Data Assimilation in Hydrological Sciences

doi:10.1017/9781009180412.008

7 - Data Assimilation in Hydrological Sciences

from Part II - ‘Fluid’ Earth Applications: From the Surface to the Space

Published online by Cambridge University Press: 20 June 2023

Edited by

Dylan Jones and

Alik Ismail-Zadeh: Affiliation:
Karlsruhe Institute of Technology, Germany
Fabio Castelli: Affiliation:
Università degli Studi, Florence
Dylan Jones: Affiliation:
University of Toronto
Sabrina Sanchez: Affiliation:
Max Planck Institute for Solar System Research, Germany

Book contents

Get access

Summary

Abstract: Hydrological sciences cover a wide variety of water-driven processes at the Earth’s surface, above, and below it. Data assimilation techniques in hydrology have developed over the years along many quite independent paths, following not only different data availabilities but also a plethora of problem-specific model structures. Most hydrologic problems that are addressed through data assimilation, however, share some distinct peculiarities: scarce or indirect observation of most important state variables (soil moisture, river discharge, groundwater level, to name a few), incomplete or conceptual modelling, extreme spatial heterogeneity, and uncertainty of controlling physical parameters. On the other side, adoption of simplified and scale-specific models allows for substantial problem reduction that partially compensates these difficulties, opening the path to the assimilation of very indirect observations (e.g. from satellite remote sensing) and efficient model inversion for parameter estimation. This chapter illustrates the peculiarities of data assimilation for state estimation and model inversion in hydrology, with reference to a number of representative applications. Sequential ensemble filters and variational methods are recognised to be the most common choices in hydrologic data assimilation, and the motivations for these choices are also discussed, with several examples.

Keywords

adjoint ensemble Kalman filter evapotranspiration flood groundwater land data assimilation remote sensing soil moisture vegetation watershed

Information

Type: Chapter
Information: Applications of Data Assimilation and Inverse Problems in the Earth Sciences , pp. 112 - 130
DOI: https://doi.org/10.1017/9781009180412.008 [Opens in a new window]
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Abdolghafoorian, A., and Farhadi, L. (2020). LIDA: A Land Integrated Data Assimilation framework for mapping land surface heat and evaporative fluxes by assimilating space-borne soil moisture and land surface temperature. Water Resources Research, 56(8), e2020WR027183.Google Scholar

Abrahart, R. J., Anctil, F., Coulibaly, P. et al. (2012). Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Progress in Physical Geography, 36(4), 480–513.CrossRef Google Scholar

Allen, G. H., Olden, J. D., Krabbenhoft, C. et al. (2020). Is our finger on the pulse? Assessing placement bias of the global river gauge network. AGU Fall Meeting Abstracts, H010-0016.Google Scholar

Altaf, M. U., El Gharamti, M., Heemink, A. W., and Hoteit, I. (2013). A reduced adjoint approach to variational data assimilation. Computer Methods in Applied Mechanics and Engineering, 254, 1–13.CrossRef Google Scholar

Altenau, E. H., Pavelsky, T. M., Durand, M. T. et al. (2021). The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A global river network for satellite data products. Water Resources Research, 57(7), e2021WR030054.Google Scholar

Alvarez-Garreton, C., Ryu, D., Western, A. W. et al. (2015). Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: Comparison between lumped and semi-distributed schemes. Hydrology and Earth System Sciences, 19(4), 1659–76.Google Scholar

Anderson, J. L. (2007). Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D: Nonlinear Phenomena, 230(1–2), 99–111.Google Scholar

Andreadis, K. M., and Schumann, G. J. (2014). Estimating the impact of satellite observations on the predictability of large-scale hydraulic models. Advances in Water Resources, 73, 44–54.Google Scholar

Annis, A., Nardi, F., and Castelli, F. (2022). Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real time flood mapping. Hydrology and Earth System Sciences, 26, 1019–41.Google Scholar

Annis, A., Nardi, F., Morrison, R. R., and Castelli, F. (2019). Investigating hydrogeomorphic floodplain mapping performance with varying DTM resolution and stream order. Hydrological Sciences Journal, 64(5), 525–38.CrossRef Google Scholar

Arellano, L. N., Good, S. P., Sanchez-Murillo, R. et al. (2020). Bayesian estimates of the mean recharge elevations of water sources in the Central America region using stable water isotopes. Journal of Hydrology: Regional Studies, 32, 100739.Google Scholar

Avellaneda, P. M., Ficklin, D. L., Lowry, C. S., Knouft, J. H., and Hall, D. M. (2020). Improving hydrological models with the assimilation of crowdsourced data. Water Resources Research, 56, e2019WR026325.Google Scholar

Azimi, S., Dariane, A. B., Modanesi, S. et al. (2020). Assimilation of Sentinel 1 and SMAP – based satellite soil moisture retrievals into SWAT hydrological model: The impact of satellite revisit time and product spatial resolution on flood simulations in small basins. Journal of Hydrology, 581, 124367.Google Scholar

Babaeian, E., Sadeghi, M., Jones, S. B. et al. (2019). Ground, proximal, and satellite remote sensing of soil moisture. Review of Geophysics, 57(2), 530–616.CrossRef Google Scholar

Balsamo, G., Agusti-Panareda, A., Albergel, C. et al. (2018). Satellite and in situ observations for advancing global earth surface modelling: A review. Remote Sensing, 10, 38.Google Scholar

Bateni, S. M., Entekhabi, D., and Castelli, F. (2013). Mapping evaporation and estimation of surface control of evaporation using remotely sensed land surface temperature from a constellation of satellites. Water Resources Research, 49, 950–68.Google Scholar

Bateni, S., Entekhabi, D., Margulis, S., Castelli, F., and Kergoat, L. (2014). Coupled estimation of surface heat fluxes and vegetation dynamics from remotely sensed land surface temperature and fraction of photosynthetically active radiation. Water Resources Research, 50, 8420–40.CrossRef Google Scholar

Bauser, H.H., Berg, D., and Roth, K. (2021). Technical Note: Sequential ensemble data assimilation in convergent and divergent systems. Hydrology and Earth System Sciences, 25, 3319–29.Google Scholar

Bauer-Marschallinger, B., Freeman, V., Cao, S. et al. (2019). Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 520–39.Google Scholar

Baugh, C., de Rosnay, P., Lawrence, H. et al. (2020). The impact of SMOA soil moisture data assimilation within the operational global flood awareness system (GloFAS). Remote. Sensing, 12(9), 1490, https://doi.org/10.3390/rs12091490.Google Scholar

Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M. (2016). Dominant flood generating mechanisms across the United States. Geophysical Research Letters, 43(9), 4382–90.Google Scholar

Berthet, L., Andréassian, V., Perrin, C., and Javelle, P. (2009). How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments. Hydrology and Earth System Sciences, 13(6), 819–31.Google Scholar

Beven, K., and Binley, A. (2014). GLUE: 20 years on. Hydrological Processes, 28(24), 5897–918.Google Scholar

Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M. (2016). The SWOT mission and its capabilities for land hydrology. In Cazenave, A., Champollion, N., Benveniste, J., and Chen, J., eds., Remote Sensing and Water Resources. Cham: Springer, pp. 117–47.Google Scholar

Birkel, C., Soulsby, C., and Tetzlaff, D. (2014), Developing a consistent process-based conceptualization of catchment functioning using measurements of internal state variables. Water Resources Research, 50, 3481–501.Google Scholar

Bolten, J. D., Crow, W. T., Jackson, T. J., Zhan, X., and Reynolds, C. A. (2010). Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1), 57–66.CrossRef Google Scholar

Boucher, M.-A., Quilty, J., and Adamowski, J. (2020). Data assimilation for streamflow forecasting using extreme learning machines and multilayer perceptrons. Water Resources Research, 56, e2019WR026226. https://doi.org/10.1029/2019WR026226.CrossRef Google Scholar

Boussetta, S., Balsamo, G., Beljaars, A. et al. (2013). Natural land carbon dioxide exchanges in the ECMWF Integrated Forecasting System: Implementation and offline validation, Journal of Geophysical Research: Atmospheres, 118, 5923–46.Google Scholar

Boussetta, S., Balsamo, G., Arduini, G. et al. (2021). ECLand: The ECMWF land surface modelling system. Atmosphere, 12, 723.Google Scholar

Burger, J., Le Brizaut, J. S., and Pogu, M. (1992). Comparison of two methods for the calculation of the gradient and of the Hessian of cost functions associated with differential systems. Mathematics and Computers in Simulation, 34, 551–62.Google Scholar

Caparrini, F., Castelli, F., and Entekhabi, D. (2003). Mapping of land-atmosphere heat fluxes and surface parameters with remote sensing data. Boundary-Layer Meteorology, 107, 605–33.CrossRef Google Scholar

Caparrini, F., Castelli, F., and Entekhabi, D. (2004). Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resources Research, 40, W12515.Google Scholar

Carrera, J., and Neuman, S.P. (1986). Estimation of aquifer parameters under transient and steady state conditions: 1. Maximum likelihood method incorporating prior information. Water Resources Research, 22(2), 199–210.Google Scholar

Castillo, A., Castelli, F., and Entekhabi, D. (2015). Gravitational and capillary soil moisture dynamics for distributed hydrologic models. Hydrology and Earth System Sciences, 19(4), 1857–69.Google Scholar

Ceccatelli, M., Del Soldato, M., Solari, L. et al. (2021). Numerical modelling of land subsidence related to groundwater withdrawal in the Firenze-Prato-Pistoia basin (central Italy). Hydrogeology Journal, 29, 629–49.Google Scholar

Cenci, L., Pulvirenti, L., Boni, G. et al. (2017). An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture-data assimilation. Advances in Geosciences, 44, 89–100.Google Scholar

Chan, S. K., Bindlihs, R., O’Neill, P. et al. (2016). Assessment of the SMAP passive soil moisture product. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4994–5007.Google Scholar

Chan, S. K., Bindlihs, R., O’Neill, P. et al. (2017). Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of Environment, 204, 931–41.Google Scholar

Chen, J. M., and Liu, J. (2020). Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sensing of Environment., 237, 111594.Google Scholar

Clark, M. P., Rupp, D. E., Woods, R. A. et al. (2008). Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model. Advances in Water Resources, 31(10), 1309–24.CrossRef Google Scholar

Cooper, E. S., Dance, S. L., Garcia-Pintado, J., Nichols, N. K., and Smith, P. J. (2019). Observation operators for assimilation of satellite observations in fluvial inundation forecasting.Hydrology and Earth System Sciences, 23(6), 2541–59.Google Scholar

Das, N. N., Entekhabi, D., Dunbar, R. S. et al. (2018). The SMAP mission combined active‐passive soil moisture product at 9 km and 3 km spatial resolutions. Remote Sensing of Environment, 211, 204–17.Google Scholar

Dasgupta, A., Hostache, R., Ramsankaran, R. et al. (2021). On the impacts of observation location, timing and frequency on flood extent assimilation performance. Water Resources Research, 57, e2020WR028238Google Scholar

DeChant, C. M., and Moradkhani, H. (2012). Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting. Water Resources Research, 48, W04518.CrossRef Google Scholar

Domeneghetti, A., Castellarin, A., and Brath, A. (2012). Assessing rating-curve uncertainty and its effects on hydraulic model calibration. Hydrology and Earth System Sciences, 16(4), 1191–202.Google Scholar

Dong, J., Crow, W. T., Tobin, K. J. et al. (2020). Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sensing of Environment., 242, 111756.Google Scholar

Emery, C. M., Biancamaria, S., Boone, A. et al. (2020). Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters.Hydrology and Earth System Sciences, 24, 2207–33.CrossRef Google Scholar

Entekhabi, D., Nakamura, H., and Njoku, E.G. (1994). Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations. IEEE Transactions on Geoscience and Remote Sensing, 32(2), 438–48.Google Scholar

Entekhabi, D., Rodriguez-Iturbe, I., and Castelli, F. (1996). Mutual interaction of soil moisture state and atmospheric processes. Journal of Hydrology, 184(1–2), 3–17.Google Scholar

Ercolani, G., and Castelli, F. (2017). Variational assimilation of streamflow data in distributed flood forecasting. Water Resources Research, 53(1), 158–83.CrossRef Google Scholar

Fabryka-Martin, J., Merz, J., and Universities Council on Water Resources (1983). Hydrology: The Study of Water and Water Problems: A Challenge for Today and Tomorrow. Carbondale, IL: Universities Council on Water Resources.Google Scholar

Fan, Y. R., Huang, W. W., Li, Y. P., Huang, G. H., and Huang, K. (2015). A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models. Journal of Hydrology, 530, 255–72.Google Scholar

Frey, S. K., Miller, K., Khader, O. et al. (2021). Evaluating landscape influences on hydrologic behavior with a fully-integrated groundwater–surface water model. Journal of Hydrology, 602, 126758.Google Scholar

Garcia-Pintado, J., Mason, D. C., Dance, S. L. et al. (2015). Satellite-supported flood forecasting in river networks: A real case study. Journal of Hydrology, 523, 706–24.Google Scholar

Ghorbanidehno, H., Kokkinaki, A., Lee, J., and Darve, E. (2020). Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology. Journal of Hydrology, 591, 125266.Google Scholar

Gomez, B., Charlton-Perez, C. L., Lewis, H., and Candy, B. (2020). The Met Office operational soil moisture analysis system. Remote, 12, 3691.Google Scholar

Good, S. P., Noone, D., and Bowen, G. (2015), Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science, 349(6244), 175–7.Google Scholar

Grimaldi, S., Li, Y., Pauwels, V. R. N., and Walker, J. P. (2016). Remote sensing-derived water extent and level to constrain hydraulic flood forecasting models: Opportunities and challenges. Surveys in Geophysics, 37(5), 977–1034.Google Scholar

Hajj, M. E., Baghdadi, N., Zribi, M., and Bazzi, H. (2017). Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sensing, 9(12), 1292. https://doi.org/10.3390/rs9121292.Google Scholar

Harrigan, S., Zsoter, E., Alfieri, L. et al. (2020). GloFAS-ERA5 operational global river discharge reanalysis 1979–present. Earth System Science Data, 12, 2043–60Google Scholar

Harvey, C. F., and Gorelick, S. M. (1995). Mapping hydraulic conductivity: Sequential conditioning with measurements of solute arrival time, hydraulic head, and local conductivity. Water Resources Research, 31(7), 1615–26.Google Scholar

Hochstetler, D. L., Barrash, W., Leven, C. et al. (2016). Hydraulic tomography: Continuity and discontinuity of high-K and low-K zones. Groundwater, 54(2), 171–85.Google Scholar

Hostache, R., Chini, M., Giustarini, L. et al. (2018). Near-real-time assimilation of SAR-derived flood maps for improving flood forecasts. Water Resources Research, 54(8), 5516–35.Google Scholar

Hou, Z.-Y., and Jin, Q.-N. (1997). Tikhonov regularization for nonlinear ill-posed problem. Nonlinear Analysis: Theory, Methods & Applications, 28(11), 1799–809.Google Scholar

Ishitsuka, Y., Gleason, C. J., Hagemann, M. W. et al. (2021). Combining optical remote sensing, McFLI discharge estimation, global hydrologic modeling, and data assimilation to improve daily discharge estimates across an entire large watershed. Water Resources Research, 56, e2020WR027794.Google Scholar

Kerr, Y. H., Waldteufel, P., Richaume, P. et al. (2012). The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1384–403.Google Scholar

King, F., Erler, A. R., Frey, S. K., and Flechter, C. G. (2020). Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada. Hydrology and Earth System Sciences, 24(10), 4887–902.Google Scholar

Kitanidis, P. K. (1996). On the geostatistical approach to the inverse problem. Advances in Water Resources. 19(6), 333–42.Google Scholar

Kolassa, J., Reichle, R. H., Liu, Q. et al. (2017). Data assimilation to extract soil moisture information from SMAP observations. Remote Sensing, 9, 1179.Google Scholar

Kustas, W. P., and Norman, J. M. (1999). Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agriculture an Forest Meteorology, 94(1), 13–29.Google Scholar

Laloy, E., Rogiers, B., Vrugt, J. A., Mallants, D., and Jacques, D. (2013). Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion. Water Resources Research, 49(5), 2664–82.Google Scholar

Le Coz, J., Patalano, A., Collins, D. et al. (2016). Crowdsourced data for flood hydrology: Feedback from recent citizen science projects in Argentina, France and New Zealand. Journal of Hydrology, 541, 766–77.Google Scholar

Le Dimet, F. X., and Talagrand, O. (1986). Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus A, 38:2, 97–110.Google Scholar

Lee, J., Yoon, H., Kitanidis, P. K., Werth, C. J., and Valocchi, A. J. (2016). Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging, Water Resources Research, 52, 5213–31.Google Scholar

Lievens, H. et al. (2017). Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters, 44(12), 6145–53.Google Scholar

Liu, Y., Weerts, AH., Clark, M. et al. (2012). Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities. Hydrology and Earth System Sciences, 16(10), 3863–87.Google Scholar

Liu, K., Huang, G., Simunek, J. et al. (2021). Comparison of ensemble data assimilation methods for the estimation of time-varying soil hydraulic parameters. Journal of Hydrology, 594, 125729.Google Scholar

Maggioni, V., Vergara, H. J., Anagnostou, E. N. et al. (2013). Investigating the applicability of error correction ensembles of satellite rainfall products in river flow simulations. Journal of Hydrometeorology, 14(4), 1194–211.Google Scholar

Maliva, G. (2016). Aquifer Characterization Techniques. Cham Springer.Google Scholar

Man, J., Zheng, Q., Wu, L., and Zeng, L. (2020). Adaptive multi-fidelity probabilistic collocation-based Kalman filter for subsurface flow data assimilation: Numerical modeling and real-world experiment. Stochastic Environmental Research and Risk Assessment, 34, 1135–46. https://doi.org/10.1007/s00477-020-01815-y Google Scholar

Margulis, S. A., and Entekhabi, D. (2001). A coupled land surface-boundary layer model and its adjoint. Journal of Hydrometeorology, 2(3), 274–96.Google Scholar

Mason, D., Garcia-Pintado, J., Cloke, H. L., Dance, S. L., and Munoz-Sabatier, J. (2020) Assimilating high resolution remotely sensed data into a distributed hydrological model to improve run off prediction. ECMWF Tech Memo, 867, European Centre for Medium-Range Weather Forecasts.Google Scholar

Massari, C., Brocca, L., Tarpanelli, A., and Moramarco, T. (2015). Data assimilation of satellite soil moisture into rainfall-runoff modelling: A complex recipe? Remote Sensing, 7(9), 11403–33.Google Scholar

Massari, C., Camici, S., Ciabatta, L., and Brocca, L. (2018). Exploiting satellite-based surface soil moisture for flood forecasting in the Mediterranean area: State update versus rainfall correction. Remote Sensing, 10(2), 292. https://doi.org/10.3390/rs10020292.Google Scholar

Mazzoleni, M., Alfonso, L., and Solomatine, D.P. (2021). Exploring assimilation of crowdsourcing observations into flood models. In Scozzari, A., Mounce, S., Han, D., Soldovier, F., and Solomatine, D., eds., ICT for Smart Water Systems: Measurements and Data Science. Handbook of Environmental Chemistry, vol. 102. Cham: Springer, pp. 209–34.Google Scholar

Moges, E., Demissie, Y., Larsen, L., and Yassin, F. (2021). Review: Sources of hydrological model uncertainties and advances in their analysis. Water, 13(1), 28, https://doi.org/10.3390/w13010028.Google Scholar

Moradkhani, H., Hsu, K.-L., Gupta, H., and Sorooshian, S. (2005). Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter. Water Resources Research, 41, W05012.Google Scholar

Mudashiru, R. B., Sabtu, N., Abustan, I., and Balogun, W. (2021). Flood hazard mapping methods: A review. Journal of Hydrology, 603, 126846.Google Scholar

Nardi, F., Cudennec, C., Abrate, T. et al. (2021). Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2020.1849707.Google Scholar

Neal, J. C., Atkinson, P. M., and Hutton, C. W. (2007). Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements. Journal of Hydrology, 336, 401–15.Google Scholar

Ng, L. W.-T., and Willcox, K. E. (2014). Multifidelity approaches for optimization under uncertainty. International Journal for Numerical Methods in Engineering, 100(10), 746–72.Google Scholar

Noilhan, J., and Planton, S. (1989). A simple parameterization of land-surface processes for meteorological models. Monthly Weather Review, 117, 536–50.Google Scholar

Ochoa-Rodriguez, S., Wang, L.-P., Gires, A. et al. (2015). Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation. Journal of Hydrology, 531(2), 389–407.Google Scholar

Pierdicca, N., Chini, M., Pulvirenti, L. et al. (2009). Using COSMO-SkyMed data for flood mapping: Some case-studies. 2009 IEEE International Geoscience and Remote Sensing Symposium, 2, II-933–6.Google Scholar

Pinnington, E., Amezcua, J., Cooper, E. et al. (2021). Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data. Hydrology and Earth System Sciences, 25, 1617–41.Google Scholar

Rajabi, M. M., Ataie-Ashtiani, B., and Simmons, C. T. (2018). Model-data interaction in groundwater studies: Review of methods, applications and future directions. Journal of Hydrology, 567, 457–77.Google Scholar

Reichle, R. H., de Lannoy, G. J. M., Liu, Q. et al. (2017). Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements. Journal of Hydrometeorology, 18(10), 2621–45.Google Scholar

Reichle, R. H, Liu, Q., Koster, R. D. et al. (2019). Version 4 of the SMAP Level-4 soil moisture algorithm and data product. Journal of Advances in Modeling Earth Systems, 11(10), 3106–30.Google Scholar

Rodriguez-Fernandez, N., de Rosnay, P., Albergel, C. et al. (2019). SMOS neural network soil moisture data assimilation in a land surface model and atmospheric impact. Remote Sensing, 11, 1334.Google Scholar

Seneviratne, S. I., Corti, T., Davin, E. L. et al. (2010). Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3–4), 125–61.Google Scholar

Sood, A., and Smakhtin, V. (2015). Global hydrological models: A review. Hydrological Sciences Journal, 60, 549–65.Google Scholar

Sprenger, M., Leistert, H., Gimbel, K., and Weiler, M. (2016a). Illuminating hydrological processes at the soil-vegetation-atmosphere interface with water stable isotopes. Reviews of Geophysics, 54(3), 674–704.Google Scholar

Sprenger, M., Seeger, S., Blume, T., and Weiler, M. (2016b). Travel times in the vadose zone: Variability in space and time. Water Resources Research, 52(8), 5727–54.CrossRef Google Scholar

Steklova, K., and Haber, E. (2017). Joint hydrogeophysical inversion: State estimation for seawater intrusion models in 3D. Computational Geosciences, 21, 75–94.Google Scholar

Tada, M., Yoshimura, K., and Toride, K. (2021). Improving weather forecasting by assimilation of water vapor isotopes. Scientific Reports, 11(1), 18067.Google Scholar

Talagrand, O., and Courtier, P. (1987). Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Quarterly Journal of the Royal Meteorological Society, 113(478), 1311–28.Google Scholar

Thacker, W. C. (1989). The role of the Hessian matrix in fitting models to measurements. Journal of Geophysical Research: Oceans, 94(C5), 6177–96.Google Scholar

Verstraeten, W. W., Veroustraete, F., van der Sande, C. J., Grootaers, I., and Feyen, J. (2006). Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sensing of Environment, 101(3), 299–314.CrossRef Google Scholar

de Vos, L. W., Droste, A .M., Zander, M. J. et al. (2020). Hydrometeorological monitoring using opportunistic sensing networks in the Amsterdam metropolitan area. Bulletin of the American Meteorological Society, 101(2), E167–E185.CrossRef Google Scholar

Wagner, W., Hahn, S., Kidd, R. et al. (2013). The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications. Meteorologische Zeitschrift, 22(1), 5–33.Google Scholar

Waller, J. A., Garcia-Pintado, J., Mason, D. C., Dance, S. L., and Nichols, N. K. (2018) Technical note: Assessment of observation quality for data assimilation in flood models. Hydrology and Earth System Sciences, 22(7), 3983–92.Google Scholar

Wang, K., and Dickinson, R. E. (2012). A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2), RG2005.Google Scholar

Wang, L. L., Chen, D. H., and Bao, H. J. (2016). The improved Noah land surface model based on storage capacity curve and Muskingum method and application in GRAPES model. Atmospheric Science Letters, 17, 190–8.Google Scholar

Weeser, B., Stenfert Kroese, J., Jacobs, S. R. et al. (2018). Citizen science pioneers in Kenya: A crowdsourced approach for hydrological monitoring. Science of the Total Environment, 631–632, 1590–9.Google Scholar

Xie, X., Meng, S., Liang, S., and Yao, Y. (2014). Improving streamflow predictions at ungauged locations with real-time updating: Application of an EnKF-based state-parameter estimation strategy. Hydrology and Earth System Sciences, 18(10), 3923–36.Google Scholar

Xu, T., Bateni, S. M., Liang, S., Entekhabi, D., and Mao, K. (2014). Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites. Journal of Geophysical Research: Atmospheres, 119(18), 10780–98.Google Scholar

Xu, T., He, X., Bateni, S. M. et al. (2019). Mapping regional turbulent heat fluxes via variational assimilation of land surface temperature data from polar orbiting satellites. Remote Sensing of Environment, 221, 444–61.Google Scholar

Yang, K., Zhu, L., Chen, Y. et al. (2016). Land surface model calibration through microwave data assimilation for improving soil moisture simulations. Journal of Hydrology, 533, 266–76.Google Scholar

Zeng, X., and Decker, M. (2009). Improving the numerical solution of soil moisture–based Richards equation for land models with a deep or shallow water table. Journal of Hydrometerology., 1081, 308–19.Google Scholar

Zhang, H., Franssen, H.-J. H., Han, X., Vrugt, J. A., and Vereecken, H. (2017). State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter. Hydrology and Earth System Sciences, 21(9), 4927–58.Google Scholar

Zhang, F., Zhang, L. W., Shi, J. J., and Huang, J. F. (2014). Soil moisture monitoring based on land surface temperature‐vegetation index space derived from MODIS data. Pedosphere, 24(4), 450–60.Google Scholar

Zheng, W., Zhan, X., Liu, J. and Ek, M. (2018). A preliminary assessment of the impact of assimilating satellite soil moisture data products on NCEP Global Forecast System. Advances in Meteorology, 1–12, 7363194.Google Scholar

Zsoter, E., Cloke, H., Stephens, E. et al. (2019). How well do operational numerical weather prediction configurations represent hydrology? Journal of Hydrometeorology, 20(8), 1533–52.Google Scholar

Accessibility standard: Unknown

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

Accessibility compliance for the HTML of this chapter is currently unknown and may be updated in the future.