Gauge Stations Research Articles

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.

Magnetogravimetric study on the Scotia Plate, in the South Atlantic Ocean for the characterization of tsunamis

The marine and coastal environments of the Scotia Sea regions in the Southern Atlantic Ocean and Antarctica are vulnerable to the potentially disastrous effects of seismic activity along the Scotia Arc. This paper presents a magnetogravimetric study of the Scotia Plate for tsunami characterization. The influence of earthquakes on the Geomagnetic Field (GMF) is investigated using data from INTERMAGNET network observatories. A tectonic model is evaluated using gravity data from NOAA and seismic refraction data from Lamont-Doherty Earth Observatory. The study also assesses the impact on water level (WL) measured at Intergovernmental Oceanographic Commission (IOC) tide gauge stations. Cross Wavelet Transform (XWT) is applied, and a frequency analysis of the GMF is conducted to identify specific frequencies during seismic events. A 2D tectonic model is constructed for the North Scotia Ridge using gravimetric and seismic data to characterize structural boundaries that may be activated during seismic events. Water level records collected from 6 tide gauge stations in the region are filtered and analyzed to identify tsunamis at each station. The results reveal anomalous frequencies in the frequency analysis of the horizontal component of the GMF during the November 25, 2013 earthquake, with high data correlation from different observatories in the study area for periods of 0.5 and 1 hour. Gravimetric modeling delineates faults activated during seismic activity and edges of structures potentially activated due to the transcurrent and compressional nature of the margin. WL anomalies up to 1.30 m are obtained following earthquakes with a magnitude greater than 8. The propagation speed in the study area averaged 460 km/h, consistent with the expected speed for those depths, except for Puerto Argentino, which exceeded them in 50%.

Generation of High‐Resolution Water Surface Slopes From Multi‐Mission Satellite Altimetry

AbstractFor nearly three decades, satellite radar altimetry has provided measurements of the water surface elevation (WSE) of rivers. These observations can be used to calculate the water surface slope (WSS), which is an essential parameter for estimating flow velocity and river discharge. In this study, we calculate a non time‐varying high‐resolution WSS of 11 Polish rivers based on multi‐mission altimetry observations from 11 satellites in the period from 1994 to 2023. The proposed approach is based on a weighted least squares adjustment with an additional Laplace condition and an a priori gradient condition. The processing is divided into river sections not interrupted by dams and reservoirs. After proper determination of the WSE for each river kilometer (bin), the WSS between adjacent bins is calculated. To assess the accuracy of the estimated WSS, it is compared with slopes between gauge stations, which are referenced to a common vertical datum. Such gauge stations are available for 8 studied rivers. The root mean squared error (RMSE) ranges from 4 mm/km to 77 mm/km, with an average of 27 mm/km. However, the mean RMSE decreases to 11 mm/km when the 2 mountain rivers are excluded. The WSS accuracies are also compared with slope data sets based on digital elevation models, ICESat‐2 altimetry, and lidar. For 6 rivers the estimated WSS shows the highest accuracy. The improvement was particularly significant for mountain rivers. The proposed approach allows an accurate, non time‐varying high‐resolution WSS even for small and medium‐sized rivers and can be applied to almost any river worldwide.

Lake surface and downstream river temperature response to the retreat of a lake‐terminating glacier

AbstractGlacier contributions to streamflow moderate summer water temperatures and help keep rivers cool during periods of hot, dry weather. As glaciers retreat, the length of channel exposed to solar radiation above a given point of interest would increase, which should ultimately result in higher water temperatures. However, formation of proglacial lakes may complicate river temperature response to glacier retreat. This study examined river and lake temperature response to the retreat of lake‐terminating Bridge Glacier, British Columbia, using a combination of manual spot and continuous river temperatures at a stream gauge, lake surface temperatures derived from Landsat imagery, and in situ lake temperatures made during a field campaign during summer 2013. Temperature data spanned the period from 1984 to 2022. Lake and river temperatures were relatively constant prior to and during a period of rapid retreat, but exhibited an upward trend following 2012, when iceberg density declined due to an apparent decrease in calving rate, resulting in an increase in lake surface area and river temperature of about 5°C even though the glacier remained lake terminating. In addition to providing the first longitudinal study of water temperature response to the retreat of a lake‐terminating glacier, this study adds to a body of literature that demonstrates that routine spot river temperatures, if collected in a consistent manner over a number of years, can provide valid information about river and stream temperature trends.

Rainfall analysis and deriving design storm for hydrological modelling of Koyna dam catchment area in Maharashtra state of India

Rainfall is a key hydro meteorological variable. Climate change is disrupting the hydrological cycle and altering the usual cycle of rainfall, which frequently results in long-lasting storms with significant rainfall. A first step in hydrologic design of project is to determine the design storm or rainfall events to be used. For deriving design storm, researchers concluded that instead of using generalized readily available curves or maps, it is better to estimate design storm based on site specific historical rainfall data. The objective of the study is to analyze the rainfall data in the koyna watershed area in order to evaluate the design storm, which will be further used as an input data for HEC-HMS event based hydrological modelling of flood peak attenuation of design storm flow at koyna dam during extreme rainfall event.In this study, 40 years (1982–2021) of rainfall data from 8 rain gauge stations in Koyna Dam Catchment area is used initially for performing trend analysis through statistical and graphical techniques and then for Isopluvial analysis. The Sen's slope test and the Mann-Kendall test are the statistical techniques employed, and Innovative Trend Analysis is the graphical technique used. IDF approach is used for deriving design storm, and using Gumbel's frequency distribution method Isohyetal maps, IDF tables and curves are prepared for 2,10,25,50,75 and 100 year return periods and 6,12,24,48 and 96 h durations. Results obtained from statistical and graphical trend analysis of annual rainfall series are consistent. No statistically significant trend in annual rainfall series is observed, however there is rising and falling trend was observed in annual as well as monthly rainfall series.From the results of design storm study, the design storm hyetograph of 10 years return period and 96 h duration is selected, which gives the rainfall intensity of 10.88 mm/h for the koyna catchment. There are various dams nearby koyna catchment, The Isohyet maps, IDF curves and table output available from this study can be more reliably used during planning and design of hydraulic structure for other areas near by koyna catchment.

Multi-century flow reconstruction of the Lhasa River, China

Study regionLhasa River Basin Study focusMulti-century records are needed to characterize flow variability including the magnitude and frequency of occurrence of extreme events. Such long records are not available without integrating well-calibrated hydrological models and climate proxies. To test whether 50–70 years of observations represent the long-term flow variability for water resources management, daily flows (1472–2000) were reconstructed by integrating the Variable Infiltration Capacity (VIC) model with proxy-based precipitation and temperature data for Lhasa River. New hydrological insights for the regionVIC performed well during calibration and validation for 3 gauging stations with the Nash-Sutcliffe coefficient of efficiency (NSE) exceeding 0.52. Proxy-based precipitation, temperature, and reconstructed flow are broadly stationary over the past 529 years, while recent decades show detectable local trends. The inter-annual/decadal variability is aligned with long-term variations in sea surface temperatures and the intensity of south Asian summer monsoon. Moreover, higher long-term average, greater variabilities, and persistent periods of above/below-average flows are detected. The maximum daily flows with small annual recurrence intervals (ARIs) less than 60 years would be overestimated based on observations, and underestimated for ARIs > 60 years. It is the first time to have reconstructed long-term daily flows using hydrological models coupled with proxy-based climate data, and this approach offers invaluable insights into the pre-instrumental flow responses to changing climate conditions in Lhasa River basin.

Intercomparisons of Three Gauge-Based Precipitation Datasets over South America during the 1901–2015 Period

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales.

Historical analysis of tropical and non‐tropical induced flooding within the James River Basin, Virginia

AbstractThe development of a basin‐specific comprehensive flood record using publicly available data for the James River Basin, Virginia. Using mixed surface analysis maps (NOAA, NWS), historical hurricane records (IBTrACS, HURDAT2), and flood and river gauge records (NWS, USGS), a database is created for analysis. Related to creating this database, a novel classification of six unique weather systems (non‐tropical and tropical) responsible for flooding is presented. The analysis includes Two‐Way tables with observed marginal and joint probabilities in various potential flood, storm, and regional combinations. Results show 233 tropical systems passed within the 500‐km study area, with 12.4%, or 29 systems of those systems causing flooding within the basin. An additional 713 events were non‐tropical, composing the majority of flood sources in the basin. Non‐tropical systems are responsible for 84% of the worst floods recorded, leading to an increased frequency of 6% yearly since 1941 and nearly 770% between 2014 and 2018. The findings of this study will be the basis for assessing land use and population patterns in the area, along with a future compound flooding model for the lower James River Basin.

Study of the Spatiotemporal Distribution Characteristics of Rainfall Using Hybrid Dimensionality Reduction-Clustering Model: A Case Study of Kunming City, China

In recent years, the frequency and intensity of global extreme weather events have gradually increased, leading to significant changes in urban rainfall patterns. The uneven distribution of rainfall has caused varying degrees of water security issues in different regions. Accurately grasping the spatiotemporal distribution patterns of rainfall is crucial for understanding the hydrological cycle and predicting the availability of water resources. This study collected rainfall data every five minutes from 62 rain gauge stations in the main urban area of Kunming City from 2019 to 2021, constructing an unsupervised hybrid dimensionality reduction-clustering (HDRC) model. The model employs the Locally Linear Embedding (LLE) algorithm from manifold learning for dimensionality reduction of the data samples and uses the dynamic clustering K-Means algorithm for cluster analysis. The results show that the model categorizes the rainfall in the Kunming area into three types: The first type has its rainfall center distributed on the north shore of Dian Lake and the southern part of Kunming’s main urban area, with spatial dynamics showing the rainfall distribution gradually developing from the Dian Lake water body towards the land. The second type’s rainfall center is located in the northern mountainous area of Kunming, with a smaller spatial dynamic change trend. The water vapor has a relatively fixed and concentrated rainfall center due to the orographic uplift effect of the mountains. The third type’s rainfall center is located in the main urban area of Kunming, with this type of rainfall showing smaller variations in all indicators, mainly occurring in May and September when the temperature is lower, related to the urban heat island effect. This research provides a general workflow for spatial rainfall classification, capable of mining the spatiotemporal distribution patterns of regional rainfall based on extensive data and generating typical samples of rainfall types.

Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin

Surface gauge measurements have been commonly employed to analyze the precipitation's high spatial and temporal variability. However, incomplete area coverage and deficiencies over most tropical and complex topography mean significant limitations of this measurement type. Satellite-derived datasets, combined with the integration of in-situ observations with satellite data, are an alternative to address these limitations by offering a more spatially homogeneous and temporally comprehensive coverage for scarce data areas of the globe. Nevertheless, applying a bias correction technique on the precipitation datasets is still necessary before they are used for research due to their considerable bias. Here, we analyze the performance of CHIRPS, WorldClim, and TerraClimate datasets compared to data from 30 rain gauge stations over the South-West of Colombia, specifically in the Upper Cauca River Basin-UCRB between 1981 and 2018. Additionally, we applied the Quantile Mapping correction to all gridded precipitation products, and subsequently, the corrected rainfall is compared to the observed data on the monthly, seasonal, and annual scale. Our results show that the CHIRPS dataset better captures the seasonal and monthly variability. CHIRPS presents the best performance during less rainy seasons and at low elevation zones (900–2000 m above sea level-m.a.s.l.), followed by TerraClimate. Utilizing the bias correction methodology, we generated a new, corrected, and more reliable monthly precipitation time series for each location from all gridded precipitation products. Additionally, we found that the correction of the CHIRPS dataset presented the best performance across all spatiotemporal scales in the UCRB. Therefore, this study provides an accurate precipitation database for a complex topographic tropical region with limited data availability.

On the selection of multi-satellite altimetry for empirical tide modelling over Indonesia

ABSTRACT Multi-satellite altimetry has been employed in tidal modelling as its accuracy has been proven acceptable in constructing good-quality models. However, the modelling process often fails to consider the varying temporal resolution and observation periods among satellites, which can affect the accuracy of the model due to aliasing and Rayleigh effects. In Indonesia, we investigate and propose a satellite selection procedure for tidal modelling utilizing the response method, validated with along-track data obtained from both older and newer generations of ERM (Exact Repeat Mission)-type altimetry satellites, including the TOPEX/Jason series (TOPEX, Jason-1, Jason-2, Jason-3, and SENTINEL-6A), ENVISAT, ERS-2, GFO, SENTINEL-3B, and SENTINEL3A. The most optimal satellite combination identified is the TOPEX/Jason series, GFO, and ENVISAT, as they contribute significantly less error compared to ERS-2, SENTINEL-3A, and SENTINEL-3B, with errors exceeding 50%, primarily due to aliasing and Rayleigh effects. This combination of three satellites with tide gauge stations achieves higher precision (11.31 cm) than the current global tidal model. This study provides empirical evidence that the accuracy of tidal models is not solely dependent on the number of altimetry satellites utilized, but implementing effective satellite selection procedures is also crucial to enhance the precision of a model.

UAV based comprehensive modelling approach for flood hazard assessment and mitigation planning

The assessment of flood hazard in semi-arid region under the scare data situation is a challenging task under the changing climatic situation. Therefore, the present research is carry out for Rel river catchment, Gujrat, India as case study and presented the comprehensive approach to identify the possible flood extend and flood mitigation measures for better flood assessment. The UAV based high resolution (3.6 cm × 3.6 cm) DEM is generated for most flood vulnerable area (i.e. Dhanera city), whereas scare precipitation data under ungauged condition is generated using GEE and National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) system. The HEC-HMS based hydrologic model is utilized for simulation of stream flow under the observed data, however, the 2D HEC-RAS based open-source model is used for flood assessment in 2D under extreme flood, dam break and levees condition. The flood decision making maps i.e water surface elevation, flood inundation, flood arrival time, and flood velocity are prepared for flood event 2017. The six flood hazard classes were selected from H1 (Low) to H6 (High) categories. The hazard maps of flood for different scenario i.e., 2D hydrodynamic analysis, 2D dam breach, 2D analysis with extended levees, and Dam break analysis with extended levees are derived. The model was validated with observed river gauge (water level) and discharge data at Dhanera City of flood 2017. The developed UAV based comprehensive modelling approach for flood hazard and mitigation planning provides the systematic approach for flood hazard assessment and would be adopted as a system framework for flood risk assessment for any similar case worldwide.

Modeling the 2022 Tonga Eruption Tsunami Recorded on Ocean Bottom Pressure and Tide Gauges Around the Pacific

AbstractTsunamis generated by the Hunga Tonga–Hunga Ha’apai volcanic eruption on January 15, 2022 were recorded on ocean bottom pressure and tide gauges around the Pacific Ocean, earlier than the expected arrival times calculated by tsunami propagation speed. Atmospheric waves from the eruption were also recorded globally with propagation speeds of ~ 310 m/s (Lamb wave) and 200–250 m/s (Pekeris wave). Previous studies have suggested that these propagating atmospheric waves caused at least the initial part of the observed tsunami. We simulated the tsunamis generated by the propagation of the Lamb and Pekeris waves by adding concentric atmospheric pressure changes. The concentric sources are parameterized by their propagation speeds, initial atmospheric wave amplitudes that decay with the distance, and a rise time. For the Lamb wave, inversions of the observed tsunami waveforms at 14 U.S. and nine New Zealand DART stations indicate the start of the positive rise at 4:16 UTC, the peak amplitude of 383 hPa, and the propagation speed of 310 m/s, assuming a rise time of 10 min. The later phases of the observed tsunami waveforms can be better reproduced by adding another propagating concentric wave (Pekeris wave) with a negative amplitude (− 50 hPa) and propagation speeds of 200–250 m/s. The DART records around the Pacific indicate that the Pekeris wave speed is faster toward the northwest and slightly slower toward the northeast. The synthetic waveforms roughly reproduced the far-field tsunami waveforms recorded at tide gauge stations, including the later phases, suggesting that the large amplitude in the later phase may be due to the coupling of the Pekeris wave and the tsunami, as well as resonance around tide gauge stations.