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New Challenges in Terrestrial Water Storage Estimation
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New Challenges in Terrestrial Water Storage Estimation

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2468

Special Issue Editor

Dr. Natthachet Tangdamrongsub

E-Mail Website
Guest Editor
Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
Interests: remote sensing; land surface modelling; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate knowledge of terrestrial water storage (including soil moisture, groundwater, snow, and surface water) is crucial to improve our understanding of the terrestrial water cycle, water scarcity, and land-atmosphere interaction. The remote sensing observations and land surface/ hydrology models have advanced our ability to assess the availability of water and climate/anthropogenic influences. Despite their functionality, the presented challenge is the coarse spatiotemporal resolution and high uncertainty of terrestrial water storage estimates. With these factors in mind, I would like to invite international research communities to discuss the benefits, limitations, and potential improvements of current and upcoming satellite datasets and models, and to submit their recent developments for publication. The Special Issue’s topics include, but are not limited to, the following:

  • Accuracy assessment of remote sensing techniques and model simulations for terrestrial water storage.
  • Applications of water resource assessment, climate variability, and natural hazards.
  • Forecast and hindcast of water storage estimates.
  • Resolution enhancement including downscaling and time series reconstruction from statistical and/or machine learning approaches.
  • Development of data processing techniques such as filtering and retrieval algorithms.
  • Univariate, multivariate data assimilation, or data fusion of remotely sensed observations to improve model simulation accuracy.
  • Demonstration of the impact of land–atmospheric interactions on terrestrial hydrology.

Dr. Natthachet Tangdamrongsub
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • terrestrial water storage
  • soil moisture
  • surface water
  • satellite remote sensing
  • hydrology model
  • water resources
  • climate
  • resolution enhancement
  • data fusion
  • data assimilation
  • accuracy assessment

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Published Papers (1 paper)

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Research

18 pages, 7048 KiB  
Article
Comparative Analysis of Global Terrestrial Water Storage Simulations: Assessing CABLE, Noah-MP, PCR-GLOBWB, and GLDAS Performances during the GRACE and GRACE-FO Era
by Natthachet Tangdamrongsub
Water 2023, 15(13), 2456; https://doi.org/10.3390/w15132456 - 4 Jul 2023
Cited by 1 | Viewed by 1981
Abstract
Hydrology and land surface and models (HM and LSM) are essential tools for estimating global terrestrial water storage (TWS), an important component of the global water budget for assessing the accessibility and long-term variability of water supplies. With the expansion of open-source and [...] Read more.
Hydrology and land surface and models (HM and LSM) are essential tools for estimating global terrestrial water storage (TWS), an important component of the global water budget for assessing the accessibility and long-term variability of water supplies. With the expansion of open-source and open-data policies, the community can now perform model TWS simulation from source codes as well as directly exploit end-user hydrologic products for water resource applications. Regardless of the model effectiveness and usability, an accuracy assessment is necessary to quantify the model’s global and regional strengths, weaknesses, and reliability. This paper compares the most recent global TWS estimates from six models, namely the PCRaster Global Water Balance (PCR-GLOBWB), Noah, Noah-Multiparameterization (Noah-MP), Catchment LSM, and Variable Infiltration Capacity (VIC), and Community Atmosphere Biosphere Land Exchange (CABLE)—the latter of which is cross validated for the first time. TWS observations from the Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions between 2002 and 2021 are used to validate the model. The analyses show that Noah-MP outperforms other models in terms of global average correlations and root mean square errors. PCR-GLOBWB performance is superior in irrigated regions because of the inclusion of human intervention components in the model. CABLE, a core LSM of the Australian climate model, significantly outperforms all others in Australia. CLSM performs reasonably well, but the TWS long-term trend appears to be incorrect due to an overestimated groundwater component. Noah performs similarly (but inferiorly) to Noah-MP, most likely due to model physics sharing. VIC has the least agreement with GRACE and GRACE-FO. The evaluation also sheds some light on the role of forcing data in model performance, particularly for ready-to-use products such as GLDAS, where incorporating MERRA-2 or ERA5 data into GLDAS Noah simulations may potentially improve its TWS accuracy, which has previously been overlooked due to limited modeling capacity. Despite each model’s unique strength, the ensemble mean TWS, particularly when Noah-MP and PCR-GLOBWB are included, yields better TWS estimates than an individual model result. The findings of this study could serve as a benchmark for future model development and the data published in this paper could aid in the scientific advancement and discoveries of the hydrology community. Full article
(This article belongs to the Special Issue New Challenges in Terrestrial Water Storage Estimation)
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