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Study region: Tsengwen Reservoir, Taiwan. Study focus: Water level (WL) and water volume (WV) are important indicators for analyzing surface water resources. Satellite remote sensing enables continuous monitoring of inland water bodies in human-inaccessible areas. We integrate Landsat imagery and satellite altimetry to derive long-term (2003–2020) WL and WV variations of Tsengwen Reservoir. First, water area (WA) was extracted from Landsat imagery by Modified Normalized Difference Water Index method and a second-order regression model is proposed to recover the entire WA from cloud-covered images to enhance the data usage. Then, WAs and WLs provided from satellite altimetry are utilized to build a linear regression model which is used to transfer WA into WL. Finally, WV was computed based on the WA and WL. New hydrological insights for the region: Results showed that the usage rate of Landsat-8 imagery utilized for conversion from WA to WL can be increased from 23% to 43%. Moreover, the root-mean-square error of the difference of WLs between the estimates and a local gauge is 2.95–5.56 m, with correlation coefficients (CC) of 0.93–0.99. In addition, the derived WV variations and ground truth showed a good agreement with CC in 0.88–0.97. The results indicated that the integration of multi-source remote sensing technologies can effectively provide long-term hydrological parameters to assist administrative agencies with an appropriate plan for water resources management.

Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry are suitable for the precise measurement of terrestrial water storage (TWS) and lake water level variations from space. In this study, two GRACE solutions, namely, spherical harmonics (SH) and mascon (MSC), are utilized with the Global Land Data Assimilation System (GLDAS) model to estimate the spatial and temporal variations of TWS in the Upper Indus Basin (UIB) for the study period of January 2003 to December 2016. The TWS estimated by SH, MSC, and the GLDAS model are consistent and generally show negative trends of -4.47 ± 0.38 mm/year, -4.81 ± 0.49 mm/year, and -3.77 ± 0.46 mm/year, respectively. Moreover, we use the GLDAS model data to understand the roles of variations in land surface state variables (snow water equivalent (SWE), soil moisture, and canopy water storage) in enhancing or dissipating the TWS in the region. Results indicate that SWE, which has a significant contribution to GRACE TWS variability, is an important parameter. Spearman's rank correlations are calculated to demonstrate the relationship of the GLDAS land surface state variables and the GRACE signals. A highly positive correlation between SWE with TWS is estimated by SH and MSC as 0.691 and 0.649, respectively, indicating that the TWS signal is mainly reliant on snow water in the study region. The ground water storages estimated by SH and MSC solutions are nearly stable with slight increasing trends of 0.63 ± 0.48 mm/year and 0.29 ± 0.51 mm/year, respectively. We also take advantage of the potential of satellite altimetry in measuring lake water level variations, and our result indicates that Cryosat-2 SARin mode altimetry data can be used in estimating small water bodies accurately in the high mountainous region of the UIB. Moreover, the climate indices data of El-Niño Southern Oscillation and Pacific Decadal Oscillation are analyzed to determine the influence of pacific climatic variability on TWS.

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

The Pacific island countries are particularly vulnerable to the effects of global warming including more frequent and intense natural disasters. Seawater inundation, one of the most serious disasters, could damage human property and life. Regional sea level rise, highest astronomic tide, vertical land motions, and extreme sea level could result in episodic, recurrent, or permanent coastal inundation. Therefore, assessing potential flooding areas is a critical task for coastal management plans. In this study, a simulation of the static flooding situation in the southwest coast of Taiwan (Tainan city) at the end of this century was conducted by using a combination of the Taiwan Digital Elevation Model (DEM), regional sea level changes reconstructed by tide gauge and altimetry data, vertical land deformation derived from leveling and GPS data, and ocean tide models. In addition, the extreme sea level situation, which typically results from high water on a spring tide and a storm surge, was also evaluated by the joint probability method using tide gauge records. To analyze the possible static flood risk and avoid overestimation of inundation areas, a region-based image segmentation method was employed in the estimated future topographic data to generate the flood risk map. In addition, an extreme sea level situation, which typically results from high water on a spring tide and a storm surge, was also evaluated by the joint probability method using tide gauge records. Results showed that the range of inundation depth around the Tainan area is 0–8 m with a mean value of 4 m. In addition, most of the inundation areas are agricultural land use (60% of total inundation area of Tainan), and two important international wetlands, 88.5% of Zengwun Estuary Wetlands and 99.5% of Sihcao Wetlands (the important Black-faced Spoonbills Refuge) will disappear under the combined situation. The risk assessment of flooding areas is potentially useful for coastal ocean and land management to develop appropriate adaptation policies for preventing disasters resulting from global climate change.

Understanding spatial and temporal changes of seasonal sea level cycles is important because of direct influence on coastal systems. The annual sea level cycle is substantially larger than semi-annual cycle in most parts of the ocean. Ensemble empirical mode decomposition (EEMD) method has been widely used to study tidal component, long-term sea level rise, and decadal sea level variation. In this work, EEMD is used to analyze the observed monthly sea level anomalies and detect annual cycle characteristics. Considering that the variations of the annual sea level variation in the Northeast Pacific Ocean are poorly studied, the trend and characteristics of annual sea level amplitudes and related mechanisms in the North Pacific Ocean are investigated using long-term tide gauge records covering 1950–2016. The average annual amplitude of coastal sea level exhibits interannual-to-decadal variability within the range of 14–220 mm. The largest value of ~174 mm is observed in the west coast of South China Sea. In the other coastal regions of North Pacific Ocean, the mean annual amplitude is relatively low between 77 and 124 mm for the western coast and 84 and 87 mm for the eastern coast. The estimated trend values for annual sea level amplitudes in the western coastal areas of South China Sea and Northeast Pacific Ocean have statistically decreased over 1952–2014 with a range of −0.77 mm·yr−1 to −0.11 mm·yr−1. Our results suggested that the decreasing annual amplitude in the west coast of South China Sea is in good agreement with the annual mean wind stress associated with the Pacific Decadal Oscillation (PDO). This wind phenomenon also explains the temporal variations of annual sea level amplitude in Northeast Pacific Ocean, especially the high correlations since 1980 (R = 0.61−0.72).

This study successfully uses satellite altimetry, including Topex/Poseidon and Jason-2, retrieved by novel retrackers to monitor vertical land motions in Southwestern Taiwan. Satellite altimetry was originally designed to measure open oceans, so waveform retracking should be applied to overcome the complex waveforms reflected from lands. Modified threshold and improved subwaveform threshold retrackers were used in the study to improve the accuracy of altimetric land surface heights (LSHs) in Southwestern Taiwan. Results indicate that the vertical motion rates derived from both retrackers coincide with those calculated by 1843 precise leveling points, with a correlation coefficient of 0.96 and mean differences of 0.43 and 0.52 cm/yr (standard deviations: 0.61 and 0.69 cm/yr). In addition, wet troposphere delay by precise point positioning with the use of Global Navigation Satellite System data was employed to evaluate the impact of the delay on the estimates of vertical motion rates compared with that traditionally derived from the European Center for Medium-Range Weather Forecasts model when the microwave radiometer is non-functional over lands. The accuracies of retracked altimetric land motion rates corrected by wet troposphere delays derived from both models show no remarkable differences in the Tuku and Yuanchang areas because the accuracy of retracked altimetric LSHs is significantly worse than that of wet troposphere delays.

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush⁻Karakoram⁻Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann⁻Kendall test and Sen’s slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995⁻2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of −198.36 km2/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is −0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region.

The Taiwanese government has established a complete tide gauge network along the coastline for accurate sea-level monitoring. In this study, we analyze several factors impacting the determination of absolute or geocentric sea-level trends—including ocean tides, inverted barometer effect, datum shift, and vertical land motion—using tide gauge records near Taiwan, from 1993–2015. The results show that datum shifts and vertical land motion have a significant impact on sea-level trends with a respective average contribution of 7.3 and 8.0 mm/yr, whereas ocean tides and inverted barometer effects have a relatively minor impact, representing 9% and 14% of the observed trend, respectively. These results indicate that datum shifts and vertical land motion effects have to be removed in the tide gauge records for accurate sea-level estimates. Meanwhile, the estimated land motions show that the southwestern plain has larger subsidence rates, for example, the Boziliao, Dongshi, and Wengang tide gauge stations exhibit a rate of 24–31 mm/yr as a result of groundwater pumping. We find that the absolute sea-level trends around Taiwan derived from tide gauges or satellite altimetry agree well with each other, and are estimated to be 2.2 mm/yr for 1993–2015, which is significantly slower than the global average sea-level rise trend of 3.2 mm/yr from satellite altimeters. Finally, a recent hiatus in sea-level rise in this region exhibits good agreement with the interannual and decadal variabilities associated with the El Niño-Southern Oscillation and Pacific Decadal Oscillation.

Spherical harmonics (SH) and mascon solutions are the two most common types of solutions for Gravity Recovery and Climate Experiment (GRACE) mass flux observations. However, SH signals are degraded by measurement and leakage errors. Mascon solutions (the Jet Propulsion Laboratory (JPL) release, herein) exhibit weakened signals at submascon resolutions. Both solutions require a scale factor examined by the CLM4.0 model to obtain the actual water storage signal. The Slepian localization method can avoid the SH leakage errors when applied to the basin scale. In this study, we estimate SH errors and scale factors for African hydrological regimes. Then, terrestrial water storage (TWS) in Africa is determined based on Slepian localization and compared with JPL-mascon and SH solutions. The three TWS estimates show good agreement for the TWS of large-sized and humid regimes but present discrepancies for the TWS of medium and small-sized regimes. Slepian localization is an effective method for deriving the TWS of arid zones. The TWS behavior in African regimes and its spatiotemporal variations are then examined. The negative TWS trends in the lower Nile and Sahara at −1.08 and −6.92 Gt/year, respectively, are higher than those previously reported.

In this study, sea level variation observed by a 1-Hz Global Positioning System (GPS) buoy system is verified by comparing with tide gauge records and is decomposed to reveal high-frequency signals that cannot be detected from 6-minute tide gauge records. Compared to tide gauges traditionally used to monitor sea level changes and affected by land motion, GPS buoys provide high-frequency geocentric measurements of sea level variations. Data from five GPS buoy campaigns near a tide gauge at Anping, Tainan, Taiwan, were processed using the Precise Point Positioning (PPP) technique with four different satellite orbit products from the International GNSS Service (IGS). The GPS buoy data were also processed by a differential GPS (DGPS) method that needs an additional GPS receiver as a reference station and the accuracy of the solution depends on the baseline length. The computation shows the average Root Mean Square Error (RMSE) difference of the GPS buoy using DGPS and tide gauge records is around 3 - 5 cm. When using the aforementioned IGS orbit products for the buoy derived by PPP, its average RMSE differences are 5 - 8 cm, 8 - 13 cm, decimeter level, and decimeter-meter level, respectively, so the accuracy of the solution derived by PPP highly depends on the accuracy of IGS orbit products. Therefore, the result indicates that the accuracy of a GPS buoy using PPP has the potential to measure the sea surface variations to several cm. Finally, high-frequency sea level signals with periods of a few seconds to a day can be successfully detected in GPS buoy observations using the Ensemble Empirical Mode Decomposition (EMD) method and are identified as waves, meteotsunamis, and tides.

Spherical harmonics (SH) and mascon solutions are the two most common types of solutions for Gravity Recovery and Climate Experiment (GRACE) mass flux observations. However, SH signals are degraded by measurement and leakage errors. Mascon solutions (the Jet Propulsion Laboratory (JPL) release, herein) exhibit weakened signals at submascon resolutions. Both solutions require a scale factor examined by the CLM4.0 model to obtain the actual water storage signal. The Slepian localization method can avoid the SH leakage errors when applied to the basin scale. In this study, we estimate SH errors and scale factors for African hydrological regimes. Then, terrestrial water storage (TWS) in Africa is determined based on Slepian localization and compared with JPL-mascon and SH solutions. The three TWS estimates show good agreement for the TWS of large-sized and humid regimes but present discrepancies for the TWS of medium and small-sized regimes. Slepian localization is an effective method for deriving the TWS of arid zones. The TWS behavior in African regimes and its spatiotemporal variations are then examined. The negative TWS trends in the lower Nile and Sahara at −1.08 and −6.92 Gt/year, respectively, are higher than those previously reported. [ABSTRACT FROM AUTHOR]