Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems

Lahoz, W. A.; Lannoy, Gabriëlle J. M. De

doi:10.1007/978-94-017-8789-5_8

Cited by 6 publications

(5 citation statements)

References 280 publications

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“…The products differ in terms of design objective, spatiotemporal resolution and coverage, data sources, algorithm, and latency. They can be broadly classified into three major categories: (i) products directly derived from active-or passive-microwave satellite observations (Zhang and Zhou, 2016;Karthikeyan et al, 2017b), (ii) hydrological or land surface models without satellite data assimilation (referred to hereafter as open-loop models; Cammalleri et al, 2015;Bierkens, 2015;Kauffeldt et al, 2016;Chen and Yuan, 2020), and (iii) hydrological or land surface models that assimilate soil moisture retrievals or brightness temperature observations from microwave satellites (Moradkhani, 2008;Pan et al, 2009;Pan and Wood, 2010;Liu et al, 2012;Lahoz and De Lannoy, 2014;Reichle et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Beck¹,

Pan²,

Miralles

et al. 2021

Hydrol. Earth Syst. Sci.

219

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Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

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Section: Introductionmentioning

confidence: 99%

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Beck¹,

Pan²,

Miralles

et al. 2021

Hydrol. Earth Syst. Sci.

219

View full text Add to dashboard Cite

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“…While being at the center of complex processes of interest across disciplines and over a wide range of scales, soil moisture cannot be directly measured over large areas, because of both cost and disruptive soil effects of direct measurements. Soil moisture upscaling is thus a major challenge in hydrological, geophysical, and climate change science [Beven, 2001;Lahoz and De Lannoy, 2014]. More generally, scaling issues lie at the heart of current research in atmospheric science and hydrology, questioning our ability to use powerful models and data retrieval techniques for the understanding of global and regional hydroclimatic processes [Bengtsson et al, 2014].…”

Section: Introductionmentioning

confidence: 99%

Data-model comparison of temporal variability in long-term time series of large-scale soil moisture

Verrot

Destouni

2016

J. Geophys. Res. Atmos.

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Soil moisture is at the heart of many processes connected to water cycle, climate, ecosystem, and societal conditions. This paper investigates the ability of a relatively simple analytical soil moisture model to reproduce temporal variability dynamics in long‐term data series for (i) remotely sensed large‐scale water storage change in 25 large catchments around the world and (ii) measured soil water content and groundwater level in individual stations within 10 smaller catchments across the United States. The model‐data comparison for large‐scale water storage change (i) shows good model ability to reproduce the observed temporal variability around long‐term average conditions in most of the large study catchments. Also, the model comparison with locally measured data for soil water content and groundwater level in the smaller U.S. catchments (ii) shows good representation of relative seasonal and longer‐term fluctuations and their timings and frequencies. Overall, the model results tend to underestimate rather than exaggerate the range of temporal soil moisture fluctuations and storage changes. The model synthesis of large‐scale hydroclimatic data is based on fundamental catchment‐scale water balance and is as such useful for identifying flux imbalance biases in the hydroclimatic data series that are used as model inputs.

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“…Soil moisture controls the partitioning of energy and water fluxes at the ground surface and is of major importance for understanding and modelling the terrestrial water cycle (Eagleson, 1978;Lahoz and De Lannoy, 2014;Trenberth and Asrar, 2014;Vereecken et al, 2015). Within the last decades, major technological progress has been made in terms of measuring soil moisture at the point scale (see Robinson et al, 2008 for an overview over the various measuring techniques).…”

Section: ) Introductionmentioning

confidence: 99%

Fluxes from Soil Moisture Measurements (FluSM v1.0). A Data-driven Water Balance Framework for Permeable Pavements

Schaffitel¹,

Schuetz²,

Weiler³

2020

Preprint

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Abstract. Water fluxes at the soil-atmosphere interface are a key information for studying the terrestrial water cycle. However, measuring and modelling water fluxes in the vadose zone poses great challenges. While direct measurements require costly lysimeters, common soil hydrologic models rely on a correct parametrization, a correct representation of the involved processes and on the selection of correct initial and boundary conditions. In contrast to lysimeter measurements, soil moisture measurements are relatively cheap and easy to perform. Using such measurements, data-driven approaches offer the possibility to derive water fluxes directly. Here we present FluSM (Fluxes from Soil Moisture measurements), which is a simple, parsimonious and robust data-driven water balancing framework. FluSM requires only one single input parameter (the infiltration capacity) and is especially valuable for cases where the application of Richards based models is critical. Since Permeable Pavements (PPs) present such a case, we apply FluSM on a recently published soil moisture dataset to obtain the water balance of 15 different PPs over a period of two years. Consistent with findings from previous studies, our results show that vertical drainage dominates the water balance of PPs, while surface runoff plays only a minor role. An additional uncertainty analysis demonstrates the ability of the FluSM-approach for water balance studies, since input and parameter uncertainties have only small effects on the characteristics of the derived water balances. Due to the lack of data on the hydrologic behavior of PPs under field conditions, our results are of special interest for urban hydrology.

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Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems

Cited by 6 publications

References 280 publications

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Data-model comparison of temporal variability in long-term time series of large-scale soil moisture

Fluxes from Soil Moisture Measurements (FluSM v1.0). A Data-driven Water Balance Framework for Permeable Pavements

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