Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Hydrological Modelling Based on Satellite Observations

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 13650

Share This Special Issue

Special Issue Editors

Dr. Hongxing Zheng

E-Mail Website
Guest Editor

Land and Water, CSIRO, Canberra, ACT 1700, Australia
Interests: hydrological modelling; remote sensing; climate change; ecohydrology; water resources management
Special Issues, Collections and Topics in MDPI journals

Dr. Ruirui Zhu

E-Mail Website
Guest Editor

Mathematical Sciences Institute, College of Science, Australian National Unviersity, Canberra, ACT 2601, Australia
Interests: hydrological prediction; remote sensing; hydrogeological modelling; surface water–groundwater interactions

Special Issue Information

Dear Colleagues,

The fast-growing satellite-based Earth observations have paved the way for developing innovative hydrological modelling techniques during the past several decades. Satellite observations from various sensors could substantially improve model performance and applicability by providing valuable model inputs, model parameters and measurements for model calibration, validation or data assimilation. An emerging and challenging research field within the hydrological community is concerned with revising the conventional modelling frameworks, which mostly rely on ground observations, by making full use of satellite observations. In the years to come, it can be expected that the increasingly available satellite observations would catalyze more sensible hydrological models, particularly along with more satellites launched for hydrological purposes. This Special Issue aims to present the most recent advances in satellite-observation-based hydrological modelling. Manuscripts submitted to this Special Issue are encouraged to focus on: (1) novel hydrological models driven mainly by remotely sensed data; (2) new model calibration or data assimilation approaches based on multiple satellite observations; (3) inspiring satellite-observation-based modelling practices in tracking regional/global hydrological cycles; (4) flood or drought modelling by incorporating high-resolution satellite observations; (5) comprehensive evaluation of the remotely sensed data for hydrological modelling; (6) reviews on the potential and limitations of satellite observations in hydrological modelling.

Dr. Hongxing Zheng
Dr. Ruirui Zhu
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.

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. Remote Sensing 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 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.

Keywords

Hydrological modelling
Satellite observations
Remote sensing
Data assimilation
Uncertainty
Hydrological cycle
Multiple-objective calibration
Prediction
Flood
Drought
Machine learning

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

25 pages, 13352 KiB

Open AccessArticle

Characterizing the 2022 Extreme Drought Event over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations and In Situ Data

by Sulan Liu, Yunlong Wu, Guodong Xu, Siyu Cheng, Yulong Zhong and Yi Zhang

Remote Sens. 2023, 15(21), 5125; https://doi.org/10.3390/rs15215125 - 26 Oct 2023

Cited by 4 | Viewed by 1190

Abstract

With advancements in remote sensing technology and the increasing availability of remote sensing platforms, the capacity to monitor droughts using multiple satellite remote sensing observations has significantly improved. This enhanced capability facilitates a comprehensive understanding of drought conditions and early warnings for extreme drought events. In this study, multiple satellite datasets, including Gravity Recovery and Climate Experiment (GRACE), the Global Precipitation Measurement (GPM) precipitation dataset, and the Global Land the Data Assimilation System (GLDAS) dataset, were used to conduct an innovative in-depth characteristic analysis and identification of the extreme drought event in the Poyang Lake Basin (PLB) in 2022. Furthermore, the drought characteristics were also supplemented by processing the synthetic aperture radar (SAR) image data to obtain lake water area changes and integrating in situ water level data as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index dataset, which provided additional instances of utilizing multi-source remote sensing satellite data for feature analysis on extreme drought events. The extreme drought event in 2022 was identified by the detection of non-seasonal negative anomalies in terrestrial water storage derived from the GRACE and GLDAS datasets. The Mann–Kendall (M-K) test results for water levels indicated a significant abrupt decrease around July 2022, passing a significance test with a 95% confidence level, which further validated the reliability of our finding. The minimum area of Poyang Lake estimated by SAR data, corresponding to 814 km², matched well with the observed drought characteristics. Additionally, the evident lower vegetation index compared to other years also demonstrated the severity of the drought event. The utilization of these diverse datasets and their validation in this study can contribute to achieving a multi-dimensional monitoring of drought characteristics and the establishment of more robust drought models. Full article

(This article belongs to the Special Issue Hydrological Modelling Based on Satellite Observations)

► Show Figures

Figure 1

15 pages, 8705 KiB

Open AccessArticle

Modelling Heat Balance of a Large Lake in Central Tibetan Plateau Incorporating Satellite Observations

by Linan Guo, Hongxing Zheng, Yanhong Wu, Liping Zhu, Junbo Wang and Jianting Ju

Remote Sens. 2023, 15(16), 3982; https://doi.org/10.3390/rs15163982 - 11 Aug 2023

Viewed by 996

Abstract

The thermodynamics of many lakes around the globe are shifting under a warming climate, affecting nutrients and oxygen transportation within the lake and altering lake biota. However, long-term variation in lake heat and water balance is not well known, particularly for regions like the Tibetan Plateau. This study investigates the long-term (1963–2019) variation in the heat balance of a large lake in the Tibetan Plateau (Nam Co) by combining the strengths of modeling and remote sensing. Remotely sensed lake surface water temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Along Track Scanning Radiometer Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) are used to calibrate and validate a conceptual model (air2water) and a thermodynamic model (LAKE) for the studied lake, for which in situ observation is limited. The results demonstrate that remotely sensed lake surface water temperature can serve as a valuable surrogate for in situ observations, facilitating effective calibration and validation of lake models. Compared with the MODIS-based lake surface water temperature (LSWT) for the period 2000–2019, the correlation coefficient and root mean square error (RMSE) of the LAKE model are 0.8 and 4.2 °C, respectively, while those of the air2water model are 0.9 and 2.66 °C, respectively. Based on modeling, we found that the water temperature of Nam Co increased significantly (p < 0.05) during the period of 1963–2019, corresponding to a warming climate. The rate of water temperature increase is highest at the surface layer (0.41 °C/10a). This warming trend is more noticeable in June and November. From 1963 to 2019, net radiation flux increased at a rate of 0.5 W/m²/10a. The increase in net radiation is primarily responsible for the warming of the lake water, while its impact on changes in lake evaporation is comparatively minor. The approaches developed in this study demonstrate the flexibility of incorporating remote sensing observations into modeling. The results on long-term changes in heat balance could be valuable for a systematic understanding of lake warming in response to a changing climate in the Tibetan Plateau. Full article

(This article belongs to the Special Issue Hydrological Modelling Based on Satellite Observations)

► Show Figures

Graphical abstract

26 pages, 5588 KiB

Open AccessArticle

Assessing Impacts of Flood and Drought over the Punjab Region of Pakistan Using Multi-Satellite Data Products

by Rahat Ullah, Jahangir Khan, Irfan Ullah, Faheem Khan and Youngmoon Lee

Remote Sens. 2023, 15(6), 1484; https://doi.org/10.3390/rs15061484 - 07 Mar 2023

Cited by 5 | Viewed by 3612

Abstract

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study’s spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon’s summer high rain and floods in the plain of Punjab (Pakistan). Full article

(This article belongs to the Special Issue Hydrological Modelling Based on Satellite Observations)

► Show Figures

Graphical abstract

27 pages, 11046 KiB

Open AccessArticle

Development of High-Resolution Soil Hydraulic Parameters with Use of Earth Observations for Enhancing Root Zone Soil Moisture Product

by Juby Thomas, Manika Gupta, Prashant K. Srivastava, Dharmendra K. Pandey and Rajat Bindlish

Remote Sens. 2023, 15(3), 706; https://doi.org/10.3390/rs15030706 - 25 Jan 2023

Cited by 2 | Viewed by 2198

Abstract

Regional quantification of energy and water balance fluxes depends inevitably on the estimation of surface and rootzone soil moisture. The simulation of soil moisture depends on the soil retention characteristics, which are difficult to estimate at a regional scale. Thus, the present study proposes a new method to estimate high-resolution Soil Hydraulic Parameters (SHPs) which in turn help to provide high-resolution (spatial and temporal) rootzone soil moisture (RZSM) products. The study is divided into three phases—(I) involves the estimation of finer surface soil moisture (1 km) from the coarse resolution satellite soil moisture. The algorithm utilizes MODIS 1 km Land Surface Temperature (LST) and 1 km Normalized difference vegetation Index (NDVI) for downscaling 25 km C-band derived soil moisture from AMSR-2 to 1 km surface soil moisture product. At one of the test sites, soil moisture is continuously monitored at 5, 20, and 50 cm depth, while at 44 test sites data were collected randomly for validation. The temporal and spatial correlation for the downscaled product was 70% and 83%, respectively. (II) In the second phase, downscaled soil moisture product is utilized to inversely estimate the SHPs for the van Genuchten model (1980) at 1 km resolution. The numerical experiments were conducted to understand the impact of homogeneous SHPs as compared to the three-layered parameterization of the soil profile. It was seen that the SHPs estimated using the downscaled soil moisture (I-d experiment) performed with similar efficiency as compared to SHPs estimated from the in-situ soil moisture data (I-b experiment) in simulating the soil moisture. The normalized root mean square error (nRMSE) for the two treatments was 0.37 and 0.34, respectively. It was also noted that nRMSE for the treatment with the utilization of default SHPs (I-a) and AMSR-2 soil moisture (I-c) were found to be 0.50 and 0.43, respectively. (III) Finally, the derived SHPs were used to simulate both surface soil moisture and RZSM. The final product, RZSM which is the daily 1 km product also showed a nearly 80% correlation at the test site. The estimated SHPs are seen to improve the mean NSE from 0.10 (I-a experiment) to 0.50 (I-d experiment) for the surface soil moisture simulation. The mean nRMSE for the same was found to improve from 0.50 to 0.31. Full article

(This article belongs to the Special Issue Hydrological Modelling Based on Satellite Observations)

► Show Figures

Figure 1

23 pages, 6867 KiB

Open AccessArticle

Seasonal Flow Forecasting Using Satellite-Driven Precipitation Data for Awash and Omo-Gibe Basins, Ethiopia

by Surafel M. Woldegebrael, Belete B. Kidanewold and Assefa M. Melesse

Remote Sens. 2022, 14(18), 4518; https://doi.org/10.3390/rs14184518 - 09 Sep 2022

Cited by 2 | Viewed by 4863

Abstract

Hydrologic extreme events such as flooding impact people and the environment and delay sustainable development in flood-prone areas when it is excessive. The present study developed a seasonal floodwater forecast system for the Awash and Omo-Gibe basins of Ethiopia using the 2021 rainy season (June to September) as a temporal case study. In Ethiopia, there is no seasonal forecasting system available to cope with the recurrent flooding impacts instead of exercising ineffective and traditional monitoring approaches. The satellite-driven precipitation and temperature forecasts, observed rainfall, discharge, reservoir water levels, land cover, and soil data were used in the hydrologic (HEC-HMS) and hydraulic (HEC-RAS) models, spreadsheet, and GIS applications. The results obtained were forecasts of the runoff, reservoir water levels, and storage. The coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), percent of bias (Pbias), and Kling–Gupta efficiency (KGE) were used to evaluate the model’s performance in addition to plots as presented in the manuscript. The R² values obtained for the Koka and Gibe-3 reservoirs’ inflows (water levels) were 0.97 (0.95) and 0.93 (0.99), respectively, and the NSE values were 0.90 (0.88) and 0.92 (0.95) for each reservoir. Similarly, the water levels (meter) and storage (Mm³) for the Koka and Gibe-3 reservoirs at the end of the 2021 flood season were 111.0 (1467.58) and 890.8 (13,638.5), respectively. Excess floodwater can be maintained in and released from reservoirs depending on the future water uses and flood monitoring activities downstream. In addition, the flood inundation extents from Earth remote sensing satellite observation and model results were examined and showed agreement. Full article

(This article belongs to the Special Issue Hydrological Modelling Based on Satellite Observations)

► Show Figures

Journal Menu

Journal Browser

Hydrological Modelling Based on Satellite Observations

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI