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Remote Sensing Data Assimilation in Hydrology: Towards an Improved Understanding of the Global Water Cycle and Human Impacts

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 14027

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Dr. Augusto Getirana

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Guest Editor

1. Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
2. Science Applications International Corporation, Greenbelt, MD 20771, USA
Interests: hydrometeorology; surface water dynamics; computational modeling; water cycle; remote sensing

Dr. Sujay Kumar

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Guest Editor

Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Interests: land surface modeling; hydrology; data assimilation; remote sensing; optimization
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Dr. Benjamin Zaitchik

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Co-Guest Editor

Department of Earth and Planetary Sciences, Johns Hopkins University, 3400 North, Charles Street, 301 Olin Hall, Baltimore, MD 21218, USA
Interests: regional climate processes; watershed hydrology; remote sensing; water resources; impacts of climate variability and change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decades, the hydrological science research has enabled significant advances in the understanding of water storage and fluxes over the continents using remote sensing data. Satellite missions such as GRACE and GRACE-FO has provided us with unprecedented information on the global water cycle and impacts of human activities on the spatial and temporal water storage variability. The GPM mission has been delivering us global hourly estimates of precipitation rates, and SMAP and SMOS has been retrieving surface soil moisture globally. The data from these missions are important not only for improving our understanding of the hydrological processes, but also for enhancing representation of extremes such as droughts and floods. Radar altimetry has been a gamechanger in surface water monitoring, measuring water levels of rivers, lakes, reservoirs and wetlands in the past 30 years. Combining water elevation change with digital elevation models or satellite-based water masks derived from Landsat and MODIS allows us to determine surface water storage change and reservoir operation impacts on river systems. The SWOT mission will further contribute to a two dimensional and temporally continuous monitoring of water bodies. Satellite-based leaf area index and evapotranspiration estimates can also inform us on plant stress and irrigation activities globally.

As a result of its global coverage at reasonable temporal resolution, hydrologists have been exploring ways to use multi-sensor satellite data to improve computational models. Data assimilation and optimization techniques have become popular tools, improving model parameters and states at different scales. Such techniques have also contributed to representations of anthropogenic activities, forecast initialization and the improvement of water resource monitoring systems.

The aim of this special issue is to gather a collection of latest developments and innovative applications of remote sensing data assimilation and integration into hydrological models. We invite contributions using the ample range of remotely sensed information through data assimilation, optimization and other innovative merging techniques to improve the numerical representation of hydrological processes, impacts of human activities on the water cycle and extreme hydrological event (e.g., droughts and floods) monitoring and forecast.

Dr. Augusto Getirana
Dr. Sujay Kumar
Dr. Benjamin Zaitchik
Guest Editors

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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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.

Published Papers (4 papers)

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Research

25 pages, 3640 KiB

Open AccessArticle

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

by Manh-Hung Le, Binh Quang Nguyen, Hung T. Pham, Amol Patil, Hong Xuan Do, RAAJ Ramsankaran, John D. Bolten and Venkataraman Lakshmi

Remote Sens. 2022, 14(7), 1607; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071607 - 28 Mar 2022

Cited by 10 | Viewed by 3204

Abstract

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km² during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology. Full article

(This article belongs to the Special Issue Remote Sensing Data Assimilation in Hydrology: Towards an Improved Understanding of the Global Water Cycle and Human Impacts)

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17 pages, 17462 KiB

Open AccessArticle

Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

by Manuela Girotto, Rolf Reichle, Matthew Rodell and Viviana Maggioni

Remote Sens. 2021, 13(6), 1223; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061223 - 23 Mar 2021

Cited by 10 | Viewed by 3233

Abstract

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques. Full article

(This article belongs to the Special Issue Remote Sensing Data Assimilation in Hydrology: Towards an Improved Understanding of the Global Water Cycle and Human Impacts)

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19 pages, 8388 KiB

Open AccessArticle

Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs

by Detang Zhong, Shusen Wang and Junhua Li

Remote Sens. 2021, 13(5), 900; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050900 - 27 Feb 2021

Cited by 15 | Viewed by 3610

Abstract

High spatiotemporal resolution of terrestrial total water storage plays a key role in assessing trends and availability of water resources. This study presents a two-step method for downscaling GRACE-derived total water storage anomaly (GRACE TWSA) from its original coarse spatiotemporal resolution (monthly, 3-degree spherical cap/~300 km) to a high resolution (daily, 5 km) through combining land surface model (LSM) simulated high spatiotemporal resolution terrestrial water storage anomaly (LSM TWSA). In the first step, an iterative adjustment method based on the self-calibration variance-component model (SCVCM) is used to spatially downscale the monthly GRACE TWSA to the high spatial resolution of the LSM TWSA. In the second step, the spatially downscaled monthly GRACE TWSA is further downscaled to the daily temporal resolution. By applying the method to downscale the coarse resolution GRACE TWSA from the Jet Propulsion Laboratory (JPL) mascon solution with the daily high spatial resolution (5 km) LSM TWSA from the Ecological Assimilation of Land and Climate Observations (EALCO) model, we evaluated the benefit and effectiveness of the proposed method. The results show that the proposed method is capable to downscale GRACE TWSA spatiotemporally with reduced uncertainty. The downscaled GRACE TWSA are also evaluated through in-situ groundwater monitoring well observations and the results show a certain level agreement between the estimated and observed trends. Full article

(This article belongs to the Special Issue Remote Sensing Data Assimilation in Hydrology: Towards an Improved Understanding of the Global Water Cycle and Human Impacts)

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20 pages, 8584 KiB

Open AccessArticle

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil

by Augusto Getirana, Dalia Kirschbaum, Felipe Mandarino, Marta Ottoni, Sana Khan and Kristi Arsenault

Remote Sens. 2020, 12(24), 4095; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244095 - 15 Dec 2020

Cited by 24 | Viewed by 3049

Abstract

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas. Full article

(This article belongs to the Special Issue Remote Sensing Data Assimilation in Hydrology: Towards an Improved Understanding of the Global Water Cycle and Human Impacts)

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