Analysis Of Hydrological Data Research Articles

Multifractal analysis of rainfall in coastal area in Pernambuco, Brazil

Global warming and climate change are the mayor concerns of scientists, engineers and policy makers because they affect every aspect of nature and human life. Rainfall and air temperature are the most important variables used to detect climate change, through the statistical analysis of set of indices that describe temperature and rainfall extremes. Over the last decades concepts and methods from complex system science were applied in analysis of hydrological data to describe variability of hydrological processes on multiple temporal and spatial scales. In this work we analyzed daily rainfall temporal series in Recife, Brazil (during the period from 1962 to 2019) using Multifractal Detrended Fluctuation analysis (MFDFA) in order to study long term correlations in subsets of small and large rainfall fluctuations. We calculated multifractal parameters (that quantify position of maximum, width and asymmetry of multifractal spectrum) which are related to different properties of rainfall fluctuations. By comparing the values of these parameters for two subperiods of 29 years, we found that after 1990, rainfall dynamics changed towards stronger persistency, weaker multifractality and decreased dominance of small fluctuations.

Analisis Distribusi Frekuensi dan Periode Ulang Hujan

Increasing water demand for various needs can be a complex problem so it is necessary to manage water resources. Analysis of hydrological data is very necessary to get information about water resources where the information can be used as a benchmark for planning a water resources builder. One of hydrological analysis is the analysis of rainfall data where this analysis uses frequency distribution analysis and rain return periods. There are four types of distribution used, namely normal distribution, normal log distribution, Gumbel distribution and type III log Pearson distribution. The goodness of fit test uses the Kolmogorov-Smirnov method, Chi-Square and Anderson-Darling. Rainfall return calculation is calculated when it is known the type of distribution of the data studied. This research uses rainfall data of Long Iram Sub-Distric, West Kutai Distric in 2013 to 2017 obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of Samarinda City. The results from research showed that the Gumbel distribution was the right distribution or distribution that was the best with the results of the return period of rain for the return period of 2 years obtained by rainfall of 519 mm, 5-year return period of 796 mm, 10-year return period of 980 mm, return period 20 years of 1.154 mm, a 50 year return period of 1.348 mm and in a 100 year return period of 1.752 mm.

Practical Research on Protection and Utilization of Urban Spatial Water Elements from the Perspective of Healthy Cities

The healthy development of urban elements would be important to sustainable urban development, especially the elements of water resources. This paper discusses the key points and methods of water elements development and protection in urban design for Healthy-city, taking the design of China Ceramic Valley in Liling as an example. Based on the analysis of hydrological and geographic data, it establishes the network structure of the river system, summarizes systematic measures for the guidance and control of sponge cities, puts forward the optimization and control methods of waterfront spatial form and compiles quantitative and elastic space guidelines. The results show that the whole-scale and systematic urban design is a necessary step of urban water resources protection and utilization. It provides practical experience and process reference for urban health and sustainable development.

Water resources and flooding risk in Kumamoto based on observed hydrologic data analysis

Abstract Variability and change of precipitation were investigated in Kumamoto on Kyushu Island in southwestern Japan, to assess water resources and flooding risk. Annual precipitation, annual maximum daily precipitation, and annual maximum hourly precipitation have increased over the period from 1891 to 2018 (128 years). Trends are 26.2 mm per decade, 6.07 mm/day per decade, and 2.17 mm/h/decade, respectively. Precipitation in the rainy season (June and July) is on average 37% (ranging from 12 to 59%) of annual precipitation for the 128-year period. Maximum daily precipitation in a year occurred at Kumamoto in the rainy season in 92/128 (72%) of the years of observation from 1891 to 2018, in the typhoon (August to November) season in 23/128 (18%), and in the March to May season in 12/128 (10%). This indicates that the rainy monsoon season poses the largest daily flooding risk. A wavelet analysis revealed that from 1891 to 2018 annual precipitation and daily maximum precipitation fluctuate with 2 and 4 years periods, which may be related to the El Nino-Southern Oscillation (ENSO). It is likely that air temperature rises, ENSO and topographical characteristics contributed to an increase in precipitation in the period. The analysis also showed that typhoons hitting or approaching Kumamoto have significantly affected annual precipitation and annual maximum daily precipitation, while the interval between typhoons affecting Kumamoto has been getting longer since the 1970s.

Lessons from the Case History of a Massive Landslide Dam

A massive landslide created a natural dam on two tributaries of Jhelum River near the town of Hattian Bala in Kashmir in October 2005. The landslide was triggered by a 7.6 Mw earthquake. The resulting unconsolidated dam and water impoundment upstream carried hazard potential of downstream damage to both infrastructure and population due to potential flooding caused by its breach. Comprehensive investigations and monitoring were implemented to analyse dam stability. Fresh topographic profiles were generated. Samples of the matrix materials were utilized in the laboratory investigations including grain-size analysis, laboratory electrical resistivity and permeability tests at varying densities and degrees of saturation, and sediment concentration assessment in the seepage discharge. A noninvasive geophysical method was employed together with new topographic information to develop transient subsurface pictures and to assess the advancement of seepage fronts within the dam body. Internal erosion/filtering potential of the matrix material was assessed by comparing grain size distributions with those of the earlier failed dams. Upstream inflows, downstream discharges, daily precipitation, and lake levels monitored during the study period were utilized in hydrological data analysis in an attempt to assess the potential seepage volume. A combination of empirical, analytical, and numerical methods and simulations, together with laboratory and field investigations, led to the interpretations regarding short- and long-term stability of the dam. This paper highlights alternative methods of investigation employed differently from those used by other national and international agencies in analysing the failure potential of this natural dam. It offers lessons learned from a case history that can be beneficial in future evaluation of seepage-induced failure of similar natural features.

Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees

Anomalous patterns are common phenomena in time series datasets. The presence of anomalous patterns in hydrological data may represent some anomalous hydrometeorological events that are significantly different from others and induce a bias in the decision-making process related to design, operation and management of water resources. Hence, it is necessary to extract those “anomalous” knowledge that can provide valuable and useful information for future hydrological analysis and forecasting from hydrological data. This paper focuses on the problem of detecting anomalous patterns from hydrological time series data, and proposes an effective and accurate anomalous pattern detection approach, TFSAX_wPST, which combines the advantages of the Trend Feature Symbolic Aggregate approximation (TFSAX) and weighted Probabilistic Suffix Tree (wPST). Experiments with different hydrological real-world time series are reported, and the results indicate that the proposed methods are fast and can correctly detect anomalous patterns for hydrological time series analysis, and thus promote the deep analysis and continuous utilization of hydrological time series data.

Temporal Scaling of Water Level Fluctuations in Shallow Lakes and Its Impacts on the Lake Eco-Environments

Managing lake water levels from an ecological perspective has become an urgent issue in recent years in efforts to protect, conserve, and restore lake eco-environments. In this study, we considered the actual situation of Ebinur Lake basin to develop a lake water balance model using a System Dynamics (SD) method. The objective of this study is based on the lake water balance model to sufficiently understand the variation and relationship between the lake depth–area–volume. We combined field investigations and hydrological data analysis to expose the major factors affecting lake water level fluctuations (WLFs), as well as the impact of WLFs on lake eco-environments. All with the aim of providing a theoretical basis to manage Ebinur Lake ecosystems for conservation and restoration. The main findings of this study include: (I) The model’s calculation results agree with the observation value, as the monthly lake surface area was used to validate the model. (II) The factors influencing the dynamic changes in the water level of the lake are ranked in ascending order (from the lowest to the highest) as follows: Precipitation, groundwater recharge, evaporation, river inflow. (III) Fluctuations in water level play a significant role in lake shoreline displacement variation, and when the lake’s water level drops below 1 m, the surface area of the water body decreases to approximately 106 km2. (IV) The magnitude and frequency of WLFs drive major differences in the ecology of lake littoral zones, influencing not only the structure and functioning of benthic assemblages but also littoral habitat structure. These results established a quantitative linkage between hydrological variables and ecosystem health for the Ebinur Lake wetlands. These findings could be widely used in managing the Ebinur Lake basin as well as other similar water bodies, and could provide a useful tool for managing lake ecosystems for conservation and restoration.

A serious gaming framework for decision support on hydrological hazards

With increasing population and human intervention on the natural environment, hazards are a growing threat, coming in many forms, including floods, droughts, soil erosion, and water pollution. A key approach to mitigate hydrological disaster risk at the community level is informed planning with decision support systems. The literature shows emerging efforts on multi-hazard decision support systems for hydrological disasters and demonstrates the need for an engaging, accessible, and collaborative serious game environment facilitating the relationship between the environment and communities. In this study, a web-based decision support tool (DST) was developed for hydrological multi-hazard analysis while employing gamification techniques to introduce a competitive element. The serious gaming environment provides functionalities for intuitive management, visualization, and analysis of geospatial, hydrological, and economic data to help stakeholders in the decision-making process regarding hydrological hazard preparedness and response. Major contributions of the presented DST include involving the community in environmental decision making by reducing the technical complexity required for analysis, increasing community awareness for the environmental and socio-economic consequences of hydrological hazards, and allowing stakeholders to discover and discuss potential trade-offs to hazardous scenarios considering the limitations in budget, regulations, and technicality. The paper describes the software design approaches and system architecture applied for a modular, secure, and scalable software as well as the framework's intuitive web-based user interfaces for real-time and collaborative data analysis and damage assessment. Finally, a case study was conducted to demonstrate the usability of DST in a formal setting and to measure user satisfaction with surveys.

Mitigation Model Development and Drought Effect Anticipation at Trenggalek Regency

The farm is an important sector that affects economic growth. El-Nino and La-Nina climate anomalies have an impact on the farm such as drought. Actions to overcome the limiting factors on marginal land need to be done through technologies application on soil conservation and water management, balanced soil nutrient management and a better understanding of climate anomalies. Therefore, mitigation models development and anticipation of the effects of drought need to be carried out to provide an alternative technology. The research stages was biophysical conditions analysis, hydrological and geological data analysis, and formulation of drought mitigation technology. Based on the research, there is a drought potential that needs to be anticipated. Technology that could be applied are water reservoirs, dykes, infiltration canals, rorak, slot mulch, and reservoir. Soil and water conservation technology can increase groundwater. Method that have a significant impact is making embankments with mulch, which can increase the soil water content up to 56%. Keywords: Drought, Farm, Groundwater, Trenggalek

Optimizing Inverse Distance Weighting with Particle Swarm Optimization

Spatial analysis of hydrological data often requires the interpolation of a variable from point samples. Commonly used methods for solving this problem include Inverse Distance Weighting (IDW) and Kriging (KG). IDW is easily extensible, has a competitive computational cost with respect to KG, hence it is usually preferred for this task. This paper proposes the optimization of finding the IDW parameter using a nature-inspired metaheuristic, namely Particle Swarm Optimization (PSO). The performance of the improved algorithm is evaluated in a complex scenario and benchmarked against the KG algorithm for 51 precipitation series from the Dobrogea region (Romania). Apart from facilitating the process of applying IDW, the PSO implementation for Optimizing IDW (OIDW) is computationally lighter than the traditional IDW approach. Compared to Kriging, OIDW is straightforward to be implemented and does not require the difficult process of identification of the most appropriate variogram for the given data.

Assessment of the large reservoirs impact on the Shu-Talas basin rivers runoff

The article based on the collection and analysis of hydrological data, the assessment of the impact of large reservoirs on the annual flow of the Shu-Talas river basin using well-known statistical methods was considered. Due to the insufficient hydrological database for the basin, the hydrological data series was restored using the analog method for the selected reporting period. The uniformity of the river series in this basin was evaluated using the StokStat program and the Student, Fisher and Wilcoxon criteria. The results of the assessment showed a positive result, so integral curves were constructed to determine the degree of human economic activity that affects the flow of the river. Using the graphical method, it is established that the flow has changed and the date of its beginning coincides with the time of construction and commissioning of reservoirs. The results of the assessment showed a positive result, so integral curves were constructed to determine the degree of human economic activity affecting the flow of the river. Using the graphical method, it is established that the flow has changed and the date of its beginning coincides with the time of construction and commissioning of reservoirs. Statistical parameters of river flow that are subject to changes in flow under the influence of large reservoirs before and after construction were determined. The impact of reservoirs on runoff was estimated by comparing the possible values of different security obtained from the total integral curves and security curves constructed during natural and disturbed periods. The results of the study are of practical use in the design of reservoirs and the assessment of their impact on the regime.

Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology

Several issues arise when extending the methods of outlier detection from a single dimension to a higher dimension. These issues include limited methods for visualization, marginal methods inadequacy, lacking a natural order and limitation in parametric modeling. The intension to overcome and address such limitations the nonparametric outlier identifier, based on depth functions, is introduced. These identifiers comprise of four threshold type outlyingness functions for outlier detection that are Mahalanobis distance, Tukey depth, spatial Mahalanobis depth, and projection depth. The object of the present research is the application of the proposed nonparametric technique in hydrology. The study is intended to be executed in two different frameworks that are multivariate hydrological data analysis and functional hydrological data analysis. The event of a flood is graphically represented by hydrograph whose components are used for computing flood characteristics that are peak(p) and volume(v). These characteristics are frequently employed for the various types of analysis in the multivariate study. Whereas, hydrograph is exhaustively employed in the analysis of functional data so that all the important information regarding flood event are not missed while analysis. The proposed technique in a multivariate framework is applied to the bivariate flood characteristics (p,v)while in functional framework proposed approach is applied to the initial two scores of principal components denoted as (z_1,z_2 ), since initial two principal components capture major variation of data employed for analysis.

Overview of software for analysis and visualization of hydrological data

Overview of software for analysis and visualization of hydrological data

Empirical and Physically Based Thresholds for the Occurrence of Shallow Landslides in a Prone Area of Northern Italian Apennines

Rainfall thresholds define the conditions leading to the triggering of shallow landslides over wide areas. They can be empirical, which exploit past rainfall data and landslide inventories, or physicallybased, which integrate slope physical–hydrological modeling and stability analyses. In this work, a comparison between these two types of thresholds was performed, using data acquired in Oltrepò Pavese (Northern Italian Apennines), to evaluate their reliability. Empirical thresholds were reconstructed based on rainfalls and landslides triggering events collected from 2000 to 2018. The same rainfall events were implemented in a physicallybased model of a representative testsite, considering different antecedent pore-water pressures, chosen according to the analysis of hydrological monitoring data. Thresholds validation was performed, using an external dataset (August 1992–August 1997). Soil hydrological conditions have a primary role on predisposing or preventing slope failures. In Oltrepò Pavese area, cold and wet months are the most susceptible periods, due to the permanence of saturated or close-to-saturation soil conditions. The lower the pore-water pressure is at the beginning of an event, the higher the amount of rain required to trigger shallow failures is. physicallybased thresholds provide a better reliability in discriminating the events which could or could not trigger slope failures than empirical thresholds. The latter provide a significant number of false positives, due to neglecting the antecedent soil hydrological conditions. These results represent a fundamental basis for the choice of the best thresholds to be implemented in a reliable earlywarning system.

The role of agent-based modeling and multi-agent systems in flood-based hydrological problems: a brief review

AbstractFlood problems are complex phenomena with a direct relationship with the hydrological cycle; these are natural processes occurring in water systems, that interact at different spatial and temporal scales. In modeling the hydrological phenomena, traditional approaches, like physics-based mathematical equations and data-driven modeling (DDM) are used. Advances in hydroinformatics are helping to understand these physical processes, with improvements in the collection and analysis of hydrological data, information and communication technologies (ICT), and geographic information systems (GIS), offering opportunities for innovations in model implementation, to improve decision support for the response to societally important floods impacting our societies. This paper offers a brief review of agent-based models (ABMs) and multi-agent systems (MASs) methodologies' applications for solutions to flood problems, their management, assessment, and efforts for forecasting stream flow and flood events. Significant observations from this review include: (i) contributions of agent technologies, as a growing methodology in hydrology; (ii) limitations; (iii) capabilities of dealing with distributed and complex domains; and (iv), the capabilities of MAS as an increasingly accepted point of view applied to flood modeling, with examples presented to show the variety of system combinations that are practical on a specialized architectural level for developing and deploying sophisticated flood forecasting systems.

Evolution From Past to Future Conditions of Fast Ice Coverage in James Bay

Fast ice is often used by coastal communities in the James Bay area for transportation in winter using snowmobiles. Therefore, the extent of fast ice along the James Bay coastline is important for land use and any changes to these extents may have significant impacts on the lifestyles of local communities. The eastern coastline has experienced changes in recent decades that might have affected the ice processes, namely hydrologic modifications due to hydroelectric development by Hydro-Québec along with climatic changes that have been observed worldwide. A statistical analysis in the form of summarized ice charts of the ice extents in the middle of winter have been compiled for the past four decades to highlight any recent changes in ice coverage using data from satellite imagery and ice charts produced by the Canadian Ice Service. A statistical analysis has also been carried out on the freeze-up and breakup dates using historical data. Moreover, a statistical analysis of hydrological data and climatic data has been carried out to determine the long-term and short-term trends in the parameters influencing the ice processes. After testing sensitivity of the past ice regime to climatic and hydrological parameters, the trends detected in the extents of fast ice, as well as freeze-up and breakup conditions, have been correlated with climatic parameters. Using the dominant parameters, a simplified model of the extents of fast ice has been developed, as well as criteria to determine freeze-up and breakup dates. Air temperature projections have also been obtained for two greenhouse gas emissions scenarios using global climate model results to establish future climatic conditions. Finally, projections of the fast ice regime around the year 2050 have been developed to determine a long-term trend covering both historical changes and future conditions. We expect around the year 2050 a recession of the landfast ice coverage of several kilometers, a delay of one to three weeks of the freeze-up dates and an advance of two to ten days of the breakup dates in comparison to the period of 1998-2016 in the James Bay area.

Hydrogeologic Behavior of a Complex and Mature Karst Aquifer System under Drought Condition

This paper aims, using classic hydrogeologic techniques, Time Series Analysis, Principal Component Analysis, Standardized Precipitation Index, and MRC methods to better understand the hydrogeological, and hydraulic characteristics of a mature karst aquifer system, and to analyze its behavior under drought conditions. The study area is the karst aquifer system of Aggitis (AAS), Northern East Greece, characterized by a mountainous terrain consisting of highly fissured and karstified Falakro marbles of Mesozoic age. AAS catchment extends 63.4 km2 and exhibits a duality of conduit and/or diffuse flow model. AAS is drained by the permanent large Maaras spring with a mean annual value of 4.656 m3/s. The dominant chemical type is Ca-HCO3. PCA and hydrochemical analysis were effectively used in combination to interpret the main hydrogeochemical mechanisms controlling karst water quality. A combined methodology provides a useful and effective tool for assessing the characteristic behavior of karst springs under drought conditions. It enables also, to gain insight into the prevailing processes in a karst aquifer system. The statistical analysis of hydrological data revealed the karst system is mainly recharged by diffuse infiltration.

Analyses of Monthly Discharges in Slovakia Using Hydrological Exploratory Methods and Statistical Methods

Abstract Detailed analyses of hydrological data are necessary in order to examine changes in their character. This article focuses on an analysis of the average monthly discharges of 14 stage-discharge gauging stations in Slovakia. The measured period is from 1931 to 2016. The approaches used are hydrological exploration methods, which were created by hydrologists to describe the behaviour of hydrological time series. The methods are used to identify a change-point through an analysis of any residuals, Pettitt´s test, and an analysis of the relationship between the mean annual discharge deviations from the long-term annual discharge and the deviations of the average monthly discharge from the long-term average monthly discharge. A considerable number of change-points were identified in the 1970s and 1980s. The results of the analyses show changes in the hydrological regimes, but to confirm the accuracy of the outcomes, it is also necessary to examine other hydrological and meteorological elements such as, e.g., precipitation and the air temperature.

Analysis of Water Level Change of Daihai Lake Based on Water Balance Over the Last 50 Years

Daihai Lake is an inland lake in north China. The water level and the water area of Daihai Lake have changed many times due to climate change and environment change. The water level of the lake has been continuously decreasing since the 1970s, and it has caused a great negative impact on the local economic construction. Based on the principle of water balance, after an analysis of hydrological data of the Daihai Lake during 1960-2014, it figures out that the decreasing in the water level of the Daihai Lake was caused by the sharp decreasing in lake runoff. With human activity in the basin, the total amount of water utilization in the basin was also rapidly increasing, which is the main reason of the sharp decrease in lake runoff.

An interactive web app for retrieval, visualization, and analysis of hydrologic and meteorological time series data

As wireless sensor networks become more prevalent tools for analyzing watershed dynamics, the integration and management of the myriad of available data streams presents a unique challenge. Web-based platforms provide tools to retrieve and visualize hydrologic and meteorologic data from various sources while addressing syntactic and semantic differences in data formats. However, current platforms are limited by a lack of data analysis tools. The Stream Hydrology And Rainfall Knowledge System (SHARKS) app was developed to expand upon existing platforms by providing a suite of exploratory data analysis features including the ability to calculate the total precipitation depth recorded for any period, interpolate the average annual recurrence interval for precipitation events, perform hydrograph separations, and calculate the volume of runoff for any period. A case study provides a conceptual description of the types of rapid analysis that can be performed using the SHARKS app.