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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 26156

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Dr. Aondover Tarhule

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State University of New York (SUNY) at Binghamton, Couper Administration Building (AD), Room 134, The Graduate School, 4400 Vestal Parkway East, Binghamton, NY 13902, USA
Interests: hydrology; water resources,;stochastic hydrology; physical geography; water scarcity
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Prof. Dr. Yang Hong

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1. School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019-3072, USA
2. National Weather Center, ARRC Suite 4610, University of Oklahoma, 120 David L. Boren Blvd, Norman, OK 73072, USA
Interests: radar and satellite remote sensing; hydrology and water security; water resource engineering and GIS
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Dr. Emad Hasan

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Department of Geological Sciences and Environmental Studies, State University of New York (SUNY) at Binghamton OJ124, 4400 Vestal Parkway East, Binghamton, NY 13902, USA
Interests: remote sensing hydrology; GIS applications in water resources
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Dr. Guoqiang Tang

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1. Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada
2. University of Saskatchewan Coldwater Lab, Canmore, AB, Canada

Special Issue Information

Dear Colleagues,

The availability of fresh water resources in sufficient quantity is critical for human well-being, agro-ecological and socio-economic systems. Yet, determining the status of available water i.e. whether adequate, vulnerable, stressed or inadequate/scarce, using ground based hydroclimatic networks, is fraught with considerable challenges. Notably, the data tend to be unavailable, inaccessible, or discontinuous over space and time. Recently, a number of studies have demonstrated considerable promise in using remote sensing data and methodologies to overcome these limitations. As example, satellite missions, such as GRACE (Gravity Recovery and Climate Experiment), and its successor program GRACE-FO (GRACE Follow-On) have provided unprecedented data on complex water cycle information significantly improving our understanding of water resources occurrence, storage, fluxes and variability over time and space. Several other platforms also provide similar or complementary advantages.

This special issue calls for original contributions that utilize remote sensing technologies in innovative ways and methodologies for observing, monitoring and assessing water scarcity regionally and globally. We encourage especially contributions that utilize frameworks that merge and integrate different remote sensing observations, numerical models and algorithms to address global water scarcity assessment and prediction. Examples include, but are not limited to:

Methods and theories that utilize remote sensing platforms and data to locate, observe and predict “available water resources”.
Contributions that refine and improve assessment of per capita water resources availability, withdrawal and use.
Changes in available water resources over time and space in response to complex interactions between climatic variability and anthropogenic processes.
New and emerging remote sensing applications for water scarcity monitoring that support decision making to mitigate possible conflicts over shared water resources.

Dr. Aondover Tarhule
Dr. Yang Hong
Dr. Emad Hasan
Dr. Guoqiang Tang
Guest Editors

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Keywords

Climate Change and Climate variability
GRACE
Hydrology and Water Resources
Remote Sensing
Water Scarcity

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Published Papers (7 papers)

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Research

26 pages, 12128 KiB

Open AccessArticle

Inversion of Groundwater Storage Variations Considering Lag Effect in Beijing Plain, from RadarSat-2 with SBAS-InSAR Technology

by Xin Zhang, Beibei Chen, Huili Gong, Kunchao Lei, Chaofan Zhou, Zhaozhao Lu and Danni Zhao

Remote Sens. 2022, 14(4), 991; https://doi.org/10.3390/rs14040991 - 17 Feb 2022

Cited by 14 | Viewed by 3187

Abstract

The long-term over-exploitation of groundwater has not only caused the compaction of aquifer thickness and surface deformation but has also further aggravated the loss of groundwater storage (GWS) in Beijing plain. The South-to-North Water Diversion Project (SNWDP) furnishes a new source of water for Beijing. By reviewing related studies, it was found that there are few studies on the realization of GWS estimation based on InSAR technology considering the lag effect. Therefore, in this study, firstly, the long-time series deformation characteristics of Beijing plain were obtained from 46 RadarSat-2 images using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology. Secondly, the seasonal components of surface deformation and hydraulic head change were extracted by means of multichannel singular spectrum analysis (MSSA), verifying the separation accuracy by means of Monto Carlo-SSA (MC-SSA). Finally, for the hydrodynamic delay (aquifer water supply/drainage) of the complex aquifer system, we introduced the time lag cross-correlation (TLCC) approach to correct the hysteresis response of seasonal deformation relative to the variation of the aquifer system head, so as to realize the estimation of aquifer storage properties and GWS loss, even unrecoverable GWS (UGWS). The results showed that the average annual variation of total GWS (TGWS) in Beijing plain was −6.702 × 10⁷ m³, of which the depletion volume of UGWS was −6.168 × 10⁷ m³, accounting for 92.03% of the TGWS. On a temporal scale, the depletion of UGWS lagged behind the total head change, with about one year of lag time. On a spatial scale, in contrast to the north of Beijing plain, the depletion of UGWS in the south only recovered briefly after 2015 and then continued to decline. This further indicated that the process of the decline of middle-deep confined head and long-term GWS loss caused by over-exploitation of groundwater was irreversible. These findings are of great significance to optimize the allocation of groundwater resources, reduce the harm of land subsidence and protect groundwater resources. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)

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23 pages, 14852 KiB

Open AccessArticle

Can GPM IMERG Capture Extreme Precipitation in North China Plain?

by Dasheng Zhang, Mingxiang Yang, Meihong Ma, Guoqiang Tang, Tsechun Wang, Xun Zhao, Suying Ma, Jin Wu and Wei Wang

Remote Sens. 2022, 14(4), 928; https://doi.org/10.3390/rs14040928 - 14 Feb 2022

Cited by 13 | Viewed by 2836

Abstract

Extreme precipitation events (EPE) often cause catastrophic floods accompanied by serious economic losses and casualties. The latest version (V06) of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) provides global satellite precipitation data from 2000 at a higher spatiotemporal resolution with improved quality. It is scientifically and practically important to assess the accuracy of the IMERG V06 in capturing extreme precipitation. This study evaluates the two widely used products of IMERG during 2000–2018, i.e., IMERG late run (IMERG-L) and IMERG final run (IMERG-F), in the densely populated and flood-prone North China Plain. The accuracy of the IMERG V06 is evaluated with ground measurements from rain gauge stations at multiple scales (hourly, daily, and seasonally). A novel target tracking method is introduced to extract three-dimensional (3D) extreme precipitation events, and the near-real-time uncalibrated PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System) and GSMAP (Global Satellite Mapping of Precipitation) satellite data are added to further evaluate IMERG’s performance during extreme precipitation. Finally, for flash flood events induced by extreme rainfall in the Hebei Province from 15 to 23 July 2016, the accuracy of capturing the event with IMERG-F and IMERG-L was verified. Results reveal that IMERG-F is better than IMERG-L at all investigated scales (hourly, daily, and seasonally), but the difference between the two products is less at higher time resolutions. Both products manifest decreased performance when capturing 3D extreme precipitation events, and comparatively, IMERG-F performs better than IMERG-L. IMERG-F exhibits a distinct discontinuity in extreme precipitation thresholds between land and ocean, which is a limitation of IMERG-F not documented in previous studies. Moreover, IMERG-L and IMERG-F are comparable at an hourly scale for some metrics, which is beyond the expectation that IMERG-F is notably better than IMERG-L. This study provides a scientific basis for the performance of satellite precipitation products and contributes to guiding users when applying global precipitation products. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)

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18 pages, 81388 KiB

Open AccessArticle

Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

by Senlin Tang, Hong Wang, Yao Feng, Qinghua Liu, Tingting Wang, Wenbin Liu and Fubao Sun

Remote Sens. 2021, 13(23), 4831; https://doi.org/10.3390/rs13234831 - 28 Nov 2021

Cited by 8 | Viewed by 2573

Abstract

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)

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18 pages, 6872 KiB

Open AccessArticle

Monitoring Drought through the Lens of Landsat: Drying of Rivers during the California Droughts

by Shang Gao, Zhi Li, Mengye Chen, Daniel Allen, Thomas Neeson and Yang Hong

Remote Sens. 2021, 13(17), 3423; https://doi.org/10.3390/rs13173423 - 28 Aug 2021

Cited by 4 | Viewed by 4127

Abstract

Water scarcity during severe droughts has profound hydrological and ecological impacts on rivers. However, the drying dynamics of river surface extent during droughts remains largely understudied. Satellite remote sensing enables surveys and analyses of rivers at fine spatial resolution by providing an alternative to in-situ observations. This study investigates the seasonal drying dynamics of river extent in California where severe droughts have been occurring more frequently in recent decades. Our methods combine the use of Landsat-based Global Surface Water (GSW) and global river bankful width databases. As an indirect comparison, we examine the monthly fractional river extent (FrcSA) in 2071 river reaches and its correlation with streamflow at co-located USGS gauges. We place the extreme 2012–2015 drought into a broader context of multi-decadal river extent history and illustrate the extraordinary change between during- and post-drought periods. In addition to river extent dynamics, we perform statistical analyses to relate FrcSA with the hydroclimatic variables obtained from the National Land Data Assimilation System (NLDAS) model simulation. Results show that Landsat provides consistent observation over 90% of area in rivers from March to October and is suitable for monitoring seasonal river drying in California. FrcSA reaches fair (>0.5) correlation with streamflow except for dry and mountainous areas. During the 2012–2015 drought, 332 river reaches experienced their lowest annual mean FrcSA in the 34 years of Landsat history. At a monthly scale, FrcSA is better correlated with soil water in more humid areas. At a yearly scale, summer mean FrcSA is increasingly sensitive to winter precipitation in a drier climate; and the elasticity is also reduced with deeper ground water table. Overall, our study demonstrates the detectability of Landsat on the river surface extent in an arid region with complex terrain. River extent in catchments of deficient water storage is likely subject to higher percent drop in a future climate with longer, more frequent droughts. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)

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24 pages, 6443 KiB

Open AccessArticle

Analysis on the Variation of Hydro-Meteorological Variables in the Yongding River Mountain Area Driven by Multiple Factors

by Kaijie Niu, Qingfang Hu, Yintang Wang, Hanbo Yang, Chuan Liang, Leizhi Wang, Lingjie Li, Xiting Li, Yong Du and Chengxi Li

Remote Sens. 2021, 13(16), 3199; https://doi.org/10.3390/rs13163199 - 12 Aug 2021

Cited by 5 | Viewed by 2373

Abstract

In recent decades, strong human activities have not only brought about climate change including both global warming and shifts in the weather patterns but have also caused anomalous variations of hydrological elements in different basins all around the world. Studying the mechanisms and causes of these hydrological variations scientifically is the basis for the management of water resources and the implementation of ecological protection. Therefore, taking the Yongding River mountain area as a representative watershed in China, the changes of different observed and simulated hydro-meteorological variables and their possible causes are analyzed on an inter-annual scale based on ground based observations and remotely sensed data of hydrology, meteorology and underlying surface characteristics from 1956 to 2016. The results show that the annual natural runoff of Guanting hydrological station in the main stream of the Yongding River, Cetian hydrological station and Xiangshuibao hydrological station in the tributary of the Yongding River all have a significant decreasing trend and abrupt changes, and all the abrupt change points of the annual natural runoff series of the three hydrological stations appear in the early 1980s. On the inter-annual scale, the water balance model with double parameters is unable to effectively simulate the natural surface runoff after the abrupt change points. The annual average precipitation after the abrupt change points decreases by no more than 10%, compared with that before the abrupt change points. However, the precipitation from July to August, which is the main runoff-production period, decreases by more than 25%, besides the intra-annual temporal distribution of precipitation becoming uniform and a significant decrease in effective rainfall, which is the source of the runoff. Meanwhile, the NDVI in the basin show an increasing trend, while the groundwater level and land water storage decrease significantly. These factors do not lead only to the continuous reduction of the annual natural runoff in the Yongding River mountain area from 1956 to 2016, but also result in significant changes of the hydro-meteorological relationship in the basin. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)

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30 pages, 10821 KiB

Open AccessArticle

Twentieth and Twenty-First Century Water Storage Changes in the Nile River Basin from GRACE/GRACE-FO and Modeling

by Emad Hasan, Aondover Tarhule and Pierre-Emmanuel Kirstetter

Remote Sens. 2021, 13(5), 953; https://doi.org/10.3390/rs13050953 - 4 Mar 2021

Cited by 21 | Viewed by 4423

Abstract

This research assesses the changes in total water storage (TWS) during the twentieth century and future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and a land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (generalized additive model for location, scale, and shape) model. The model performance was tested using standard indicators including coevolution plots, the Nash–Sutcliffe coefficient, cumulative density function, standardized residuals, and uncertainty bounds. All model evaluation results are satisfactory to excellent. The drought and flooding severity/magnitude, duration, and recurrence frequencies were assessed during the studied period. The results showed, (1) The NRB between 2002 to 2020 has witnessed a substantial transition to wetter conditions. Specifically, during the wet season, the NRB received between ~50 Gt./yr. to ~300 Gt./yr. compared to ~30 Gt./yr. to ~70 Gt./yr. of water loss during the dry season. (2) The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS of ~17% during the wet season. Moreover, the TWS storage had witnessed a recovery of ~28% during the dry season. (3) The projected TWSA between 2021 to 2050 unveiled a positive increase in the TWS during the rainy season. While during the dry season, the water storage showed insubstantial TWS changes. Despite these projections, the future storage suggested a reduction between 10 to 30% in TWS. The analysis of drought and flooding frequencies between 1901 to 2050 revealed that the NRB has ~64 dry-years compared to ~86 wet-years. The exceedance probabilities for the normal conditions are between 44 to 52%, relative to a 4% chance of extreme events. The recurrence interval of the normal to moderate wet or dry conditions is ~6 years. These TWSA trajectories call for further water resources planning in the region, especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)

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20 pages, 10431 KiB

Open AccessArticle

Comprehensive Comparisons of State-of-the-Art Gridded Precipitation Estimates for Hydrological Applications over Southern China

by Zhen Gao, Bensheng Huang, Ziqiang Ma, Xiaohong Chen, Jing Qiu and Da Liu

Remote Sens. 2020, 12(23), 3997; https://doi.org/10.3390/rs12233997 - 6 Dec 2020

Cited by 43 | Viewed by 3615

Abstract

Satellite-based precipitation estimates with high quality and spatial-temporal resolutions play a vital role in forcing global or regional meteorological, hydrological, and agricultural models, which are especially useful over large poorly gauged regions. In this study, we apply various statistical indicators to comprehensively analyze the quality and compare the performance of five newly released satellite and reanalysis precipitation products against China Merged Precipitation Analysis (CMPA) rain gauge data, respectively, with 0.1° × 0.1° spatial resolution and two temporal scales (daily and hourly) over southern China from June to August in 2019. These include Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5-Land), Fengyun-4 (FY-4A), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). Results indicate that: (1) all five products overestimate the accumulated rainfall in the summer, with FY-4A being the most severe; additionally, FY-4A cannot capture the spatial and temporal distribution characteristics of precipitation over southern China. (2) IMERG and GSMaP perform better than the other three datasets at both daily and hourly scales; IMERG correlates slightly better than GSMaP against CMPA data, while it performs worse than GSMaP in terms of probability of detection (POD). (3) ERA5-Land performs better than PERSIANN-CCS and FY-4A at daily scale but shows the worst correlation coefficient (CC), false alarm ratio (FAR), and equitable threat score (ETS) of all precipitation products at hourly scale. (4) The rankings of overall performance on precipitation estimations for this region are IMERG, GSMaP, ERA5-Land, PERSIANN-CCS, and FY-4A at daily scale; and IMERG, GSMaP, PERSIANN-CCS, FY-4A, and ERA5-Land at hourly scale. These findings will provide valuable feedback for improving the current satellite-based precipitation retrieval algorithms and also provide preliminary references for flood forecasting and natural disaster early warning. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)

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