Gauge Stations Research Articles

Exploring short- and long-term meteorological drought parameters in the Vaippar Basin of Southern India

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates.

Bootstrap Reduced Major Axis (BRMA) to optimize the satellite‐derived discharge rating curves

AbstractWe estimate river discharge by utilizing limited in‐situ and multi‐satellite data through Bootstrap Reduced Major Axis (BRMA) optimization method (). We establish the BRMA by analyzing the functional relationships (H, W, H.W vs. Q) at four distinct locations along the Ganga (Shahzadpur and Azmabad) and the Narmada (Hoshangabad and Mandleshwer) rivers. To measure the channel width (W) we have used the Landsat 5, 7, and 8 images (2006–2019). We use the water level (H) data from satellite altimeter (Jason 2, Jason 3, Envisat, and Sentinel 3A). We have used BRMA to establish functional relationships between channel width (W), water level (H), and H.W to their corresponding discharge at each gauge stations in the study reach. Efficacy of the proposed approach is evaluated through a comparative analysis with the traditional ordinary least squares (OLS) regression method. We observed that the BRMA exhibits better performance as compared to the best fit curve obtained by using the OLS regression. We noticed that the functional relationship between the WH‐Q outperforms as compared to the other empirical curves at the both gauge stations of the Ganga River. The accuracy estimates of the Ganga River is in a range of (0.76–0.95), (1–23) and (0.25–0.44). In the Narmada River, the functional relationship between H‐Q outperforms. The accuracy of discharge at both the gauge stations of the Narmada River are found to be in a range (0.76–0.95), (1–23), to (0.25–0.44). This study is a step towards estimating discharge from satellites data.

The dual role of meltwater in buffering river runoff in the Yarlung Zangbo Basin, Tibetan Plateau

Study regionThe Yarlung Zangbo Basin (YZB) on the Tibetan Plateau, the world's highest river basin, features a significant cryosphere with glaciers and seasonal snow cover crucial to its hydrology. The study focuses on the region between the Nuxia and Dexing river gauging stations, where glaciers cover 15.4 % of the area. Study focusThe research quantifies the contributions of snow melt (SM) runoff, glacier melt (GM) runoff, rainfall runoff, and baseflow to the total runoff in the YZB. The Spatial Processes in Hydrology (SPHY) model, enhanced with a cryosphere module, was utilized, calibrated with runoff data from the Nuxia station and evapotranspiration data from 2003 to 2014. New hydrological insightsThe study found rainfall runoff to be the primary contributor to annual runoff (66.3 %), followed by snow melt runoff (19.7 %), glacier melt runoff (6.2 %), and baseflow (7.8 %). Snow melt runoff is dominant in early spring, while baseflow prevails in winter. Glacier melt runoff contributes directly to river flow (90.1 %) and replenishes groundwater (9.9 %), which then drains as baseflow. In glacier-rich areas, percolated glacier meltwater significantly recharges groundwater, underscoring its vital role in sustaining river flow in the YZB. This research enhances the understanding of hydrological processes in large alpine river basins and highlights the crucial role of glacier and snow melt in maintaining the Tibetan Plateau's water resources.

Comparative Performance of Non-Stationary Intensity-Duration-Frequency (NS-IDF) Models for Selected Gauge Stations in the Niger Delta

This study focused on a comparative analysis of developed Non-stationary rainfall intensity-duration-frequency (NS-IDF) models with existing IDF models for the Niger Delta with Uyo, Benin, Port Harcourt, and Warri as selected stations. Applied was 24-hourly (daily) annual maximum series (AMS) data with downscaling models also used to downscale the time series data. Uyo and Benin had statistically significant trends with Port Harcourt and Warri showing mild trends. The best linear behavioural parameter extremes model integrating time as co-variate was selected for each station for computation of the General extreme value (GEV) distribution fitted NS-IDF models with the open-access R-studio software. The Non-stationary intensity values were higher than computed stationary ones, with significant differences at a 5% significance level for a given return period. For example, for 2 and 10-year return periods for 1-hour storms the differences of 22.71% & 17.0%, 15.24% & 9.40%, 5.09% & 4.04%, and 6.15% & 4.43% for Uyo, Benin, Port Harcourt and Warri, respectively were recorded. While, the percentage difference in intensities was very high between the Non-stationary and existing, Stationary IDF models. For a return period of 2 years at 15 and 60 min durations, the differences were 97.9 & 3.2%, 240.6 & 67.2%, 78.2 & 0%, and 121.6 & 50.1% for Uyo, Benin, Port Harcourt and Warri, respectively. Such extreme value difference in intensity underestimates the peak flood and exagerate the flood risk. The general NS-IDF calibrated models showed very good match and fit with R<sup>2</sup> = 0.977, 0.999, 0.999 & 0.999, and MSE accuracy = 193.5, 1.011, 4.1552 & 1.011 for Uyo, Benin, Port Harcourt, and Warri, respectively. Erosion and flood control facilities in the Niger Delta require upgrading using the calibrated general NS-IDF models to accommodate extra-value rainfall intensities due to climate change.

Strategic stream gauging network design for sustainable water management

Strategic stream gauging network design for sustainable water management

Influence of global teleconnections on long-term variability in flood seasonality across peninsular India

Climate change and natural climate variability impact not only the frequency and magnitude of floods but also flood seasonality. However, limited to no study has investigated the seasonality in flood occurrence in peninsular Indian rivers. The Central Water Commission opening its long-term records of river stage and discharge gathered from many monitoring stations offers a unique opportunity to analyse flood seasonality. The primary aims of this study are to identify the time of the year when floods are most likely, investigate whether the occurrence of floods has changed over time due to the influence of climate change and natural climate variability, and determine the most significant large-scale and local climate drivers of flood seasonality. Stage and discharge data from 281 gauging stations across ten peninsular river basins are considered to identify the annual maximum gauge level for a gauging point while retaining the corresponding Julian day (also known as the date of occurrence or DO) for further analysis. Several attributes related to circular statistics are calculated from the DO series to find that 68% of the gauging stations experience floods during August, indicating the dominance of the monsoon system on DO. Preliminary analysis confirms that while most stations experience a non-stationarity in the DO series, a few stations exhibit a non-linear trend. Furthermore, our study develops a machine learning model with DO as predictand and 36 local and large-scale predictors to identify the dominant predictors of DO. The predictor importance metric shows that the Pacific Decadal Oscillation (PDO) and the El Niño and Southern Oscillation (ENSO) explain DO variability better than other drivers. Moreover, since ENSO and PDO are teleconnected with the onset of monsoon and annual maximum rainfall (Pradhan et al 2017 Sci. Rep. 7 14229; Choudhury et al 2021a Q. J. R. Meteorol. Soc. 147 3111–23), flood seasonality demonstrates a connection with both PDO and ENSO.

Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions

AbstractClimate change has exacerbated water stress and water‐related disasters, necessitating more precise streamflow simulations. However, in the majority of global regions, a deficiency of streamflow data constitutes a significant constraint on modeling endeavors. Traditional distributed hydrological models and regionalization approaches have shown suboptimal performance. While current deep learning (DL)‐related models trained on large data sets excel in spatial generalization, the direct applicability of these models in certain regions with unique hydrological processes can be challenging due to the limited representativeness within the training data set. Furthermore, transfer learning DL models pre‐trained on large data sets still necessitate local data for retraining, thereby constraining their applicability. To address these challenges, we present a physics‐informed DL model based on a distributed framework. It involves spatial discretization and the establishment of differentiable hydrological models for discrete sub‐basins, coupled with a differentiable Muskingum method for channel routing. By introducing upstream‐downstream relationships, model errors in sub‐basins propagate through the river network to the watershed outlet, enabling the optimization using limited downstream streamflow data, thereby achieving spatial simulation of ungauged internal sub‐basins. The model, when trained solely on the downstream‐most station, outperforms the distributed hydrological model in streamflow simulation at both the training station and upstream held‐out stations. Additionally, in comparison to transfer learning models, our model requires fewer gauge stations for training, but achieves higher precision in simulating streamflow on spatially held‐out stations, indicating better spatial generalization ability. Consequently, this model offers a novel approach to hydrological simulation in data‐scarce regions, especially those with poor hydrological representativeness.

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.