Gauge Stations Research Articles

Comparative analysis of HEC-HMS and SWAT hydrological models for simulating the streamflow in sub-humid tropical region in India.

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km2. Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash-Sutcliffe Efïciency (NSE), Coefficient of Determination (R2), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions.

Hydraulic River Models From ICESat‐2 Elevation and Water Surface Slope

AbstractForecasting flood and drought events requires accurate modeling tools. Hydraulic river models are based on estimates of riverbed geometry which are traditionally collected in situ. The novel Ice, Cloud and Land Elevation Satellite 2 [ICESat‐2] lidar altimetry mission with 6 simultaneous high‐resolution laser beams provides the opportunity to define river cross‐section geometries as well as observe water surface elevation [WSE] and water surface slope spatially resolved along the river chainage. This paper describes a method to utilize terrain altimetry and water surface slope estimates to define complete river geometries from ICESat‐2 data products, using the diffusive wave approximation to calculate depth in the submerged section not penetrated by the lidar. Exemplifying the method, cross‐sections are defined for a stretch of the Mekong River. Hydrodynamic model results of the stretch are compared with ICESat‐2 WSE estimates and in situ gauging station time series. Insights in river characteristics from satellite imagery and the ICESat‐2 slope estimates allow for fine‐tuning of the cross‐sections using spatially varying Manning numbers. The final model achieves a root mean square error against the ICESat‐2 WSE of 0.676 m and average Kling‐Gupta Efficiency against gauging station time series of 0.880. The method is limited by the diffusive wave approximation resulting in inaccurate cross‐section estimates in sections with supercritical flow or significant acceleration. Errors can be identified from ICESat‐2 WSE estimates and reduced with additional cross‐sections. Combined with hydrological models, the method will allow for cross‐section definition without in situ data.

Vegetation greening accelerated hydrological drought in two-thirds of river basins over China

Although the vegetation-greening-induced water supply reduction has been widely recognized, little is known about the impacts of vegetation greening on hydrological drought. Here Standardized Water Availability Index (SWAI, based on surface water-balance), driven by evapotranspiration (ET, with vegetation dynamics), was developed to quantify the impacts of vegetation greening on hydrological drought at multiple timescales over China from 1982 to 2015. The ET with vegetation dynamics was modeled by the improved PT-JPL model, and was validated with in-situ observed data from 8 flux tower sites and 4 on-site field experiments (covered with 12 different land-cover types in China). SWAI was also validated with monthly runoff records (from 59 gauging stations over different climatic regimes in China), and proved to effectively capture the response of water surplus/deficit to vegetation dynamics. Results reveal that percentage drought-affected areas increased (increased by 2 % to 9 %) and drought intensity enhanced in two-thirds of Chinese river basins due to vegetation greening. Moreover, in nearly half of the river basins in China, drought duration increased by 0.035–0.131 months, and drought frequency increased by 0.016–0.085 months/34-year. Hydrological drought accelerated by vegetation greening mainly occurred in arid and semi-arid river basins (water-limited areas), while it was not obvious in humid and sub-humid river basins due to limited energy for ET in these areas. Our work and findings provide new insights for understanding the vegetation-induced water resource shortage and have potential applications for government and water managers to timely monitor and assess the balance between ecological restoration and water resources.

Precipitation and hydrological extremes during the warm season in response to climate change: the example from the Polish Carpathians

This study assesses the long-term variability of extreme precipitation events in the Carpathian region of Poland in relation to climate change and its potential impact on river regime. Focusing on eight catchments with undisturbed runoff regimes, the research utilized daily data from May to October between 1951 and 2022. This data included precipitation totals from 23 gauging stations, discharge values from eight water level gauging stations, and gridded precipitation data from the E-OBS dataset. Additionally, future projections for 2026–2060 under two RCP scenarios (4.5 and 8.5) were generated using five GCM-RCM model combinations from the EURO-CORDEX simulations. The study explored the relationship between precipitation and discharge, using probability techniques to identify extreme events. This study highlights the strong linkages between extreme precipitation and discharge in the region, indicating an elevated flood risk due to increased precipitation variability and volume. Despite no significant trends in the frequency of precipitation extremes being observed in the historical analysis, an increase in seasonal precipitation totals is projected, although with considerable uncertainty.

Infilling of high‐dimensional rainfall networks through multiple imputation by chained equations

AbstractAccurate precipitation records are an essential component when monitoring the climate and studying its changes. However, analysis is typically limited by the large quantities of missing values present. This article proposes two new imputation techniques for incomplete monthly data collected from a rainfall monitoring network in the Republic of Ireland from 1981 to 2010. The data considered is high‐dimensional due to the large number of over 1100 rain gauge stations present, and the methods presented are designed to handle such cases. These are Elastic‐Net Chained Equations (ENCE) and Multiple Imputation by Chained Equations with Direct use of Regularized Regression by elastic‐net (MICE DURR). Both methods predict missing data by a series of regularized regression models, where MICE DURR differs from ENCE by also using multiple imputation. Through various evaluations across different levels of missingness, ENCE and MICE DURR consistently outperformed existing imputation methods in terms of RMSE and . Moreover, they have provided the best results both seasonally and for accurately predicting extreme values. An RMSE of 14.16 and 14.17 mm per month were reported for ENCE and MICE DURR, respectively, when stations that were at least 50% complete during the study period were included. For increasingly sparser data, the imputation accuracy achieved from MICE DURR surpasses ENCE, demonstrating the efficacy of multiple imputation when handling a substantial amount of missing data. Validation metrics indicate that these methods compare very favourably to existing methods in the literature, such as those that use random forests or multiple linear regression.

Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand

Floods cause disastrous damage to the environment, economy, and humanity. Flood losses can be reduced if adequate management is implemented in the pre-disaster period. Flood hazard maps comprise disaster risk information displayed on geo-location maps and the potential flood events that occur in an area. This paper proposes a spatiotemporal flood hazard map framework to generate a flood hazard map using spatiotemporal data. The framework has three processes: (1) temporal prediction, which uses the LSTM technique to predict water levels and rainfall for the next time; (2) spatial interpolation, which uses the IDW technique to estimate values; and (3) map generation, which uses the CNN technique to predict flood events and generate flood hazard maps. The study area is Chiang Mai Province, Thailand. The generated hazard map covers 20,107 km2. There are 14 water-level telemetry stations and 16 rain gauge stations. The proposed model accurately predicts water level and rainfall, as demonstrated by the evaluation results (RMSE, MAE, and R2). The generated map has a 95.25% mean accuracy and a 97.25% mean F1-score when compared to the actual flood event. The framework enhances the accuracy and responsiveness of flood hazard maps to reduce potential losses before floods occur.

Method of Data Preprocessing on the Basis of Pulse Neural Network to Improve the Accuracy of Water Level Forecast on the Example of Ufa City of the Republic of Bashkortostan

The developed method of data preprocessing based on impulse neural network is considered. The essence of this method is to improve the quality of the initial dataset by minimising data noise. The peculiarity is to improve the accuracy of the predicted values of the time series of water levels obtained at the output of the pulse neural network. Retrospective data were obtained from hydrological posts (river gauge) and automatic stations with the help of FSUE «Centre of Register and Cadastre» from 01.01.1997 to 30.06.2023. On the example of hydrological post 76289 (Ufa) the experiment on forecasting of water levels with the help of the developed system «Flood 2.0» was carried out. The experiment proves the efficiency of the data preprocessing method developed in this study to improve the accuracy of water level forecasts.

Evaluating of the accuracy of the DTU22MDT mean dynamic topography model based on the tidal gauge data in Vietnam’s coastal areas

In this study, the accuracy of the latest mean dynamic topography model (MDT), DTU22MDT, is determined based on data collected from 14 tide gauge stations along the coast of Vietnam, from the North to the South. The process of assessing the accuracy of the DTU22MDT model is carried out carefully and precisely, involving determining the model’s height at tide gauge stations, quality checks of the data, and evaluation of the model’s accuracy. The research results indicate that, compared to the local sea surface in Vietnam, the DTU22MDT model has a higher height of approximately 0.7 meters. This model represents the global MDT with the highest accuracy in Vietnam, with a mean square error of about 9.0 cm. This information provides a basis for applying the model in scientific research and practical applications, such as constructing a national height system, planning the economic development of coastal areas, generating specialized maps, designing structures, and other activities in the maritime regions of Vietnam. The method used to evaluate the accuracy of the MDT in this study can be applied to other research areas and to different MDTs.

Improved tide level prediction model combined GA-BP neural networks and GNSS SNR data

High-precision tidal forecasting plays a crucial role in providing reliable tidal information to coastal communities, ship operators, fishermen, and government agencies. Existing tidal prediction models, such as backpropagation neural networks (BPNN) and genetic algorithm-optimized BPNN (GA-BPNN), frequently difficulty in in achieving high precision because they rely solely on past tidal variations for real-time predictions. Signal-to-Noise Ratio (SNR) data have emerged as a valuable indicator closely related to real-time sea level changes due to the widespread adoption of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. In this paper, we propose a nowcasting tide level prediction method by adding SNR data to BPNN, i.e., Improved BPNN and Improved GA-BPNN, which predicts the next momentary tide level value by using the previous 15 tidal values and real-time SNR as input factors to BPNN. The results of 100-day, 200-day and 1-year tide prediction based on Astoria and Friday Harbor tide gauge stations show that the improved model prediction accuracies are better than the traditional model. Meanwhile, among the two improved models, Improved GA-BPNN has the higher prediction accuracy and can adapt to different time lengths of tide level prediction.

Effects of episodic stream dewatering on brook trout spatial population structure

Abstract Stream dewatering is expected to become more prevalent due to climate change, and we explored the potential consequences for brook trout (Salvelinus fontinalis) within a temperate forest ecosystem in eastern North America. We estimated fish density within stream pools (n = 386) from electrofishing surveys over 10 years (2012–2021) to compare a stream that exhibits episodic dewatering (Paine Run) against a stream of similar size that remains flow‐connected (Staunton River) within Shenandoah National Park, Virginia (U.S.A.). Annual surveys encompassed fluvial distances ranging from 2.6 to 4.4 km in each stream. Mean annual fish density (fish/pool m2) was not different between streams for juvenile or adult age classes, but spatial variation in density was greater in Paine Run for both age classes of fish. Paine Run also included a greater proportion of unoccupied pools than Staunton River and exhibited stronger spatial autocorrelation in fish density among nearby pools, suggesting dispersal limitation due to surface flow fragmentation. Fish density in pools increased during years with low summer precipitation, and this effect was observed in both streams but was stronger in Paine Run than Staunton River, further indicating the importance of fish movement into pools in response to low‐flow thresholds. Our results indicate the importance of pools as ecological refuges during low‐flow conditions and that episodic dewatering may affect extirpation risks for brook trout by sequestering more fish into fewer areas. Our findings also highlight the importance of hydrological variation within stream networks because downstream river gages could not predict the observed spatial heterogeneity in fish density or pool occupancy.

Evaluating the significance of wetland representation in isotope-enabled distributed hydrologic modeling in mesoscale Precambrian shield watersheds

Evaluation of regional-scale models against both streamflow and stable isotopes of water is a relatively new and evolving area of distributed hydrological modeling. While applications of isoWATFLOOD, an isotope-enabled distributed hydrological model are increasing, evaluation of the impacts of model structural features, such as wetland connectivity, on simulation of runoff components is still limited. Wetlands are important water bodies that impact streamflow generation and its spatial variation in Precambrian shield watersheds in parts of Canada and elsewhere. This study investigates the efficiency of isoWATFLOOD in simulating streamflow and instream δ18O and the effects of model percent connected wetlands (%CW) in a mesoscale (∼12,000 km2) Precambrian Shield watershed in Northeastern Ontario, Canada. Model calibration and validation are performed using daily streamflow for five hydrometric gauging stations and biweekly to monthly instream δ18O from 11 river sampling locations from across the watershed. Five model versions with different %CW are generated, calibrated, and validated separately. Results show all models can simulate the timing and pattern of streamflow and δ18O. %CW impacts simulated streamflow during both calibration and validation periods, calibrated parameter sets and simulated internal hydrological processes. Model performance varies spatially with no single value of %CW providing best performance across sub-catchments with 40 % CW providing best mean streamflow simulations in calibration. Increasing %CW (10 % to 40 %) results in higher contribution of lower soil zone water to streamflow that markedly improves baseflow simulation. This study builds on recent isotope-enabled hydrologic simulation capacity of isoWATFLOOD, contributing new examination of model representation of wetland connectivity, a critical component of Precambrian Shield watersheds and evidence of how stable isotopes can contribute to addressing the challenge of equifinality. Results suggest the need to develop model capability for representation of spatial variation in wetland connectivity to recognize this important aspect of heterogeneity in watersheds influencing hydrologic function.

Assessing vulnerability and enhancing resilience of port systems in southeast Texas facing sea-level rise

Climate change and the associated sea level rise (SLR) are presenting newfound challenges to the port systems and coastal transportation infrastructure of southeast Texas. This paper introduces a Geographic Information Systems (GIS) based model designed to simulate inundation scenarios under various sea-level projections, aiming to assess the vulnerabilities of both port facilities and road networks. The study area encompasses a specific region within Jefferson County, southeast Texas, encompassing three major ports: Port Arthur, Beaumont, and Orange. Utilizing a high-resolution (1-m) Digital Elevation Model (DEM) derived from the 2017 LiDAR dataset, this model is integrated with NASA’s sea-level rise projections to compute the extent and volume of inundation across low, medium, and high SLR scenarios. Drawing from monthly mean sea level data spanning from 1958 to 2020, the lowest SLR projections, derived from the relative sea-level trend measured at the Sabine Pass, TX gauge station, indicate a yearly increase of 6.16 mm, with a 95% confidence interval of +/- 0.74 mm. Projections for 2050 and 2,100 show the lowest SLR at 0.17 m and 0.48 m, respectively. In contrast, the medium to high RSLR projections under the IPCC SSP3-7.0 scenario for 2050 and 2,100 stand at 0.54 m and 1.34 m, respectively. The findings reveal that, under medium to high SLR scenarios, the extent of inundated areas in the study region is expected to expand by 12.4% in 2050 and 19.9% in 2,100, compared to the lowest SLR projection. Additionally, the length of submerged roadways is predicted to increase by 6.9% in 2050 and 13.3% in 2,100, in comparison to the lowest SLR projection. It is worth noting that some margin of error may be introduced due to factors such as the width of the port area and access roads, the high-resolution DEM, and the alignment of computed inundated areas with the existing topography. Overall, the manuscript highlights the urgency of proactive planning and underscores the importance of safeguarding critical infrastructure in the context of climate change and SLR.

Estimating rainfall-runoff parameters for ungauged catchments: a case study in Turkiye

ABSTRACT Determining water yield and flood discharges in catchments is a vital aspect of hydrology. This entails considering precipitation and runoff as key hydrological parameters. Constructing infrastructure like hydroelectric plants and regulators over streams necessitates continuous, accurate flow, and meteorological observations spanning at least 25 years. However, in developing nations, economic factors often impede such observations. This study proposes a method to estimate peak rainfall and flow values for ungauged basins with varying return periods by utilizing from gauged basins and spatial variables. Flood calculations were carried out for the ungauged Rabat River basin. In this study, regional flood frequency analysis was carried out using the flow values of the flow gauging stations neighboring the basin. In addition, maximum flow values were calculated using Moscus and the DSI synthetic method. Two- and three-parameter distributions were used to estimate 50-, 100-, 200- and 500-year flood return values at stations with observation periods ranging from 15 to 64 years. Kolmogorov–Smirnov and Probability Line Correlation coefficient (Chi-square) tests were applied to check the suitability of these distributions and the most appropriate distributions were found. This yielded an estimation for the flow values of the Rabat River, indicating the method's reliability for forecasting runoff-rainfall in the ungauged basin.

Advancing freshwater science with sensor data collected by community scientists

Autonomous sensor networks providing real‐time data are growing in popularity with community scientists due to instant availability of high‐frequency data. What role does this monitoring play in watershed assessment alongside agency‐run monitoring programs? How accessible, interoperable, and reusable are the data for other researchers? We compared a community science‐led stream monitoring network—EnviroDIY—in the Delaware River Basin, in which more than 50 watershed organizations have deployed more than 100 stations monitoring temperature, electric conductivity, depth, and sometimes turbidity, with the Basin's US Geological Survey (USGS) stream gauge network. The EnviroDIY network (n = 124) complemented the USGS network (n = 102) by monitoring sites with different watershed sizes and land‐use distributions. Although data were accessible and interoperable using a web data portal, community scientists had difficulty sharing metadata that would enable data reuse outside this project and they required support analyzing these large datasets to understand threats to watershed conditions. We address those needs here with a conceptual framework for interpreting data and communicating results.

Estimation of Suspended Sediment Concentration along the Lower Brazos River Using Satellite Imagery and Machine Learning

This article focuses on developing models that estimate suspended sediment concentrations (SSCs) for the Lower Brazos River, Texas, U.S. Historical samples of SSCs from gauge stations and satellite imagery from Landsat Missions and Sentinel Mission 2 were utilized to develop models to estimate SSCs for the Lower Brazos River. The models used in this study to accomplish this goal include support vector machines (SVMs), artificial neural networks (ANNs), extreme learning machines (ELMs), and exponential relationships. In addition, flow measurements were used to develop rating curves to estimate SSCs for the Brazos River as a baseline comparison of the models that used satellite imagery to estimate SSCs. The models were evaluated using a Taylor Diagram analysis on the test data set developed for the Brazos River data. Fifteen of the models developed using satellite imagery as inputs performed with a coefficient of determination R2 above 0.69, with the three best performing models having an R2 of 0.83 to 0.85. One of the best performing models was then utilized to estimate the SSCs before, during, and after Hurricane Harvey to evaluate the impact of this storm on the sediment dynamics along the Lower Brazos River and the model’s ability to estimate SSCs.

Estimation of Catchment Parameters of Snyder's Synthetic Method for Water Resources Planning and Flood Management in Sri Lanka

Snyder's synthetic unit hydrograph method is widely recognized for synthesizing unit hydrographs. This paper illustrates a study demonstrating how to estimate catchment parameters using Snyder's synthetic method for hydro-meteorological stations maintained by the Irrigation Department and its application to Sri Lanka. Updated Snyder's Unit Hydrograph parameters, including coefficient accounting for flood wave and storage condition (Cp) and coefficient representing variations of catchment slop and storage (Ct) for 15 river gauge stations spread across the island, have been presented in this paper. The Cp values vary from 0.22 to 0.7, and the Ct values vary from 0.48 to 5.76. Updating the Snyder parameters is a long-term requirement, and these values can be used to update the previous values estimated in 1980.

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect solely with water. It is acknowledged that the ATL13 product has noise elevations of the adjacent land, resulting in biased high mean WSEs at VSs. Earlier studies have relied on human intervention or water masks to resolve this. Both approaches are unsatisfactory solutions for large river basins where the issue becomes pronounced due to many tributaries and meanders. There is no automated procedure to partition the truly representative water height from the totality of the along-track ICESAT-2 photon segments (portions of photon points along a beam) for increasing precision of the mean WSE at VSs. We have developed an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparing the ATL13-derived WSEs with direct water level observations at 10 different gauging stations on 37 different dates along the Lower Murray River, Australia. The concordance between the two datasets is significantly high and without detectable bias. In addition, we evaluated the effects of four methods for calculating the mean WSEs at VSs after auto-segmentation processing. Our results reveal that all methods perform almost equally well, with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189 m) and MAE (0.130–0.142 m). We also found that the R2, RMSE and MAE are better under the high flow condition (0.999, 0.124 and 0.111 m) than those under the normal-low flow condition (0.997, 0.208 and 0.160 m). Overall, our auto-segmentation method is an effective and efficient approach for deriving accurate mean WSEs at river VSs. It will contribute to the improvement of ICESAT-2 ATL13 altimetry data utility on rivers.

Tsunami waveform inversion using Green’s functions with advection effects: application to the 2003 Tokachi–Oki earthquake

We explored nonlinear effects within the context of tsunami waveform inversion, wherein Green's functions were linearly superimposed to estimate earthquake slips. We focused on these effects while developing a source model for the 2003 Tokachi–Oki earthquake off Hokkaido, Japan. A source model for this earthquake was developed based on linear tsunami waveform inversion using Green’s functions and tsunami waveforms observed at tide gauge stations. Subsequently, tsunami waveforms from the source were simulated at the stations using nonlinear long-wave theory and compared with those estimated by inversion. The comparisons demonstrated that the waveforms had a non-negligible discrepancy that was attributed to advection effects, even for the primary wave used in the inversion at the two stations. This result strongly suggests that advection effects should be considered in the source modeling of the 2003 earthquake based on tsunami waveforms observed by tide gauges. Based on these results, a new tsunami waveform inversion technique that incorporates linearly approximated advection effects and maintain the framework of linear tsunami waveform inversion using Green’s functions is proposed and applied. The proposed method successfully mimicked the advection effects during the 2003 tsunami, reproduced better tsunami waveforms, and developed a source model for the 2003 earthquake using these effects. The peak slip amount and seismic moment were greater in the source model with advection effects than those without the effects. This finding suggests that the values in the source models developed for other earthquake events without considering these effects may have been underestimated.Graphical abstract

Hydrological Variability in the El Cielo Biosphere Reserve, Mexico: A Watershed-Scale Analysis Using Tree-Ring Records

The El Cielo Biosphere Reserve (CBR) stands as a vital forested region in eastern Mexico due to its high biodiversity in flora and fauna and provision of environmental services. This study established a network of 10 ring-width chronologies of different species within the CBR and adjacent watersheds. The objective was to analyze their climatic response and reconstruct the seasonal streamflow contribution of each sub-basin to the main stream, utilizing data from a gauge network of eight hydrological stations located at strategic locations of the CBR. With chronologies ranging from 116 to 564 years, most exhibited association with the accumulated streamflow between January and June. Based on the adjusted R2, Akaike Information Criteria, and Variance Inflation Factor, the stepwise regression procedure was selected among different statistical methods for developing the reconstruction model. In spite of differences in the seasonal reconstructed periods, all the species showed potential to develop hydrological reconstructions as indicated by their common response to streamflow variability, as occurred in the wet years of 1976, 1993, 2000, and 2008, and dry years of 1980, 1982, 1996, and 2011. It was found that the response of the chronologies to gauge records increased as a function of the chronologies’ interseries correlation, average mean sensitivity, and distance of the tree-ring series to the gauge station. Streamflow reconstructions at the sub-basin level allowed a better understanding of the hydroclimatic variability characterizing the CBR, but also suggested the need to increase the network of chronologies for some particular sub-basins lacking tree-ring series to improve the reconstructed models.

Asymmetry response of storm surges along the eastern coast of the Taiwan Strait

Spatiotemporal variation of storm surges in the Taiwan Strait (TWS) is studied using water level datasets from 14 tidal gauge stations located in the TWS from summer to fall of 2016. The effects of bathymetry on storm surges and tropical cyclone (TC)-induced continental shelf wave (CSW) are explored. By comparing water level response along the east coast of TWS, it is found that storm surges are asymmetric on the north and south sides of the Zhan-Yun Ridge (ZYR), regardless of the different categories and tracks of TCs passing by. Observations indicate that the ZYR could modulate the storm surges and the CSW propagation; ZYR can not only amplify the storm surges that generally peak around the ZYR, but also block the CSW propagation by dramatically dissipating its kinetic energy as revealed by the dispersion relation for the first mode CSW. Moreover, local wind work and the remote forcing, which are induced by TCs and cold fronts, respectively, can also modulate the synoptic variations of water level in the TWS.