Hydrological Stations Research Articles

Analysis of Spatial and Temporal Characteristics of Runoff Erosion Power in Fujiang River Basin Based on the SWAT Model

As an erosion dynamic index considering the three elements of flood, runoff erosion power (REP) can better reflect the influence of precipitation, underlying surface, and other factors on the erosion and sediment transport (ST) of flood events. Therefore, it is of great significance to study the variation characteristics of the REP and its relationship with ST in the basin for soil erosion control. In this paper, the Fujiang River Basin (FRB) was selected to analyze the characteristics of runoff and ST at four hydrological stations in the basin from 2009 to 2018, including Santai, Jiangyou, Shehong, and Xiaoheba. Combined with the concept of the REP, six kinds of water–sediment relationship were compared and analyzed. Furthermore, by constructing the SWAT model, the spatial distribution characteristics of runoff, ST, and REP in the FRB were analyzed in depth, and the spatial scale effect of the REP in the basin was explored. The conclusions are as follows: (1) The power function relationship between REP and sediment transport modulus (STM) is better than the other five kinds of water–sediment relationship. (2) Based on the SWAT model, the evaluation indexes of the monthly runoff and ST of the four hydrological stations are credible, good, and excellent in the rating period (RP) and the validation period (VP). (3) The annual REP in the main stream from upstream to downstream is mostly a single change trend, while in each primary tributary, the overall value is larger than that of the main stream and the interannual difference is obvious. The average annual REP generally shows the distribution characteristics of ‘large at the junction of the upper and middle reaches and small in the rest of the area’. With the increase in the control area, the multi-year average REP has a decreasing trend, especially when the catchment area above the sub-watershed is >7318 km2; the change of the multi-year average REP is single and obviously slows down, with an average value of 23.8 mm·m3·s−1·km−2.

Machine Learning based IoT Flood Rediction Using Data Modeling and Decision Support System

An essential step in supplying data for climate impact studies and evaluations of hydrological processes is rainfall prediction. However, rainfall events are complex phenomenon’s that continue to be difficult to forecast. In this paper , we present unique hybrid models for the prediction of monthly precipitation that include Seasonal Artificial Neural Networks and Discrete wavelet transforms are two pre-processing methods, together with Artificial Neural Networks have two feed forward neural networks. The temporal series of observed monthly rainfall from Vietnam’s Ca Mau hydrological station were decomposed into three subsets by seasonal decomposition and five sub signals and four levels by wavelet analysis. The methods for predicting rainfall that use feed forward artificial neural networks (ANN) and seasonal artificial neural network (SANN) were fed with the processed data. The classic genetic method and simulated annealing method backed by using an integrated moving average and autoregressive moving was contrasted with the predicted models for model evaluation. The results showed that non-stationary regarding issues with non-linear time series, such forecasting rainfall could be satisfactorily simulated. The SANN model was integrated with the wavelet transform and seasonal decomposition are both used. Techniques, however the wavelet transform method produced the most accurate monthly rainfall data, Predictions. Due to the effects of climate change, nations including the Japan, China, the United States of America, and Taiwan, etc., have recently experienced severe and devastating natural disasters. One of the biggest causes of the destruction in Asian nations like china, India, Bangladesh, Sri Lanka, etc. is the flood. The danger of fatality from these floods is increased by 78% as information technology advances; there is a demand for simple access to massive amounts of cloud storage and computing capacity.

Changes in flow and sediment transport caused by cascade hydropower in the upper reaches of Yangtze River and their influence on spawning of Coreius heterodon

The upper reaches of the Yangtze River (YR) constitute the largest group of hydropower cascade projects in the world. The construction of cascade hydropower systems has a significant impact on the flow regimes and sediment transport in rivers, ultimately affecting fish spawning. Previous studies have examined the effects of fluctuating flow and water temperature on spawning; however, insufficient attention has been paid to their effects on sediment transport. This study analysed changes in runoff and sediment load (SL) at the Xiangjiaba hydrologic station (XJB) and Zhutuo hydrologic station (ZT) in the upper reaches of the YR over the past 60 years. Based on the indicators of hydrological regimes and sediment transport, we used the generalized additive models (GAM) to investigate the effects of cascade hydropower operations from 2009 to 2018 on the spawning abundance of Coreius heterodon (C. H) in the upper YR Fish Reserve. Following the operation of cascade hydropower, the SL and sediment concentration decreased by 56.8% and 56.6%, respectively, while the median size of the bed-material load demonstrated an initial thinning trend followed by subsequent coarsening. In addition, the frequency of the high-flow pulse increased by 32.1%, whereas the peak flow and duration of the high-flow pulse decreased by 41.2% and 62.6%, respectively. However, no significant changes were noted in the indicators of the flooding process. The decrease in sediment transport and changes in the flow regime led to a reduction in the spawning abundance of C. H. Reductions in sediment transport and changes in high-flow pulses caused by cascade hydropower can negatively affect C. H spawning, and SL is the most important indicator of impacts on spawning abundance (contribution of 77.7%). These findings are critical for evaluating the long-term impacts of cascade hydropower on fish habitats.

Daily flow prediction of the Huayuankou hydrometeorological station based on the coupled CEEMDAN–SE–BiLSTM model

Enhancing flood forecasting accuracy, promoting rational water resource utilization and management, and mitigating river disasters all hinge on the crucial role of improving the accuracy of daily flow prediction. The coupled model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SE), and Bidirectional Long Short-Term Memory (BiLSTM) demonstrates higher stability when faced with nonlinear and non-stationary data, stronger adaptability to various types and lengths of time series data by utilizing sample entropy, and significant advantages in processing sequential data through the BiLSTM network. In this study, in the context of predicting daily flow at the Huayuankou Hydrological Station in the lower reaches of the Yellow River, a coupled CEEMDAN–SE–BiLSTM model was developed and utilized. The results showed that the CEEMDAN–SE–BiLSTM coupled model achieved the utmost accuracy in prediction and optimal fitting performance. Compared with the CEEMDAN–SE–LSTM, CEEMDAN–BiLSTM, and BiLSTM coupled models, the root mean square error (RMSE) of this model is reduced by 42.77, 182.02, and 193.71, respectively; the mean absolute error (MAE) is reduced by 37.62, 118.60, and 126.67, respectively; and the coefficient of determination (R2) is increased by 0.0208, 0.1265, 0.1381.

Response Mechanism of Annual Streamflow Decline to Vegetation Growth and Climate Change in the Han River Basin, China

Vegetation changes have a significant impact on the underlying surface of a watershed and alter hydrological processes. To clarify the synergistic evolution relationship between climate, vegetation, and hydrology, this study aims to reveal how vegetation restoration influences streamflow decline. This study first applied the trend-free pre-whitening Mann–Kendall (TFPW-MK) method to identify variation trends of various elements at Baihe and Shayang hydrologic stations from 1982 to 2015. Secondly, an extended Budyko equation was improved by fitting the linear relationship between annual NDVI and Budyko parameter (ω). Finally, based on the extended Budyko formula, the elastic coefficient method was applied to identify the influence of vegetation changes on runoff changes of the Baihe and Shayang stations from 1982 to 2015. The results displayed that (1) the annual NDVI of Baihe and Shayang hydrologic stations both presented an increasing trend, and streamflow presented an insignificant decrease trend. The mutation year of the annual runoff depth of Baihe and Shayang stations both occurred in 1990. (2) The annual NDVI had a significant and positive linear relationship with ω. (3) The streamflow decline of Baihe and Shayang stations is mainly influenced by precipitation variation and human activities. (4) Vegetation growth had a positive effect on the streamflow decline of Baihe and Shayang stations, with a contribution rate of 14.06% and 17.87%. This effect of vegetation growth on discharge attenuation should be given high priority.

Monthly Runoff Prediction by Combined Models Based on Secondary Decomposition at the Wulong Hydrological Station in the Yangtze River Basin

Accurate runoff prediction plays a pivotal role in facilitating effective watershed management and the rational allocation of water resources. Given the inherent challenges posed by the nonlinear and nonstationary nature of runoff sequences, this study introduces a novel coupled model, combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), long short-term memory (LSTM), and Informer techniques for monthly runoff prediction at the Wulong hydrological station of the Yangtze River Basin from 1973 to 2022. In addition, by comparing the prediction results of the traditional statistical model, which is the seasonal exponential smoothing model, with those of the machine learning model, the prediction accuracy of the machine learning model was found to be much higher than that of the traditional statistical mode. And the coupled model of secondary decomposition and secondary prediction was compared with other types of coupled models, such as one decomposition and one prediction. The CEEMDAN-VMD-LSTM-Informer model exhibited superior performance, as evidenced by an NSE value of 0.997, MAE of 1.327 × 108 m3, MAPE of 2.57%, and RMSE of 2.266 × 108 m3. The combined model proposed in this paper has the highest prediction accuracy, rendering it suitable for long-time series prediction. Accurate runoff prediction plays a pivotal role in facilitating effective watershed management and the rational allocation of water resources.

Improvement of the sediment flux estimation in the Yangtze River Estuary with a GOCI data adjusted numerical model

Sediment flux (SF) in the estuary is vital to the coastal and estuarine environment, especially the morphodynamical and ecological processes. However, its quantitative estimation with high accuracy is difficult because it is controlled by complex mechanisms and multiple processes. This study corrects the seasonal variations of the simulated suspended sediment concentration (SSC) by using GOCI-derived surface SSC and calculates the variations of SFs at the main cross-sections in and out of the Yangtze River Estuary (YRE). The results show that in 2013, 159 Mt and 143 Mt of sediments passed through Xuliujing hydrological station in YRE and estuarine mouth section, respectively. In the inner estuary, the significant seasonal variations of sediment transport are noted that the most seaward transport happens in summer (43.8%) and the least occurs in winter (7.3%). In the outer estuary, the southward transport towards Hangzhou Bay is the most critical pathway, accounting for 109.0% of total transport at mouth section, and is prevalent in autumn and winter. With considerations of sand mining and land reclamation, obviously erosions appear in the whole estuary during both 2013 and 2015. With stronger wind conditions in 2013, severer erosion (161 Mt) happens in outer estuary than that in 2015 (86 Mt). By combining the GOCI-derived surface SSC and the numerical model results, this study can better represent high-frequency hydro- and sediment-dynamical processes to calculate the annual, seasonal, and vertical SFs with improved accuracy. Hence this method may provide a viable way to infer locally averaged morphological changes.

Changes in Magnitude and Shifts in Timing of Australian Flood Peaks

We analysed changes in magnitude and timing of the largest annual observed daily flow (Amax), in each water year, for 596 stations in high-value water resource catchments and flood risk locations across Australia. These stations are either included in the Bureau of Meteorology’s Hydrologic Reference Stations or used in its operational flood forecasting services. Monotonic trend (which is either consistently increasing or decreasing) analyses of the magnitude and timing of flood peaks (estimated using Amax) were performed using the Theil–Sen and Mann–Kendall approaches and circular statistics to identify the strength of seasonality and timing. We analysed regional significance across different drainage divisions using the Walker test. Monotonic decreasing trends in Amax flood magnitude were found in the Murray–Darling River Basin and in other drainage divisions in Victoria, southwest and midwest of Western Australia and South Australia. No significant obvious pattern in Amax magnitude was detected in northern Queensland, coastal NSW, central Australia and Tasmania. Monotonic increasing trends were only found in the Tanami–Timor Sea Coast drainage division in northern Australia. Monotonic trends in Amax magnitude were regionally significant at the drainage division scale. We found two distinct patterns in flood seasonality and timing. In the northern and southern parts of Australia, flood peaks generally occur from February to March and August to October, respectively. The strength of this seasonality varies across the country. Weaker seasonality was detected for locations in the Murray–Darling River Basin, and stronger seasonality was evident in northern Australia, the southwest of Western Australia, South Australia, Victoria and Tasmania. The trends of seasonality and timing reveal that in general, flood peaks have occurred later in the water year in recent years. In northern Australia, flood peaks have generally occurred earlier, at a rate of 12 days/decade. In Victoria, New South Wales and Tasmania, the trends in timing are generally mixed. However, in the southwest of Western Australia, the largest change in timing was evident, with Amax peaks commencing later at a rate of 15 days/decade. Decadal variability in flood timing was found at the drainage division scale as well. Most stations show a decreasing trend in Amax magnitude, but how that trend is associated with the change in timing is not clear.

Regional contributions of climate change and human activities to altered flow of the Lancang-mekong river

Study regionThe Lancang-Mekong River (LMR) in Southeast Asia. Study focusThe LMR provides unparalleled economic and ecological resources for six Asian countries. However, the intensified impact of human activities, coupled with the effects of climate change, has led to alterations in its natural flow, thereby disrupting the river's flow regime. Ensuring the future sustainable management and downstream ecological integrity of this transboundary river requires understanding the regional contributions to the river's altered flow at the sub-basin level. We conducted an analysis for the last four decades using a hydrological model, which divided the entire basin into eight sub-basins, each associated with one of the eight main hydrological stations. New hydrological insights for the regionOur analysis showed significant regional differences in precipitation variation across the LMR, from its headwaters to the lower regions, during both periods. Furthermore, a spatial variation was observed for the impacts of human activities on the natural runoff in both seasons, indicating a lack of effective coordinated management efforts among riparian countries. Notably, our findings indicated that agricultural expansion and the construction of tributary dams in certain sub-basins had a more significant impact on the reduction of the mainstream flow compared to the mainstream dams. These findings underscore the enormous potential of regional water governance to alleviate the impacts of changing climate, offering valuable insights for addressing this critical issue.

A Method for Calculating Water Demand for Sediment Transport Based on the Principles of River Dynamics

Sufficient water is pivotal in maintaining the stability of boundaries in sandy river systems. However the current methodologies employed for computing the water demand for sediment transport in rivers frequently neglect this component. This research utilizes data spanning 1960 to 2020 from seven principal hydrological stations located in the lower Yellow River to establish the correlation between key factors pertaining to the sediment transport capacity of flow. A closed equation system was established based on the principles of river dynamics to solve unknown hydraulic parameters. Finding a suitable hydraulic geometric relationship equation as a supplementary equation is a key step in constructing a closed equation system. The findings indicate that sediment transport water demands are 71.79, 133.24, 226.89, 286.12, and 313.6 × 108 m3, respectively, when sediment inflow is at 1, 2, 4, 6, and 8 × 108 t, with a bankfull discharge of 4000 m3/s. As the sediment inflow diminishes and the unit water demand for sediment transport increases, the sediment transport efficiency of the lower Yellow River reduces. The outcomes of this research can serve as a foundation for the joint operation of the Yellow River’s main and branch reservoirs, as well as for designing water resource allocation schemes within the basin.

Effects of vegetation distribution and landscape pattern on water conservation in the Dongjiang River basin

Water conservation is important for maintaining a well-functioning ecosystem. Using the Soil & Water Assessment Tool (SWAT) model, we simulated the hydrological cycle and evaluated the spatial and temporal variability of water conservation in the Dongjiang River. Spatial autocorrelation analysis and geographically weighted regression (GWR) were used to investigate the effects of landscape patterns and normalized difference vegetation index (NDVI) on water conservation. Four hydrological stations with regular rate, validation period deterministic coefficient R2, and efficiency coefficient ENS (Nash–Sutcliffe) met the adaptation requirements, and the model accurately simulated the hydrological process of the Dongjiang River. The interannual variation of water conservation showed a non-significant upward trend (0.12 mm/10a), and the spatial distribution is given as follows: midstream > downstream > upstream. Regression coefficients of NDVI of high, medium, and low-density forest land in the sub-basin with water conservation decreased from the upper to lower reaches. The negative correlation of LPI, CONTAG, and IJI was concentrated in the upper reaches and southeast of the basin, and that of SHDI was concentrated in the middle and lower reaches; the regression coefficients of LPI and PLAND of forest land decreased from middle reaches to north and south, and ED decreased from southwest to northeast. The positive correlation of grassland was concentrated in the watershed’s upper reaches; the regression coefficient of construction land showed an increasingly negative correlation from the lower to upper reaches. This study provides scientific support for promoting the hydrological ecosystem and comprehensive utilization of water resources in the Dongjiang River basin.

Твердый сток реки Дон и поступление взвеси в дельту при нагонах: статистическое моделирование и сопоставление в период маловодья

Statistical modeling of the Don River solid runoff based on water discharge and turbidity measurements obtained at the hydrological station in the village of Razdorskaya for the twelve-year period 2009–2020 was carried out. The WRTDS (Weighted Regressions on Time, Discharge, and Season) and WRTDSKalman (WRTDS with Kalman filtering) methods were applied. The developed statistical model is aimed at solving the problem of the imbalance between the regularity of collecting data on water discharge and data on the concentration of suspended matter by “restoring” the concentration values on days without measurements based on data on the most “similar” days with measurements in terms of time, discharge and season and does not claim to describe a relationship between the concentration of the constituent of interest and discharge. The quality of the developed statistical model and its modification were checked. The average daily concentrations and fluxes of suspended matter were compared with estimates of the volumes of suspended material deposited during periods of recurring strong surge phenomena. A comparative assessment of sea and river factors contributions to the transport and sedimentation of suspended matter in the Don River delta was fulfilled.

Determination of the maximum run‐off sediment transport capacity during a rainstorm flood in a small watershed on the Loess Plateau

AbstractThe channel sediment transport capacity is an important parameter in soil erosion models, and it determines the proportion of soil erosion that is sent outside the watershed. However, the calculation and judgment methods for the sediment transport capacity of channel are still incomplete, and further research is needed. Therefore, this paper takes Chabagou, a typical watershed in the hilly and gully region of the Loess Plateau, as an example and, based on the measured hydrological and sediment data collected during rainstorm floods in 1961–1969 at three hydrological stations, Caoping, Shejiagou, and Tuanshangou, studies the sediment transport capacity of channels in a small watershed from the perspective of energy dissipation. Clearly, the sediment transport capacity is better determined by using a single flood and sediment concentration peak process. The discriminant conditions are determined for the sediment concentration to reach sediment transport capacity during the flood rising and falling sections of the sediment process of the small watershed storm flood. When determining sediment transport capacity based on the characteristics of the flood and sediment concentration peak, we find that sediment transport capacity is reached in the flood discharge peak when the flood discharge peak occurs earlier than the sediment concentration peak. When determining sediment transport capacity based on the flow discharge and sediment concentration characteristics of the sediment fall section, we find that when the flood event exhibits a counterclockwise hysteresis curve and a time‐lagged effect occurs, the sediment concentration at that time reaches sediment transport capacity. In this study, the sediment concentration as a function of run‐off amount is logarithmic at different subbasin scales when run‐off reaches its maximum capacity to transport sediment. The R2 is high, ranging from 0.78 to 0.92. The results of this research provide a reference for the study of sediment transport capacity.

Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition

The Sentinel-1 imaging radar mission provides a short revisit-time, continuous all-weather, and day-and-night imagery at the C-band, which brings opportunities for the dynamic extraction of lake water areas. For wetland-type lakes, it is difficult to distinguish between the water, submerged plants, and mudflats at the edge of a lake, which leads to difficulty in recognizing the water edge of a lake and affects the accuracy of lake water area extraction. In this paper, a water area extraction and water level prediction algorithm based on Sentinel-1 dual-polarization data decomposition is proposed to solve the problem. We can accurately extract lake water through generalized Stokes polarization decomposition. At the same time, we can verify the accuracy of water area extraction by establishing the water area and in situ water level elevation (A–E) relationship, and predicting the water level according to the calculated water area. In this study, dual-polarization Sentinel-1 time series SAR data covering the Dongting Lake wetland from 2018 to 2022 are used to verify the proposed water area extraction algorithm and establish the A–E relationship of the East Dongting Lake basin. The results show that the generalized Stokes decomposition parameters are very sensitive to the water boundary, and the R2 of the water area and the water level can reach 0.98 by using the piecewise linear function. It confirms the accuracy of the water area inversion, which is of extremely important significance for the high-precision monitoring of the water area of Dongting Lake with long-term Sentinel-1 data. Meanwhile, the predicted lake water level acquired using the A–E relationship established in this paper is compared with the field water level, with an RMSE of 0.4857 m and R2 of 0.9930. This means that the water level derived using the method in this study is quite compatible with the field observations, which provides a good idea for the water level monitoring of lakes lacking hydrological monitoring stations.

Monitoring Lake Level Changes in Bosten Lake from 2003-2021 based on Multi-source Satellite Altimetry Data

The investigation of freshwater lake water resource changes in arid regions is crucial for understanding the evolution of regional lake dynamics under climate change, given their value and limited availability. This study aims to overcome the limitations of traditional water level monitoring methods and address the practical need for monitoring water level changes in Lake Bosten, located in the arid and semi-arid regions of China. To this end, we employed ICESat, ICESat-2, and CryoSat-2 altimetry data, as well as Landsat 8-OLI optical remote sensing data, to obtain the water level information for Bosten Lake from 2003 to 2022. We analyzed the temporal variation of the lake level and discussed the response of the lake level to climate and human activities in the context of temperature, precipitation data, and human water use. The water level of Lake Bosten exhibited a decreasing and then increasing trend, with three rising and three falling intervals, respectively. The intra-year variation of the lake water level exhibited bimodal characteristics, and the fluctuation range of water level during abundant water periods was greater than that during dry periods. Finally, we correlated the extracted lake water level with the measured water level at the hydrological station and found a significant positive linear correlation between the observed water level of Bosten Lake based on satellite altimetry and the measured water level at the hydrological station, with an overall correlation coefficient of 0.896 and 0.934 (P < 0.01), indicating high accuracy of the water level results obtained in this study. The dynamics of Lake Bosten are influenced by a combination of climate change, hydrological elements, and human activities, with a relatively weak relationship between climate change and lake volume and a strong influence of human activities. Multi-source altimetry satellites provide a powerful tool for monitoring lake water levels over a long time scale, which is critical for studying lake water level changes and their response to climate and the environment.

Simulation of runoff process based on the 3-D river network

Many rivers originating from mountainous areas cannot be adequately measured in the field to obtain sufficient basic data due to the challenging terrain, which poses difficulties for conducting relevant research such as hydrodynamic and sediment transport simulations, fluvial material fluxes. This study utilized a combination of 71 river cross sections extracted from multisource remote sensing data and 34 cross sections measured by hydrological stations between 2010 and 2018 to generate generalized cross sections for all river orders in the middle reaches of the Yellow River. By integrating the generalized cross sections, river surfaces, and traditional DEM river network, a comprehensive 3-D river network has been established to provide a more accurate representation of the morphological changes along the river channel. To tackle the challenge of simulating runoff in data-deficient areas, the Digital Yellow River Integrated Model (DYRIM), a potent distributed hydrological model, was enhanced by incorporating geometric parameters derived from a 3-D river network into almost all input parameters that can be obtained from multi-source remote sensing. This enhancement enabled the model to accurately simulate runoff in representative data-deficient rivers of the Loess Plateau. The Nash Efficiency coefficients for the simulated runoff exhibited improvement of 23% compared to previous studies, surpassing 0.8 for the period between 2010 and 2018. Additionally, the simulated errors for flow velocity and water depth were 7% and 9% respectively. This approach can provide essential data for the simulation of hydrological processes and improving hydrological models, as well as technical methodologies for related research such as sediment transport simulations and river carbon emissions.

Identifying changes in flood characteristics and their causes from an event-based perspective in the Central Taihu Basin

Increasing rainstorms induced by climate change and modification in the land surface due to urbanization have greatly altered floods at different spatio-temporal scales. However, investigating flood events in urbanized plains is challenging as anthropogenic behaviors can change river flow without rainfall. In addition, while the frequency and magnitude of floods have been well examined, knowledge about variations in the rate of flood change is still limited. To fill these gaps, we proposed a scheme that focused on flood responses to rainfall to detect changes in flood characteristics in the Central Taihu Basin, a highly urbanized region in the Yangtze River Delta of China. Four characteristic metrics were adopted to summarize the flood hydrograph, including the peak, increment, rising rate, and falling rate. We then examined trends of these metrics based on the selected rainfall-flood events from ten hydrological stations during 1970–2020. Subsequently, the reduction method was used to separate the impacts of regional climate change and human activities on flood characteristics alterations. Furthermore, the importance of fifteen factors was quantified by the random forest model. We found that there is a significant upward trend in the evolution of flood characteristics, except for the increment of floods. Flood characteristics exhibit higher values when rainfall accumulates, indicating stronger responses of floods to a large amount of rainfall. The results also show that human activities dominate and impact the peak, rising rate, and falling rate of floods more than climate change. Meanwhile, although cumulative precipitation is the most important factor, flood characteristics are also susceptible to anthropogenic factors, such as land use change and hydraulic engineering construction. Our findings, which provide insights into flood event identification and enhance the understanding of regional flood changes, will serve as a reference for water resource management and flood mitigation in urbanized areas.

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM’s fast-learning ability and discrete wavelet transform’s ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.

Effect of Surface Characteristics and Rainfall in Infiltration Rate: A Case Study of Kaligandaki River Basin

Study of infiltration characteristics is crucial for effective watershed management, as it helps determine the water availability for crops, the need of irrigation, groundwater recharge, and surface runoff. This study is aimed at identifying the relation between infiltration rate and surface characteristics as well as rainfall rate in the Kaligandaki River basin of Nepal using Hydrologic Modelling System (HEC-HMS). HEC-HMS is a physically based, semi-distributed model that uses the weather and spatial information of soil and landuse of the basin for deriving the inputs for the model. A hydrological rainfall-runoff model is developed for the Kaligandaki River basin, with outlet at hydrological station 420, Kotgau, of Department of Hydrology and Meteorology (DHM). The model was then validated at DHM station 420, Angsing, of Kaligandaki River. Model generated subbasin wise infiltration rates and existing landuse were analysed and it was found that sub-basins with dominant landuse as forest had high infiltration rate among prevailing landuse types. Conversely, low infiltration rate was observed for barren area, snow/glacier and grassland landcover type dominated subbasins. A positive correlation among rainfall rate and infiltration rate was seen with linear increase in infiltration rate for an exponential increase of average rainfall intensity. Infiltration rate was found to have positive correlation with the Hydrologic Soil Group types as suggested by NRCS. Moreover, the study signified a greater impact of landcover over soil types on infiltration rates. Whereas, no significant impact of basin slope on infiltration rate could be identified, which may be due to preclusion of multiple topographic features. Understanding the impacts of rainfall intensity and surface characteristics on infiltration enables proactive measures to mitigate the issues like landslide and reduce flooding and soil erosion by improving infiltration rates through surface condition assessment.

Flood risk cascade analysis and vulnerability assessment of watershed based on Bayesian network

Under the dual impact of climate change and accelerated urbanization, urban flood disasters are frequent, resulting in severe losses, and there has been widespread concern. Through constructing the topological structure of flood control network and collecting the hydrological information from hydrological stations, this study proposes a Bayesian flood network model of Haihe river basin (Beisanhe and Yongdinghe river basin) to assess the flood risk. Flood Probability (FP) and Flood Network Cascade (flood network cascade) are proposed to characterize the vulnerability and flood cascade of flood control networks respectively. The accuracy of the flood cascade simulation of the Beisanhe river basin exceeds 80%, and the accuracy of the Yongdinghe river basin exceeds 85%. It is proved that the Bayesian network model proposed in this study has good simulation performance. The derived vulnerability assessment indicates that the central and southern regions of the Beisanhe River and Yongdinghe River basin are easily affected by floods at other nodes. The proposed Bayesian network modeling framework can also simulate the flood cascade in flood control infrastructure, so it can be used for scenario planning and real-time flood forecasting to provide information for flood management and operation.