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Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

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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 (30 July 2021) | Viewed by 37547

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Special Issue Editors

Prof. Dr. Yongqiang Zhang

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Guest Editor

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing; hydrology; climate change; evapotranspiration; runoff; predictions; catchment
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Dongryeol Ryu

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Guest Editor

Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Austrilia
Interests: microwave remote sensing of soil moisture; hydrological applications of remote sensing; hydrological data assimilation
Special Issues, Collections and Topics in MDPI journals

Dr. Donghai Zheng

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Guest Editor

National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: land surface modeling; microwave remote sensing of soil moisture

Special Issue Information

Dear Colleagues,

We are living in a world where geophysical datasets, particularly, remote sensing datasets, are created at fast increasing rates. The efficient and innovative use of these datasets for understanding hydrological processes in various climatic and vegetation regimes under anthropogenic influence has become an important challenge, which offers a wide range of research opportunities. This is particularly urgent for the hydrological community at large who has relied on distributed and lumped hydrological models for hydrological simulations/predictions over the last several decades. Increasingly accurate water information requires more insightful understanding and more skilful predictions, at resolutions commensurate with the available data, which is challenging the more traditional hydrological modelling. This Special Issue provides a great opportunity to stimulate investigations on how a better and smarter use of high-to-moderate-resolution remote sensing datasets can improve hydrological simulations and predictions.

We invite researchers to contribute their original research articles that use various remote sensing techniques to improve hydrological simulations and predictions and that will stimulate our collective efforts. We invite papers covering the following, but not exclusive, topics:

(1) Detecting hydrological and other related changes using state-of-the-art remote sensing techniques;

(2) Mapping eco-hydrological and hydrological processes and their driving factors using large samples and high-resolution datasets;

(3) Understanding hydrological processes under a rapidly changing world by using hydrological modelling together with high-to-moderate-resolution (several meters to hundred meters) remote sensing data;

(4) Improving hydrological prediction skills by modifying hydrological model structures to incorporate remote sensing data and by using various model calibrations against remote sensing data;

(5) Developing hydrological modelling frameworks using advanced cloud cluster computation techniques and tools, such as the Google Earth Engine;

(6) Using remote sensing data together with machine learning techniques to improve predictions of various hydrological variables and hydrological signatures;

(7) Using remote sensing techniques for water-related studies, such as on the water–food–energy security nexus.

Prof. Yongqiang Zhang
Dr. Dongryeol Ryu
Dr. Donghai Zheng
Guest Editors

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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.

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Keywords

remote sensing
high resolution
model
GEE
machine learning
evapotranspiration
runoff
soil moisture
hydrological signatures

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Published Papers (11 papers)

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Editorial

Jump to: Research

7 pages, 903 KiB

Open AccessEditorial

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

by Yongqiang Zhang, Dongryeol Ryu and Donghai Zheng

Remote Sens. 2021, 13(19), 3865; https://doi.org/10.3390/rs13193865 - 27 Sep 2021

Cited by 4 | Viewed by 2716

Abstract

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions, on how to make the most out of the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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Research

Jump to: Editorial

22 pages, 5635 KiB

Open AccessArticle

Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records

by Zhihua Zhu, Yueying Yang, Yanpeng Cai and Zhifeng Yang

Remote Sens. 2021, 13(11), 2204; https://doi.org/10.3390/rs13112204 - 4 Jun 2021

Cited by 4 | Viewed by 2945

Abstract

Analyzing flooding in urban areas is a great challenge due to the lack of long-term rainfall records. This study hereby seeks to propose a modeling framework for urban flood analysis in ungauged drainage basins. A platform called “RainyDay” combined with a nine-year record of hourly, 0.1° remotely sensed rainfall data are used to generate extreme rainfall events. These events are used as inputs to a hydrological model. The comprehensive characteristics of urban flooding are reflected through the projection pursuit method. We simulate runoff for different return periods for a typical urban drainage basin. The combination of RainyDay and short-record remotely sensed rainfall can reproduce recent observed rainfall frequencies, which are relatively close to the design rainfall calculated by the intensity-duration-frequency formula. More specifically, the design rainfall is closer at high (higher than 20-yr) return period or long duration (longer than 6 h). Contrasting with the flood-simulated results under different return periods, RainyDay-based estimates may underestimate the flood characteristics under low return period or short duration scenarios, but they can reflect the characteristics with increasing duration or return period. The proposed modeling framework provides an alternative way to estimate the ensemble spread of rainfall and flood estimates rather than a single estimate value. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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19 pages, 6519 KiB

Open AccessArticle

Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

by Yuchen Liu, Jia Liu, Chuanzhe Li, Fuliang Yu and Wei Wang

Remote Sens. 2021, 13(11), 2103; https://doi.org/10.3390/rs13112103 - 27 May 2021

Cited by 6 | Viewed by 2712

Abstract

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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15 pages, 6267 KiB

Open AccessArticle

Estimating Urban Evapotranspiration at 10m Resolution Using Vegetation Information from Sentinel-2: A Case Study for the Beijing Sponge City

by Xuanze Zhang and Peilin Song

Remote Sens. 2021, 13(11), 2048; https://doi.org/10.3390/rs13112048 - 22 May 2021

Cited by 15 | Viewed by 3617

Abstract

Estimating accurately evapotranspiration (ET) in urban ecosystems is difficult due to the complex surface conditions and a lack of fine measurement of vegetation dynamics. To overcome such difficulties using recent developments of remote sensing technology, we estimate leaf area index (LAI) from Sentinel-2-based Normalized Difference Vegetation Index (NDVI) using the NDVI–LAI nonlinear relationship. By applying Sentinel-2-based LAI and land cover classification (LCC) to a carbon-water coupling model (PML-V2.1) with surface meteorological forcing data as input, we, for the first time, estimate monthly ET at 10m × 10m resolution for the Beijing Sponge City. Results show that for the whole sponge city during June 2018, the LAI, ET and gross primary productivity (GPP) are 0.83 m² m⁻², 1.6 mm d⁻¹ and 2.8 gC m⁻² d⁻¹, respectively. For different LCCs, lakes and rivers have the highest ET (≥8 mm d⁻¹), followed by mixed forests and croplands (ET is 4–6 mm d⁻¹ and LAI is 2–3 m² m⁻²) with dominant contribution (>80%) from plant transpiration, while grasslands (2–4 mm d⁻¹) have 50–70% from transpiration due to smaller LAI (1~2 m² m⁻²). The impervious surfaces occupying ~60% of the sponge city area, have the smallest ET (<2.0 mm d⁻¹) in which interception evaporation by impervious surface contributes 20–30%, and transpiration from greenbelts (0.5–1.0 m² m⁻² of LAI) contributes 40–50%. These findings can provide a valuable scientific basis for policymaking and urban water use planning. This study proposes a Sentinel-2-based technology for estimating ET as a feasible framework to evaluate city-level hydrological dynamics in urban ecosystems. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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23 pages, 3162 KiB

Open AccessArticle

Impending Hydrological Regime of Lhasa River as Subjected to Hydraulic Interventions—A SWAT Model Manifestation

by Muhammad Yasir, Tiesong Hu and Samreen Abdul Hakeem

Remote Sens. 2021, 13(7), 1382; https://doi.org/10.3390/rs13071382 - 3 Apr 2021

Cited by 9 | Viewed by 3011

Abstract

The damming of rivers has altered their hydrological regimes. The current study evaluated the impacts of major hydrological interventions of the Zhikong and Pangduo hydropower dams on the Lhasa River, which was exposed in the form of break and change points during the double-mass curve analysis. The coefficient of variability (CV) for the hydro-meteorological variables revealed an enhanced climate change phenomena in the Lhasa River Basin (LRB), where the Lhasa River (LR) discharge varied at a stupendous magnitude from 2000 to 2016. The Mann–Kendall trend and Sen’s slope estimator supported aggravated hydro-meteorological changes in LRB, as the rainfall and LR discharge were found to have been significantly decreasing while temperature was increasing from 2000 to 2016. The Sen’s slope had a largest decrease for LR discharge in relation to the rainfall and temperature, revealing that along with climatic phenomena, additional phenomena are controlling the hydrological regime of the LR. Reservoir functioning in the LR is altering the LR discharge. The Soil and Water Assessment Tool (SWAT) modeling of LR discharge under the reservoir’s influence performed well in terms of coefficient of determination (R²), Nash–Sutcliffe coefficient (NSE), and percent bias (PBIAS). Thus, simulation-based LR discharge could substitute observed LR discharge to help with hydrological data scarcity stress in the LRB. The simulated–observed approach was used to predict future LR discharge for the time span of 2017–2025 using a seasonal AutoRegressive Integrated Moving Average (ARIMA) model. The predicted simulation-based and observation-based discharge were closely correlated and found to decrease from 2017 to 2025. This calls for an efficient water resource planning and management policy for the area. The findings of this study can be applied in similar catchments. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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19 pages, 3920 KiB

Open AccessArticle

Analyzing the Suitability of Remotely Sensed ET for Calibrating a Watershed Model of a Mediterranean Montane Forest

by Steven M. Jepsen, Thomas C. Harmon and Bin Guan

Remote Sens. 2021, 13(7), 1258; https://doi.org/10.3390/rs13071258 - 26 Mar 2021

Cited by 9 | Viewed by 2684

Abstract

The ability to spatially characterize runoff generation and forest health depends partly on the accuracy and resolution of evapotranspiration (ET) simulated by numerical models. A possible strategy to increase the accuracy and resolution of numerically modeled ET is the use of remotely sensed ET products as an observational basis for parameter estimation (model calibration) of those numerical models. However, the extent to which that calibration strategy leads to a realistic representation of ET, relative to ground conditions, is not well understood. We examined this by comparing the spatiotemporal accuracy of ET from a remote sensing product, MODIS MOD16A2, to that from a watershed model (SWAT) calibrated to flow measured at an outlet streamgage. We examined this in the upper Kings River watershed (3999 km²) of California’s Sierra Nevada, a snow-influenced watershed in a Mediterranean climate. We assessed ET accuracies against observations from three eddy-covariance flux towers at elevations of 1160–2700 m. The accuracy of ET from the stream-calibrated watershed model surpassed that of MODIS in terms of Nash-Sutcliffe efficiency (+0.36 versus −0.43) and error in elevational trend (+7.7% versus +81%). These results indicate that for this particular experiment, an outlet streamgage would provide a more effective observational basis than remotely sensed ET product for watershed-model parameter estimation. Based on analysis of ET-weather relationships, the relatively large errors we found in MODIS ET may be related to weather-based corrections to water limitation not representative of the hydrology of this snow-influenced, Mediterranean-climate area. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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21 pages, 6917 KiB

Open AccessArticle

Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity

by Wei Wang, Jia Liu, Chuanzhe Li, Yuchen Liu and Fuliang Yu

Remote Sens. 2021, 13(4), 595; https://doi.org/10.3390/rs13040595 - 7 Feb 2021

Cited by 12 | Viewed by 3079

Abstract

The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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17 pages, 6922 KiB

Open AccessArticle

The Effect of Water Transfer during Non-growing Season on the Wetland Ecosystem via Surface and Groundwater Interactions in Arid Northwestern China

by Shufeng Qiao, Rui Ma, Ziyong Sun, Mengyan Ge, Jianwei Bu, Junyou Wang, Zheng Wang and Han Nie

Remote Sens. 2020, 12(16), 2516; https://doi.org/10.3390/rs12162516 - 5 Aug 2020

Cited by 14 | Viewed by 3431

Abstract

The use of ecological water transfer to maintain the ecological environment in arid or semiarid regions has become an important means of human intervention to alleviate vegetation ecosystem degradation in arid and semiarid areas. The water transfer to downstream in a catchment is often carried out during the non-growing season, due to the competitive water use between the upper and middle reaches and lower reaches of rivers. However, the impacts and mechanism of artificial water transfer on vegetation and wetland ecosystem restoration have not been thoroughly investigated, especially in northwest China. Taking the Qingtu Lake wetland system in the lower reaches of the Shiyang River Catchment as the study area, this study analyzed the spatial and temporal distribution surface area of Qingtu Lake and the surrounding vegetation coverage before and after water transfer, by interpreting remote sensing data, the variation of water content in the vadose zone, and the groundwater level by obtaining field monitoring data, as well as the correlation between the water body area of Qingtu Lake and the highest vegetation coverage area in the following year. The conclusion is that there is a positive correlation between the water body area of Qingtu Lake in autumn and the vegetation coverage in each fractional vegetation coverage (FVC) interval in the next summer, especially in terms of the FVC of 30–50%. The groundwater level and soil water content increase after water transfer and remain relatively high for the following months, which suggests that transferred water from upstream can be stored as groundwater or soil water in the subsurface through surface water and subsurface water interaction. These water sources can provide water for the vegetation growth the next spring, or support plants in the summer. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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15 pages, 6723 KiB

Open AccessArticle

Application of the GPM-IMERG Products in Flash Flood Warning: A Case Study in Yunnan, China

by Meihong Ma, Huixiao Wang, Pengfei Jia, Guoqiang Tang, Dacheng Wang, Ziqiang Ma and Haiming Yan

Remote Sens. 2020, 12(12), 1954; https://doi.org/10.3390/rs12121954 - 17 Jun 2020

Cited by 47 | Viewed by 3945

Abstract

NASA’s Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is a major source of precipitation data, having a larger coverage, higher precision, and a higher spatiotemporal resolution than previous products, such as the Tropical Rainfall Measuring Mission (TRMM). However, there rarely has been an application of IMERG products in flash flood warnings. Taking Yunnan Province as the typical study area, this study first evaluated the accuracy of the near-real-time IMERG Early run product (IMERG-E) and the post-real-time IMERG Final run product (IMERG-F) with a 6-hourly temporal resolution. Then the performance of the two products was analyzed with the improved Rainfall Triggering Index (RTI) in the flash flood warning. Results show that (1) IMERG-F presents acceptable accuracy over the study area, with a relatively high hourly correlation coefficient of 0.46 and relative bias of 23.33% on the grid, which performs better than IMERG-E; and (2) when the RTI model is calibrated with the gauge data, the IMERG-F results matched well with the gauge data, indicating that it is viable to use MERG-F in flash flood warnings. However, as the flash flood occurrence increases, both gauge and IMERG-F data capture fewer flash flood events, and IMERG-F overestimates actual precipitation. Nevertheless, IMERG-F can capture more flood events than IMERG-E and can contribute to improving the accuracy of the flash flood warnings in Yunnan Province and other flood-prone areas. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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22 pages, 18340 KiB

Open AccessArticle

Performance of an Unmanned Aerial Vehicle (UAV) in Calculating the Flood Peak Discharge of Ephemeral Rivers Combined with the Incipient Motion of Moving Stones in Arid Ungauged Regions

by Shengtian Yang, Chaojun Li, Hezhen Lou, Pengfei Wang, Juan Wang and Xiaoyu Ren

Remote Sens. 2020, 12(10), 1610; https://doi.org/10.3390/rs12101610 - 18 May 2020

Cited by 22 | Viewed by 2825

Abstract

Ephemeral rivers are vital to ecosystem balance and human activities as essential surface runoff, while convenient and effective ways of calculating the peak discharge of ephemeral rivers are scarce, especially in ungauged areas. In this study, a new method was proposed using an unmanned aerial vehicle (UAV) combined with the incipient motion of stones to calculate the peak discharge of ephemeral rivers in northwestern China, a typical arid ungauged region. Two field surveys were conducted in dry seasons of 2017 and 2018. Both the logarithmic and the exponential velocity distribution methods were examined when estimating critical initial velocities of moving stones. Results reveal that centimeter-level orthoimages derived from UAV data can demonstrate the movement of stones in the ephemeral river channel throughout one year. Validations with peak discharge through downstream culverts confirmed the effectiveness of the method. The exponential velocity distribution method performs better than the logarithmic method regardless of the amount of water through the two channels. The proposed method performs best in the combination of the exponential method and the river channel with evident flooding (>20 m³/s), with the relative accuracy within 10%. In contrast, in the river channel with a little flow (around 1 m³/s), the accuracies are weak because of the limited number of small moving stones found due to the current resolution of UAV data. The poor performance in the river channel with a little flow could further be improved by identifying smaller moving stones, especially using UAV data with better spatial resolution. The presented method is easy and flexible to apply with appropriate accuracy. It also has great potential for extensive applications in obtaining runoff information of ephemeral rivers in ungauged regions, especially with the quick advance of UAV technology. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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20 pages, 3243 KiB

Open AccessArticle

Rainfall Monitoring Based on Next-Generation Millimeter-Wave Backhaul Technologies in a Dense Urban Environment

by Congzheng Han, Juan Huo, Qingquan Gao, Guiyang Su and Hao Wang

Remote Sens. 2020, 12(6), 1045; https://doi.org/10.3390/rs12061045 - 24 Mar 2020

Cited by 29 | Viewed by 4770

Abstract

High-resolution and accurate rainfall monitoring is of great importance to many applications, including meteorology, hydrology, and flood monitoring. In recent years, microwave backhaul links from wireless communication networks have been suggested for rainfall monitoring purposes, complementing the existing monitoring systems. With the advances in microwave technology, new microwave backhaul solutions have been proposed and applied for 5G networks. Examples of the latest microwave technology include E-band (71–76 and 81–86 GHz) links, multi-band boosters, and line-of-sight multiple-input multiple-output (LOS-MIMO) backhaul links. They all rely on millimeter-wave (mmWave) technology, which is the fastest small-cell backhaul solution. In this paper, we will study the rain attenuation characteristics of these new microwave backhaul techniques at different mmWave frequencies and link lengths. We will also study the potential of using these new microwave solutions for rainfall monitoring. Preliminary results indicate that all the test mmWave links can be very effective for estimating the path-averaged rain rates. The correlation between the mmWave link measurement-derived rain rate and the local rain gauge is in the range of 0.8 to 0.9, showing a great potential to use these links for precipitation and flood monitoring in urban areas. Full article

(This article belongs to the Special Issue Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World)

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