Articles | Volume 26, issue 7
https://doi.org/10.5194/hess-26-1727-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1727-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
05 Apr 2022
Technical note |
| 05 Apr 2022
| 05 Apr 2022
Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks
Huiying Ren
Earth Systems Science Division, Pacific Northwest National Laboratory, Richland, WA, USA
Erol Cromwell
Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, WA, USA
Ben Kravitz
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
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Ewa M. Bednarz, Amy H. Butler, Daniele Visioni, Yan Zhang, Ben Kravitz, and Douglas G. MacMartin
Atmos. Chem. Phys., 23, 13665–13684, https://doi.org/10.5194/acp-23-13665-2023, https://doi.org/10.5194/acp-23-13665-2023, 2023
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We use a state-of-the-art Earth system model and a set of stratospheric aerosol injection (SAI) strategies to achieve the same level of global mean surface cooling through different combinations of location and/or timing of the injection. We demonstrate that the choice of SAI strategy can lead to contrasting impacts on stratospheric and tropospheric temperatures, circulation, and chemistry (including stratospheric ozone), thereby leading to different impacts on regional surface climate.
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Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
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Geoengineering indicates methods aiming to reduce the temperature of the planet by means of reflecting back a part of the incoming radiation before it reaches the surface or allowing more of the planetary radiation to escape into space. It aims to produce modelling experiments that are easy to reproduce and compare with different climate models, in order to understand the potential impacts of these techniques. Here we assess its past successes and failures and talk about its future.
Daniele Visioni, Ewa M. Bednarz, Walker R. Lee, Ben Kravitz, Andy Jones, Jim M. Haywood, and Douglas G. MacMartin
Atmos. Chem. Phys., 23, 663–685, https://doi.org/10.5194/acp-23-663-2023, https://doi.org/10.5194/acp-23-663-2023, 2023
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Ewa M. Bednarz, Daniele Visioni, Ben Kravitz, Andy Jones, James M. Haywood, Jadwiga Richter, Douglas G. MacMartin, and Peter Braesicke
Atmos. Chem. Phys., 23, 687–709, https://doi.org/10.5194/acp-23-687-2023, https://doi.org/10.5194/acp-23-687-2023, 2023
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Building on Part 1 of this two-part study, we demonstrate the role of biases in climatological circulation and specific aspects of model microphysics in driving the differences in simulated sulfate distributions amongst three Earth system models. We then characterize the simulated changes in stratospheric and free-tropospheric temperatures, ozone, water vapor, and large-scale circulation, elucidating the role of the above aspects in the surface responses discussed in Part 1.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
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Mari R. Tye, Katherine Dagon, Maria J. Molina, Jadwiga H. Richter, Daniele Visioni, Ben Kravitz, and Simone Tilmes
Earth Syst. Dynam., 13, 1233–1257, https://doi.org/10.5194/esd-13-1233-2022, https://doi.org/10.5194/esd-13-1233-2022, 2022
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We examined the potential effect of stratospheric aerosol injection (SAI) on extreme temperature and precipitation. SAI may cause daytime temperatures to cool but nighttime to warm. Daytime cooling may occur in all seasons across the globe, with the largest decreases in summer. In contrast, nighttime warming may be greatest at high latitudes in winter. SAI may reduce the frequency and intensity of extreme rainfall. The combined changes may exacerbate drying over parts of the global south.
Ilaria Quaglia, Daniele Visioni, Giovanni Pitari, and Ben Kravitz
Atmos. Chem. Phys., 22, 5757–5773, https://doi.org/10.5194/acp-22-5757-2022, https://doi.org/10.5194/acp-22-5757-2022, 2022
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Carbonyl sulfide is a gas that mixes very well in the atmosphere and can reach the stratosphere, where it reacts with sunlight and produces aerosol. Here we propose that, by increasing surface fluxes by an order of magnitude, the number of stratospheric aerosols produced may be enough to partially offset the warming produced by greenhouse gases. We explore what effect this would have on the atmospheric composition.
Pin Shuai, Xingyuan Chen, Utkarsh Mital, Ethan T. Coon, and Dipankar Dwivedi
Hydrol. Earth Syst. Sci., 26, 2245–2276, https://doi.org/10.5194/hess-26-2245-2022, https://doi.org/10.5194/hess-26-2245-2022, 2022
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Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947, https://doi.org/10.5194/gmd-15-2917-2022, https://doi.org/10.5194/gmd-15-2917-2022, 2022
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Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Andy Jones, Jim M. Haywood, Adam A. Scaife, Olivier Boucher, Matthew Henry, Ben Kravitz, Thibaut Lurton, Pierre Nabat, Ulrike Niemeier, Roland Séférian, Simone Tilmes, and Daniele Visioni
Atmos. Chem. Phys., 22, 2999–3016, https://doi.org/10.5194/acp-22-2999-2022, https://doi.org/10.5194/acp-22-2999-2022, 2022
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Simulations by six Earth-system models of geoengineering by introducing sulfuric acid aerosols into the tropical stratosphere are compared. A robust impact on the northern wintertime North Atlantic Oscillation is found, exacerbating precipitation reduction over parts of southern Europe. In contrast, the models show no consistency with regard to impacts on the Quasi-Biennial Oscillation, although results do indicate a risk that the oscillation could become locked into a permanent westerly phase.
Daniele Visioni, Simone Tilmes, Charles Bardeen, Michael Mills, Douglas G. MacMartin, Ben Kravitz, and Jadwiga H. Richter
Atmos. Chem. Phys., 22, 1739–1756, https://doi.org/10.5194/acp-22-1739-2022, https://doi.org/10.5194/acp-22-1739-2022, 2022
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Aerosols are simulated in a simplified way in climate models: in the model analyzed here, they are represented in every grid as described by three simple logarithmic distributions, mixing all different species together. The size can evolve when new particles are formed, particles merge together to create a larger one or particles are deposited to the surface. This approximation normally works fairly well. Here we show however that when large amounts of sulfate are simulated, there are problems.
Yan Zhang, Douglas G. MacMartin, Daniele Visioni, and Ben Kravitz
Earth Syst. Dynam., 13, 201–217, https://doi.org/10.5194/esd-13-201-2022, https://doi.org/10.5194/esd-13-201-2022, 2022
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Adding SO2 to the stratosphere could temporarily cool the planet by reflecting more sunlight back to space. However, adding SO2 at different latitude(s) and season(s) leads to significant differences in regional surface climate. This study shows that, to cool the planet by 1–1.5 °C, there are likely six to eight choices of injection latitude(s) and season(s) that lead to meaningfully different distributions of climate impacts.
Dawn L. Woodard, Alexey N. Shiklomanov, Ben Kravitz, Corinne Hartin, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 4751–4767, https://doi.org/10.5194/gmd-14-4751-2021, https://doi.org/10.5194/gmd-14-4751-2021, 2021
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We have added a representation of the permafrost carbon feedback to the simple, open-source global carbon–climate model Hector and calibrated the results to be consistent with historical data and Earth system model projections. Our results closely match previous work, estimating around 0.2 °C of warming from permafrost this century. This capability will be useful to explore uncertainties in this feedback and for coupling with integrated assessment models for policy and economic analysis.
Daniele Visioni, Douglas G. MacMartin, Ben Kravitz, Olivier Boucher, Andy Jones, Thibaut Lurton, Michou Martine, Michael J. Mills, Pierre Nabat, Ulrike Niemeier, Roland Séférian, and Simone Tilmes
Atmos. Chem. Phys., 21, 10039–10063, https://doi.org/10.5194/acp-21-10039-2021, https://doi.org/10.5194/acp-21-10039-2021, 2021
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A new set of simulations is used to investigate commonalities, differences and sources of uncertainty when simulating the injection of SO2 in the stratosphere in order to mitigate the effects of climate change (solar geoengineering). The models differ in how they simulate the aerosols and how they spread around the stratosphere, resulting in differences in projected regional impacts. Overall, however, the models agree that aerosols have the potential to mitigate the warming produced by GHGs.
Nikolas O. Aksamit, Ben Kravitz, Douglas G. MacMartin, and George Haller
Atmos. Chem. Phys., 21, 8845–8861, https://doi.org/10.5194/acp-21-8845-2021, https://doi.org/10.5194/acp-21-8845-2021, 2021
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There exist robust and influential material features evolving within turbulent fluids that behave as the skeleton for fluid transport pathways. Recent developments in applied mathematics have made the identification of these time-varying structures more rigorous and insightful than ever. Using short-range wind forecasts, we detail how and why these material features can be exploited in an effort to optimize the spread of aerosols in the stratosphere for climate geoengineering.
Ben Kravitz, Douglas G. MacMartin, Daniele Visioni, Olivier Boucher, Jason N. S. Cole, Jim Haywood, Andy Jones, Thibaut Lurton, Pierre Nabat, Ulrike Niemeier, Alan Robock, Roland Séférian, and Simone Tilmes
Atmos. Chem. Phys., 21, 4231–4247, https://doi.org/10.5194/acp-21-4231-2021, https://doi.org/10.5194/acp-21-4231-2021, 2021
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This study investigates multi-model response to idealized geoengineering (high CO2 with solar reduction) across two different generations of climate models. We find that, with the exception of a few cases, the results are unchanged between the different generations. This gives us confidence that broad conclusions about the response to idealized geoengineering are robust.
Andy Jones, Jim M. Haywood, Anthony C. Jones, Simone Tilmes, Ben Kravitz, and Alan Robock
Atmos. Chem. Phys., 21, 1287–1304, https://doi.org/10.5194/acp-21-1287-2021, https://doi.org/10.5194/acp-21-1287-2021, 2021
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Two different methods of simulating a geoengineering scenario are compared using data from two different Earth system models. One method is very idealised while the other includes details of a plausible mechanism. The results from both models agree that the idealised approach does not capture an impact found when detailed modelling is included, namely that geoengineering induces a positive phase of the North Atlantic Oscillation which leads to warmer, wetter winters in northern Europe.
Walker Lee, Douglas MacMartin, Daniele Visioni, and Ben Kravitz
Earth Syst. Dynam., 11, 1051–1072, https://doi.org/10.5194/esd-11-1051-2020, https://doi.org/10.5194/esd-11-1051-2020, 2020
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The injection of aerosols into the stratosphere to reflect sunlight could reduce global warming, but this type of
geoengineeringwould also impact other variables like precipitation and sea ice. In this study, we model various climate impacts of geoengineering on a 3-D graph to show how trying to meet one climate goal will affect other variables. We also present two computer simulations which validate our model and show that geoengineering could regulate precipitation as well as temperature.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
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It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Cited articles
Alvera-Azcárate, A., Barth, A., Parard, G., and Beckers, J.-M.:
Analysis of SMOS sea surface salinity data using DINEOF,
Remote Sens Environ.,
180, 137–145, 2016. a
Amaranto, A., Munoz-Arriola, F., Corzo, G., Solomatine, D. P., and Meyer, G.:
Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland,
J. Hydroinform.,
20, 1227–1246, 2018. a
Amaranto, A., Munoz-Arriola, F., Solomatine, D., and Corzo, G.:
A spatially enhanced data-driven multimodel to improve semiseasonal groundwater forecasts in the High Plains aquifer, USA,
Water Resour. Res.,
55, 5941–5961, 2019. a
Banerjee, S., Carlin, B. P., and Gelfand, A. E.: Hierarchical modeling and analysis for spatial data, CRC Press, https://doi.org/10.1201/9780203487808, 2014. a
Beckers, J.-M. and Rixen, M.:
EOF calculations and data filling from incomplete oceanographic datasets,
J. Atmos. Ocean. Tech.,
20, 1839–1856, 2003. a
Beckers, J.-M., Barth, A., and Alvera-Azcárate, A.: DINEOF reconstruction of clouded images including error maps – application to the Sea-Surface Temperature around Corsican Island, Ocean Sci., 2, 183–199, https://doi.org/10.5194/os-2-183-2006, 2006. a
Calculli, C., Fassò, A., Finazzi, F., Pollice, A., and Turnone, A.:
Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy,
Environmetrics,
26, 406–417, 2015. a
Chen, S., Wang, X., Guo, H., Xie, P., and Sirelkhatim, A. M.:
Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series,
IEEE J. Sel. Top. Appl.,
13, 2251–2263, 2020. a
Chen, X., Murakami, H., Hahn, M. S., Hammond, G. E., Rockhold, M. L., Zachara, J. M., and Rubin, Y.: Three-dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data, Water Resour. Res., 48, W06501, https://doi.org/10.1029/2011WR010675, 2012. a
Chen, X., Hammond, G. E., Murray, C. J., Rockhold, M. L., Vermeul, V. R., and Zachara, J. M.:
Application of ensemble-based data assimilation techniques for aquifer characterization using tracer data at Hanford 300 area,
Water Resour. Res.,
49, 7064–7076, https://doi.org/10.1002/2012WR013285, 2013. a
Cheng, T., Haworth, J., and Wang, J.:
Spatio-temporal autocorrelation of road network data,
J. Geogr. Syst.,
14, 389–413, https://doi.org/10.1007/s10109-011-0149-5, 2012. a
Cheng, T., Haworth, J., Anbaroglu, B., Tanaksaranond, G., and Wang, J.:
Spatiotemporal data mining, in: Handbook of regional science, Springer, 1173–1193, https://doi.org/10.1007/978-3-642-23430-9_68, 2014. a
Contractor, S. and Roughan, M.: Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature, Front. Mar. Sci., 8, 637759, https://doi.org/10.3389/fmars.2021.637759, 2021. a
Datta, A., Banerjee, S., Finley, A. O., and Gelfand, A. E.:
Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets,
J. Am. Stat. Assoc.,
111, 800–812, 2016. a
Eidsvik, J., Shaby, B. A., Reich, B. J., Wheeler, M., and Niemi, J.:
Estimation and prediction in spatial models with block composite likelihoods,
J. Comput. Graph. Stat.,
23, 295–315, 2014. a
Fang, K., Shen, C., Kifer, D., and Yang, X.:
Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network,
Geophys. Res. Lett.,
44, 11030–11039, https://doi.org/10.1002/2017GL075619, 2017. a
Faruk, D. Ö.:
A hybrid neural network and ARIMA model for water quality time series prediction,
Eng. Appl. Artif. Intel.,
23, 586–594, 2010. a
Finley, A. O., Banerjee, S., and Gelfand, A. E.:
spBayes for large univariate and multivariate point-referenced spatio-temporal data models,
arXiv [preprint], arXiv:1310.8192, 2013. a
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.:
Could Machine Learning Break the Convection Parameterization Deadlock?,
Geophys. Res. Lett.,
45, 5742–5751, https://doi.org/10.1029/2018GL078202, 2018. a
Ghil, M., Allen, M., Dettinger, M., Ide, K., Kondrashov, D., Mann, M., Robertson, A. W., Saunders, A., Tian, Y., Varadi, F., and Yiou, P.: Advanced spectral methods for climatic time series, Rev. Geophys., 40, 3.1–3.41, https://doi.org/10.1029/2000RG000092, 2002. a
Grant, G. E. and Dietrich, W. E.:
The frontier beneath our feet,
Water Resour. Res.,
53, 2605–2609, 2017. a
Graves, A.:
Generating sequences with recurrent neural networks,
arXiv [preprint], arXiv:1308.0850, 2013. a
Graves, A., Abdel-rahman, M., and Geoffrey, H.: Speech recognition with deep recurrent neural networks, in: 2013 IEEE international conference on acoustics, speech and signal processing, 6645–6649,
https://doi.org/10.1109/ICASSP.2013.6638947, 2013. a
Griffith, D. A.:
Modeling spatio-temporal relationships: retrospect and prospect,
J. Geogr. Syst.,
12, 111–123, 2010. a
Grinsted, A., Moore, J. C., and Jevrejeva, S.: Application of the cross wavelet transform and wavelet coherence to geophysical time series, Nonlin. Processes Geophys., 11, 561–566, https://doi.org/10.5194/npg-11-561-2004, 2004. a, b
Grossmann, A. and Morlet, J.:
Decomposition of Hardy functions into square integrable wavelets of constant shape,
SIAM J. Math. Anal.,
15, 723–736, 1984. a
Güler, C. and Thyne, G. D.:
Hydrologic and geologic factors controlling surface and groundwater chemistry in Indian Wells-Owens Valley area, southeastern California, USA,
J. Hydrol.,
285, 177–198, 2004. a
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
Han, P., Wang, P. X., Zhang, S. Y., and Zhu, D. H.:
Drought forecasting based on the remote sensing data using ARIMA models,
Math. Comput. Model.,
51, 1398–1403, 2010. a
Ho, S., Xie, M., and Goh, T.:
A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction,
Comput. Ind. Eng.,
42, 371–375, 2002. a
Hochreiter, S. and Schmidhuber, J.:
Long Short-Term Memory,
Neural Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a
Hochreiter, S., Bengio, Y., Frasconi, P., and Schmidhuber, J.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, http://www.bioinf.jku.at/publications/older/ch7.pdf (last access: 5 April 2022), 2001. a
Hocke, K. and Kämpfer, N.: Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram, Atmos. Chem. Phys., 9, 4197–4206, https://doi.org/10.5194/acp-9-4197-2009, 2009. a
Hyndman, R. J. and Khandakar, Y.: Automatic time series for forecasting: the forecast package for R, 6/07, Monash University, Department of Econometrics and Business Statistics, https://doi.org/10.18637/jss.v000.i00, 2007. a
Hyndman, R. J. and Khandakar, Y.:
Automatic time series forecasting: the forecast package for R,
J. Stat. Softw.,
27, 1–22, 2008. a
Jordan, M.:
Attractor dynamics and parallelism in a connectionist sequential machine,
in: Proc. of the Eighth Annual Conference of the Cognitive Science Society, Erlbaum, Hillsdale, NJ, 112–127, https://ci.nii.ac.jp/naid/10018634949/en/ (last access: January 1990), 1986. a
Kamarianakis, Y. and Prastacos, P.: Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches, Transp. Res. Record, 1857, 74–84, https://doi.org/10.3141/1857-09, 2003. a
Kamarianakis, Y. and Prastacos, P.:
Space–time modeling of traffic flow,
Comput. Geosci.,
31, 119–133, 2005. a
Kandasamy, S., Baret, F., Verger, A., Neveux, P., and Weiss, M.: A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products, Biogeosciences, 10, 4055–4071, https://doi.org/10.5194/bg-10-4055-2013, 2013. a
Katzfuss, M. and Cressie, N.:
Spatio-temporal smoothing and EM estimation for massive remote-sensing data sets,
J. Time Ser. Anal.,
32, 430–446, 2011. a
Kingma, D. P. and Ba, J.:
Adam: A Method for Stochastic Optimization, CoRR, abs/1412.6980,
arXiv [preprint], arXiv:1412.6980, 2014. a
Kondrashov, D. and Ghil, M.: Spatio-temporal filling of missing points in geophysical data sets, Nonlin. Processes Geophys., 13, 151–159, https://doi.org/10.5194/npg-13-151-2006, 2006. a
Kondrashov, D., Shprits, Y., and Ghil, M.: Gap filling of solar wind data by singular spectrum analysis, Geophys. Res. Lett., 37, L15101, https://doi.org/10.1029/2010GL044138, 2010. a
Körner, P., Kronenberg, R., Genzel, S., and Bernhofer, C.:
Introducing Gradient Boosting as a universal gap filling tool for meteorological time series,
Meteorol. Z.,
27, 369–376, 2018. a
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a
Längkvist, M., Karlsson, L., and Loutfi, A.:
A review of unsupervised feature learning and deep learning for time-series modeling,
Pattern Recogn. Lett.,
42, 11–24, https://doi.org/10.1016/j.patrec.2014.01.008, 2014. a
Lin, C. Y., Abdullah, M. H., Praveena, S. M., Yahaya, A. H. B., and Musta, B.:
Delineation of temporal variability and governing factors influencing the spatial variability of shallow groundwater chemistry in a tropical sedimentary island,
J. Hydrol.,
432, 26–42, 2012. 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
Pfeifer, P. E. and Deutrch, S. J.:
A three-stage iterative procedure for space-time modeling phillip,
Technometrics,
22, 35–47, 1980. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science,
Nature,
566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Sarafanov, M., Kazakov, E., Nikitin, N. O., and Kalyuzhnaya, A. V.: A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI, Remote Sens.-Basel, 12, 3865, https://doi.org/10.3390/rs12233865, 2020. a
SBRSFA: Using Deep Learning to Fill Spatio-Temporal Data Gaps in Hydrological Monitoring Networks: A Case Study at the U.S. Department of Energy's Hanford Site, SBRSFA [data set], https://sbrsfa.velo.pnnl.gov/datasets/?UUID=14febd81-05b6-47fb-be52-439c4382decd, last access: 5 April 2022. a
Schmidhuber, J.:
Deep learning in neural networks: An overview,
Neural Networks,
61, 85–117, https://doi.org/10.1016/j.neunet.2014.09.003, 2015. a
Shen, C.:
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists,
Water Resour. Res.,
54, 8558–8593, https://doi.org/10.1029/2018WR022643, 2018. a
Shuai, P., Chen, X., Song, X., Hammond, G. E., Zachara, J., Royer, P., Ren, H., Perkins, W. A., Richmond, M. C., and Huang, M.:
Dam Operations and Subsurface Hydrogeology Control Dynamics of Hydrologic Exchange Flows in a Regulated River Reach,
Water Resour. Res.,
55, 2593–2612, https://doi.org/10.1029/2018WR024193, 2019. a
Song, X., Chen, X., Stegen, J., Hammond, G., Song, H.-S., Dai, H., Graham, E., and Zachara, J. M.: Drought Conditions Maximize the Impact of High-Frequency Flow Variations on Thermal Regimes and Biogeochemical Function in the Hyporheic Zone, Water Resour. Res., 54, 7361–7382,
https://doi.org/10.1029/2018WR022586, 2018. a, b
Stockwell, R. G., Mansinha, L., and Lowe, R.:
Localization of the complex spectrum: the S transform,
IEEE T. Signal Proces.,
44, 998–1001, 1996. a
Strobl, R. O. and Robillard, P. D.:
Network design for water quality monitoring of surface freshwaters: A review,
J. Environ. Manage.,
87, 639–648, 2008. a
Stroud, J. R., Stein, M. L., and Lysen, S.:
Bayesian and maximum likelihood estimation for Gaussian processes on an incomplete lattice,
J. Comput. Graph. Stat.,
26, 108–120, 2017. a
Sun, A. Y.:
Discovering State-Parameter Mappings in Subsurface Models Using Generative Adversarial Networks,
Geophys. Res. Lett.,
45, 11,137–11,146, https://doi.org/10.1029/2018GL080404, 2018. a
Sun, A. Y., Scanlon, B. R., Zhang, Z., Walling, D., Bhanja, S. N., Mukherjee, A., and Zhong, Z.:
Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch?,
Water Resour. Res.,
55, 1179–1195, https://doi.org/10.1029/2018WR023333, 2019. a
Taylor, C. J. and Alley, W. M.: Ground-water-level monitoring and the importance of long-term water-level data, 1217–2002, US Geological Survey, https://doi.org/10.3133/cir1217, 2002. a
Vacha, L. and Barunik, J.:
Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis,
Energ. Econ.,
34, 241–247, 2012. a
Valenzuela, O., Rojas, I., Rojas, F., Pomares, H., Herrera, L. J., Guillén, A., Marquez, L., and Pasadas, M.:
Hybridization of intelligent techniques and ARIMA models for time series prediction,
Fuzzy Set. Syst.,
159, 821–845, 2008. a
Wang, G., Garcia, D., Liu, Y., De Jeu, R., and Dolman, A. J.:
A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations,
Environ. Modell. Softw.,
30, 139–142, 2012. a
Wett, B., Jarosch, H., and Ingerle, K.:
Flood induced infiltration affecting a bank filtrate well at the River Enns, Austria,
J. Hydrol.,
266, 222–234, 2002. a
Wikle, C. K., Berliner, L. M., and Cressie, N.:
Hierarchical Bayesian space-time models,
Environ. Ecol. Stat.,
5, 117–154, 1998. a
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, X., Łukasz Kaiser, Gouws, S., Kato, Y., Kudo, T., Kazawa, H., Stevens, K., Kurian, G., Patil, N., Wang, W., Young, C., Smith, J., Riesa, J., Rudnick, A., Vinyals, O., Corrado, G., Hughes, M., and Dean, J.:
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, CoRR,
arXiv [preprint], arXiv:1609.08144, 2016 a
You, Q., Jin, H., Wang, Z., Fang, C., and Luo, J.: Image Captioning with Semantic Attention, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4651–4659, https://doi.org/10.1109/CVPR.2016.503, 2016. a
Zachara, J. M., Long, P. E., Bargar, J., Davis, J. A., Fox, P., Fredrickson, J. K., Freshley, M. D., Konopka, A. E., Liu, C., McKinley, J. P., Rockhold, M. L., Williams, K. H., and Yabusaki, S. B.: Persistence of uranium groundwater plumes: contrasting mechanisms at two DOE sites in the groundwater–river interaction zone, J. Contam. Hydrol., 147, 45–72, https://doi.org/10.1016/j.jconhyd.2013.02.001, 2013. a
Zachara, J. M., Chen, X., Song, X., Shuai, P., Murray, C., and Resch, C. T.:
Kilometer-scale hydrologic exchange flows in a gravel-bed river corridor and their implications to solute migration, Water Resour. Res., 56, e2019WR025258, https://doi.org/10.1029/2019WR025258, 2020. a
Zhang, D., Lindholm, G., and Ratnaweera, H.:
Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring,
J. Hydrol.,
556, 409–418, https://doi.org/10.1016/j.jhydrol.2017.11.018, 2018. a
Zhang, G. P.:
Time series forecasting using a hybrid ARIMA and neural network model,
Neurocomputing,
50, 159–175, 2003. a
Zhao, J., Lange, H., and Meissner, H.: Gap-filling continuously-measured soil respiration data: A highlight of time-series-based methods, Agr. Forest Meteorol., 285, 107912, https://doi.org/10.1016/j.agrformet.2020.107912, 2020. a
Short summary
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data...
