Total Discharge Estimation in the Korean Peninsula Using Multi-Satellite Products

2018

Sensors

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Consistent observations of lakes and reservoirs that comprise the majority of surface freshwater globally are limited, especially in Africa where water bodies are exposed to unfavorable climatic conditions and human interactions. Publicly available satellite imagery has increased the ability to monitor water bodies of various sizes without much financial hassle. Landsat 7 and 8 images were used in this study to estimate area changes around Lake Chad. The Automated Water Extraction Index (AWEI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) were compared for the remote sensing retrieval process of surface water. Otsu threshold method was used to separate water from non-water features. With an overall accuracy of ~96% and an inter-rater agreement (kappa coefficient) of 0.91, the MNDWI was a better indicator for mapping recent area changes in Lake Chad and was used to estimate the lake’s area changes from 2003–2016. Extracted monthly areas showed an increasing trend and ranged between ~1242 km2 and 2231 km2 indicating high variability within the 13-year period, 2003–2016. In addition, we combined Landsat measurements with Total Water Storage Anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) satellites. This combination is well matched with our estimated surface area trends. This work not only demonstrates the importance of remote sensing in sparsely gauged developing countries, it also suggests the use of freely available high-quality imagery data to address existing lake crisis.

Section: Methodsmentioning

confidence: 99%

“…To reduce the noise caused by orbit resonance during the determination of the Stokes coefficients and aliasing errors, the data set was truncated at 60 degrees and smoothed with a Gaussian filter (300 km) [65]. It is important to restore the signals lost during data processing using an appropriate scaling factor.…”

Section: Methodsmentioning

confidence: 99%

Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE

Buma

2018

Sensors

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“…Particularly, the TMPA3B42 and TMPA3B43 products provide precipitation data by merging multi-satellite (SSMI, SSMIS, MHS, AMSU-B, and AMSR E) precipitation data and global observed precipitation data (Global Precipitation Climatology Project; GPCP; [24]). TMPA3B42 and 3B43 precipitation data for the Korean Peninsula has been validated in previous studies [25,26]. We applied TMPA3B43 (ver.…”

Section: Grace Satellitementioning

confidence: 99%

Spatio-Temporal Groundwater Drought Monitoring Using Multi-Satellite Data Based on an Artificial Neural Network

2019

Water

Drought is a complex phenomenon caused by lack of precipitation that affects water resources and human society. Groundwater drought is difficult to assess due to its complexity and the lack of spatio-temporal groundwater observations. In this study, we present an approach to evaluate groundwater drought based on relatively high spatial resolution groundwater storage change data. We developed an artificial neural network (ANN) that employed satellite data (Gravity Recovery and Climate Experiment (GRACE) and Tropical Rainfall Measuring Mission (TRMM)) as well as Global Land Data Assimilation System (GLDAS) models. The Standardized Groundwater Level Index (SGI) was calculated by normalizing ANN-predicted groundwater storage changes from 2003 to 2015 across South Korea. The ANN-predicted 25 km groundwater storage changes correlated well with both the in situ and the water balance equation (WBE)-estimated groundwater storage changes, with mean correlation coefficients of 0.87 and 0.64, respectively. The Standardized Precipitation–Evapotranspiration Index (SPEI), having an accumulation time of 1–6 months, and the Palmer Drought Severity Index (PDSI) were used to validate the SGI. The results showed that the SGI had a pattern similar to that of SPEI-1 and SPEI-2 (1- and 2-month accumulation periods, respectively), and PDSI. However, the SGI performance fluctuated slightly due to its relatively short study period (13 years) as compared to SPEI and PDSI (more than 30 years). The SGI, which was developed using a new approach in this study, captured the characteristics of groundwater drought, thus presenting a framework for the assessment of these characteristics.

“…These studies have demonstrated the applicability of the GRACE-based water balance method to various regions of the world. For instance, the approach for evaluating the total discharge of the Korean Peninsula using multi-satellite data from GRACE, the MODIS (Moderate Resolution Imaging Spectroradiometer), and the TRMM (tropical rainfall measuring mission) satellites were examined by Seo and Lee [11]. The three different satellite-based total discharges showed good relationships with the results of the global land data assimilation system (GLDAS), the precipitation-runoff modeling system (PRMS), and with the streamflow gauge measurements.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Multi-Satellite Data and Artificial Neural Network for Predicting Total Discharge

2020

Remote Sensing

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As research on the use of satellites in combination with previous hydrological monitoring techniques increases, interest in the application of the machine-learning approach to the prediction of hydrological variables is growing. Ground-based measurements are often limited due to the difficulties in measuring spatiotemporal variations, especially in ungauged areas. In addition, there are no existing satellites capable of measuring total discharge directly. In this study, Artificial neural network (ANN) machine-learning approaches are examined for the prediction of 0.25° total discharge data over the Korean Peninsula using the data fusion of multi-satellites, reanalysis data, and ground-based observations. Terrestrial water storage changes (TWSC) of the Gravity Recovery and Climate Experiment (GRACE) satellite, precipitation of the tropical rainfall measuring mission (TRMM), and soil moisture storage and average temperature of the global land data assimilation system (GLDAS) models are used as ANN model input data. The results demonstrate the relatively good performance of the ANN approach for predicting the total discharge in terms of the correlation coefficient (r = 0.65–0.95), maximum absolute error (MAE = 13.28–20.35 mm/month), root mean square error (RMSE = 22.56–34.77 mm/month), and Nash-Sutcliff efficiency (NSE = 0.42–0.90). The precipitation is identified as the most influential input parameter through a sensitivity analysis. Overall, the ANN-predicted total discharge shows similar spatial patterns to those from other methods, while GLDAS underestimates the total discharge with a smaller dynamic range than the other models. Thus, the potential of the ANN approach described herein shows promise for predicting the total discharge based on the data fusion of multi-satellites, reanalysis data, and ground-based observations.