Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-751-2023
© Author(s) 2023. 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-16-751-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
31 Jan 2023
Model description paper |
| 31 Jan 2023
| 31 Jan 2023
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Institute for Risk and Disaster Reduction, University College London, Gower Street, WC1E 6BT, UK
Fuqiang Tian
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Guang-Heng Ni
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Related authors
Ruidong Li, Jiapei Liu, Ting Sun, Shao Jian, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3780, https://doi.org/10.5194/egusphere-2024-3780, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This work presents a new approach to simulate sewer drainage effects for urban flooding with key missing information like flow directions and nodal depths estimated from incomplete information. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.
Ruidong Li, Jiapei Liu, Ting Sun, Shao Jian, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3780, https://doi.org/10.5194/egusphere-2024-3780, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This work presents a new approach to simulate sewer drainage effects for urban flooding with key missing information like flow directions and nodal depths estimated from incomplete information. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.
Khosro Morovati, Keer Zhang, Lidi Shi, Yadu Pokhrel, Maozhou Wu, Paradis Someth, Sarann Ly, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 5133–5147, https://doi.org/10.5194/hess-28-5133-2024, https://doi.org/10.5194/hess-28-5133-2024, 2024
Short summary
Short summary
This study examines large daily river flow fluctuations in the dammed Mekong River, developing integrated 3D hydrodynamic and response time models alongside a hydrological model with an embedded reservoir module. This approach allows estimation of travel times between hydrological stations and contributions of subbasins and upstream regions. Findings show a power correlation between upstream discharge and travel time, and significant fluctuations occurred even before dam construction.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-2966, https://doi.org/10.5194/egusphere-2024-2966, 2024
Short summary
Short summary
We assessed the value of high-resolution data and parameters transferability across temporal scales based on 7 catchments in northern China. We found that higher resolution data does not always improve model performance, questioning the need for such data; Model parameters are transferable across different data resolutions, but not across computational time steps. It is recommended to utilize smaller computational time step when building hydrological models even without high-resolution data.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Zhen Cui and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-2177, https://doi.org/10.5194/egusphere-2024-2177, 2024
Short summary
Short summary
This study investigates stormflow patterns in a forested watershed in North China, revealing that delayed stormflow is influenced by soil water content and groundwater levels. When soil moisture exceeds its storage capacity, excess water recharges groundwater, which then flows into streams more slowly. As groundwater levels rise, they enhance water movement and connectivity, causing a delayed stormflow peak to merge with the direct stormflow peak.
Zhen Cui, Fuqiang Tian, Zilong Zhao, Zitong Xu, Yongjie Duan, Jie Wen, and Mohd Yawar Ali Khan
Hydrol. Earth Syst. Sci., 28, 3613–3632, https://doi.org/10.5194/hess-28-3613-2024, https://doi.org/10.5194/hess-28-3613-2024, 2024
Short summary
Short summary
We investigated the response characteristics and occurrence conditions of bimodal hydrographs using 10 years of hydrometric and isotope data in a semi-humid forested watershed in north China. Our findings indicate that bimodal hydrographs occur when the combined total of the event rainfall and antecedent soil moisture index exceeds 200 mm. Additionally, we determined that delayed stormflow is primarily contributed to by shallow groundwater.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1438, https://doi.org/10.5194/egusphere-2024-1438, 2024
Short summary
Short summary
Common intuition holds that higher input data resolution leads to better results. To assess the benefits of high-resolution data, we conducted simulation experiments using data with various temporal resolutions across multiple catchments, and found that higher resolution data does not always improve model performance, challenging the necessity of pursuing such data. In catchments with small areas or significant flow variability, high-resolution data is more valuable.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1464, https://doi.org/10.5194/egusphere-2024-1464, 2024
Short summary
Short summary
Our study conducted a detailed analysis of runoff component and future trend in the Yarlung Tsangpo River basin owing to the existed differences in the published results, and find that the contributions of snowmelt and glacier melt runoff to streamflow were limited, both for ~5 % which were much lower than previous results. The streamflow there will continuously increase in the future, but the overestimated contribution from glacier melt would lead to an underestimation on such increasing trend.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
Short summary
Short summary
This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
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
Short summary
Short summary
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.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
Short summary
Short summary
Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
Yongping Wei, Jing Wei, Gen Li, Shuanglei Wu, David Yu, Mohammad Ghoreishi, You Lu, Felipe Augusto Arguello Souza, Murugesu Sivapalan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 26, 2131–2146, https://doi.org/10.5194/hess-26-2131-2022, https://doi.org/10.5194/hess-26-2131-2022, 2022
Short summary
Short summary
There is increasing tension among the riparian countries of transboundary rivers. This article proposes a socio-hydrological framework that incorporates the slow and less visible societal processes into existing hydro-economic models, revealing the slow and hidden feedbacks between societal and hydrological processes. This framework will contribute to process-based understanding of the complex mechanism that drives conflict and cooperation in transboundary river management.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
Liying Guo, Jing Wei, Keer Zhang, Jiale Wang, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 26, 1165–1185, https://doi.org/10.5194/hess-26-1165-2022, https://doi.org/10.5194/hess-26-1165-2022, 2022
Short summary
Short summary
Data support is crucial for the research of conflict and cooperation on transboundary rivers. Conventional, manual constructions of datasets cannot meet the requirements for fast updates in the big data era. This study brings up a revised methodological framework, based on the conventional method, and a toolkit for the news media dataset tracking of conflict and cooperation dynamics on transboundary rivers. A dataset with good tradeoffs between data relevance and coverage is generated.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 25, 6151–6172, https://doi.org/10.5194/hess-25-6151-2021, https://doi.org/10.5194/hess-25-6151-2021, 2021
Short summary
Short summary
Hydrological modeling has large problems of uncertainty in cold regions. Tracer-aided hydrological models are increasingly used to reduce uncertainty and refine the parameterizations of hydrological processes, with limited application in large basins due to the unavailability of spatially distributed precipitation isotopes. This study explored the utility of isotopic general circulation models in driving a tracer-aided hydrological model in a large basin on the Tibetan Plateau.
Kunbiao Li, Fuqiang Tian, Mohd Yawar Ali Khan, Ran Xu, Zhihua He, Long Yang, Hui Lu, and Yingzhao Ma
Earth Syst. Sci. Data, 13, 5455–5467, https://doi.org/10.5194/essd-13-5455-2021, https://doi.org/10.5194/essd-13-5455-2021, 2021
Short summary
Short summary
Due to complex climate and topography, there is still a lack of a high-quality rainfall dataset for hydrological modeling over the Tibetan Plateau. This study aims to establish a high-accuracy daily rainfall product over the southern Tibetan Plateau through merging satellite rainfall estimates based on a high-density rainfall gauge network. Statistical and hydrological evaluation indicated that the new dataset outperforms the raw satellite estimates and several other products of similar types.
Yi Nan, Lide Tian, Zhihua He, Fuqiang Tian, and Lili Shao
Hydrol. Earth Syst. Sci., 25, 3653–3673, https://doi.org/10.5194/hess-25-3653-2021, https://doi.org/10.5194/hess-25-3653-2021, 2021
Short summary
Short summary
This study integrated a water isotope module into the hydrological model THREW. The isotope-aided model was subsequently applied for process understanding in the glacierized watershed of Karuxung river on the Tibetan Plateau. The model was used to quantify the contribution of runoff component and estimate the water travel time in the catchment. Model uncertainties were significantly constrained by using additional isotopic data, improving the process understanding in the catchment.
You Lu, Fuqiang Tian, Liying Guo, Iolanda Borzì, Rupesh Patil, Jing Wei, Dengfeng Liu, Yongping Wei, David J. Yu, and Murugesu Sivapalan
Hydrol. Earth Syst. Sci., 25, 1883–1903, https://doi.org/10.5194/hess-25-1883-2021, https://doi.org/10.5194/hess-25-1883-2021, 2021
Short summary
Short summary
The upstream countries in the transboundary Lancang–Mekong basin build dams for hydropower, while downstream ones gain irrigation and fishery benefits. Dam operation changes the seasonality of runoff downstream, resulting in their concerns. Upstream countries may cooperate and change their regulations of dams to gain indirect political benefits. The socio-hydrological model couples hydrology, reservoir, economy, and cooperation and reproduces the phenomena, providing a useful model framework.
Jing Wei, Yongping Wei, Fuqiang Tian, Natalie Nott, Claire de Wit, Liying Guo, and You Lu
Hydrol. Earth Syst. Sci., 25, 1603–1615, https://doi.org/10.5194/hess-25-1603-2021, https://doi.org/10.5194/hess-25-1603-2021, 2021
Liming Wang, Songjun Han, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 25, 375–386, https://doi.org/10.5194/hess-25-375-2021, https://doi.org/10.5194/hess-25-375-2021, 2021
Short summary
Short summary
It remains unclear at which timescale the complementary principle performs best in estimating evaporation. In this study, evaporation estimation was assessed over 88 eddy covariance monitoring sites at multiple timescales. The results indicate that the generalized complementary functions perform best in estimating evaporation at the monthly scale. This study provides a reference for choosing a suitable time step for evaporation estimations in relevant studies.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, https://doi.org/10.5194/gmd-13-4891-2020, 2020
Short summary
Short summary
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, and socio-economic factors and in response to environmental conditions.
Songjun Han and Fuqiang Tian
Hydrol. Earth Syst. Sci., 24, 2269–2285, https://doi.org/10.5194/hess-24-2269-2020, https://doi.org/10.5194/hess-24-2269-2020, 2020
Short summary
Short summary
The complementary principle is an important methodology for estimating actual evaporation by using routinely observed meteorological variables. This review summaries its 56-year development, focusing on how related studies have shifted from adopting a symmetric linear complementary relationship to employing generalized nonlinear functions. We also compare the polynomial and sigmoid types of generalized complementary functions and discuss their future development.
Yu Ma, Guangheng Ni, Chandrasekar V. Chandra, Fuqiang Tian, and Haonan Chen
Hydrol. Earth Syst. Sci., 23, 4153–4170, https://doi.org/10.5194/hess-23-4153-2019, https://doi.org/10.5194/hess-23-4153-2019, 2019
Short summary
Short summary
Raindrop size distribution (DSD) information is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation. This study extensively investigates the DSD characteristics during rainy seasons in the Beijing urban area using 5-year DSD observations from a Parsivel2 disdrometer. The statistical distributions of DSD parameters are examined and the polarimetric radar rainfall algorithms are derived to support the ongoing development of an X-band radar network.
Ting Sun and Sue Grimmond
Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, https://doi.org/10.5194/gmd-12-2781-2019, 2019
Short summary
Short summary
A Python-enhanced urban land surface model, SuPy (SUEWS in Python), is presented with its development (the SUEWS interface modification, F2PY configuration and Python frontend implementation), cross-platform deployment (PyPI, Python Package Index) and demonstration (online tutorials in Jupyter notebooks for users of different levels). SuPy represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality.
Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, https://doi.org/10.5194/gmd-12-2119-2019, 2019
Short summary
Short summary
The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
Mohd Yawar Ali Khan and Fuqiang Tian
Proc. IAHS, 379, 61–66, https://doi.org/10.5194/piahs-379-61-2018, https://doi.org/10.5194/piahs-379-61-2018, 2018
Short summary
Short summary
This study has been conducted on Ramganga River, a major tributary of Ganges River, India, to observe the spatial variation of DOC, dissolved inorganic carbon (DIC), SOC and suspended inorganic carbon (SIC) in river water. The significant conclusions of this investigation revealed that the river and its tributaries show abundance amount of TSC (SOC and SIC) and TDC (DOC and DIC) both in the upstream and downstream. TDC accounts more in river concentration as compared to TSC.
Guanghui Ming, Hongchang Hu, Fuqiang Tian, Zhenyang Peng, Pengju Yang, and Yiqi Luo
Hydrol. Earth Syst. Sci., 22, 3075–3086, https://doi.org/10.5194/hess-22-3075-2018, https://doi.org/10.5194/hess-22-3075-2018, 2018
Short summary
Short summary
The purpose of this research was to detect the effect of plastic film mulching (PFM), a widely applied cultivation method, on soil respiration. We found that soil respiration was not only affected by PFM, but it was also affected by irrigation and precipitation, and whether the PFM increases soil respiration compared to a non-mulched field largely depends on precipitation in the field. The result has an important meaning for agricultural carbon sequestration in the context of global warming.
Ran Xu, Hongchang Hu, Fuqiang Tian, Chao Li, and Mohd Yawar Ali Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-251, https://doi.org/10.5194/hess-2018-251, 2018
Manuscript not accepted for further review
Short summary
Short summary
We provide a comprehensive and updated assessment of the impacts of climate change on YBR streamflow by integrating a physically based hydrological model, regional climate integrations, different bias correction methods, and Bayesian model averaging method. By the year 2035, the annual mean streamflow is projected to change respectively by 6.8 % (12.9 %), −0.4 % (13.1 %), and −4.1 % (19.9 %) under RCP4.5 (8.5) relative to the historical period at the Bahadurabad, the upper Brahmaputra outlet, and Nuxia.
Ting Sun, Zhi-Hua Wang, Walter C. Oechel, and Sue Grimmond
Geosci. Model Dev., 10, 2875–2890, https://doi.org/10.5194/gmd-10-2875-2017, https://doi.org/10.5194/gmd-10-2875-2017, 2017
Short summary
Short summary
The diurnal hysteresis behaviour found between the net storage heat flux and net all-wave radiation has been captured in the Objective Hysteresis Model (OHM). To facilitate use, and enhance physical interpretations of the OHM coefficients, we develop the Analytical Objective Hysteresis Model (AnOHM) using an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the surface energy balance relationship.
Songjun Han, Fuqiang Tian, Ye Liu, and Xianhui Duan
Hydrol. Earth Syst. Sci., 21, 3619–3633, https://doi.org/10.5194/hess-21-3619-2017, https://doi.org/10.5194/hess-21-3619-2017, 2017
Short summary
Short summary
The history of the co-evolution of the coupled human–groundwater system in Cangzhou (a region with the most serious depression cone in the North China Plain) is analyzed with a particular focus on how the groundwater crisis unfolded and how people attempted to settle the crisis. The evolution of the system was substantially impacted by two droughts. Further restoration of groundwater environment could be anticipated, but the occurrence of drought still remains an undetermined external forcing.
Wen-Yu Yang, Guang-Heng Ni, You-Cun Qi, Yang Hong, and Ting Sun
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-388, https://doi.org/10.5194/amt-2016-388, 2016
Revised manuscript has not been submitted
Short summary
Short summary
Using a dataset consisting of one-year measurements by an X-band radar and distrometer, we found that error corrections greatly improve X-band-radar-based rainfall estimation. Specifically, the greatest improvement is realized by the beam integration. Derivation of localized Z-R relationships for specific rainfall systems is also of great importance. Moreover, wind drift correction improves quantitative estimates and temporal consistency.
Zhenyang Peng, Hongchang Hu, Fuqiang Tian, Qiang Tie, and Sihan Zhao
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-112, https://doi.org/10.5194/hess-2016-112, 2016
Manuscript not accepted for further review
Short summary
Short summary
Preferential flow (PF) occurred by a frequency of 40.7 % in a semi humid catchment. Possibility of PF occurrence is positively correlated with rainfall features, i.e. rainfall amount, duration, maximum and average intensity, among which the rainfall amount is the dominant driven factor of PF. PF is more likely to occur on gentle slopes with thick surface covers, while high antecedent soil moisture is more likely to be consequence of infiltration capacity, rather than an inducer of PF.
Fuqiang Tian, Yu Sun, Hongchang Hu, and Hongyi Li
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-88, https://doi.org/10.5194/hess-2016-88, 2016
Preprint withdrawn
Z. H. He, F. Q. Tian, H. V. Gupta, H. C. Hu, and H. P. Hu
Hydrol. Earth Syst. Sci., 19, 1807–1826, https://doi.org/10.5194/hess-19-1807-2015, https://doi.org/10.5194/hess-19-1807-2015, 2015
D. Liu, F. Tian, M. Lin, and M. Sivapalan
Hydrol. Earth Syst. Sci., 19, 1035–1054, https://doi.org/10.5194/hess-19-1035-2015, https://doi.org/10.5194/hess-19-1035-2015, 2015
Short summary
Short summary
A simplified conceptual socio-hydrological model based on logistic growth curves is developed for the Tarim River basin in western China and is used to illustrate the explanatory power of a co-evolutionary model. The socio-hydrological system is composed of four sub-systems, i.e., the hydrological, ecological, economic, and social sub-systems. The hydrological equation focusing on water balance is coupled to the evolutionary equations of the other three sub-systems.
Z. H. He, J. Parajka, F. Q. Tian, and G. Blöschl
Hydrol. Earth Syst. Sci., 18, 4773–4789, https://doi.org/10.5194/hess-18-4773-2014, https://doi.org/10.5194/hess-18-4773-2014, 2014
Short summary
Short summary
In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDFS) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Snow density is estimated as the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. DDFS values are estimated as the ratio between changes in the snow water equivalent and difference between the daily temperature and a threshold value for days with snowmelt.
Z. Zhang, H. Hu, F. Tian, X. Yao, and M. Sivapalan
Hydrol. Earth Syst. Sci., 18, 3951–3967, https://doi.org/10.5194/hess-18-3951-2014, https://doi.org/10.5194/hess-18-3951-2014, 2014
Y. Liu, F. Tian, H. Hu, and M. Sivapalan
Hydrol. Earth Syst. Sci., 18, 1289–1303, https://doi.org/10.5194/hess-18-1289-2014, https://doi.org/10.5194/hess-18-1289-2014, 2014
Z. Zhang, F. Tian, H. Hu, and P. Yang
Hydrol. Earth Syst. Sci., 18, 1053–1072, https://doi.org/10.5194/hess-18-1053-2014, https://doi.org/10.5194/hess-18-1053-2014, 2014
L. Yang, F. Tian, Y. Sun, X. Yuan, and H. Hu
Hydrol. Earth Syst. Sci., 18, 775–786, https://doi.org/10.5194/hess-18-775-2014, https://doi.org/10.5194/hess-18-775-2014, 2014
Z. He, F. Tian, H. C. Hu, H. V. Gupta, and H. P. Hu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-1253-2014, https://doi.org/10.5194/hessd-11-1253-2014, 2014
Revised manuscript not accepted
Y. Sun, Z. Hou, M. Huang, F. Tian, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 17, 4995–5011, https://doi.org/10.5194/hess-17-4995-2013, https://doi.org/10.5194/hess-17-4995-2013, 2013
Y. Tang, Q. Tang, F. Tian, Z. Zhang, and G. Liu
Hydrol. Earth Syst. Sci., 17, 4471–4480, https://doi.org/10.5194/hess-17-4471-2013, https://doi.org/10.5194/hess-17-4471-2013, 2013
H. Liu, F. Tian, H. C. Hu, H. P. Hu, and M. Sivapalan
Hydrol. Earth Syst. Sci., 17, 805–815, https://doi.org/10.5194/hess-17-805-2013, https://doi.org/10.5194/hess-17-805-2013, 2013
Related subject area
Earth and space science informatics
Checking the consistency of 3D geological models
The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11
GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity
Random forests with spatial proxies for environmental modelling: opportunities and pitfalls
An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
Remote sensing-based high-resolution mapping of the forest canopy height: some models are useful, but might they be even more if combined?
Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6
Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
The 4D reconstruction of dynamic geological evolution processes for renowned geological features
Moving beyond post-hoc XAI: Lessons learned from dynamical climate modeling
Machine learning for numerical weather and climate modelling: a review
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
CSDMS: a community platform for numerical modeling of Earth surface processes
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
A new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)
HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution
Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Global hydro-climatic biomes identified via multitask learning
A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)
Marion N. Parquer, Eric A. de Kemp, Boyan Brodaric, and Michael J. Hillier
Geosci. Model Dev., 18, 71–100, https://doi.org/10.5194/gmd-18-71-2025, https://doi.org/10.5194/gmd-18-71-2025, 2025
Short summary
Short summary
This is a proof-of-concept paper outlining a general approach to how 3D geological models would be checked to be geologically
reasonable. We do this with a consistency-checking tool that looks at geological feature pairs and their spatial, temporal, and internal polarity characteristics. The idea is to assess if geological relationships from a specific 3D geological model match what is allowed in the real world from the perspective of geological principles.
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024, https://doi.org/10.5194/gmd-17-8909-2024, 2024
Short summary
Short summary
Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called
enstools-compressionthat makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev., 17, 8455–8468, https://doi.org/10.5194/gmd-17-8455-2024, https://doi.org/10.5194/gmd-17-8455-2024, 2024
Short summary
Short summary
In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural-network-based spatiotemporal regression models and an open-source Python package named Geographically Neural Network Weighted Regression to capture relationships between factors. This makes it a valuable tool for researchers in fields such as environmental science, urban planning, and public health.
Carles Milà, Marvin Ludwig, Edzer Pebesma, Cathryn Tonne, and Hanna Meyer
Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024, https://doi.org/10.5194/gmd-17-6007-2024, 2024
Short summary
Short summary
Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
Chunhua Jiang, Xiang Gao, Huizhong Zhu, Shuaimin Wang, Sixuan Liu, Shaoni Chen, and Guangsheng Liu
Geosci. Model Dev., 17, 5939–5959, https://doi.org/10.5194/gmd-17-5939-2024, https://doi.org/10.5194/gmd-17-5939-2024, 2024
Short summary
Short summary
With ERA5 hourly data, we show spatiotemporal characteristics of pressure and zenith wet delay (ZWD) and propose an empirical global pressure and ZWD grid model with a broader operating space which can provide accurate pressure, ZWD, zenith hydrostatic delay, and zenith tropospheric delay estimates for any selected time and location over globe. IGPZWD will be of great significance for the tropospheric augmentation in real-time GNSS positioning and atmospheric water vapor remote sensing.
Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer
Geosci. Model Dev., 17, 5897–5912, https://doi.org/10.5194/gmd-17-5897-2024, https://doi.org/10.5194/gmd-17-5897-2024, 2024
Short summary
Short summary
Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
Nikola Besic, Nicolas Picard, Cédric Vega, Lionel Hertzog, Jean-Pierre Renaud, Fajwel Fogel, Agnès Pellissier-Tanon, Gabriel Destouet, Milena Planells-Rodriguez, and Philippe Ciais
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-95, https://doi.org/10.5194/gmd-2024-95, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The creation of advanced mapping models for forest attributes, utilizing remote sensing data and incorporating machine or deep learning methods, has become a key area of interest in the domain of forest observation and monitoring. This paper introduces a method where we blend and collectively interpret five models dedicated to estimating forest canopy height. We achieve this through Bayesian model averaging, offering a comprehensive approach to height estimation in forest ecosystems.
Lars Hoffmann, Kaveh Haghighi Mood, Andreas Herten, Markus Hrywniak, Jiri Kraus, Jan Clemens, and Mingzhao Liu
Geosci. Model Dev., 17, 4077–4094, https://doi.org/10.5194/gmd-17-4077-2024, https://doi.org/10.5194/gmd-17-4077-2024, 2024
Short summary
Short summary
Lagrangian particle dispersion models are key for studying atmospheric transport but can be computationally intensive. To speed up simulations, the MPTRAC model was ported to graphics processing units (GPUs). Performance optimization of data structures and memory alignment resulted in runtime improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for future high-performance computing systems.
Mohamad Hakam Shams Eddin and Juergen Gall
Geosci. Model Dev., 17, 2987–3023, https://doi.org/10.5194/gmd-17-2987-2024, https://doi.org/10.5194/gmd-17-2987-2024, 2024
Short summary
Short summary
In this study, we use deep learning and a climate simulation to predict the vegetation health as it would be observed from satellites. We found that the developed model can help to identify regions with a high risk of agricultural drought. The main applications of this study are to estimate vegetation products for periods where no satellite data are available and to forecast the future vegetation response to climate change based on climate scenarios.
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
Short summary
Short summary
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
Short summary
Short summary
The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
Short summary
Short summary
This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Ryan O'Loughlin, Dan Li, and Travis O'Brien
EGUsphere, https://doi.org/10.5194/egusphere-2023-2969, https://doi.org/10.5194/egusphere-2023-2969, 2024
Short summary
Short summary
We draw from traditional climate modeling practices to make recommendations for AI-driven climate science. In particular, we show how component-level understanding–which is obtained when scientists can link model behavior to parts within the overall model–should guide the development and evaluation of AI models. Better understanding can lead to a stronger basis for trust in these models. We highlight several examples to demonstrate.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
Short summary
Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
Short summary
Short summary
We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary
Short summary
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
Short summary
Short summary
Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
Short summary
Short summary
We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
Short summary
Short summary
The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
Short summary
Short summary
A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
Short summary
Short summary
Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
Short summary
Short summary
The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
Short summary
Short summary
The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
Short summary
Short summary
We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
Short summary
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
Short summary
Short summary
The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
Short summary
Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
Short summary
Short summary
We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
Short summary
Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
Short summary
Short summary
The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
Short summary
Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
Short summary
Short summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
Short summary
Short summary
The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
Short summary
Short summary
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
Short summary
Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
Short summary
Short summary
This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
Short summary
Short summary
This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
Short summary
Short summary
Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
Short summary
Short summary
Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
Short summary
Short summary
The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
Short summary
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
Short summary
Short summary
The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
Short summary
Short summary
We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
Short summary
Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
Short summary
Short summary
Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
Short summary
Short summary
Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
Short summary
Short summary
For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
Cited articles
Bengio, Y., Courville, A., and Vincent, P.: Representation Learning: A Review
and New Perspectives, IEEE T. Pattern Anal., 35,
1798–1828, https://doi.org/10.1109/tpami.2013.50, 2013. a, b
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, 1996. a
Brunner, D., Lemoine, G., Bruzzone, L., and Greidanus, H.: Building Height
Retrieval From VHR SAR Imagery Based on an Iterative Simulation and
Matching Technique, IEEE T. Geosci. Remote, 48, 1487–1504,
https://doi.org/10.1109/tgrs.2009.2031910, 2010. a
Bruwier, M., Maravat, C., Mustafa, A., Teller, J., Pirotton, M., Erpicum, S.,
Archambeau, P., and Dewals, B.: Influence of urban forms on surface flow in
urban pluvial flooding, J. Hydrol., 582, 124493,
https://doi.org/10.1016/j.jhydrol.2019.124493, 2020. a
Burke, M., Driscoll, A., Lobell, D. B., and Ermon, S.: Using satellite imagery
to understand and promote sustainable development, Science, 371, eabe8626,
https://doi.org/10.1126/science.abe8628, 2021. a, b
Cao, W., Dong, L., Wu, L., and Liu, Y.: Quantifying urban areas with
multi-source data based on percolation theory, Remote Sens. Environ., 241,
111730, https://doi.org/10.1016/j.rse.2020.111730, 2020. a, b
Carrera-Hernández, J.: Not all DEMs are equal: An evaluation of six
globally available 30 m resolution DEMs with geodetic benchmarks and
LiDAR in Mexico, Remote Sens. Environ., 261, 112474,
https://doi.org/10.1016/j.rse.2021.112474, 2021. a
Caruana, R.: Multitask learning, Mach. Learn., 28, 41–75, 1997. a
Chen, T. and Guestrin, C.: XGBoost, in: Proceedings of the 22nd ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, KDD '16,
ACM, New York, NY, USA, 13–17 August 2016, 785–794, https://doi.org/10.1145/2939672.2939785, 2016. a, b
Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A.: Gradnorm:
Gradient normalization for adaptive loss balancing in deep multitask
networks, in: International Conference on Machine Learning, Stockholm, Sweden,
10–15 July 2018,
arXiv [preprint], https://doi.org/10.48550/arXiv.1711.02257, 794–803,
PMLR, 2018. a
Cipolla, R., Gal, Y., and Kendall, A.: Multi-task Learning Using Uncertainty to
Weigh Losses for Scene Geometry and Semantics, in: 2018 IEEE Conference on
Computer Vision and Pattern Recognition, Salt Lake City, USA,
18–23 June 2018, 7482–7491, IEEE,
https://doi.org/10.1109/cvpr.2018.00781, 2018. a, b
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F.,
Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F.,
Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: Esa's Optical
High-Resolution Mission for GMES Operational Services, Remote Sens.
Environ., 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026, 2012. a
Eigen, D. and Fergus, R.: Predicting Depth, Surface Normals and Semantic Labels
with a Common Multi-scale Convolutional Architecture, in: 2015 IEEE
International Conference on Computer Vision, Santiago, Chile,
7–13 December 2015, 2650–2658, IEEE,
https://doi.org/10.1109/iccv.2015.304, 2015. a
Esch, T., Brzoska, E., Dech, S., Leutner, B., Palacios-Lopez, D.,
Metz-Marconcini, A., Marconcini, M., Roth, A., and Zeidler, J.: World
Settlement Footprint 3D – A first three-dimensional survey of the global
building stock, Remote Sens. Environ., 270, 112877,
https://doi.org/10.1016/j.rse.2021.112877, 2022. a, b
Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden,
S., and Hostert, P.: National-scale mapping of building height using
Sentinel-1 and Sentinel-2 time series, Remote Sens. Environ., 252, 112128,
https://doi.org/10.1016/j.rse.2020.112128, 2021. a, b, c, d, e, f, g, h, i, j, k, l
Frolking, S., Milliman, T., Seto, K. C., and Friedl, M. A.: A global
fingerprint of macro-scale changes in urban structure from 1999 to 2009,
Environ. Res. Lett., 8, 024004, https://doi.org/10.1088/1748-9326/8/2/024004, 2013. a
Geiß, C., Leichtle, T., Wurm, M., Pelizari, P. A., Standfuß, I., Zhu, X. X.,
So, E., Siedentop, S., Esch, T., and Taubenböck, H.: Large-Area
Characterization of Urban Morphology–Mapping of Built-Up Height and Density
Using TanDEM-X and Sentinel-2 Data,
IEEE J. Sel. Top. Appl., 12, 2912–2927,
https://doi.org/10.1109/JSTARS.2019.2917755, 2019. a
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017. a
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X.,
and Briggs, J. M.: Global Change and the Ecology of Cities, Science, 319,
756–760, https://doi.org/10.1126/science.1150195, 2008. a
Guo, H., Shi, Q., Du, B., Zhang, L., Wang, D., and Ding, H.: Scene-Driven
Multitask Parallel Attention Network for Building Extraction in
High-Resolution Remote Sensing Images, IEEE T. Geosci. Remote, 59,
4287–4306, https://doi.org/10.1109/tgrs.2020.3014312, 2021. a, b
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a, b
Haralick, R. M., Shanmugam, K., and Dinstein, I.: Textural Features for Image
Classification, IEEE T. Syst. Man Cyb., SMC-3,
610–621, https://doi.org/10.1109/tsmc.1973.4309314, 1973. a
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical
Learning, Springer New York, https://doi.org/10.1007/978-0-387-84858-7, 2009. a, b, c
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern
Recognition, Las Vegas, USA,
27–30 June 2016, 770–778, IEEE, https://doi.org/10.1109/cvpr.2016.90, 2016. a, b
Hu, J., Shen, L., and Sun, G.: Squeeze-and-Excitation Networks, in: 2018 IEEE
Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA,
18–23 June 2018, 7132–7141, IEEE,
https://doi.org/10.1109/cvpr.2018.00745, 2018. a, b, c
Huber, P. J.: Robust Estimation of a Location Parameter,
Ann. Math. Stat., 35, 73–101, https://doi.org/10.1214/aoms/1177703732, 1964. a
Ji, C. and Tang, H.: Number of Building Stories Estimation from Monocular
Satellite Image Using a Modified Mask R-CNN, Remote Sensing, 12, 3833,
https://doi.org/10.3390/rs12223833, 2020. a
Joachims, T.: Making large-scale SVM learning practical,
Technical report, http://hdl.handle.net/10419/77178 (last access date: 20 January 2023), 1998. a
Koppel, K., Zalite, K., Voormansik, K., and Jagdhuber, T.: Sensitivity of
Sentinel-1 backscatter to characteristics of buildings, Int. J. Remote Sens.,
38, 6298–6318, https://doi.org/10.1080/01431161.2017.1353160, 2017. a, b
Lagacherie, P., Rabotin, M., Colin, F., Moussa, R., and Voltz, M.:
Geo-MHYDAS: A landscape discretization tool for distributed
hydrological modeling of cultivated areas, Comput. Geosci., 36,
1021–1032, https://doi.org/10.1016/j.cageo.2009.12.005, 2010. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015. a, b
Li, R. and Sun, T.: Reference Datasets for: SHAFTS (v2022.3): a deep-learning-based Python package for Simultaneous extraction of building Height And FootprinT from Sentinel Imagery, Zenodo [data set], https://doi.org/10.5281/zenodo.6587510, 2022. a
Liebel, L., Bittner, K., and Körner, M.: A generalized multi-task learning
approach to stereo DSM filtering in urban areas, ISPRS J. Photogramm., 166, 213–227, https://doi.org/10.1016/j.isprsjprs.2020.03.005, 2020. a
Liu, Y., Bates, P. D., Neal, J. C., and Yamazaki, D.: Bare-Earth DEM
Generation in Urban Areas for Flood Inundation Simulation Using Global
Digital Elevation Models, Water Resour. Res., 57, e2020WR028516,
https://doi.org/10.1029/2020wr028516, 2021. a
Loew, A. and Mauser, W.: Generation of geometrically and radiometrically
terrain corrected SAR image products, Remote Sens. Environ., 106, 337–349,
https://doi.org/10.1016/j.rse.2006.09.002, 2007. a
Loshchilov, I. and Hutter, F.: SGDR: Stochastic Gradient Descent with Warm
Restarts, in: 5th International Conference on Learning Representations,
OpenReview.net, Toulon, France, 24–26 April 2017,
arXiv [preprint], https://doi.org/10.48550/arXiv.1608.03983, 2017. a
Masson, V., Heldens, W., Bocher, E., Bonhomme, M., Bucher, B., Burmeister, C.,
de Munck, C., Esch, T., Hidalgo, J., Kanani-Sühring, F., Kwok, Y.-T.,
Lemonsu, A., Lévy, J.-P., Maronga, B., Pavlik, D., Petit, G., See, L.,
Schoetter, R., Tornay, N., Votsis, A., and Zeidler, J.: City-descriptive
input data for urban climate models: Model requirements, data sources and
challenges, Urban Clim., 31, 100536, https://doi.org/10.1016/j.uclim.2019.100536,
2020a. a
Masson, V., Lemonsu, A., Hidalgo, J., and Voogt, J.: Urban Climates and Climate
Change, Annu. Rev. Env. Resour., 45, 411–444,
https://doi.org/10.1146/annurev-environ-012320-083623, 2020b. a
Mirzaei, P. A. and Haghighat, F.: Approaches to study Urban Heat Island –
Abilities and limitations, Build. Environ., 45, 2192–2201,
https://doi.org/10.1016/j.buildenv.2010.04.001, 2010. a
Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., and
Papathanassiou, K. P.: A tutorial on synthetic aperture radar,
IEEE Geoscience and Remote Sensing Magazine, 1, 6–43,
https://doi.org/10.1109/MGRS.2013.2248301, 2013. a
Mottaghi, R., Chen, X., Liu, X., Cho, N.-G., Lee, S.-W., Fidler, S., Urtasun,
R., and Yuille, A.: The Role of Context for Object Detection and Semantic
Segmentation in the Wild, in: 2014 IEEE Conference on Computer Vision and
Pattern Recognition, Columbus, USA, 23–28 June 2014, 891–898, https://doi.org/10.1109/CVPR.2014.119, 2014. a
Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler,
S. J., Buck, S., Chambers, C. D., Chin, G., Christensen, G., Contestabile,
M., Dafoe, A., Eich, E., Freese, J., Glennerster, R., Goroff, D., Green,
D. P., Hesse, B., Humphreys, M., Ishiyama, J., Karlan, D., Kraut, A., Lupia,
A., Mabry, P., Madon, T., Malhotra, N., Mayo-Wilson, E., McNutt, M., Miguel,
E., Paluck, E. L., Simonsohn, U., Soderberg, C., Spellman, B. A., Turitto,
J., VandenBos, G., Vazire, S., Wagenmakers, E. J., Wilson, R., and Yarkoni,
T.: Promoting an open research culture, Science, 348, 1422–1425,
https://doi.org/10.1126/science.aab2374, 2015. a
Pesaresi, M. and Benediktsson, J.: A new approach for the morphological
segmentation of high-resolution satellite imagery,
IEEE T. Geosci. Remote, 39, 309–320, https://doi.org/10.1109/36.905239, 2001. a
Piantanida, R., Hajduch, G., and Poullaouec, J.: Sentinel-1 Level 1 Detailed
Algorithm Definition, ESA, techreport SEN-TN-52-7445,
https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/document-library/-/asset_publisher/1dO7RF5fJMbd/content/id/2477988 (last access: 1 November 2022),
2021. a
Qin, Z., Zhang, P., Wu, F., and Li, X.: FcaNet: Frequency Channel Attention
Networks, Montreal, Canada, 10–17 October 2021.
783–792,
https://doi.org/10.1109/ICCV48922.2021.00082, 2021. a
Ruder, S.: An Overview of Multi-Task Learning in Deep Neural Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1706.05098, 15 June 2017. a
Salvadore, E., Bronders, J., and Batelaan, O.: Hydrological modelling of
urbanized catchments: A review and future directions, J. Hydrol., 529,
62–81, https://doi.org/10.1016/j.jhydrol.2015.06.028, 2015. a
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and
Freer, J. E.: A high-resolution global flood hazard model, Water Resour.
Res., 51, 7358–7381, https://doi.org/10.1002/2015wr016954, 2015. a
Schaefli, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process.,
21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007. a
Sener, O. and Koltun, V.: Multi-task learning as multi-objective optimization,
in: Proceedings of the 32nd International Conference on Neural Information
Processing Systems, Montreal, Canada,
3–8 December 2018,
arXiv [preprint], https://doi.org/10.48550/arXiv.1810.04650, 525–536, 2018. a, b
Shi, Y., Li, Q., and Zhu, X. X.: Building segmentation through a gated graph
convolutional neural network with deep structured feature embedding,
ISPRS J. Photogramm., 159, 184–197,
https://doi.org/10.1016/j.isprsjprs.2019.11.004, 2020. a, b
Shorten, C. and Khoshgoftaar, T. M.: A survey on Image Data Augmentation for
Deep Learning, Journal of Big Data, 6, 1–48,
https://doi.org/10.1186/s40537-019-0197-0, 2019. a
Simonyan, K. and Zisserman, A.: Very Deep Convolutional Networks for
Large-Scale Image Recognition, in: 3rd International Conference on Learning
Representations, edited by: Bengio, Y. and LeCun, Y., San Diego, USA,
7–9 May 2015,
arXiv [preprint], https://doi.org/10.48550/arXiv.1409.1556,
2015. a
Soergel, B., Kriegler, E., Weindl, I., Rauner, S., Dirnaichner, A., Ruhe, C.,
Hofmann, M., Bauer, N., Bertram, C., Bodirsky, B. L., Leimbach, M.,
Leininger, J., Levesque, A., Luderer, G., Pehl, M., Wingens, C., Baumstark,
L., Beier, F., Dietrich, J. P., Humpenöder, F., von Jeetze, P., Klein, D.,
Koch, J., Pietzcker, R., Strefler, J., Lotze-Campen, H., and Popp, A.: A
sustainable development pathway for climate action within the UN 2030
Agenda, Nat. Clim. Change, 11, 656–664, https://doi.org/10.1038/s41558-021-01098-3,
2021. a
Stilla, U., Soergel, U., and Thoennessen, U.: Potential and limits of InSAR
data for building reconstruction in built-up areas, ISPRS J. Photogramm., 58, 113–123, https://doi.org/10.1016/s0924-2716(03)00021-2, 2003. a
Sun, Y., Mou, L., Wang, Y., Montazeri, S., and Zhu, X. X.: Large-scale building
height retrieval from single SAR imagery based on bounding box regression
networks, ISPRS J. Photogramm., 184, 79–95,
https://doi.org/10.1016/j.isprsjprs.2021.11.024, 2022. a
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. E., Anguelov, D., Erhan,
D., Vanhoucke, V., and Rabinovich, A.: Going deeper with convolutions, in:
2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 7–12 June 2015, 1–9,
IEEE, https://doi.org/10.1109/CVPR.2015.7298594, 2015. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192,
https://doi.org/10.1029/2000jd900719, 2001. a, b, c, d
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E.,
Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P.,
Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R.,
Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission,
Remote Sens. Environ., 120, 9–24, https://doi.org/10.1016/j.rse.2011.05.028, 2012. a
van der Maaten, L. and Hinton, G.: Visualizing non-metric similarities in
multiple maps, Mach. Learn., 87, 33–55, https://doi.org/10.1007/s10994-011-5273-4,
2011.
a
Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., and
Mueller, A.: Scikit-learn, GetMobile: Mobile Computing and Communications,
19, 29–33, https://doi.org/10.1145/2786984.2786995, 2015. a
Yu, B., Liu, H., Wu, J., Hu, Y., and Zhang, L.: Automated derivation of urban
building density information using airborne LiDAR data and object-based
method, Landscape Urban Plan., 98, 210–219,
https://doi.org/10.1016/j.landurbplan.2010.08.004,
2010. a
Yu, Z., Hu, L., Sun, T., Albertson, J., and Li, Q.: Impact of heat storage on
remote-sensing based quantification of anthropogenic heat in urban
environments, Remote Sens. Environ., 262, 112520,
https://doi.org/10.1016/j.rse.2021.112520, 2021. a
Zhang, T., Huang, X., Wen, D., and Li, J.: Urban Building Density Estimation
From High-Resolution Imagery Using Multiple Features and Support Vector
Regression, IEEE J. Sel. Top. Appl., 10, 3265–3280, https://doi.org/10.1109/jstars.2017.2669217, 2017. a
Zheng, Z., Zhou, W., Wang, J., Hu, X., and Qian, Y.: Sixty-Year Changes in
Residential Landscapes in Beijing: A Perspective from Both the Horizontal
(2D) and Vertical (3D) Dimensions, Remote Sensing, 9, 992,
https://doi.org/10.3390/rs9100992, 2017. a
Zhong, Y., Su, Y., Wu, S., Zheng, Z., Zhao, J., Ma, A., Zhu, Q., Ye, R., Li,
X., Pellikka, P., and Zhang, L.: Open-source data-driven urban land-use
mapping integrating point-line-polygon semantic objects: A case study of
Chinese cities, Remote Sens. Environ., 247, 111838,
https://doi.org/10.1016/j.rse.2020.111838, 2020. a
Zhu, X., Cheng, D., Zhang, Z., Lin, S., and Dai, J.: An Empirical Study of
Spatial Attention Mechanisms in Deep Networks, in: 2019 IEEE International
Conference on Computer Vision, Seoul, Korea, 27 October–2 November 2019, 6688–6697, IEEE,
https://doi.org/10.1109/iccv.2019.00679, 2019a. a
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer,
F.: Deep Learning in Remote Sensing: A Comprehensive Review and List of
Resources, IEEE Geoscience and Remote Sensing Magazine, 5, 8–36,
https://doi.org/10.1109/mgrs.2017.2762307, 2017. a
Zhu, X. X., Qiu, C., Hu, J., Shi, Y., Wang, Y., Schmitt, M., and Taubenböck,
H.: The urban morphology on our planet – Global perspectives from space,
Remote Sens. Environ., 269, 112794, https://doi.org/10.1016/j.rse.2021.112794, 2022. a
Zhu, Z., Zhou, Y., Seto, K. C., Stokes, E. C., Deng, C., Pickett, S. T., and
Taubenböck, H.: Understanding an urbanizing planet: Strategic directions
for remote sensing, Remote Sens. Environ., 228, 164–182,
https://doi.org/10.1016/j.rse.2019.04.020, 2019b. a, b
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel...
