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Applications of Remotely Sensed Data in Hydrology and Climatology

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 33317

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Dr. Arun Mondal

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Guest Editor

School of Earth, Ocean and Environment, University of South Carolina, Columbia, SC, USA
Interests: hydrological modeling and soil erosion; climate change; remote sensing and GIS

Prof. Dr. Yuei-An Liou

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Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli District, Taoyuan City 320317, Taiwan
Interests: remoet sensing; hydrometeorological extremes; vulnerability assessment
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Special Issue Information

Dear Colleagues,

This Special Issue is mainly focused on evaluating the individual and integrated studies of hydroclimatic analysis using satellite observations. The main intention of this issue is to present precise and novel information regarding variations of the hydrological and climatic characteristics and improvement of future planning and policy. Remotely sensed data are nowadays commonly used in hydrological and climatological studies on regional or global scales. Satellite observations from passive and active sensors, onboard both geostationary and polar-orbiting satellites, collect information and data in dangerous or inaccessible areas that are very useful for hydrological and climatological studies. For monitoring of the terrestrial hydrology for various applications (rainfall, soil moisture, flood extent, surface water level, terrestrial water storage, groundwater, evapotranspiration, discharge, snow and ice, floods, etc.), great numbers of satellites observations are being used. Similarly, consistent long-term Earth satellite observations and data records are becoming indispensable for providing information for improved detection, attribution, and prediction of global climate and environmental changes, in addition to helping decision makers and society to respond and adapt to the changes and variability in a resilient fashion. Finally, remote sensing data can be very useful for improving warning, forecasting, and preparedness, being therefore also useful in hydroclimatic disaster risk management.

Special focus will be given to the hybrid methods, modeling, and recent advances in the fields of spatiotemporal variation in water and climatic changes using satellite observations. Of interest to this Special Issue are a wide range of topics including but not limited to:

Time series analysis of hydrometeorological parameters using satellite data.
Watershed modeling using remote sensing products or in situ observations.
Application of satellite data on flood, evapotranspiration, snow, soil moisture, groundwater, and soil erosion studies (modeling, improvement, policy, etc.).
Assessment of climate change impacts on extremes, like flood and drought, using satellite data.
Assessment of climate change impacts on water resources or hydrological cycle using remote sensing products.
Statistical and machine learning application to satellite-based hydrological and climatological data.
Assessment of climate change impacts on available water resources and agricultural production using satellite observations.
Assessment and improvement of hydroclimatic study at regional or global scales using remote sensing data.

Dr. Arun Mondal
Prof. Dr. Yuei-An Liou
Guest Editors

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Keywords

Climate change
Satellite observation
Water resources
Global water and energy cycles
Remote Sensing
Water reservoir monitoring
Cloud, temperature, humidity, precipitation, wind, etc

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Published Papers (9 papers)

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Research

27 pages, 8702 KiB

Open AccessArticle

Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin

by Rabeea Noor, Arfan Arshad, Muhammad Shafeeque, Jinping Liu, Azhar Baig, Shoaib Ali, Aarish Maqsood, Quoc Bao Pham, Adil Dilawar, Shahbaz Nasir Khan, Duong Tran Anh and Ahmed Elbeltagi

Remote Sens. 2023, 15(2), 318; https://doi.org/10.3390/rs15020318 - 5 Jan 2023

Cited by 15 | Viewed by 4035

Abstract

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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25 pages, 17045 KiB

Open AccessArticle

A Statistical Approach to Using Remote Sensing Data to Discern Streamflow Variable Influence in the Snow Melt Dominated Upper Rio Grande Basin

by Khandaker Iftekharul Islam, Emile Elias, Christopher Brown, Darren James and Sierra Heimel

Remote Sens. 2022, 14(23), 6076; https://doi.org/10.3390/rs14236076 - 30 Nov 2022

Cited by 4 | Viewed by 2710

Abstract

Since the middle of the 20th century, the peak snowpack in the Upper Rio Grande (URG) basin of United States has been decreasing. Warming influences snowpack characteristics such as snow cover, snow depth, and Snow Water Equivalent (SWE), which can affect runoff quantity and timing in snowmelt runoff-dominated river systems of the URG basin. The purpose of this research is to investigate which variables are most important in predicting naturalized streamflow and to explore variables’ relative importance for streamflow dynamics. We use long term remote sensing data for hydrologic analysis and deploy R algorithm for data processing and synthesizing. The data is analyzed on a monthly and baseflow/runoff basis for nineteen sub-watersheds in the URG. Variable importance and influence on naturalized streamflow is identified using linear standard regression with multi-model inference based on the second-order Akaike information criterion (AICc) coupled with the intercept only model. Five predictor variables: temperature, precipitation, soil moisture, sublimation, and SWE are identified in order of relative importance for streamflow prediction. The most influential variables for streamflow prediction vary temporally between baseflow and runoff conditions and spatially by watershed and mountain range. Despite the importance of temperature on streamflow, it is not consistently the most important factor in streamflow prediction across time and space. The dominance of precipitation over streamflow is more obvious during baseflow. The impact of precipitation, SWE, sublimation, and minimum temperature on streamflow is evident during the runoff season, but the results vary for different sub-watersheds. The association between sublimation and streamflow is positive in the runoff season, which may relate to temperature and requires further research. This research sheds light on the primary drivers and their spatial and temporal variability on streamflow generation. This work is critical for predicting how warming temperatures will impact water supplies serving society and ecosystems in a changing climate. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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25 pages, 6909 KiB

Open AccessArticle

Evaluation of the Spatiotemporal Distribution of Precipitation Using 28 Precipitation Indices and 4 IMERG Datasets over Nepal

by Rocky Talchabhadel, Suraj Shah and Bibek Aryal

Remote Sens. 2022, 14(23), 5954; https://doi.org/10.3390/rs14235954 - 24 Nov 2022

Cited by 10 | Viewed by 3343

Abstract

Accurate accounting of spatiotemporal variability of precipitation is essential for understanding the changing climate. Among the available precipitation estimates, the Global Precipitation Measurement (GPM) is an international satellite network providing advanced global precipitation estimates. The integrated multi-satellite retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation and yields a better performance in detecting precipitation events and spatial resolution. Here, we used twenty years (2001–2020) of IMERG Final data over the entire Nepal to analyze the spatial and temporal distribution of precipitation. This study evaluates the dynamic characteristics of the precipitation amounts, intensities, frequencies, and other relevant data across Nepal, using four IMERG datasets: (i) microwave only, (ii) infrared only, (iii) multi satellites gauge uncalibrated, and (iv) multi satellites gauge calibrated. A total of 28 precipitation indices was computed: threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, proportion-based indices, and additional seasonal indices. Results show that all four IMERG datasets are promising in capturing spatial details. The frequency of wet days corresponds with ground-based precipitation. Still, most indices, including consecutive wet days, annual and monsoon precipitation, and days when precipitation equaled or exceeded 20 and 50 mm, were substantially underestimated. In addition, the microwave-only dataset highly underestimated the precipitation amount. Notably, a substantial proportion of false alarms is a problem for all four IMERG datasets. Moreover, our results demonstrate that the IMERG uncalibrated dataset tends to overestimate precipitation during heavy precipitation events. These advantages and shortcomings of IMERG datasets over the rugged terrain of Nepal can provide useful feedback for sensor and algorithm developers to overcome limitations and improve retrieval algorithms. The study findings are helpful to the broader data users and practitioners for effective water decision applications. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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27 pages, 10034 KiB

Open AccessArticle

Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China

by Ying Zhang, Jinliang Hou and Chunlin Huang

Remote Sens. 2022, 14(21), 5355; https://doi.org/10.3390/rs14215355 - 26 Oct 2022

Cited by 9 | Viewed by 2998

Abstract

Precipitation monitoring is important for earth system modeling and environmental management. Low spatial representativeness limits gauge measurements of rainfall and low spatial resolution limits satellite-derived rainfall. SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture (SM) observations, can be an alternative. However, the quality of SM data limits the accuracy of rainfall. The goal of this work was to improve the accuracy of rainfall estimation through merging multiple soil moisture (SM) datasets. This study proposed an integration framework, which consists of multiple machine learning methods, to use satellite and ground-based soil moisture observations to derive a precipitation product. First, three machine learning (ML) methods (random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN)) were used, respectively to generate three SM datasets (RF-SM, LSTM-SM, and CNN-SM) by merging satellite (SMOS, SMAP, and ASCAT) and ground-based SM observations. Then, these SM datasets were merged using the Bayesian model averaging method and validated by wireless sensor network (WSN) observations. Finally, the merged SM data were used to produce a rainfall dataset (SM2R) using SM2RAIN. The SM2R dataset was validated using automatic meteorological station (AMS) rainfall observations recorded throughout the Upper Heihe River Basin (China) during 2014–2015 and compared with other rainfall datasets. Our results revealed that the quality of the SM2R data outperforms that of GPM-SM2RAIN, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ERA5-Land (ERA5) and multi-source weighted-ensemble Precipitation (MSWEP). Triple-collocation analysis revealed that SM2R outperformed China Meteorological Data and the China Meteorological Forcing Dataset. Ultimately, the SM2R rainfall product was considered successful with acceptably low spatiotemporal errors (RMSE = 3.5 mm, R = 0.59, and bias = −1.6 mm). Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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30 pages, 8920 KiB

Open AccessArticle

Evaluation of the Performance of Multi-Source Satellite Products in Simulating Observed Precipitation over the Tensift Basin in Morocco

by Wiam Salih, Abdelghani Chehbouni and Terence Epule Epule

Remote Sens. 2022, 14(5), 1171; https://doi.org/10.3390/rs14051171 - 26 Feb 2022

Cited by 24 | Viewed by 3991

Abstract

The Tensift basin in Morocco is prominent for its ecological and hydrological diversity. This is marked by rivers flowing into areas such as Ourika. In addition to agriculture, the basin is a hub of variable land use systems. As such, it is important to gain a better understanding of the relationship between simulated and observed precipitation in this region to be able to better understand the role of precipitation in impacting the climate and water resources in the basin. This study evaluates the performance of multi-source satellite products against weather station precipitation in the basin. The satellite-product-based data were first collected for seven satellite products, namely PERSIANN, PERSIANN CDR, TRMM3B42, ARC2, RFE2, CHIRPS, and ERA5 (simulated precipitation) from the following repositories (CHRS iRain, RainSphere, NASA, EUMETSAT, NOAA, FEWS NET, ECMWF). Precipitation observation data from six weather stations, located at Tachedert (2343 m), Imskerbour (1404 m), Asni (1170 m), Grawa (550 m), Agdal (489 m), and Agafay (487 m), at different altitudes, latitudes, and temporal scales (1D, 1M, 1Y), over the period 13 May 2007 and 31 September 2019 over the Tensift basin were collected. The data were compared and analyzed through inferential statistics such as the Nash–Sutcliffe efficiency coefficient, bias, root-mean-square error (RMSE), root-mean-square deviation (RMSD), the standard deviation, the correlation coefficient (R), and the coefficient of determination (R²) and visualized through Taylor diagrams and scatterplots to visualize the closeness between the seven satellite products and the observed precipitation data. A second analysis was carried out on the monthly precipitation, resulting from the six weather stations, and based on the standardized precipitation index (SPI) to determine the onset, duration, and magnitude of the meteorological drought. The results show that PERSIANN CDR performs best and is more reliable regarding its ability to simulate precipitation over the basin. This is seen as PERSIANN CDR has significant rates for the different statistics (Bias: −0.05 (Daily Asni), RMSE: 2.86 (Daily Agdal), R: 0.83, R²:0.687 (Monthly Agdal)). The results also show that there are no major differences between the observed weather station and the satellite precipitation data. The best performance was attributed to PERSIANN CDR (for monthly and annual precipitation at all altitudes and for daily precipitation at high altitudes). However, most of the time, this product records low or negative Nash values (−6.06 (Annual Grawa)), due to the insufficient weather station data in the study area (Tensift). It was observed that TRMM overestimates precipitation during heavy precipitation and underestimates it during low precipitation. This makes it important for the latter observations to be viewed with caution due to the quality of annual comparison results and underscores the need to develop more efficient precipitation comparison approaches and datasets. Additionally, the performance of the satellite products is better at low altitudes and during wet years. Finally, it was concluded from the SPI that Tensift region has experienced 13 drought periods over the study period, with the longest event of 12 months being from Marsh 2015 to February 2016, and the most intense event with the highest drought severity (19.6) and the lowest SPI value (−2.66) being in 2019. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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26 pages, 8595 KiB

Open AccessArticle

Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate

by Seyyed Hasan Hosseini, Hossein Hashemi, Ahmad Fakheri Fard and Ronny Berndtsson

Remote Sens. 2022, 14(2), 270; https://doi.org/10.3390/rs14020270 - 7 Jan 2022

Cited by 7 | Viewed by 3240

Abstract

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km² size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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19 pages, 4489 KiB

Open AccessArticle

Analysis of Climate Change Effects on Surface Temperature in Central-Italy Lakes Using Satellite Data Time-Series

by Davide De Santis, Fabio Del Frate and Giovanni Schiavon

Remote Sens. 2022, 14(1), 117; https://doi.org/10.3390/rs14010117 - 28 Dec 2021

Cited by 11 | Viewed by 3825

Abstract

Evaluation of the impact of climate change on water bodies has been one of the most discussed open issues of recent years. The exploitation of satellite data for the monitoring of water surface temperatures, combined with ground measurements where available, has already been shown in several previous studies, but these studies mainly focused on large lakes around the world. In this work the water surface temperature characterization during the last few decades of two small–medium Italian lakes, Lake Bracciano and Lake Martignano, using satellite data is addressed. The study also takes advantage of the last space-borne platforms, such as Sentinel-3. Long time series of clear sky conditions and atmospherically calibrated (using a simplified Planck’s Law-based algorithm) images were processed in order to derive the lakes surface temperature trends from 1984 to 2019. The results show an overall increase in water surface temperatures which is more evident on the smallest and shallowest of the two test sites. In particular, it was observed that, since the year 2000, the surface temperature of both lakes has risen by about 0.106 °C/year on average, which doubles the rate that can be retrieved by considering the whole period 1984–2019 (0.053 °C/year on average). Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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19 pages, 21261 KiB

Open AccessArticle

Evaluation of Seasonal, Drought, and Wet Condition Effects on Performance of Satellite-Based Precipitation Data over Different Climatic Conditions in Iran

by Salman Qureshi, Javad Koohpayma, Mohammad Karimi Firozjaei and Ata Abdollahi Kakroodi

Remote Sens. 2022, 14(1), 76; https://doi.org/10.3390/rs14010076 - 24 Dec 2021

Cited by 16 | Viewed by 3720

Abstract

The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially in the areas with sparse or no other robust sources. This study explored the accuracy and precision of satellite data products over a span of 18 years (2000–2017) using synoptic ground station data for three regions in Iran with different climates, namely (a) humid and high rainfall, (b) semi-arid, and (c) arid. The results show that the monthly precipitation products of GPM and TRMM overestimate the rainfall. On average, they overestimated the precipitation amount by 11% in humid, by 50% in semi-arid, and by 43% in arid climate conditions compared to the ground-based data. This study also evaluated the satellite data accuracy in drought and wet conditions based on the standardized precipitation index (SPI) and different seasons. The results showed that the accuracy of satellite data varies significantly under drought, wet, and normal conditions and different timescales, being lowest under drought conditions, especially in arid regions. The highest accuracy was obtained on the 12-month timescale and the lowest on the 3-month timescale. Although the accuracy of the data is dependent on the season, the seasonal effects depend on climatic conditions. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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18 pages, 6446 KiB

Open AccessArticle

Cross Validation of GOES-16 and NOAA Multi-Radar Multi-Sensor (MRMS) QPE over the Continental United States

by Luyao Sun, Haonan Chen, Zhe Li and Lei Han

Remote Sens. 2021, 13(20), 4030; https://doi.org/10.3390/rs13204030 - 9 Oct 2021

Cited by 7 | Viewed by 3036

Abstract

The Geostationary Operational Environmental Satellite-R (GOES-R) series provides new opportunities for continuous observation of precipitation at large scales with a high resolution. An operational quantitative precipitation estimation (QPE) product has been produced based on multi-channel measurements from the Advanced Baseline Imager (ABI) aboard the GOES-16 (formerly known as GOES-R). This paper presents a comprehensive evaluation of this GOES-16 QPE product against a ground reference QPE product from the National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor (MRMS) system over the continental United States (CONUS) during the warm seasons of 2018 and 2019. For the first time, the accuracy of GOES-16 QPE product was quantified using the gauge-corrected MRMS (GC-MRMS) QPE product, and a number of evaluation metrics were applied to adequately resolve the associated errors. The results indicated that precipitation occurrence and intensity estimated by the GOES-16 QPE agreed with GC-MRMS fairly well over the eastern United States (e.g., the probability of detection was close to 1.0, and the Pearson’s correlation coefficient was 0.80 during September 2019), while the discrepancies were noticeable over the western United States (e.g., the Pearson’s correlation coefficient was 0.64 for the same month). The performance of GOES-16 QPE was downgraded over the western United States, in part due to the limitations of the GOES-16 rainfall retrieval algorithm over complex terrains, and in part because of the poor radar coverage analyzed by the MRMS system. In addition, it was found that the GOES-16 QPE product significantly overestimated rainfall induced by the mesoscale convective systems in the midwestern United States, which must be addressed in the future development of GOES satellite rainfall retrieval algorithms. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology)

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