Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-5087-2024
© Author(s) 2024. 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-28-5087-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Nov 2024
Research article |
| 27 Nov 2024
| 27 Nov 2024
Deducing land–atmosphere coupling regimes from SMAP soil moisture
Department of Civil, Environmental, and Architectural Engineering, University of Kansas, Lawrence, Kansas, USA
Joseph A. Santanello
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Patricia M. Lawston-Parker
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Joshua K. Roundy
Department of Civil, Environmental, and Architectural Engineering, University of Kansas, Lawrence, Kansas, USA
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Yuna Lim, Andrea M. Molod, Randal D. Koster, and Joseph A. Santanello
Hydrol. Earth Syst. Sci., 29, 3435–3445, https://doi.org/10.5194/hess-29-3435-2025, https://doi.org/10.5194/hess-29-3435-2025, 2025
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To better utilize a given set of predictions, identifying “forecasts of opportunity” is valuable as this helps anticipate when prediction skill will be higher. This study shows that when strong land–atmosphere (L–A) coupling is detected 3–4 weeks into a forecast, the surface air temperature prediction skill at this lead time increases across the Midwest and northern Great Plains. Regions experiencing strong L–A coupling exhibit warm and dry anomalies, enhancing predictions of abnormally warm events.
Manisha Ganeshan, Dong L. Wu, Joseph A. Santanello, Jie Gong, Chi Ao, Panagiotis Vergados, and Kevin J. Nelson
Atmos. Meas. Tech., 18, 1389–1403, https://doi.org/10.5194/amt-18-1389-2025, https://doi.org/10.5194/amt-18-1389-2025, 2025
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This study explores the potential of two newly launched commercial Global Navigation Satellite System (GNSS) radio occultation (RO) satellite missions for advancing Arctic lower-atmospheric studies. The products have a good sampling of the lower Arctic atmosphere and are useful to derive the planetary boundary layer (PBL) height during winter months. This research is a step towards closing the observation gap in polar regions due to the decomissioning of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) GNSS RO mission and the lack of high-latitude coverage by its successor (COSMIC-2).
Admin Husic, John Hammond, Adam N. Price, and Joshua K. Roundy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3235, https://doi.org/10.5194/egusphere-2024-3235, 2024
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We used explainable machine learning to evaluate the accuracy of two continental-scale hydrologic models. We analyzed a suite of catchment attributes and found that soil water content had the biggest impact on model performance, especially in dry areas. Key thresholds for variables like precipitation and road density were identified, which could guide future improvements in these models. Our findings highlight the potential of data-driven methods to inform process-based models.
Gaoyun Wang, Rong Fu, Yizhou Zhuang, Paul A. Dirmeyer, Joseph A. Santanello, Guiling Wang, Kun Yang, and Kaighin McColl
Atmos. Chem. Phys., 24, 3857–3868, https://doi.org/10.5194/acp-24-3857-2024, https://doi.org/10.5194/acp-24-3857-2024, 2024
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This study investigates the influence of lower-tropospheric humidity on land–atmosphere coupling (LAC) during warm seasons in the US Southern Great Plains. Using radiosonde data and a buoyancy model, we find that elevated LT humidity is crucial for generating afternoon precipitation events under dry soil conditions not accounted for by conventional LAC indices. This underscores the importance of considering LT humidity in understanding LAC over dry soil during droughts in the SGP.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Patricia Lawston-Parker, Joseph A. Santanello Jr., and Nathaniel W. Chaney
Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
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Irrigation has been shown to impact weather and climate, but it has only recently been considered in prediction models. Prescribing where (globally) irrigation takes place is important to accurately simulate its impacts on temperature, humidity, and precipitation. Here, we evaluated three different irrigation maps in a weather model and found that the extent and intensity of irrigated areas and their boundaries are important drivers of weather impacts resulting from human practices.
Andrew Tangborn, Belay Demoz, Brian J. Carroll, Joseph Santanello, and Jeffrey L. Anderson
Atmos. Meas. Tech., 14, 1099–1110, https://doi.org/10.5194/amt-14-1099-2021, https://doi.org/10.5194/amt-14-1099-2021, 2021
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Accurate prediction of the planetary boundary layer is essential to both numerical weather prediction (NWP) and pollution forecasting. This paper presents a methodology to combine these measurements with the models through a statistical data assimilation approach that calculates the correlation between the PBLH and variables like temperature and moisture in the model. The model estimates of these variables can be improved via this method, and this will enable increased forecast accuracy.
Hendrik Wouters, Irina Y. Petrova, Chiel C. van Heerwaarden, Jordi Vilà-Guerau de Arellano, Adriaan J. Teuling, Vicky Meulenberg, Joseph A. Santanello, and Diego G. Miralles
Geosci. Model Dev., 12, 2139–2153, https://doi.org/10.5194/gmd-12-2139-2019, https://doi.org/10.5194/gmd-12-2139-2019, 2019
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The free software CLASS4GL (http://class4gl.eu) is designed to investigate the dynamic atmospheric boundary layer (ABL) with weather balloons. It mines observational data from global radio soundings, satellite and reanalysis data from the last 40 years to constrain and initialize an ABL model and automizes multiple experiments in parallel. CLASS4GL aims at fostering a better understanding of land–atmosphere feedbacks and the drivers of extreme weather.
Patricia M. Lawston, Joseph A. Santanello Jr., Trenton E. Franz, and Matthew Rodell
Hydrol. Earth Syst. Sci., 21, 2953–2966, https://doi.org/10.5194/hess-21-2953-2017, https://doi.org/10.5194/hess-21-2953-2017, 2017
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Irrigation can affect the weather by making the air cooler and more humid, potentially causing changes to clouds and rainfall. This study uses new datasets to test how well irrigation is simulated in a model. We find the model applies more water than farmers' data show, but the water is applied at the right time in the growing season and improves the modeled wetness of the soil. These results will help improve irrigation modeling and thus understanding of human impacts on the water cycle.
S. V. Kumar, C. D. Peters-Lidard, J. A. Santanello, R. H. Reichle, C. S. Draper, R. D. Koster, G. Nearing, and M. F. Jasinski
Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, https://doi.org/10.5194/hess-19-4463-2015, 2015
Z. Tao, J. A. Santanello, M. Chin, S. Zhou, Q. Tan, E. M. Kemp, and C. D. Peters-Lidard
Atmos. Chem. Phys., 13, 6207–6226, https://doi.org/10.5194/acp-13-6207-2013, https://doi.org/10.5194/acp-13-6207-2013, 2013
Related subject area
Subject: Global hydrology | Techniques and Approaches: Stochastic approaches
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Novel extensions to the Fisher copula to model flood spatial dependence over North America
Combining Recurrent Neural Networks with Variational Mode Decomposition and Multifractals to Predict Rainfall Time Series
Revisiting the global hydrological cycle: is it intensifying?
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How streamflow has changed across Australia since the 1950s: evidence from the network of hydrologic reference stations
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Francesco Serinaldi, Federico Lombardo, and Chris G. Kilsby
Hydrol. Earth Syst. Sci., 29, 1159–1181, https://doi.org/10.5194/hess-29-1159-2025, https://doi.org/10.5194/hess-29-1159-2025, 2025
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Non-asymptotic probability distributions of block maxima (BM) have been proposed as an alternative to asymptotic distributions from classic extreme-value theory. We show that the non-asymptotic models are unnecessary and redundant approximations of the corresponding parent distributions, which are readily available, are not affected by serial dependence, have simpler expression, and describe the probability of all quartiles of the process of interest and not only the probability of BM.
Luis-Enrique Olivera-Guerra, Catherine Ottlé, Nina Raoult, and Philippe Peylin
Hydrol. Earth Syst. Sci., 29, 261–290, https://doi.org/10.5194/hess-29-261-2025, https://doi.org/10.5194/hess-29-261-2025, 2025
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We assimilate the recent ESA-CCI land surface temperature (LST) product to optimize parameters of a land surface model (ORCHIDEE). We test different assimilation strategies to evaluate the best strategy over various in situ stations across Europe. We also provide advice on how to assimilate this LST product to better simulate LST and surface energy fluxes. Finally, we demonstrate the effectiveness of this optimization, which is essential to better simulate future projections.
Duy Anh Alexandre, Chiranjib Chaudhuri, and Jasmin Gill-Fortin
Hydrol. Earth Syst. Sci., 28, 5069–5085, https://doi.org/10.5194/hess-28-5069-2024, https://doi.org/10.5194/hess-28-5069-2024, 2024
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Estimating extreme river discharges at single stations is relatively simple. However, flooding is a spatial phenomenon as rivers are connected. We develop a statistical method to estimate extreme flows with global coverage, accounting for spatial dependence. Using our model, synthetic flood events are simulated with more information than the limited historical events. This event catalog can be used to produce spatially coherent flood depth maps for flood risk assessment.
Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia
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The hybrid VMD-RNN model provides a reliable one-step-ahead prediction, with better performance in predicting high and low values than the pure LSTM model. The universal multifractals technique is also introduced to evaluate prediction performance, thus validating the usefulness and applicability of the hybrid model.
Demetris Koutsoyiannis
Hydrol. Earth Syst. Sci., 24, 3899–3932, https://doi.org/10.5194/hess-24-3899-2020, https://doi.org/10.5194/hess-24-3899-2020, 2020
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We overview and retrieve a great amount of global hydroclimatic data sets. We improve the quantification of the global hydrological cycle, its variability and its uncertainties through the surge of newly available data sets. We test (but do not confirm) established climatological hypotheses, according to which the hydrological cycle should be intensifying due to global warming. We outline a stochastic view of hydroclimate, which provides a reliable means of dealing with its variability.
Yves Tramblay, Louise Mimeau, Luc Neppel, Freddy Vinet, and Eric Sauquet
Hydrol. Earth Syst. Sci., 23, 4419–4431, https://doi.org/10.5194/hess-23-4419-2019, https://doi.org/10.5194/hess-23-4419-2019, 2019
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In the present study the flood trends have been assessed for a large sample of 171 basins located in southern France, which has a Mediterranean climate. Results show that, despite the increase in rainfall intensity previously observed in this area, there is no general increase in flood magnitude. Instead, a reduction in the annual number of floods is found, linked to a decrease in soil moisture caused by the increase in temperature observed in recent decades.
Raghavendra B. Jana, Ali Ershadi, and Matthew F. McCabe
Hydrol. Earth Syst. Sci., 20, 3987–4004, https://doi.org/10.5194/hess-20-3987-2016, https://doi.org/10.5194/hess-20-3987-2016, 2016
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Interactions between soil moisture and terrestrial evaporation affect responses between land surface and the atmosphere across scales. We present an analysis of the link between soil moisture and evaporation estimates from three distinct models. The relationships were examined over nearly 2 years of observation data. Results show that while direct correlations of raw data were mostly not useful, the root-zone soil moisture and the modelled evaporation estimates reflect similar distributions.
Xiaoyong Sophie Zhang, Gnanathikkam E. Amirthanathan, Mohammed A. Bari, Richard M. Laugesen, Daehyok Shin, David M. Kent, Andrew M. MacDonald, Margot E. Turner, and Narendra K. Tuteja
Hydrol. Earth Syst. Sci., 20, 3947–3965, https://doi.org/10.5194/hess-20-3947-2016, https://doi.org/10.5194/hess-20-3947-2016, 2016
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The hydrologic reference stations website (www.bom.gov.au/water/hrs/), developed by the Australia Bureau of Meteorology, is a one-stop portal to access long-term and high-quality streamflow information for 222 stations across Australia. This study investigated the streamflow variability and inferred trends in water availability for those stations. The results present a systematic analysis of recent hydrological changes in Australian rivers, which will aid water management decision making.
Takayuki Sugimoto, András Bárdossy, Geoffrey G. S. Pegram, and Johannes Cullmann
Hydrol. Earth Syst. Sci., 20, 2705–2720, https://doi.org/10.5194/hess-20-2705-2016, https://doi.org/10.5194/hess-20-2705-2016, 2016
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This paper is aims to detect the climate change impacts on the hydrological regime from the long-term discharge records. A new method for stochastic analysis using copulas, which has the advantage of scrutinizing the data independent of marginal, is suggested in this paper. Two measures are used in the copula domain: one focuses on the asymmetric characteristic of data and the other compares the distances between the copulas. These are calculated for 100 years of daily discharges and the results are discussed.
L. Gudmundsson and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 19, 2859–2879, https://doi.org/10.5194/hess-19-2859-2015, https://doi.org/10.5194/hess-19-2859-2015, 2015
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Water storages and fluxes on land are key variables in the Earth system. To provide context for local investigations and to understand phenomena that emerge at large spatial scales, information on continental freshwater dynamics is needed. This paper presents a methodology to estimate continental-scale runoff on a 0.5° spatial grid, which combines the advantages of in situ observations with the power of machine learning regression. The resulting runoff estimates compare well with observations.
J. P. Boisier, N. de Noblet-Ducoudré, and P. Ciais
Hydrol. Earth Syst. Sci., 18, 3571–3590, https://doi.org/10.5194/hess-18-3571-2014, https://doi.org/10.5194/hess-18-3571-2014, 2014
R. Alkama, L. Marchand, A. Ribes, and B. Decharme
Hydrol. Earth Syst. Sci., 17, 2967–2979, https://doi.org/10.5194/hess-17-2967-2013, https://doi.org/10.5194/hess-17-2967-2013, 2013
E. Arnone, D. Pumo, F. Viola, L. V. Noto, and G. La Loggia
Hydrol. Earth Syst. Sci., 17, 2449–2458, https://doi.org/10.5194/hess-17-2449-2013, https://doi.org/10.5194/hess-17-2449-2013, 2013
B. Li and M. Rodell
Hydrol. Earth Syst. Sci., 17, 1177–1188, https://doi.org/10.5194/hess-17-1177-2013, https://doi.org/10.5194/hess-17-1177-2013, 2013
S. M. Papalexiou, D. Koutsoyiannis, and C. Makropoulos
Hydrol. Earth Syst. Sci., 17, 851–862, https://doi.org/10.5194/hess-17-851-2013, https://doi.org/10.5194/hess-17-851-2013, 2013
A. K. Gain, W. W. Immerzeel, F. C. Sperna Weiland, and M. F. P. Bierkens
Hydrol. Earth Syst. Sci., 15, 1537–1545, https://doi.org/10.5194/hess-15-1537-2011, https://doi.org/10.5194/hess-15-1537-2011, 2011
J. O. Haerter, S. Hagemann, C. Moseley, and C. Piani
Hydrol. Earth Syst. Sci., 15, 1065–1079, https://doi.org/10.5194/hess-15-1065-2011, https://doi.org/10.5194/hess-15-1065-2011, 2011
K. Stahl, H. Hisdal, J. Hannaford, L. M. Tallaksen, H. A. J. van Lanen, E. Sauquet, S. Demuth, M. Fendekova, and J. Jódar
Hydrol. Earth Syst. Sci., 14, 2367–2382, https://doi.org/10.5194/hess-14-2367-2010, https://doi.org/10.5194/hess-14-2367-2010, 2010
Cited articles
AIRS Project: AIRS/Aqua L3 Daily Standard Physical Retrieval (AIRS-only) 1° × 1° V7. Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [Temperature profile, Humidity profile, Surface Pressure, Surface Air Temperature and Surface Humidity], https://doi.org/10.5067/UO3Q64CTTS1U, 2019.
Alexander, G. A., Holmes, H. A., Sun, X., Caputi, D., Faloona, I. C., and Oldroyd, H. J.: Simulating land-atmosphere coupling in the Central Valley, California: Investigating soil moisture impacts on boundary layer properties, Agr. Forest Meteorol., 317, 108898, https://doi.org/10.1016/j.agrformet.2022.108898, 2022.
Anderson, W. B., Zaitchik, B. F., Hain, C. R., Anderson, M. C., Yilmaz, M. T., Mecikalski, J., and Schultz, L.: Towards an integrated soil moisture drought monitor for East Africa, Hydrol. Earth Syst. Sci., 16, 2893–2913, https://doi.org/10.5194/hess-16-2893-2012, 2012.
Arshad, M., Ma, X., Yin, J., Ullah, W., Liu, M., and Ullah, I.: Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan, Weather and Climate Extremes, 33, 100373, https://doi.org/10.1016/j.wace.2021.100373, 2021.
Beamesderfer, E. R., Buechner, C., Faiola, C., Helbig, M., Sanchez-Mejia, Z. M., Yáñez-Serrano, A. M., Zhang, Y., and Richardson, A. D.: Advancing Cross-Disciplinary Understanding of Land–Atmosphere Interactions, J. Geophys. Res.-Biogeo., 127, e2021JG006707, https://doi.org/10.1029/2021JG006707, 2022.
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, S., Bidlot, J., Haimberger, L., Woollen, J., Buontempo, C., and Thépaut, J.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteor. Soc., 147, 4186–4227, https://doi.org/10.1002/qj.4174, 2021.
Bennet, M. J., Kingston, D. G., and Cullen, N. J.: Extreme Compound and Seesaw Hydrometeorological Events in New Zealand: An Initial Assessment, J. Geophys. Res.-Atmos., 128, e2022JD038346, https://doi.org/10.1029/2022JD038346, 2023.
Chen, C., He, M., Chen, Q., Zhang, J., Li, Z., Wang, Z., and Duan, Z.: Triple collocation-based error estimation and data fusion of global gridded precipitation products over the Yangtze River basin, J. Hydrol., 605, 127307, https://doi.org/10.1016/j.jhydrol.2021.127307, 2022.
Dirmeyer, P. A., Halder, S., and Bombardi, R.: On the Harvest of Predictability From Land States in a Global Forecast Model, J. Geophys. Res.-Atmos., 123, 13111–13127, https://doi.org/10.1029/2018JD029103, 2018.
Dong, J. and Crow, W. T.: L-band remote-sensing increases sampled levels of global soil moisture-air temperature coupling strength, Remote Sens. Environ., 220, 51–58, https://doi.org/10.1016/j.rse.2018.10.024, 2019.
Dong, X., Wang, Y., Hou, S., Ding, M., Yin, B., and Zhang, Y.: Robustness of the Recent Global Atmospheric Reanalyses for Antarctic Near-Surface Wind Speed Climatology, J. Climate, 33, 4027–4043, https://doi.org/10.1175/JCLI-D-19-0648.1, 2020.
Durre, I. and Yin, X.: Enhanced Radiosonde Data For Studies of Vertical Structure, B. Am. Meteorol. Soc., 89, 1257–1262, https://doi.org/10.1175/2008BAMS2603.1, 2008.
Durre, I., Yin, X., Vose, R. S., Applequist, S., Arnfield, J., Korzeniewski, B., and Hundermark, B.: Integrated Global Radiosonde Archive (IGRA), Version 2, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5X63K0Q, 2016.
Entekhabi, D., Rodriguez-Iturbe, I., and Castelli, F.: Mutual interaction of soil moisture state and atmospheric processes, J. Hydrol., 184, 3–17, https://doi.org/10.1016/0022-1694(95)02965-6, 1996.
Entekhabi, D., Das, N., Njoku, E., Johnson, J., and Shi, J.: SMAP L3 Radar/Radiometer Global Daily 9km EASE-Grid Soil Moisture, Version 3 [Surface Soil Moisture], NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/7KKNQ5UURM2W, 2016.
Feng, F. and Wang, K.: Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets, Remote Sens., 10, 115, https://doi.org/10.3390/rs10010115, 2018.
Ferguson, C. R. and Wood, E. F.: Observed Land–Atmosphere Coupling from Satellite Remote Sensing and Reanalysis, J. Hydrometeorol., 12, 1221–1254, https://doi.org/10.1175/2011JHM1380.1, 2011.
Findell, K. L. and Eltahir, E. A. B.: Atmospheric Controls on Soil Moisture–Boundary Layer Interactions. Part II: Feedbacks within the Continental United States, J. Hydrometeorol., 4, 570–583, https://doi.org/10.1175/1525-7541(2003)004<0570:ACOSML>2.0.CO;2, 2003.
Findell, K. L., Yin, Z., Seo, E., Dirmeyer, P. A., Arnold, N. P., Chaney, N., Fowler, M. D., Huang, M., Lawrence, D. M., Ma, P.-L., and Santanello Jr., J. A.: Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output, Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, 2024.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., Da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
GMAO (Global Modeling and Assimilation Office): inst3_3d_asm_Cp: MERRA-2 3D IAU State, Meteorology Instantaneous 3-hourly (p-coord, 0.625 × 0.5L42), version 5.12.4, Greenbelt, MD, USA: Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC) [data set], https://doi.org/10.5067/VJAFPLI1CSIV, 2015.
Gruber, A., Dorigo, W. A., Crow, W., and Wagner, W.: Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals, IEEE T. Geosci. Remote, 55, 6780–6792, https://doi.org/10.1109/TGRS.2017.2734070, 2017.
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet, J.-C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi, M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C., Muñoz-Sabater, J., Peng, J., Reichle, R., Richaume, P., Rüdiger, C., Scanlon, T., Van Der Schalie, R., Wigneron, J.-P., and Wagner, W.: Validation practices for satellite soil moisture retrievals: What are (the) errors?, Remote Sens. Environ., 244, 111806, https://doi.org/10.1016/j.rse.2020.111806, 2020.
Hassler, B. and Lauer, A.: Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5, Atmosphere, 12, 1462, https://doi.org/10.3390/atmos12111462, 2021.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Hsu, H. and Dirmeyer, P. A.: Soil moisture-evaporation coupling shifts into new gears under increasing CO2, Nat. Commun., 14, 1162, https://doi.org/10.1038/s41467-023-36794-5, 2023.
Jach, L., Schwitalla, T., Branch, O., Warrach-Sagi, K., and Wulfmeyer, V.: Sensitivity of land–atmosphere coupling strength to changing atmospheric temperature and moisture over Europe, Earth Syst. Dynam., 13, 109–132, https://doi.org/10.5194/esd-13-109-2022, 2022.
Kim, S., Dong, J., and Sharma, A.: A Triple Collocation-Based Comparison of Three L-Band Soil Moisture Datasets, SMAP, SMOS-IC, and SMOS, Over Varied Climates and Land Covers, Front. Water, 3, 693172, https://doi.org/10.3389/frwa.2021.693172, 2021.
Kozubek, M., Krizan, P., and Lastovicka, J.: Homogeneity of the Temperature Data Series from ERA5 and MERRA2 and Temperature Trends, Atmosphere, 11, 235, https://doi.org/10.3390/atmos11030235, 2020.
Liu, Y., Yao, L., Jing, W., Di, L., Yang, J., and Li, Y.: Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA, J. Hydrol., 590, 125406, https://doi.org/10.1016/j.jhydrol.2020.125406, 2020.
Lorenzo, A. T., Morzfeld, M., Holmgren, W. F., and Cronin, A. D.: Optimal interpolation of satellite and ground data for irradiance nowcasting at city scales, Sol. Energy, 144, 466–474, https://doi.org/10.1016/j.solener.2017.01.038, 2017.
Lu, J., Wang, G., Chen, T., Li, S., Hagan, D. F. T., Kattel, G., Peng, J., Jiang, T., and Su, B.: A harmonized global land evaporation dataset from model-based products covering 1980–2017, Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, 2021.
Lyu, F., Tang, G., Behrangi, A., Wang, T., Tan, X., Ma, Z., and Xiong, W.: Precipitation Merging Based on the Triple Collocation Method Across Mainland China, IEEE T. Geosci. Remote, 59, 3161–3176, https://doi.org/10.1109/TGRS.2020.3008033, 2021.
Makhasana, P., Roundy, J., Santanello, J. A., and Lawston-Parker, P. M.: Triple Collocation based Merged Dataset for Convective Triggering Potential (CTP) and Humidity Index (HI), HydroShare [data set], https://doi.org/10.4211/hs.90bf9b575b684c849e617f620c2d63fb, 2024.
Miranda Espinosa, M. T., Giuliani, G., and Ray, N.: Reviewing the discoverability and accessibility to data and information products linked to Essential Climate Variables, Int. J. Digit. Earth, 13, 236–252, https://doi.org/10.1080/17538947.2019.1620882, 2020.
Mishra, A., Vu, T., Veettil, A. V., and Entekhabi, D.: Drought monitoring with soil moisture active passive (SMAP) measurements, J. Hydrol., 552, 620–632, https://doi.org/10.1016/j.jhydrol.2017.07.033, 2017.
Mladenova, I. E., Bolten, J. D., Crow, W., Sazib, N., and Reynolds, C.: Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model, Front. Big Data, 3, 10, https://doi.org/10.3389/fdata.2020.00010, 2020.
Mukherjee, S. and Mishra, A. K.: Global Flash Drought Analysis: Uncertainties From Indicators and Datasets, Earth's Future, 10, 1–14, https://doi.org/10.1029/2022EF002660, 2022.
Nguyen, G. V., Le, X.-H., Van, L. N., Jung, S., Yeon, M., and Lee, G.: Application of Random Forest Algorithm for Merging Multiple Satellite Precipitation Products across South Korea, Remote Sens., 13, 4033, https://doi.org/10.3390/rs13204033, 2021.
ONeill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., Chaubell, M. J., and Colliander, A.: SMAP Enhanced L3 Ra- diometer Global and Polar Grid Daily 9km EASE-Grid Soil Moisture, Version 5 [Surface Soil Moisture], NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/4DQ54OUIJ9DL, 2021.
Park, S., Son, S.-W., Jung, M.-I., Park, J., and Park, S. S.: Evaluation of tropospheric ozone reanalyses with independent ozonesonde observations in East Asia, Geosci. Lett., 7, 12, https://doi.org/10.1186/s40562-020-00161-9, 2020.
Pratola, C., Barrett, B., Gruber, A., and Dwyer, E.: Quality Assessment of the CCI ECV Soil Moisture Product Using ENVISAT ASAR Wide Swath Data over Spain, Ireland and Finland, Remote Sens., 7, 15388–15423, https://doi.org/10.3390/rs71115388, 2015.
Qi, Y., Chen, H., and Zhu, S.: Influence of Land–Atmosphere Coupling on Low Temperature Extremes Over Southern Eurasia, J. Geophys. Res.-Atmos., 128, e2022JD037252, https://doi.org/10.1029/2022JD037252, 2023.
Qiu, J., Dong, J., Crow, W. T., Zhang, X., Reichle, R. H., and De Lannoy, G. J. M.: The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis, Hydrol. Earth Syst. Sci., 25, 1569–1586, https://doi.org/10.5194/hess-25-1569-2021, 2021.
Reichle, R., De Lannoy, G., Koster, R., Crow, W., Kimball, J., and Liu, Q.: SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 6, [Surface Soil Moisture], NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/08S1A6811J0U, 2021.
Reichle, R. H., De Lannoy, G. J. M., Liu, Q., Ardizzone, J. V., Colliander, A., Conaty, A., Crow, W., Jackson, T. J., Jones, L. A., Kimball, J. S., Koster, R. D., Mahanama, S. P., Smith, E. B., Berg, A., Bircher, S., Bosch, D., Caldwell, T. G., Cosh, M., González-Zamora, Á., Holifield Collins, C. D., Jensen, K. H., Livingston, S., Lopez-Baeza, E., Martínez-Fernández, J., McNairn, H., Moghaddam, M., Pacheco, A., Pellarin, T., Prueger, J., Rowlandson, T., Seyfried, M., Starks, P., Su, Z., Thibeault, M., Van Der Velde, R., Walker, J., Wu, X., and Zeng, Y.: Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements, J. Hydrometeorol., 18, 2621–2645, https://doi.org/10.1175/JHM-D-17-0063.1, 2017.
Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., and Walker, J. P.: Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product, J. Adv. Model Earth Sy., 11, 3106–3130, https://doi.org/10.1029/2019MS001729, 2019.
Reichle, R. H., Liu, Q., Ardizzone, J. V., Crow, W. T., De Lannoy, G. J. M., Dong, J., Kimball, J. S., and Koster, R. D.: The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product, J. Hydrometeorol., 22, 405–424, https://doi.org/10.1175/JHM-D-20-0217.1, 2021.
Roundy, J. K. and Santanello, J. A.: Utility of Satellite Remote Sensing for Land–Atmosphere Coupling and Drought Metrics, J. Hydrometeorol., 18, 863–877, https://doi.org/10.1175/JHM-D-16-0171.1, 2017.
Roundy, J. K. and Wood, E. F.: The Attribution of Land–Atmosphere Interactions on the Seasonal Predictability of Drought, J. Hydrometeorol., 16, 793–810, https://doi.org/10.1175/JHM-D-14-0121.1, 2015.
Roundy, J. K., Ferguson, C. R., and Wood, E. F.: Temporal Variability of Land–Atmosphere Coupling and Its Implications for Drought over the Southeast United States, J. Hydrometeorol., 14, 622–635, https://doi.org/10.1175/JHM-D-12-090.1, 2013.
Roundy, J. K., Ferguson, C. R., and Wood, E. F.: Impact of land-atmospheric coupling in CFSv2 on drought prediction, Clim. Dynam., 43, 421–434, https://doi.org/10.1007/s00382-013-1982-7, 2014.
Saha, K., Dash, P., Zhao, X., and Zhang, H.: Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis, Remote Sens., 12, 590, https://doi.org/10.3390/rs12040590, 2020.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., Chuang, H., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Van Den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010.
Saha, S. et al.: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D61C1TXF, 2011.
Saini, R., Wang, G., and Pal, J. S.: Role of Soil Moisture Feedback in the Development of Extreme Summer Drought and Flood in the United States, J. Hydrometeorol., 17, 2191–2207, https://doi.org/10.1175/JHM-D-15-0168.1, 2016.
Santanello, J. A., Roundy, J., and Dirmeyer, P. A.: Quantifying the Land–Atmosphere Coupling Behavior in Modern Reanalysis Products over the U.S. Southern Great Plains, J. Climate, 28, 5813–5829, https://doi.org/10.1175/JCLI-D-14-00680.1, 2015.
Santanello, J. A., Dirmeyer, P. A., Ferguson, C. R., Findell, K. L., Tawfik, A. B., Berg, A., Ek, M., Gentine, P., Guillod, B. P., Van Heerwaarden, C., Roundy, J., and Wulfmeyer, V.: Land–Atmosphere Interactions: The LoCo Perspective, B. Am. Meteorol. Soc., 99, 1253–1272, https://doi.org/10.1175/BAMS-D-17-0001.1, 2018.
Seneviratne, S. I. and Stöckli, R.: The Role of Land–Atmosphere Interactions for Climate Variability in Europe, in: Climate Variability and Extremes during the Past 100 Years, edited by: Brönnimann, S., Luterbacher, J., Ewen, T., Diaz, H. F., Stolarski, R. S., and Neu, U., Springer Netherlands, Dordrecht, 179–193, https://doi.org/10.1007/978-1-4020-6766-2_12, 2008.
Seo, E. and Dirmeyer, P. A.: Understanding the diurnal cycle of land–atmosphere interactions from flux site observations, Hydrol. Earth Syst. Sci., 26, 5411–5429, https://doi.org/10.5194/hess-26-5411-2022, 2022.
Seo, Y.-W. and Ha, K.-J.: Changes in land-atmosphere coupling increase compound drought and heatwaves over northern East Asia, NPJ Clim. Atmos. Sci., 5, 100, https://doi.org/10.1038/s41612-022-00325-8, 2022.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and calibration using triple collocation, J. Geophys. Res., 103, 7755–7766, https://doi.org/10.1029/97JC03180, 1998.
Sun, L. and Fu, Y.: A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner, Earth Syst. Sci. Data, 13, 2293–2306, https://doi.org/10.5194/essd-13-2293-2021, 2021.
Tavakol, A., Rahmani, V., Quiring, S. M., and Kumar, S. V.: Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States, Remote Sens. Environ., 229, 234–246, https://doi.org/10.1016/j.rse.2019.05.006, 2019.
Teixeira, J., Chen, S., Clayson, C. A., Fridlind, A. M., Lebsock, M., McCarty, W., Salmun, H., Santanello, J. A., Turner, D. D., Wang, Z., and Zeng, X.: Toward a Global Planetary Boundary Layer Observing System: The NASA PBL Incubation Study Team Report, NASA PBL Incubation Study Team, 134 pp., https://science.nasa.gov/earth-science/decadal-surveys/decadal-pbl/ (last access: 3 May 2024), 2021.
Van Vuuren, D. P., Batlle Bayer, L., Chuwah, C., Ganzeveld, L., Hazeleger, W., Van Den Hurk, B., Van Noije, T., O’Neill, B., and Strengers, B. J.: A comprehensive view on climate change: coupling of earth system and integrated assessment models, Environ. Res. Lett., 7, 024012, https://doi.org/10.1088/1748-9326/7/2/024012, 2012.
Velpuri, N. M., Senay, G. B., and Morisette, J. T.: Evaluating New SMAP Soil Moisture for Drought Monitoring in the Rangelands of the US High Plains, Rangelands, 38, 183–190, https://doi.org/10.1016/j.rala.2016.06.002, 2016.
Wakefield, R. A., Basara, J. B., Furtado, J. C., Illston, B. G., Ferguson, Craig. R., and Klein, P. M.: A Modified Framework for Quantifying Land–Atmosphere Covariability during Hydrometeorological and Soil Wetness Extremes in Oklahoma, J. Appl. Meteorol. Clim., 58, 1465–1483, https://doi.org/10.1175/JAMC-D-18-0230.1, 2019.
Wang, G., Fu, R., Zhuang, Y., Dirmeyer, P. A., Santanello, J. A., Wang, G., Yang, K., and McColl, K.: Influence of lower-tropospheric moisture on local soil moisture–precipitation feedback over the US Southern Great Plains, Atmos. Chem. Phys., 24, 3857–3868, https://doi.org/10.5194/acp-24-3857-2024, 2024.
Wilson, A. G. and Fronczyk, K. M.: Bayesian Reliability: Combining Information, Qual. Eng., 9, 119–129, https://doi.org/10.1080/08982112.2016.1211889, 2016.
Wu, X., Lu, G., Wu, Z., He, H., Scanlon, T., and Dorigo, W.: Triple Collocation-Based Assessment of Satellite Soil Moisture Products with In Situ Measurements in China: Understanding the Error Sources, Remote Sens., 12, 2275, https://doi.org/10.3390/rs12142275, 2020.
Xu, X.: Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the Great Lakes Region, Remote Sens., 12, 3785, https://doi.org/10.3390/rs12223785, 2020.
Yilmaz, M. T., Crow, W. T., Anderson, M. C., and Hain, C.: An objective methodology for merging satellite- and model-based soil moisture products: Objectively merging soil moisture products, Water Resour. Res., 48, W11502, https://doi.org/10.1029/2011WR011682, 2012.
Yingshan, W., Weijun, S., Lei, W., Yanzhao, L., Wentao, D., Jizu, C., and Xiang, Q.: How Do Different Reanalysis Radiation Datasets Perform in West Qilian Mountains?, Front. Earth Sci., 10, 852054, https://doi.org/10.3389/feart.2022.852054, 2022.
Zhang, L., Ding, M., Zheng, X., Chen, J., Guo, J., and Bian, L.: Assessment of AIRS Version 7 Temperature Profiles and Low-Level Inversions with GRUAN Radiosonde Observations in the Arctic, Remote Sens., 15, 1270, https://doi.org/10.3390/rs15051270, 2023a.
Zhang, L. N., Short Gianotti, D. J., and Entekhabi, D.: Land Surface Influence on Convective Available Potential Energy (CAPE) Change during Interstorms, J. Hydrometeorol., 24, 1365–1376, https://doi.org/10.1175/JHM-D-22-0191.1, 2023b.
Zhang, S.-Q., Ren, G.-Y., Ren, Y.-Y., Zhang, Y.-X., and Xue, X.-Y.: Comprehensive evaluation of surface air temperature reanalysis over China against urbanization-bias-adjusted observations, Advances in Climate Change Research, 12, 783–794, https://doi.org/10.1016/j.accre.2021.09.010, 2021.
Zhang, X., Zhang, T., Zhou, P., Shao, Y., and Gao, S.: Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements, Remote Sens., 9, 104, https://doi.org/10.3390/rs9020104, 2017.
Zhou, A., Cai, Z., Wei, L., and Qian, W.: M-kernel merging: towards density estimation over data streams, in: Eighth International Conference on Database Systems for Advanced Applications, Proceedings Eighth International Conference on Database Systems for Advanced Applications (DASFAA 2003), Kyoto, Japan, 26–28 March 2003, 285–292, https://doi.org/10.1109/DASFAA.2003.1192393, 2003.
Zhou, S., Williams, A. P., Berg, A. M., Cook, B. I., Zhang, Y., Hagemann, S., Lorenz, R., Seneviratne, S. I., and Gentine, P.: Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity, P. Natl. Acad. Sci. USA, 116, 18848–18853, https://doi.org/10.1073/pnas.1904955116, 2019.
Zhu, L., Tian, G., Wu, H., Ding, M., Zhu, A.-X., and Ma, T.: Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture, Remote Sens., 16, 1225, https://doi.org/10.3390/rs16071225, 2024.
Short summary
This study examines how soil moisture impacts land–atmosphere interactions, crucial for understanding Earth's water and energy cycles. The study used two different soil moisture datasets from the SMAP satellite to measure how strongly soil moisture influences the atmosphere's ability to retain moisture (called coupling strength). Leveraging SMAP soil moisture data and integrating multiple atmospheric datasets, the study offers new insights into the dynamics of land–atmosphere coupling strength.
This study examines how soil moisture impacts land–atmosphere interactions, crucial for...
