/10.1029/97WR03495, 1998. a, b
Haddeland, I., Lettenmaier, D. P., and Skaugen, T.: Effects of
irrigation on the water and energy balances of the Colorado and Mekong river basins, J. Hydrol.,
324, 210–223, https://doi.org/10.1016/j.jhydrol.2005.09.028, 2006a. a
Haddeland, I., Skaugen, T., and Lettenmaier, D. P.:
Anthropogenic impacts on continental surface water fluxes, Geophys. Res. Lett., 33, L08406,
https://doi.org/10.1029/2006GL026047, 2006b. a
Haddeland,
I., Skaugen, T., and Lettenmaier, D. P.: Hydrologic effects of land and water management in North
America and Asia: 1700–1992, Hydrol. Earth Syst. Sci., 11, 1035–1045,
https://doi.org/10.5194/hess-11-1035-2007, 2007. a
Hadley, O. L. and Kirchstetter, T. W.:
Black-carbon reduction of snow albedo, Nat. Clim. Change, 2, 437–440, https://doi.org/10.1038/nclimate1433,
2012. a
Hamman,
J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y.: The Variable Infiltration Capacity
model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility,
Geosci. Model Dev., 11, 3481–3496, https://doi.org/10.5194/gmd-11-3481-2018, 2018. a
Hansen, J. and Nazarenko, L.: Soot
climate forcing via snow and ice albedos, P. Natl. Acad. Sci. USA, 101, 423–428,
https://doi.org/10.1073/pnas.2237157100, 2004. a
Hansen, J., Sato, M., and
Ruedy, R.: Radiative forcing and climate response, J. Geophys. Res.-Atmos. 102, 6831–6864,
https://doi.org/10.1029/96JD03436, 1997. a
Hock, R.: Temperature index melt modelling in mountain
areas, J. Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9, 2003. a
Hrachowitz, M., Savenije, H., Blöschl,
G., McDonnell, J., Sivapalan, M., Pomeroy, J., Arheimer, B., Blume, T., Clark, M., Ehret, U.,
Fenicia, F., Freer, J., Gelfan, A., Gupta, H., Hughes, D., Hut, R., Montanari, A., Pande, S.,
Tetzlaff, D., Troch, P., Uhlenbrook, S., Wagener, T., Winsemius, H., Woods, R., Zehe, E., and
Cudennec, C.: A decade of Predictions in Ungauged Basins (PUB) – a review, Hydrol. Sci.
J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013. a
IPCC, W. G. I.: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY,
USA, https://doi.org/10.1017/CBO9781107415324, 2013. a
Jacobson, M. Z.: Climate response of fossil fuel and
biofuel soot, accounting for soot's feedback to snow and sea ice albedo and emissivity,
J. Geophys. Res.-Atmos. 109, D21201, https://doi.org/10.1029/2004JD004945, 2004. a
King, D. E.: Dlib-ml: A Machine Learning Toolkit,
J. Machine Learn. Res., 10, 1755–1758, 2009. a
Kirchner, J. W.: Getting the right answers for the
right reasons: Linking measurements, analyses, and models to advance the science of hydrology,
Water Resour. Res., 42, W03S04,
https://doi.org/10.1029/2005WR004362, 2006. a
Kirchner, J. W.: Catchments as simple dynamical
systems: Catchment characterization, rainfall-runoff modeling, and doing hydrology backward, Water
Resour. Res., 45, W2429, https://doi.org/10.1029/2008WR006912, 2009. a
Kolberg, S. and Bruland, O.:
Open-Source as a strategy for operational software – the case of Enki, in: EGU General Assembly
Conference Abstracts, EGU General Assembly Conference Abstracts, p. 11334, 2014. a
Kolberg, S., Rue,
H., and Gottschalk, L.: A Bayesian spatial assimilation scheme for snow coverage observations in a
gridded snow model, Hydrol. Earth Syst. Sci., 10, 369–381, https://doi.org/10.5194/hess-10-369-2006, 2006. a
Liang, X.,
Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land
surface water and energy fluxes for general circulation models, J. Geophys. Res., 99,
14415–14428, https://doi.org/10.1029/94JD00483, 1994. a
Liechti,
K., Panziera, L., Germann, U., and Zappa, M.: The potential of radar-based ensemble forecasts for
flash-flood early warning in the southern Swiss Alps, Hydrol. Earth Syst. Sci., 17, 3853–3869,
https://doi.org/10.5194/hess-17-3853-2013, 2013. a
Lindström, G., Johansson, B., Persson, M., Gardelin,
M., and Bergström, 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. a, b
Matt, F. N.,
Burkhart, J. F., and Pietikäinen, J.-P.: Modelling hydrologic impacts of light absorbing
aerosol deposition on snow at the catchment scale, Hydrol. Earth Syst. Sci., 22, 179–201,
https://doi.org/10.5194/hess-22-179-2018, 2018. a
McCabe, M. F., Rodell, M.,
Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest,
N. E. C., Franz, T. E., Shi, J., Gao, H., and Wood, E. F.: The future of Earth observation in
hydrology, Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, 2017. a
Merz, R. and Blöschl,
G.: Regionalisation of catchment model parameters, J. Hydrol., 287, 95–123, https://doi.org/10.1016/j.jhydrol.2003.09.028, 2004. a
Moreno, H. A., Vivoni,
E. R., and Gochis, D. J.: Utility of Quantitative Precipitation Estimates for high resolution
hydrologic forecasts in mountain watersheds of the Colorado Front Range, J. Hydrol., 438, 66–83,
https://doi.org/10.1016/j.jhydrol.2012.03.019, 2012. a
Moreno, H. A., Vivoni,
E. R., and Gochis, D. J.: Addressing uncertainty in reflectivity-rainfall relations in mountain
watersheds during summer convection, Hydrol. Process., 28, 688–704, https://doi.org/10.1002/hyp.9600, 2014. a
Nash, J. and Sutcliffe, J.: River
flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10,
282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Pagano, T. C., Wood, A. W., Ramos,
M.-H., Cloke, H. L., Pappenberger, F., Clark, M. P., Cranston, M., Kavetski, D., Mathevet, T.,
Sorooshian, S., and Verkade, J. S.: Challenges of Operational River Forecasting,
J. Hydrometeorol., 15, 1692–1707, https://doi.org/10.1175/JHM-D-13-0188.1, 2014. a
Painter, T. H.,
Bryant, A. C., and Skiles, S. M.: Radiative forcing by light absorbing impurities in snow from
MODIS surface reflectance data, Geophys. Res. Lett., 39, L17502, https://doi.org/10.1029/2012GL052457,
2012. a
Painter, T. H., McKenzie Skiles, S., Deems, J. S., Tyler Brandt,
W., and Dozier, J.: Variation in rising limb of Colorado River snowmelt runoff hydrograph
controlled by dust radiative forcing in snow, Geophys. Res. Lett., 45, 797–808,
https://doi.org/10.1002/2017GL075826, 2017. a
Pappenberger, F., Pagano, T. C., Brown, J. D., Alfieri, L.,
Lavers, D. A., Berthet, L., Bressand, F., Cloke, H. L., Cranston, M., Danhelka, J., Demargne, J.,
Demuth, N., de Saint-Aubin, C., Feikema, P. M., Fresch, M. A., Garçon, R., Gelfan, A., He, Y.,
Hu, Y. Z., Janet, B., Jurdy, N., Javelle, P., Kuchment, L., Laborda, Y., Langsholt, E., Le Lay,
M., Li, Z. J., Mannessiez, F., Marchandise, A., Marty, R., Meißner, D., Manful, D., Organde,
D., Pourret, V., Rademacher, S., Ramos, M. H., Reinbold, D., Tibaldi, S., Silvano, P., Salamon,
P., Shin, D., Sorbet, C., Sprokkereef, E., Thiemig, V., Tuteja, N. K., van Andel, S. J., Verkade,
J. S., Vehviläinen, B., Vogelbacher, A., Wetterhall, F., Zappa, M., Van der Zwan, R. E., and
Thielen-del Pozo, J.: Hydrological Ensemble Prediction Systems Around the Globe, in: Handbook of
Hydrometeorological Ensemble Forecasting, 1–35,
https://doi.org/10.1007/978-3-642-40457-3_47-1,
2016. a
Pomeroy, J. W., Gray, D. M., Brown, T., Hedstrom, N. R., Quinton,
W. L., Granger, R. J., and Carey, S. K.: The cold regions hydrological model: a platform for
basing process representation and model structure on physical evidence, Hydrol. Process., 21,
2650–2667, https://doi.org/10.1002/hyp.6787, 2007. a
Powell, M.: The BOBYQA algorithm for bound constrained
optimization without derivatives, DAMTP, available at: http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf (last access: 28 January 2021), 2009. a
Priestley, C. H. B. and Taylor, R. J.:
On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Mon. Weather
Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972. a
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.: Aerosols, Climate, and the
Hydrological Cycle, Science, 294, 2119–2124, https://doi.org/10.1126/science.1064034, 2001. a
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.: Understanding predictive
uncertainty in hydrologic modeling: The challenge of identifying input and structural errors,
Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009WR008328, 2010. a
Riboust,
P., Thirel, G., Moine, N. L., and Ribstein, P.: Revisiting a Simple Degree-Day Model for
Integrating Satellite Data: Implementation of Swe-Sca Hystereses, J. Hydrol. Hydromech., 67,
70–81, https://doi.org/10.2478/johh-2018-0004, 2019. a
Sanderson, C. and Curtin, R.:
Armadillo: a template-based C++ library for linear algebra, J. Open Source Softw., 1, 26,
https://doi.org/10.21105/joss.00026, 2016. a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P.,
Bouttier, F., Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational Model,
Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a
Shepard, D.: A two-dimensional interpolation function
for irregularly-spaced data, ACM '68: Proceedings of the 1968 23rd ACM national conference,
517–524, https://doi.org/10.1145/800186.810616, 1968. a
Shyft: shyft, GitHub, available at: https://gitlab.com/shyft-os/shyft, last access: 31 January 2021a. a
Shyft: shyft-doc, GitHub, available at: https://gitlab.com/shyft-os/shyft-doc, last access: 31 January 2021b. a
Shyft: dockers, GitHub, available at: https://gitlab.com/shyft-os/dockers, last access: 31 January 2021c. a
Skaugen, T. and Onof, C.: A rainfall-runoff
model parameterized from GIS and runoff data, Hydrol. Process., 28, 4529–4542,
https://doi.org/10.1002/hyp.9968, 2014. a
Skaugen, T. and Randen, F.: Modeling the
spatial distribution of snow water equivalent, taking into account changes in snow-covered area,
Ann. Glaciol., 54, 305–313, https://doi.org/10.3189/2013AoG62A162, 2013. a
Skaugen, T. and Weltzien, I. H.: A model
for the spatial distribution of snow water equivalent parameterized from the spatial variability
of precipitation, The Cryosphere, 10, 1947–1963, https://doi.org/10.5194/tc-10-1947-2016, 2016. a
Skiles, S. M. and Painter, T.: Daily
evolution in dust and black carbon content, snow grain size, and snow albedo during snowmelt,
Rocky Mountains, Colorado, J. Glaciol., 63, 118–132, https://doi.org/10.1017/jog.2016.125, 2016. a
Teweldebrhan, A., Burkhart, J., Schuler, T., and Xu, C.-Y.:
Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based
Ensemble Smoother Data Assimilation Frameworks, Remote Sensing, 11, 28, https://doi.org/10.3390/rs11010028,
2018a. a, b
Teweldebrhan, A. T., Burkhart, J. F., and Schuler, T. V.:
Parameter uncertainty analysis for an operational hydrological model using residual-based and
limits of acceptability approaches, Hydrol. Earth Syst. Sci., 22, 5021–5039,
https://doi.org/10.5194/hess-22-5021-2018, 2018b. a, b, c, d
Teweldebrhan, A. T., Schuler, T. V., Burkhart, J. F.,
and Hjorth-Jensen, M.: Coupled machine learning and the limits of acceptability approach applied
in parameter identification for a distributed hydrological model, Hydrol. Earth Syst. Sci., 24,
4641–4658, https://doi.org/10.5194/hess-24-4641-2020, 2020. a
Twomey, S. A.,
Piepgrass, M., and Wolfe, T. L.: An assessment of the impact of pollution on global cloud albedo,
Tellus B, 36, 356–366, https://doi.org/10.3402/tellusb.v36i5.14916, 1984. a
Vivoni, E. R.,
Entekhabi, D., and Hoffman, R. N.: Error Propagation of Radar Rainfall Nowcasting Fields through a
Fully Distributed Flood Forecasting Model, J. Appl. Meteorol. Climatol., 46, 932–940,
https://doi.org/10.1175/JAM2506.1, 2007. a
Wang, X., Doherty, S. J.,
and Huang, J.: Black carbon and other light-absorbing impurities in snow across Northern China,
J. Geophys. Res.-Atmos. 118, 1471–1492, https://doi.org/10.1029/2012JD018291, 2013. a
Warren, S. G. and Wiscombe, W. J.: A Model
for the Spectral Albedo of Snow. II: Snow Containing Atmospheric Aerosols, J. Atmos. Sci., 37,
2734–2745, https://doi.org/10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2, 1980. a
Weiler, M. and Beven, K.: Do we need a
Community Hydrological Model, Water Resour. Res., 51, 7777–7784, https://doi.org/10.1002/2014WR016731,
2015. a
Westerberg, I. K. and McMillan,
H. K.: Uncertainty in hydrological signatures, Hydrol. Earth Syst. Sci., 19, 3951–3968,
https://doi.org/10.5194/hess-19-3951-2015, 2015.
a
Wiscombe, W. J. and Warren, S. G.: A
Model for the Spectral Albedo of Snow. I: Pure Snow, J. Atmos. Sci., 37, 2712–2733,
https://doi.org/10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2, 1980. a
World Energy, Council:
World Energy Resources 2016, Strategic Report, World Energy Council, available at: https://www.worldenergy.org/assets/images/imported/2016/10/World-Energy-Resources-Full-report-2016.10.03.pdf (last access: 28 January 2021), 2016. a
World Meteorological Organization: Guidelines on the Role, Operation and Management of National
Hydrological Services, Operational Hydrology Report, World Meteorological Organization, available at: https://library.wmo.int/index.php?lvl=notice_display&id=8875#.X_4QtVNKh_V (last access: 28 January 2021), 49, 1–83, 2006. a
Wu,
W., Emerton, R., Duan, Q., Wood, A. W., Wetterhall, F., and Robertson, D. E.: Ensemble flood
forecasting: Current status and future opportunities, WIREs Water, 7, e1432, https://doi.org/10.1002/wat2.1432, 2020. a
Zappa, M.,
Rotach, M. W., Arpagaus, M., Dorninger, M., Hegg, C., Montani, A., Ranzi, R., Ament, F., Germann,
U., Grossi, G., Jaun, S., Rossa, A., Vogt, S., Walser, A., Wehrhan, J., and Wunram, C.: MAP
D-PHASE: real-time demonstration of hydrological ensemble prediction systems, Atmos. Sci. Lett.,
9, 80–87, https://doi.org/10.1002/asl.183, 2008. a
Zsoter, E.,
Pappenberger, F., and Richardson, D.: Sensitivity of model climate to sampling configurations and
the impact on the Extreme Forecast Index, Meteorol. Appl., 22, 236–247, https://doi.org/10.1002/met.1447,
2015. a
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.
We present a new hydrologic modeling framework for interactive development of inflow forecasts...
