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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 March 2021) | Viewed by 40939

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Dr. Hyun-Han Kwon

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Department of Civil and Environmental Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: stochastic hydrology; water resources modeling; Bayesian modeling; time series analysis and forecasting; climate change; hydro-meteorology; machine learning; weather forecasting; risk analysis; big data analysis; soil moisture modeling
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Dr. Qiang Dai

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Guest Editor

Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Interests: urban hydrology; radar hydrology; precipitation remote sensing; multi-hazards; weather forecasting; geographical information science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Streamflow information is an important component which is commonly required to evaluate how much water is available in different locations for both human societies and natural ecosystems. Especially, the impacts of climate change on the available water have been an issue in certain areas, however, the lack of effective models and long-term streamflow observation data and their associated uncertainties are still challenging to assess the impacts in many parts of the world. The purpose of the proposed special issue on “Remote Sensing for Streamflow Simulation” is to present an integrated approach to streamflow modelling that incorporates and combines new hydro-meteorological information including satellite-based, airborne and ground-based observations, so as to foster a scientific framework for better understanding the impact of climate and social-environmental change on water resources. Topics to be addressed include but are not limited to the following.

Use of in situ and remote sensing observations of hydrologic processes for a better simlation of streamflow
Downscaling of large-scaled remote sensing observations for local streamflow simulation
Physcially or statisticall-based or their combined models could be developed and employed to simulate streamflow, dealing with their associated uncertainties; hydrological models with the use of satellite-based products are particularly welcome.
Remote sensing of precipitation and its relationship with streamflow under changing climate

Dr. Hyun-Han Kwon
Dr. Qiang Dai
Guest Editors

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Keywords

Streamflow simulation
Remote sensing observations
Hydrologic modeling
Uncertainty
Downscaling
Climate change

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Published Papers (10 papers)

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18 pages, 4642 KiB

Open AccessArticle

Contribution of Snow-Melt Water to the Streamflow over the Three-River Headwater Region, China

by Sisi Li, Mingliang Liu, Jennifer C. Adam, Huawei Pi, Fengge Su, Dongyue Li, Zhaofei Liu and Zhijun Yao

Remote Sens. 2021, 13(8), 1585; https://doi.org/10.3390/rs13081585 - 19 Apr 2021

Cited by 15 | Viewed by 3497

Abstract

Snowmelt water is essential to the water resources management over the Three-River Headwater Region (TRHR), where hydrological processes are influenced by snowmelt runoff and sensitive to climate change. The objectives of this study were to analyse the contribution of snowmelt water to the total streamflow (f_Q,snow) in the TRHR by applying a snowmelt tracking algorithm and Variable Infiltration Capacity (VIC) model. The ratio of snowfall to precipitation, and the variation of the April 1 snow water equivalent (SWE) associated with f_Q,snow, were identified to analyse the role of snowpack in the hydrological cycle. Prior to the simulation, the VIC model was validated based on the observed streamflow data to recognize its adequacy in the region. In order to improve the VIC model in snow hydrology simulation, Advanced Scanning Microwave Radiometer E (ASMR-E) SWE product data was used to compare with VIC output SWE to adjust the snow parameters. From 1971 to 2007, the averaged f_Q,snow was 19.9% with a significant decreasing trend over entire TRHR (p < 0.05).The influence factor resulted in the rate of change in f_Q,snow which were different for each sub-basin TRHR. The decreasing rate of f_Q,snow was highest of 0.24%/year for S_Lantsang, which should be due to the increasing streamflow and the decreasing snowmelt water. For the S_Yangtze, the increasing streamflow contributed more than the stable change of snowmelt water to the decreasing f_Q,snow with a rate of 0.1%/year. The April 1 SWE with the minimum value appearing after 2000 and the decreased ratio of snowfall to precipitation during the study period, suggested the snow solid water resource over the TRHR was shrinking. Our results imply that the role of snow in the snow-hydrological regime is weakening in the TRHR in terms of water supplement and runoff regulation due to the decreased f_Q,snow and snowfall. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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28 pages, 3640 KiB

Open AccessArticle

Usefulness of Global Root Zone Soil Moisture Product for Streamflow Prediction of Ungauged Basins

by Jeonghyeon Choi, Jeongeun Won, Okjeong Lee and Sangdan Kim

Remote Sens. 2021, 13(4), 756; https://doi.org/10.3390/rs13040756 - 18 Feb 2021

Cited by 9 | Viewed by 3015

Abstract

Using modelling approaches to predict stream flow from ungauged basins requires new model calibration strategies and evaluation methods that are different from the existing ones. Soil moisture information plays an important role in hydrological applications in basins. Increased availability of remote sensing data presents a significant opportunity to obtain the predictive performance of hydrological models (especially in ungauged basins), but there is still a limit to applying remote sensing soil moisture data directly to models. The Soil Moisture Active Passive (SMAP) satellite mission provides global soil moisture data estimated by assimilating remotely sensed brightness temperature to a land surface model. This study investigates the potential of a hydrological model calibrated using only global root zone soil moisture based on satellite observation when attempting to predict stream flow in ungauged basins. This approach’s advantage is that it is particularly useful for stream flow prediction in ungauged basins since it does not require observed stream flow data to calibrate a model. The modelling experiments were carried out on upstream watersheds of two dams in South Korea with high-quality stream flow data. The resulting model outputs when calibrated using soil moisture data without observed stream flow data are particularly impressive when simulating monthly stream flows upstream of the dams, and daily stream flows also showed a satisfactory level of predictive performance. In particular, the model calibrated using soil moisture data for dry years showed better predictive performance than for wet years. The performance of the model calibrated using soil moisture data was significantly improved under low flow conditions compared to the traditional regionalization approach. Additionally, the overall stream flow was also predicted better. In addition, the uncertainty of the model calibrated using soil moisture was not much different from that of the model calibrated using observed stream flow data, and showed more robust outputs when compared to the traditional regionalization approach. These results prove that the application of the global soil moisture product for predicting stream flows in ungauged basins is promising. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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24 pages, 10026 KiB

Open AccessArticle

Comparison of River Basin Water Level Forecasting Methods: Sequential Neural Networks and Multiple-Input Functional Neural Networks

by Chih-Chiang Wei

Remote Sens. 2020, 12(24), 4172; https://doi.org/10.3390/rs12244172 - 20 Dec 2020

Cited by 11 | Viewed by 3290

Abstract

To precisely forecast downstream water levels in catchment areas during typhoons, the deep learning artificial neural networks were employed to establish two water level forecasting models using sequential neural networks (SNNs) and multiple-input functional neural networks (MIFNNs). SNNs, which have a typical neural network structure, are network models constructed using sequential methods. To develop a network model capable of flexibly consolidating data, MIFNNs are employed for processing data from multiple sources or with multiple dimensions. Specifically, when images (e.g., radar reflectivity images) are used as input attributes, feature extraction is required to provide effective feature maps for model training. Therefore, convolutional layers and pooling layers were adopted to extract features. Long short-term memory (LSTM) layers adopted during model training enabled memory cell units to automatically determine the memory length, providing more useful information. The Hsintien River basin in northern Taiwan was selected as the research area and collected relevant data from 2011 to 2019. The input attributes comprised one-dimensional data (e.g., water levels at river stations, rain rates at rain gauges, and reservoir release) and two-dimensional data (i.e., radar reflectivity mosaics). Typhoons Saola, Soudelor, Dujuan, and Megi were selected, and the water levels 1 to 6 h after the typhoons struck were forecasted. The results indicated that compared with linear regressions (REG), SNN using dense layers (SNN-Dense), and SNN using LSTM layers (SNN-LSTM) models, superior forecasting results were achieved for the MIFNN model. Thus, the MIFNN model, as the optimal model for water level forecasting, was identified. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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25 pages, 6177 KiB

Open AccessArticle

Correlation Analysis between Air Temperature and MODIS Land Surface Temperature and Prediction of Air Temperature Using TensorFlow Long Short-Term Memory for the Period of Occurrence of Cold and Heat Waves

by Jeehun Chung, Yonggwan Lee, Wonjin Jang, Siwoon Lee and Seongjoon Kim

Remote Sens. 2020, 12(19), 3231; https://doi.org/10.3390/rs12193231 - 4 Oct 2020

Cited by 20 | Viewed by 4474

Abstract

The purpose of this study is to analyze the correlation between surface air temperature (SAT) and land surface temperature (LST) based on land use when heat and cold waves occur and to predict the distribution of SAT using the long short-term memory (LSTM) of TensorFlow. For the correlation analysis, the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime LST and maximum, minimum, and mean SAT were measured at 79 weather stations of the Korea Meteorological Administration (KMA) from 2008 to 2018. As a result of the correlation analysis between SAT and LST, the maximum SAT (T_MX) had a good correlation with the daytime LST of Terra MODIS, with a Pearson’s correlation coefficient (R) of 0.92 and root mean square error (RMSE) of 4.8 °C, and the minimum SAT (T_MN) showed a good correlation with the nighttime LST of Terra MODIS, with an R of 0.93 and RMSE of 4.2 °C. When analyzing temperature characteristics by land use (urban, paddy, upland crop, forest, grass, wetland, bare field, and water), it was confirmed that the climate mitigation effect of the wetland and vegetation area appeared in the LSTs and the observed SAT. In the cold wave period, the average temperatures for urban and wetland areas was the highest, and the average temperature for wetland and forest was not higher than that of other land use classes. As the SAT results predicted through the LSTM model, the accuracy of the T_MN during the cold wave period was 0.59 for the coefficient of determination (R²), 3.1 °C for RMSE, and 0.76 for the index of agreement (IoA), while the accuracy of the T_MX for the heat wave period was 0.24 for R², 2.23 °C for RMSE, and 0.63 for IoA. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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19 pages, 5700 KiB

Open AccessArticle

A New and Simplified Approach for Estimating the Daily River Discharge of the Tibetan Plateau Using Satellite Precipitation: An Initial Study on the Upper Brahmaputra River

by Tian Zeng, Lei Wang, Xiuping Li, Lei Song, Xiaotao Zhang, Jing Zhou, Bing Gao and Ruishun Liu

Remote Sens. 2020, 12(13), 2103; https://doi.org/10.3390/rs12132103 - 1 Jul 2020

Cited by 8 | Viewed by 3058

Abstract

Collecting in situ observations from remote, high mountain rivers presents major challenges, yet real-time, high temporal resolution (e.g., daily) discharge data are critical for flood hazard mitigation and river management. In this study, we propose a method for estimating daily river discharge (RD) based on free, operational remote sensing precipitation data (Tropical Rainfall Measuring Mission (TRMM), since 2001). In this method, an exponential filter was implemented to produce a new precipitation time series from daily basin-averaged precipitation data to model the time lag of precipitation in supplying RD, and a linear-regression relationship was constructed between the filtered precipitation time series and observed discharge records. Because of different time lags in the wet season (rainfall-dominant) and dry season (snowfall-dominant), the precipitation data were processed in a segmented way (from June to October and from November to May). The method was evaluated at two hydrological gauging stations in the Upper Brahmaputra (UB) river basin, where Nash–Sutcliffe Efficiency (NSE) coefficients for Nuxia (>0.85) and Yangcun (>0.80) indicate good performance. By using the degree-day method to estimate the snowmelt and acquire the time series of new active precipitation (rainfall plus snowmelt) in the target basins, the discharge estimations were improved (NSE > 0.9 for Nuxia) compared to the original data. This makes the method applicable for most rivers on the Tibetan Plateau, which are fed mainly by precipitation (including snowfall) and are subject to limited human interference. The method also performs well for reanalysis precipitation data (Chinese Meteorological Forcing Dataset (CMFD), 1980–2000). The real-time or historical discharges can be derived from satellite precipitation data (or reanalysis data for earlier historical years) by using our method. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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21 pages, 6937 KiB

Open AccessArticle

A Hybrid Approach Combining Conceptual Hydrological Models, Support Vector Machines and Remote Sensing Data for Rainfall-Runoff Modeling

by Moonhyuk Kwon, Hyun-Han Kwon and Dawei Han

Remote Sens. 2020, 12(11), 1801; https://doi.org/10.3390/rs12111801 - 2 Jun 2020

Cited by 23 | Viewed by 4214

Abstract

Understanding catchment response to rainfall events is important for accurate runoff estimation in many water-related applications, including water resources management. This study introduced a hybrid model, the Tank-least squared support vector machine (LSSVM), that incorporated intermediate state variables from a conceptual tank model within the least squared support vector machine (LSSVM) framework in order to describe aspects of the rainfall-runoff (RR) process. The efficacy of the Tank-LSSVM model was demonstrated with hydro-meteorological data measured in the Yongdam Catchment between 2007 and 2016, South Korea. We first explored the role of satellite soil moisture (SM) data (i.e., European Space Agency (ESA) CCI) in the rainfall-runoff modeling. The results indicated that the SM states inferred from the ESA CCISWI provided an effective means of describing the temporal dynamics of SM. Further, the Tank-LSSVM model’s ability to simulate daily runoff was assessed by using goodness of fit measures (i.e., root mean square error, Nash Sutcliffe coefficient (NSE), and coefficient of determination). The Tank-LSSVM models’ NSE were all classified as “very good” based on their performance during the training and testing periods. Compared to individual LSSVM and Tank models, improved daily runoff simulations were seen in the proposed Tank-LSSVM model. In particular, low flow simulations demonstrated the improvement of the Tank-LSSVM model compared to the conventional tank model. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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16 pages, 3840 KiB

Open AccessArticle

Performance Evaluation of the Multiple Quantile Regression Model for Estimating Spatial Soil Moisture after Filtering Soil Moisture Outliers

by Chunggil Jung, Yonggwan Lee, Jiwan Lee and Seongjoon Kim

Remote Sens. 2020, 12(10), 1678; https://doi.org/10.3390/rs12101678 - 23 May 2020

Cited by 7 | Viewed by 3717

Abstract

The spatial distribution of soil moisture (SM) was estimated by a multiple quantile regression (MQR) model with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and filtered SM data from 2013 to 2015 in South Korea. For input data, observed precipitation and SM data were collected from the Korea Meteorological Administration and various institutions monitoring SM. To improve the work of a previous study, prior to the estimation of SM, outlier detection using the isolation forest (IF) algorithm was applied to the observed SM data. The original observed SM data resulted in IF_SM data following outlier detection. This study obtained an average data removal rate of 20.1% at 58 stations. For various reasons, such as instrumentation, environment, and random errors, the original observed SM data contained approximately 20% uncertain data. After outlier detection, this study performed a regression analysis by estimating land surface temperature quantiles. The soil characteristics were considered through reclassification into four soil types (clay, loam, silt, and sand), and the five-day antecedent precipitation was considered in order to estimate the regression coefficient of the MQR model. For all soil types, the coefficient of determination (R²) and root mean square error (RMSE) values ranged from 0.25 to 0.77 and 1.86% to 12.21%, respectively. The MQR results showed a much better performance than that of the multiple linear regression (MLR) results, which yielded R² and RMSE values of 0.20 to 0.66 and 1.08% to 7.23%, respectively. As a further illustration of improvement, the box plots of the MQR SM were closer to those of the observed SM than those of the MLR SM. This result indicates that the cumulative distribution functions (CDF) of MQR SM matched the CDF of the observed SM. Thus, the MQR algorithm with outlier detection can overcome the limitations of the MLR algorithm by reducing both the bias and variance. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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21 pages, 9707 KiB

Open AccessArticle

Suitability of Satellite-Based Precipitation Products for Water Balance Simulations Using Multiple Observations in a Humid Catchment

by Dan Zhang, Xiaomang Liu, Peng Bai and Xiang-Hu Li

Remote Sens. 2019, 11(2), 151; https://doi.org/10.3390/rs11020151 - 15 Jan 2019

Cited by 16 | Viewed by 3464

Abstract

This study assesses the suitability of five popular satellite-based precipitation products in modeling water balance in a humid region of China during the period 1998–2012. The satellite-based precipitation products show similar spatial patterns with varying degrees of overestimation or underestimation, compared with the gauged precipitation. A distributed hydrological model is used to evaluate the suitability of satellite-based precipitation products in simulating streamflow, evapotranspiration and soil moisture. The simulations of streamflow and evapotranspiration forced by the MSWEP precipitation perform best among the five satellite-based precipitation products, where the Kling-Gupta efficiency (KGE) between the simulated and observed streamflow ranges from 0.75 to 0.91, and the KGE between the simulated and observed evapotranspiration ranges from 0.46 to 0.61. However, the KGE between the simulated and observed soil moisture is negative, indicating that the performance of soil moisture simulation forced by satellite-based precipitation is poor. In addition, this study finds the spatial pattern of simulated streamflow is dominated by the distribution of precipitation, whereas the distribution of evapotranspiration and soil moisture is controlled by the parameters of the hydrological model. This study is useful for the improvement of hydrological modeling based on remote sensing and the monitoring of regional water resources. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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31 pages, 12817 KiB

Open AccessArticle

Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

by Fei Yuan, Limin Zhang, Khin Min Wun Soe, Liliang Ren, Chongxu Zhao, Yonghua Zhu, Shanhu Jiang and Yi Liu

Remote Sens. 2019, 11(2), 140; https://doi.org/10.3390/rs11020140 - 12 Jan 2019

Cited by 91 | Viewed by 7051

Abstract

Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM- and GPM-era satellite-based precipitation products (SPPs) in both near- and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash–Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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12 pages, 2960 KiB

Open AccessLetter

Discharge Estimation Using Harmonized Landsat and Sentinel-2 Product: Case Studies in the Murray Darling Basin

by Zhuolin Shi, Yun Chen, Qihang Liu and Chang Huang

Remote Sens. 2020, 12(17), 2810; https://doi.org/10.3390/rs12172810 - 30 Aug 2020

Cited by 15 | Viewed by 3535

Abstract

Quantifying river discharge is a critical component for hydrological studies, floodplain ecological conservation research, and water resources management. In recent years, a series of remote sensing-based discharge estimation methods have been developed. An example is the use of the near infrared (NIR) band of optical satellite images, with the principle of calculating the ratio between a stable land pixel for calibration (C) and a pixel within the river for measurement (M), applying a linear regression between C/M series and observed discharge series. This study trialed the C/M method, utilizing the Harmonized Landsat and Sentinel-2 (HLS) surface reflectance product on relatively small rivers with 30~100 m widths. Two study sites with different river characteristics and geographic settings in the Murray-Darling Basin (MDB) of Australia were selected as case studies. Two independent sets of HLS data and gauged discharge data for the 2017 and 2018 water years were acquired for modeling and validation, respectively. Results reveal high consistency between the HLS-derived discharge and gauged discharge at both sites. The Relative Root Mean Square Errors are 53% and 19%, and the Nash-Sutcliffe Efficiency coefficients are 0.24 and 0.69 for the two sites. This study supports the effectiveness of applying the fine-resolution HLS for modeling discharge on small rivers based on the C/M methodology, which also provides evidence of using multisource synthesized datasets as the input for discharge estimation. Full article

(This article belongs to the Special Issue Remote Sensing for Streamflow Simulation)

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