•The flow coefficient of Aksu River Basin in Türkiye is estimated by using SMRGT method.•SMGRT method was applied effectively in hydrological analysis to estimate the flow coefficient.•SMGRT method ...contributes more accurate flood prediction, water resource management and flood mitigation strategies.
The estimation of the flow coefficient is a vital hydrological procedure that holds considerable importance in flood prediction, water resource management, and flood mitigation. The precise estimation of the flow coefficient is imperative in mitigating flood-related damages, administering flood alert mechanisms, and regulating water discharge. It is hard to accurately determine the flow coefficient without a good understanding of the river basin’s hydrology, climate, topography, and soil characteristics. A range of methodologies have been documented in the most recent body of literaturefor flow coefficient modeling. The majority of these methods, however, depend on opaque techniques that lack generalizability. Therefore, this research employed three distinct methodologies—specifically, the Adaptive Neural Fuzzy Inference System (ANFIS), the Simple Membership Function, and the Fuzzy Rules Generation Technique (SMRGT) are all examples of fuzzy inference systems, and Artificial Neural Network (ANN), to achieve its objectives. The Aksu River Basin in Antalya, Turkey, was chosen as the study area. The models underwent multiple permutations of precipitation (P), temperature (T), relative humidity (Rh), wind speed (Ws), land use (LU), and soil properties (Sp) data that were tailored to the particular study region. The study analyzed the results using various performance metrics of the model such as mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). The results indicate that the SMRGT method resulted in a remarkable degree of accuracy in forecasting the flow coefficient, as demonstrated with the minimal RMSE and MAE values and high correlation coefficient values. The study’s findings suggest that the SMRGT method was applied effectively in hydrological analysis to estimate the flow coefficient, contributing to more accurate flood prediction, water resource management, and flood mitigation strategies.
•Stochastic modelling reproducing any marginal distribution and linear correlation.•Applicable in univariate, cyclostationary and multivariate cases.•Precise modelling of precipitation, river ...discharge, wind, etc. at any time scale.•Parametric correlation transformation functions unify and simplify the scheme.•Empirical correlation representation through parsimonious parametric functions.
Hydroclimatic processes come in all “shapes and sizes”. They are characterized by different spatiotemporal correlation structures and probability distributions that can be continuous, mixed-type, discrete or even binary. Simulating such processes by reproducing precisely their marginal distribution and linear correlation structure, including features like intermittency, can greatly improve hydrological analysis and design. Traditionally, modelling schemes are case specific and typically attempt to preserve few statistical moments providing inadequate and potentially risky distribution approximations. Here, a single framework is proposed that unifies, extends, and improves a general-purpose modelling strategy, based on the assumption that any process can emerge by transforming a specific “parent” Gaussian process. A novel mathematical representation of this scheme, introducing parametric correlation transformation functions, enables straightforward estimation of the parent-Gaussian process yielding the target process after the marginal back transformation, while it provides a general description that supersedes previous specific parameterizations, offering a simple, fast and efficient simulation procedure for every stationary process at any spatiotemporal scale. This framework, also applicable for cyclostationary and multivariate modelling, is augmented with flexible parametric correlation structures that parsimoniously describe observed correlations. Real-world simulations of various hydroclimatic processes with different correlation structures and marginals, such as precipitation, river discharge, wind speed, humidity, extreme events per year, etc., as well as a multivariate example, highlight the flexibility, advantages, and complete generality of the method.
•Relief and vegetation should be considered for DEM resolution selection.•High resolution is needed where heterogeneous and dispersed vegetation is present.•Distance to sinks may explain spatial ...differences in the index of connectivity.•Results show a balance between accuracy and processing for selecting DEM resolution.•Our findings can be considered for hydrological modelling.
Assessment of hydrological connectivity is a valuable tool for water management. It enables various environmental factors related to water-mediated transport of matter and energy within or between elements of a system to be identified and incorporated into hydrological models. The index of connectivity (IC) is widely used to quantify hydrological connectivity; however, it may be influenced by the spatial resolution of the digital elevation model (DEM), which is one of the common inputs, as pixel size modifies some basin parameters. Several DEMs available on open-access platforms, with spatial resolutions from 5 to 30 m (LiDAR, ALOS, MEC, ASTER, SRTM), were compared to identify differences between IC in two basins with contrasting environments (Site 1: steep slope terrain with warm-humid climate, and Site 2: gentle hill slopes with semi-arid climate). Differences in perimeters were analyzed using GLM Repeated Measures ANOVA, while for differences in IC, one-way ANOVA was used. Relationships between IC subtracted maps and basin parameters were explored with classification and regression trees (CART). Basin perimeters and IC varied as a function of DEM resolution, but environmental factors such as relief and land use/cover seemed to influence IC maps. Distance to sinks was one of the variables explaining spatial differences in IC between DEMs. The selection of DEM spatial resolution for hydrological connectivity analysis should be based on the characteristics of the study area, economic and technological issues, and whether the objectives can be met with the available DEM resolution.
This paper introduces HydroLang, an open-source and integrated community-driven computational web framework for hydrology and water resources research and education. HydroLang employs client-side web ...technologies and standards to carry out various routines aimed at acquiring, managing, transforming, analyzing, and visualizing hydrological datasets. HydroLang consists of four major high-cohesion low-coupling modules: (1) retrieving, manipulating, and transforming raw hydrological data, (2) statistical operations, hydrological analysis, and model creation, (3) generating graphical and tabular data representations, and (4) mapping and geospatial data visualization. To demonstrate the framework's capabilities, portability, and interoperability, two detailed case studies (assessment of lumped models and construction of a rainfall disaggregation model) have been presented. HydroLang's unique modular architecture and open-source nature allow it to be easily tailored into any use case and web framework, and it encourages iterative enhancements with community involvement to establish the comprehensive next-generation hydrological software toolkit.
•Open source web-based programming framework for hydrological research and education.•A software toolkit for hydrological data retrieval, analysis, and visualization.•Modular architecture and detailed guidelines for simple adoption and extension.•Sharing models, data, and case studies for large-scale community participation.
Land use and land cover change can increase or decrease landslide
susceptibility (LS) in the mountainous areas. In the hilly and mountainous
part of southwestern China, land use and land cover ...change (LUCC) has
taken place in the last decades due to infrastructure development and rapid
economic activities. This development and activities can worsen the slope
susceptible to sliding due to mostly the cutting of slopes. This study,
taking Zhushan Town, Xuan'en County, as the study area, aims to evaluate the
influence of land use and land cover change on landslide susceptibility at a
regional scale. Spatial distribution of landslides was determined in terms
of visual interpretation of aerial photographs and remote sensing images,
supported by field surveys. Two types of land use and land cover (LUC) maps,
with a time interval covering 21 years (1992–2013), were prepared: the first was obtained by the neural net classification of images acquired in 1992 and the second by the object-oriented classification of images in 2002 and 2013. Landslide-susceptible areas were analyzed using the logistic regression model (LRM) in which six influencing factors were chosen as the landslide susceptibility indices. In addition, the hydrologic analysis method was applied to optimize the partitioning of the terrain. The results indicated that the LUCC in the region was mainly the transformation from the grassland and arable land to the forest land, which is increased by 34.3 %. An increase of 1.9 % is shown in the area where human engineering activities concentrate. The comparison of landslide susceptibility maps among different periods revealed that human engineering activities were the most important factor in increasing LS in this region. Such results emphasize the requirement of a reasonable land use planning activity process.
Accurate and efficient models for rainfall–runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are process-driven; i.e., they solve either simplified ...empirical formulas or some variation of the St. Venant (shallow water) equations. With the development of machine-learning techniques, we may now be able to emulate rainfall models using, for example, neural networks. In this study, a data-driven RR model using a sequence-to-sequence long-short-term-memory (LSTM) network was constructed. The model was tested for a watershed in Houston, TX, known for severe flood events. The LSTM network’s capability in learning long-term dependencies between the input and output of the network allowed modeling RR with high resolution in time (15 min). Using 10-year precipitation from 153 rainfall gages and river channel discharge data (more than 5.3 million data points), and by designing several numerical tests, the developed model performance in predicting river discharge was tested. The model results were also compared with the output of a process-driven model gridded surface subsurface hydrologic analysis (GSSHA). Moreover, physical consistency of the LSTM model was explored. The model results showed that the LSTM model was able to efficiently predict discharge and achieve good model performance. When compared to GSSHA, the data-driven model was more efficient and robust in terms of prediction and calibration. Interestingly, the performance of the LSTM model improved (test Nash–Sutcliffe model efficiency from 0.666 to 0.942) when a selected subset of rainfall gages based on the model performance, were used as input instead of all rainfall gages.
Fluctuations in water surface elevation (WSE) along rivers have important implications for water resources, flood hazards, and biogeochemical cycling. However, current in situ and remote sensing ...methods exhibit key limitations in characterizing spatiotemporal hydraulics of many of the world's river systems. Here we analyze new measurements of river WSE and slope from AirSWOT, an airborne analogue to the Surface Water and Ocean Topography (SWOT) mission aimed at addressing limitations in current remotely sensed observations of surface water. To evaluate its capabilities, we compare AirSWOT WSEs and slopes to in situ measurements along the Tanana River, Alaska. Root‐mean‐square error is 9.0 cm for WSEs averaged over 1 km2 areas and 1.0 cm/km for slopes along 10 km reaches. Results indicate that AirSWOT can accurately reproduce the spatial variations in slope critical for characterizing reach‐scale hydraulics. AirSWOT's high‐precision measurements are valuable for hydrologic analysis, flood modeling studies, and for validating future SWOT measurements.
Key Points
AirSWOT provides a new method for measuring river water surface elevations (WSEs) and slopes without the need for in situ data
AirSWOT detects decimeter‐level variations in WSEs for 1 km2 areas and cm/km‐level variations in river slopes along 10 km reaches
Results indicate that AirSWOT is capable of producing measurements useful for validating SWOT‐quality measurements of river WSEs and slopes
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•Proposed an ecological network framework based on bird movement characteristics.•Minimum threshold for ecological source area using the granularity inverse method.•Better ...connectivity of ecological networks extracted using multiple methods.•We extracted a total of 51 ecological corridors and 88 ecological nodes.
With the rapid pace of global urbanization, the conflict between the needs of human economic development and ecological conservation is becoming increasingly prominent. As an important habitat along the global migratory routes of migratory birds in East Asia, the construction of an ecological network for the bird communities in Foshan City is essential to protect urban biodiversity. In this research, we employs the Maximum Entropy Model (MaxEnt) and the Remote Sensing Ecological Index (RSEI) to identify ecological source sites in Foshan City based on bird migration characteristics and habitat requirements, and calculated the minimum threshold for the area of ecological source area by using the granularity inverse method. Secondly, the entropy weight method (EWM) and the analytical hierarchy process (AHP) are combined to establish a comprehensive resistance surface. Finally, circuit theory and hydrological analysis principles are utilized to construct ecological network. The findings reveal the following: The landscape component structure is best at a grain size of 1400 m, and a minimum threshold area of 1.96 km2 is identified for ecological sources in Foshan. A total of 19 ecological sources, spanning a combined area of 636.09 km2, have been identified. These sites are mainly clustered in the north, south-west and east of Foshan City. In terms of ecological corridors, this research shows the existence of 51 corridors with a total length of 501.84 km. These corridors include 7 first-level corridors, 25 s-level corridors, and 19 potential corridors (including 15 radiating routes). At the same time, we identified 88 ecological nodes, including 10 critical ecological nodes and 78 general ecological nodes. Lastly, the ecological network closure index (α), connectivity index (β), connectivity rate index (γ), and connectivity density index (ρ) after optimization of hydrological analysis grew to 0.84, 2.43, 0.89, and 0.16, respectively. This study can provide data support for optimizing the future ecological security pattern and bird diversity conservation in Foshan.
Satellite radar altimetry has been widely used in the monitoring of water levels of lakes, rivers and wetlands in the past decades. The conventional pulse-limited radar altimeters have a relatively ...large ground footprint, which limits their capability to retrieve surface elevation information over small and medium-sized water bodies. A new generation of satellite radar altimeter system, a dual-frequency SAR radar altimeter (SRAL) onboard the Copernicus Sentinel-3 satellite, has produced densely sampled elevation measurements with a smaller footprint for the Earth's surfaces since June 2016, owing to the Delay-Doppler processing technique. Four standard SRAL SAR altimetry waveform retracking algorithms (known as retrackers) have been designed to retrieve elevation measurements for different types of surfaces: Ice-Sheet retracker for polar ice sheets, SAMOSA-3 retracker for open ocean and coastal zones, OCOG retracker for sea-ice margins, and Sea-Ice retracker for sea ice. In this research, we evaluated the performances of the Sentinel-3 SRAL SAR altimetry retrackers over lakes, particularly over seasonally ice-covered lakes in one hydrological cycle. For 15 lakes and reservoirs with different sizes and at varying latitudes in the northern hemisphere, we compared the lake water levels estimated by each of standard SRAL SAR retrackers against in-situ water level measurements for different seasons (a full hydrologic cycle) during 2016–2017. Our evaluation shows that Sea-Ice retracker was unable to provide continuous estimates of lake water levels, as a result of the high rate of missing data. Although the precision and relative accuracy of lake water level estimates from these three standard SRAL SAR retrackers are similar, the SAMOSA-3 retracker has the least bias in comparison with ground-based gauge measurements. When the lakes in the mid- and high-latitude regions were covered by ice in the winter season, these three standard SAR retrackers generated erroneous lake water level measurements, significantly lower than the true lake water levels recorded by in-situ gauge stations. The measurement errors of these three standard retrackers increase with the growth of the lake ice thickness. To address the negative effect of the seasonal ice cover, we developed a new bimodal correction algorithm. We demonstrate that our bimodal correction algorithm can retrieve the ice thickness and reliably estimate water levels for the ice-covered lakes in winter, hence enabling the generation of temporally consistent lake water level measurements throughout all seasons for lake hydrological analysis.
•The performances of Sentinel-3 SRAL SAR retrackers over inland lakes are evaluated.•The mechanism of how lake ice affects SRAL SAR water level estimates is revealed.•We develop a bimodal algorithm to derive consistent water levels for ice-covered lake.
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