Analysis Of Hydrological Data Research Articles

Use of Modern GIS Technologies in the Analysis of Hydrological Data for the Alkabeer Alshamali River in Syria

Use of Modern GIS Technologies in the Analysis of Hydrological Data for the Alkabeer Alshamali River in Syria

Defining and characterizing the phenomenon of river-bottom tearing scour (RBTS): A case study of the Middle Yellow River

River-bottom tearing scour (RBTS) in natural rivers refers to the incipient motion and transport processes of clay blocks formed by fine cohesive sediment after deposition and consolidation in riverbeds. The RBTS phenomenon can lead to significant channel erosion and changes in river planview morphology, and has, thus, attracted the attention of hydrologists and engineers. In the study, a new determination measure (K) for the occurrence of a RBTS event is derived based on the theoretical expression for the critical incipient velocity of the clay block, K=α2(CL+2λ22), which is a combination of the coefficients of the velocity (i.e., α is a local velocity coefficient), lift force (i.e., CL is the lift coefficient), and clay block size (i.e., λ2 is the ratio of block thickness to length). Furthermore, to explain river behavior during a RBTS event, the Fugu, Xiaobeiganliu, and Weihe river reaches in the Middle Yellow River (MYR) were selected as study areas. Analysis of hydrological data from 1950 to 2023 in the examined reaches implies that a single discharge or sediment threshold cannot predict the occurrence of RBTS. The cross-sectional erosion and deposition volume (CEDV) and the migration intensity of the channel thalweg (MI) also were calculated at the section and reach scales for the examined reaches during the RBTS events. It was observed that RBTS typically causes significant channel erosion originating from the most upstream portion of the study reach with CEDV values ranging from 61 to 6034 m2, while MI values during the RBTS events were close to the multi-year average for the study reach. Finally, a flume experiment simulating RBTS was done to discuss and verify the threshold value of K. Field survey and experimental results indicated that RBTS occurs if K > 0.5, thus, providing theoretical support for the prediction and prevention of RBTS.

A Study on the Factors Controlling the Kinematics of a Reactivated and Slow-Moving Landslide in the Eastern Liguria Region (NW Italy) through the Integration of Automatic Geotechnical Sensors

This paper deals with the investigation of factors influencing the movement patterns of a reactivated slow-moving landslide situated in the eastern Liguria region (NW Italy) through the analysis of extensive ground-based hydrological and geotechnical monitoring data. Subsurface horizontal displacement and pore water pressure data were acquired simultaneously by means of automatic sensors positioned at pre-existing and localized failure zones. The joint examination of field measurements enabled us to explore the connections between rain, pore water pressure, and displacements. The results of continuous displacement monitoring showed that the landslide kinematics involved phases of extremely slow movements alternated with periods of relative inactivity. Both stages occurred prevalently at seasonal scale displaying similar durations. The slow-motion phases took place at relatively constant pore water pressure and were ascribed to mechanisms of viscous shear displacements along failure surfaces. Inactive phases entailed no significant deformations, mostly corresponding to prolonged dry periods. The two motion patterns were interrupted by episodic sharp deformations triggered by delayed (preparation periods from 4 to 11 days) rainfall-induced pore water pressure peaks, which were ascribed to sliding mechanisms taking place through rigid-plastic frictional behaviour. During these deformation events, hysteresis relationships between pore water pressure and displacement were found, revealing far more complex hydro-mechanical behaviour.

Changes of the Speed of Rock Destruction in the Upper Sublitoral Zone, 2017–2022

A five-year observation (2017–2022) of abrasion destruction of rocks in the littoral and upper sublittoral zone of the Dalnezelenetskaya Bay (Murmansk coast) was carried out. During this period, a slow rate of destruction of rocks was observed, not only in the upper sublittoral zone, but also along the entire littoral of the coast. It was revealed that the rate of destruction of coarse-grained material in 2017–2022 averaged 15±3 µm/g. The greatest influence on the intensity of destruction of rocks was exerted by storms generated by winds with a force of 7–12 m/s from the east and southeast directions. It is established that in 2021–2022 there was an activation of abrasion destruction of rocks in the sublittoral zone of the Murmansk coast. Based on the analysis of hydrological, meteorological and seismic data, it is assumed that the activation of the abrasion process was caused by an increase in the number of storm periods, perhaps the tectonic activity of disjunctive structures on the coast played an important role in this. The data obtained indicate that changes in the temperature and salinity of the water mass in the bay do not have a significant effect on the rate of the abrasion process.

Quantitative assessment of runoff change and its drivers in a multi-scale framework

ABSTRACT Identifying runoff changes and quantifying the impacts of climate change and human activities are important for water resources planning and management in river basins. The impacts of climate change and human activities on runoff can be more accurately assessed through scientific hydrological modeling and data analysis methods. In this study, an integrated assessment framework was established to quantitatively separate the driving mechanisms of runoff at different time scales. The results show that the runoff of Wu River has shown a decreasing trend since 2004, with a change degree of 56%, and the monthly average flow indexes of August and September have changed significantly, both are over 90%. The NSE coefficient of the SWAT simulation effect is above 0.8, we validate the simulation results based on the LSTM model. It was found that climate change was the main factor affecting the runoff of Wu River, with the contribution rates reaching 60 and 57%, respectively. Pearson correlation analysis found that rainfall was the most important factor affecting the runoff. The results of this study are helpful to formulate effective water resources management policies and measures to ensure the sustainable utilization and management of water resources in the Wu River Basin.

A Novel Online Hydrological Data Quality Control Approach Based on Adaptive Differential Evolution

The quality of hydrological data has a significant impact on hydrological models, where stable and anomaly-free hydrological time series typically yield more valuable patterns. In this paper, we conduct data analysis and propose an online hydrological data quality control method based on an adaptive differential evolution algorithm according to the characteristics of hydrological data. Taking into account the characteristics of continuity, periodicity, and seasonality, we develop a Periodic Temporal Long Short-Term Memory (PT-LSTM) predictive control model. Building upon the real-time nature of the data, we apply the Adaptive Differential Evolution algorithm to optimize PT-LSTM, creating an Online Composite Predictive Control Model (OCPT-LSTM) that provides confidence intervals and recommended values for control and replacement. The experimental results demonstrate that the proposed data quality control method effectively manages data quality; detects data anomalies; provides suggested values; reduces reliance on manual intervention; provides a solid data foundation for hydrological data analysis work; and helps hydrological personnel in water resource scheduling, flood control, and other related tasks. Meanwhile, the proposed method can also be applied to the analysis of time series data in other industries.

Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets

The open-source HYPEtools R package aims to address current challenges in hydrological modeling and data analysis by providing a suite of tools for data management, visualization, interpretation, and exploration. Originally developed as a supporting toolbox for the HYPE hydrological model, HYPEtools has since evolved into a standalone package with features ranging from diverse aggregation and evaluation routines to interactive apps and mapping. Specifically, functions are included for data manipulation and conversion, data summarization, plotting and mapping, and interactive data exploration. Two case studies are provided to highlight how HYPEtools can simplify hydrological workflows and analyses both independently of and within HYPE modeling contexts. First, HYPEtools is used for data exploration and mapping of water transfers in South Africa. Then, a case study for the Baltic Sea region of a pan-European HYPE model setup illustrates how HYPEtools can assist model calibration and validation.

Analisis Dampak Luapan Sungai Citarum Terhadap Stabilitas Bendungan Saguling

The overflow of the Citarum River is one of the main challenges in maintaining the stability of the Saguling Dam in Indonesia. This research aims to analyze the impact of the Citarum River overflow on the stability of the dam. The methods used include field surveys, hydrological data analysis, and numerical modeling. This research makes an important contribution to understanding the interaction between river overflows and dam stability, and provides a basis for developing disaster risk mitigation strategies in areas vulnerable to floods and river overflows.

Towards transient connectivity of river networks during rainfall events: Insight from hydrological observation and functional data analysis

River connectivity, which is critical for river networks to perform their functions, is deteriorating in highly urbanized areas. While it is well known about the structural and functional connectivity of river networks using traditional indicators, much less is known about the process connectivity and its influence factor. To this end, we propose a new approach based on hydrological observations and functional data analysis that has the advantage of capturing transient changes in process connectivity and responses to rainfall events. Applying the method to a typical river-dense plain in China, the results show that 1) the highest and the lowest values of process connectivity were concentrated in 0.6–0.95 and 0.04–0.61 during 2015–2020, respectively. The average values of process connectivity were lower in 2015, 2016, and 2020 with 0.49, 0.44, and 0.46, respectively, and higher in 2017 to 2019 with 0.52, 0.57, and 0.58, respectively. 2) There is a significant negative correlation between rainfall intensity and process connectivity, especially when heavy rainfall occurs. Under the same rainfall intensity, the process connectivity was weaker in the river network with lower structural connectivity, lower overflow capacity of main streams, strong concentration of rivers, more sluices and larger unit areas. 3) The main influencing factors on process connectivity were structural connectivity and rainfall, with contributions of 34.67% and 22.67%, respectively. Following closely are unit features and human regulation, contributing 14.07% and 12.61% to process connectivity, respectively. 4) Structural connectivity and unit features are the primary factors influencing process connectivity during low-intensity rainfall events. During high-intensity rainfall events, rainfall and unit features become the predominant factors affecting process connectivity. Our results provide a new insight for quantifying process connectivity in the plain and are expected to support the study of the changing mechanism of hydrological connectivity.

Water Table Fluctuations and Degradation Risk Assessment at the Waterlogged Site of La Draga (Lake Banyoles, Spain)

ABSTRACT Pile dwelling sites in Mediterranean lakes face increasing threats to their conservation and safeguarding from climate change and its associated impacts. The primary objective of this study is to investigate the influence of climate change and water table fluctuations on the preservation of archaeological layers, with a particular focus on organic layers and wooden remains. Based on a combination of lake water level fluctuation analysis, water table measurements, and stratigraphic data, we managed to gauge the effects of recent water table fluctuations on the preservation of the occupation levels at the Neolithic site of La Draga (Lake Banyoles). By simulating and analysing the relationship between water table levels and their effects on the stratigraphic sequence, we were able to identify areas that are most susceptible to the impact of water table fluctuations and therefore potential post-depositional processes. Moreover, statistical analysis of recent hydrological data has shown that there is a direct relationship between lake level and water table at the settlement. This would have been a factor affecting the preservation of both artefacts and deposits since the occupation of La Draga. Thus, the importance of considering hydrological factors when analysing and interpreting lakeside archaeological sites is highlighted.

FLOODING AND LANDSLIDE POTENTIAL MAPPING OF PUBLIC HOUSING AFTER LANDUSED CHANGE

Semarang City, the capital of Central Java Province, is divided into three parts based on its topography: hills, lowlands, and coastal areas. As a result, the city is at risk of natural disasters. According to BNPB data from 2014 to 2023, Semarang City has the second-highest disaster history in Central Java Province, with 741 disaster events recorded. Floods and landslides are the most common disasters in the area. Permata Jangli Housing is located in the hills, where changes in land use have transformed the surrounding forest into road infrastructure. An analysis of hydrological data and soil characteristics at the site indicates that these changes have led to increased surface flow, resulting in an increased flood discharge. This is one of the reasons for the high potential for disaster. Therefore, it is essential to map the disaster potential in Permata Jangli Housing. Factors such as land use, rainfall intensity, soil texture, slope, and soil elevation affect flooding disasters, while rainfall intensity, slope, soil texture, and land use affect landslides. To assess and map the potential for disasters, we used overlay techniques with the QGIS application. Our findings indicate that Permata Jangli Residential area has a moderate potential for flooding, covering 68% of the area or 1.1 ha, and a high potential of 32% or 0.5 ha. In contrast, the potential for landslides in Permata Jangli is medium, covering 74% or 1.2 ha of the area, and high potential of 26% or 0.4 ha. In conclusion, our study emphasizes the importance of mapping disaster potential in areas with a history of natural disasters. By doing so, we can take preventive measures and mitigate the impact of disasters in the future.

Evaluating climate Change's impact on hydroelectricity in the Zambezi river basin

This study investigates the effects of climate change on energy security in Africa, specifically focusing on the Upper and Lower Zambezi Basin. Data from the Kariba River basin sub-catchments, annual reports, the Climate Data Store, and Teal Tool Earth's country-by-country climate data were analysed through quantitative and qualitative data analysis techniques. The Mann-Kendal Trend Analysis was used to analyse time series and test the significance of changes to the climate. The historical climate and hydrological data analysis showed evidence of a slight increase in average rainfall amounts in the Zambezi River Basin but with high rainfall variability in some areas. Despite droughts increasing in frequency, there is a general increase in hydrological annual average water flow in the Zambezi River at two of the three sample sites. The increased water flow through the region could be attributed to population growth-induced land clearance and the degradation of wetlands in Angola's highlands. Although there is an increase in hydrological water flow into Kariba, there are water shortages for hydroelectricity generation due to increased generation capacity, resulting in increased demand for more water than in previous years. The unsustainable water abstraction to meet growing energy demands contributes to low water levels in the lake. The study recommends energy diversification and new hydroelectricity where the potential exists in the basin to reduce over-reliance on Lake Kariba with care not to disrupt the basin's hydrology and other economic activities. The study results provide insight into the potential effects of climate change on energy security in Africa.

Analyzing the primary hydrological components (rainfall and discharge) within the context of Cipunagara Watershed management, West Java

The Cipunagara River Basin is a component of the Citarum Watershed, necessitating a thorough analysis of hydrological data particularly rainfall and discharge for effective management. The watershed spans a moderate area of 1,363 km2 across three administrative divisions: Subang Regency, Indramayu Regency, and a segment of Sumedang Regency. Hydrological data analysis is imperative in watershed management. This study aims to analyze the key hydrological elements relevant to Cipunagara Watershed management. Rigorous consistency and homogeneity tests were conducted on rainfall data from each station. Changes in watershed flow patterns were scrutinized by examining shifts in runoff coefficient values and base flow magnitudes. The upward trend in runoff during the rainy season and the corresponding decline in base flow throughout the dry season may be attributed to land use alterations within the Cipunagara Watershed zone. To proactively address drought concerns in watershed management, the study calculated the 80% dependable flow to assess water availability during arid months, yielding an 80% dependable flow rate of 16.25 m3/s for the Cipunagara Watershed.

Feasibility Study of a Micro Hydro Power Plant for Rural Electrification in Lalumpe Village, North Sulawesi, Indonesia

This feasibility study aims to assess the potential of implementing a micro hydro system in Lalumpe Village, located in North Sulawesi, Indonesia. The study focuses on evaluating the technical and economic aspects of the proposed micro hydro project. Data collection was carried out through field surveys, interviews with local stakeholders, and analysis of available hydrological and topographical data. The technical assessment involved conducting a resource assessment to determine the water availability and potential for harnessing hydroelectric power in the area. The study also examined the village’s energy demand and determined the suitable capacity of the micro hydro system required to meet the community’s needs. Economic analysis included estimating the initial investment costs, operation and maintenance expenses, and potential revenue generation from the sale of excess electricity to the local grid. Financial viability indicators such as payback period and net present value were calculated to assess the economic feasibility of the project.

Robust imputation method with context-aware voting ensemble model for management of water-quality data.

Water-quality monitoring and management are crucial for ensuring the safety and sustainability of water resources. However, missing data is a frequent problem in water-quality datasets, which can result in biased results in hydrological modeling and data analysis. While classic statistical methods and emerging machine/deep learning methods have been applied for imputing missing values, most existing studies perform well in specific missing scenarios, but not in universal scenarios. Therefore, existing imputation methods often fail to robustly impute missing values across various scenarios. To address the problem, we propose an imputation method that uses a context-aware voting-ensemble model to dynamically select optimal weights to integrate various imputation models across different missingness scenarios. For first identify the attributes of missingness scenarios that influence imputation accuracy. Then after introducing missing values in collected data according to the missingness scenarios, we measure the accuracy of various imputation models across the missingness scenarios. Weights of imputation models are optimized by estimating non-linear functions with regression model that can capture relationships between missingness scenarios and imputation accuracies of models. The final imputed value of the ensemble model for a missing scenario can be determined by multiplying each imputation model's weight by its imputed value, then summing the products. The method inherits the advantages of state-of-art imputation models, including the ability to learn long-term dependencies in time series, as well as the flexibility of using a dynamic weighting strategy to process various missingness scenarios. To validate the superiority of our method, we evaluate on real-world water-quality data from a river in South Korea. The proposed method achieves higher accuracy and lower variation of imputed values than baseline models across various missingness scenarios. Furthermore, we showed the applicability of our method to various hydrological environment by validating our method on industrial water quality dataset. This study highlights the potential value of the ensemble model with dynamic weighting in robust imputation of water-quality data.

Automatic Extraction of Watershed Model by using Python Programming

In earlier days, watershed delineation was mainly conducted by the method of hand delineation. Now a days GIS tools are being widely used for delineation of watershed boundary and identifying stream network. The advancements and new techniques in information technologies are making it possible to acquire large-scale spatial data through satellite data, elevation models with help of user-friendly programming. Watershed delineation is a process for creating a boundary that represents the contributing area for a specific control point or water outlet, with the intent of characterization and analysis of portions of a study area. Although many GIS tools and software are available for watershed analysis, there is a need for optimized libraries for client-side and for better illustration of data in interactive environment for hydrological data analysis. In this connection, presently we are developing a model for the automatic delineation of watershed area from Shuttle Radar Topographical Mission (SRTM) digital elevation model by using Python program. The data required by the model are digital elevation and the spatial coordinate of spill way, outlets etc. In this project, we can be developed and demonstrated several watershed delineation techniques on large scale by using Python programming.

Frequency Analysis of Hydrological Data for Urban Floods—Review of Traditional Methods and Recent Developments, Especially an Introduction of Japanese Proper Methods

Frequency analysis has long been an important theme of hydrology research. Although meteorological techniques (physical approaches) such as radar nowcasting, remote sensing, and forecasting heavy rainfall events using meteorological simulation models are quite effective for urban disaster prevention, statistical and stochastic theories that include frequency analysis, which are usually used in flood control plans, are also valuable for flood control plans for disaster prevention. Master plans for flood control projects in urban areas often use the concept of T-year hydrological values with a T-year return period. A flood control target is a “landside area that is safe against heavy rainfall or floods with a return period of T years”. This review emphasizes discussions of parameter estimation of stochastic models and selection of optimal statistical models, which include evaluation of goodness-of-fit techniques of statistical models. Based on those results, the authors criticize Japanese standard procedures recommended by the central government. Consistency between parameter estimation and evaluation of goodness-of-fit is necessary. From this perspective, we recommend using the maximum likelihood method and AIC, both of which are related to Kullback–Leibler divergence. If one prefers using SLSC, we recommend not SLSC itself but SLSC’s non-exceedance probability. One important purpose of this review is the introduction of well-used Japanese methods. Because some techniques that are slightly different from the international standard have been used for many years in Japan, we introduce those in the review article.

Tracking Morphological Change Trends along River Nile in Egypt Using Hydrological Data Analysis

Tracking Morphological Change Trends along River Nile in Egypt Using Hydrological Data Analysis

A comparison of numerical approaches for statistical inference with stochastic models

Due to our limited knowledge about complex environmental systems, our predictions of their behavior under different scenarios or decision alternatives are subject to considerable uncertainty. As this uncertainty can often be relevant for societal decisions, the consideration, quantification and communication of it is very important. Due to internal stochasticity, often poorly known influence factors, and only partly known mechanisms, in many cases, a stochastic model is needed to get an adequate description of uncertainty. As this implies the need to infer constant parameters, as well as the time-course of stochastic model states, a very high-dimensional inference problem for model calibration has to be solved. This is very challenging from a methodological and a numerical perspective. To illustrate aspects of this problem and show options to successfully tackle it, we compare three numerical approaches: Hamiltonian Monte Carlo, Particle Markov Chain Monte Carlo, and Conditional Ornstein-Uhlenbeck Sampling. As a case study, we select the analysis of hydrological data with a stochastic hydrological model. We conclude that the performance of the investigated techniques is comparable for the analyzed system, and that also generality and practical considerations may be taken into account to guide the choice of which technique is more appropriate for a particular application.

A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 2: Historical context and relation to climate change

Abstract. Heavy precipitation over western Germany and neighboring countries in July 2021 led to widespread floods, with the Ahr and Erft river catchments being particularly affected. Following the event characterization and process analysis in Part 1, here we put the 2021 event in the historical context regarding precipitation and discharge records and in terms of the temporal transformation of the valley morphology. Furthermore, we evaluated the role of ongoing and future climate change on the modification of rainfall totals and the associated flood hazard, as well as implications for flood management. The event was among the five heaviest precipitation events of the past 70 years in Germany. However, consideration of the large LAERTES-EU regional climate model (RCM) ensemble revealed a substantial underestimation of both return levels and periods based on extreme value statistics using only observations. An analysis of homogeneous hydrological data of the last 70 years demonstrated that the event discharges exceeded by far the statistical 100-year return levels. Nevertheless, the flood peaks at the Ahr river were comparable to the reconstructed major historical events of 1804 and 1910, which were not included in the flood risk assessment so far. A comparison between the 2021 and past events showed differences in terms of the observed hydro-morphodynamic processes which enhanced the flood risk due to changes in the landscape organization and occupation. The role of climate change and how the 2021 event would unfold under warmer or colder conditions (within a −2 to +3 K range) was considered based on both a pseudo global warming (PGW) model experiments and the analysis of an RCM ensemble. The PGW experiments showed that the spatial mean precipitation scales with the theoretical Clausius–Clapeyron (CC) relation, predicting a 7 % to 9 % increase per degree of warming. Using the PGW rainfall simulations as input to a hydrological model of the Ahr river basin revealed a strong and non-linear effect on flood peaks: for the +2 K scenario, the 18 % increase in areal rainfall led to a 39 % increase of the flood peak at gauge Altenahr. The analysis of the high-resolution convection-permitting KIT-KLIWA RCM ensemble confirmed the CC scaling for moderate spatial mean precipitation but showed a super CC scaling of up to 10 % for higher intensities. Moreover, the spatial extent of such precipitation events is also expected to increase.