Rainfall Network Research Articles

Infilling of high‐dimensional rainfall networks through multiple imputation by chained equations

AbstractAccurate precipitation records are an essential component when monitoring the climate and studying its changes. However, analysis is typically limited by the large quantities of missing values present. This article proposes two new imputation techniques for incomplete monthly data collected from a rainfall monitoring network in the Republic of Ireland from 1981 to 2010. The data considered is high‐dimensional due to the large number of over 1100 rain gauge stations present, and the methods presented are designed to handle such cases. These are Elastic‐Net Chained Equations (ENCE) and Multiple Imputation by Chained Equations with Direct use of Regularized Regression by elastic‐net (MICE DURR). Both methods predict missing data by a series of regularized regression models, where MICE DURR differs from ENCE by also using multiple imputation. Through various evaluations across different levels of missingness, ENCE and MICE DURR consistently outperformed existing imputation methods in terms of RMSE and . Moreover, they have provided the best results both seasonally and for accurately predicting extreme values. An RMSE of 14.16 and 14.17 mm per month were reported for ENCE and MICE DURR, respectively, when stations that were at least 50% complete during the study period were included. For increasingly sparser data, the imputation accuracy achieved from MICE DURR surpasses ENCE, demonstrating the efficacy of multiple imputation when handling a substantial amount of missing data. Validation metrics indicate that these methods compare very favourably to existing methods in the literature, such as those that use random forests or multiple linear regression.

Geospatial Interpolative Analytics of Gauged Rainfall in Network under Semi-arid Situation of Krishna Basin on a Watershed Scale

Kriging as a Geospatial interpolative delineation of optimal radial distance around existing rainfall stations with in the purview of flat land (1-3 %) watershed (Hti-2 4D7B3) situated in Krishna basin. The smaller (15 and 30 min) and medium (45 min to 1h 45 min) events of the were analyzed focusing on their distribution whereas events of longer duration (>2 h) analyzed with their depth across 14 gauging stations. Analysis of 14 gauged rainfall network stations and their corresponding rainfall data during 2014-2020 used to derive relationship between recurring events of classified duration (15 min to 2 h) vis-a-vis weighted influence of number and depth due to spatial morphology and limitations of watershed. The event vise rainfall network distribution of Huti-2 watershed found to be significantly uniform in number and depth within the radial distance upto 3 km around each station prevailed by strong coefficient of determinant (R2) more than 0.8.

Comparison Link Function from Summer Rainfall Network in Amazon Basin

The Amazon Basin is the largest rainforest in the world, and studying the rainfall in this region is crucial for understanding the functioning of the entire rainforest ecosystem and its role in regulating the regional and global climate. This work is part of the application of complex networks, which refer to a network modeled by graphs and are characterized by their high versatility, as well as the extraction of key information from the system under study. The main objective of this article is to examine the precipitation system in the Amazon basin during the austral summer. The networks are defined by nodes and connections, where each node represents a precipitation time series, while the connections can be represented by different similarity functions. For this study, three rainfall networks were created, which differ based on the correlation function used (Pearson, Spearman, and Kendall). By comparing these networks, we can identify the most effective method for analyzing the data and gain a better understanding of rainfall’s spatial structure, thereby enhancing our knowledge of its impact on different Amazon basin regions. The results reveal the presence of three important regions in the Amazon basin. Two areas were identified in the northeast and northwest, showing incursions of warm and humid winds from the oceans and favoring the occurrence of large mesoscale systems, such as squall lines. Additionally, the eastern part of the central Andes may indicate an outflow region from the basin with winds directed toward subtropical latitudes. The networks showed a high level of activity and participation in the center of the Amazon basin and east of the Andes. Regarding information transmission, the betweenness centrality identified the main pathways within a basin, and some of these are directly related to certain rivers, such as the Amazon, Purus, and Madeira. Indicating the relationship between rainfall and the presence of water bodies. Finally, it suggests that the Spearman and Kendall correlation produced the most promising results. Although they showed similar spatial patterns, the major difference was found in the identification of communities, this is due to the meridional differences in the network’s response. Overall, these findings highlight the importance of carefully selecting appropriate techniques and methods when analyzing complex networks.

Traditional and Novel Methods of Rainfall Observation and Measurement: A Review

Abstract Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from “citizen science” and “crowdsourcing,” allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems.

Exploring the relationship between flood insurance claims, crowdsourced rainfall, and tide levels for coastal urban communities: Case study for the mid-Atlantic United States

The Federal Emergency Management Agency (FEMA) recently released a large dataset (∼2.5 million records) of flood insurance claim transactions from the National Flood Insurance Program (NFIP) that are geolocated by census tract. In this study, we present a methodology for using these NFIP flood claims along with agency rainfall and tide level data, combined with high-resolution rainfall data collected from growing networks of crowdsourced Personal Weather Stations (PWSs), to investigate the characteristics of storm events that result in flood insurance claims. Using 25 cities/counties in five metropolitan areas in the Mid-Atlantic region as case studies for testing the methodology, we find that the majority of flood claim events in this region were associated with rainfall below a 1-year return period and a tide level below the NOAA minor flood risk tide level. When storm events exceeded these rainfall and tide thresholds, the probability of a flood claim occurring could reach 100% for rainfall only events and 33% for tidal only events, depending on the region. While compound events of both high rainfall and high tide were rare, when seen in Virginia Beach, the probability of insurance claims being made during these events far exceeded the probability that would be expected if rainfall and tide were independent, showing how compound events in this region exacerbate flood impacts. Additionally, analysis of 110 heavy rainfall events showed that crowdsourced PWSs, can better capture rainfall extremes associated with insurance claims due to their higher spatial density compared to federal agency rainfall networks. This suggests that the PWS networks, once quality controlled, can offer greater insights into which rainfall events are likely to contribute to insurance claims. These region-specific findings and the general methodology presented in this study can benefit multiple stakeholders including researchers, risk management officials, insurers as well as the general public in understanding the impacts of rainfall, tidal, and compound flooding, along with the value of crowdsourced data in assessing flood risk.

Surveillance audio-based rainfall observation: An enhanced strategy for extreme rainfall observation

The increased frequency of extreme rainfall events (EREs) causing disastrous effects on society has become an indisputable fact in recent years. The weak performance of the current observation network in terms of accuracy and spatiotemporal resolution makes the development of new rainfall estimation techniques essential to reduce the impact of ERE-driven disasters. The rainfall sounds collected by widespread surveillance cameras provide an opportunity to produce high-resolution, all-weather rainfall data. However, the complex structure of the ground surface and the random background noise make building an effective surveillance audio-based ERE estimation system challenging. In this study, from the viewpoint of surveillance sound space, a 3D printer was used to create a shelter for the surveillance camera to define the underlying surface of falling raindrops artificially. Combining the knowledge of meteorology, micro-physics, and acoustics of rainfall, the shelter structure was designed to standardize the acoustical behavior while enhancing the consistency and specificity of raindrop sound, especially in complex scenarios such as those disturbed by different levels of wind. After that, convolutional neural network-based deep learning algorithms were used to classify ERE levels, and an audio-based ERE classification system was built. The experimental results show that the shelter facilitates audio-based rainfall representation; moreover, with the help of shelter, our proposed system achieved performance with about 93.4% accuracy in complex rainfall scenarios. Our study supports high-resolution rainfall data production on existing surveillance resources, developing a novel and reliable alternative for the perception of ERE and the calibration of observations from current rainfall networks.

“A comprehensive assessment of suitability of Global Precipitation Products for hydro-meteorological applications in a data-sparse Himalayan region”

Observation rainfall networks in developing countries such as Nepal face many challenges (like availability and quality). Global Precipitation Products (GPPs) could be an alternative to gauge-based observed rainfall (GOR) in hydro-meteorological studies. However, GPPs performance across the Himalayan regions is still unknown and is influenced by several factors such as spatial and temporal resolutions, primary data sources, etc. We have comprehensively assessed the suitability of the latest GPPs using categorical and continuous variable performance metrics for the Gandak River Basin in the Nepalese Himalayas. We then ranked GPPs for the first time using the Multicriteria Decision-Making technique. 11 out of 12 GPPs considered underestimated the annual rainfall in the basin. Performance of GPPs was also inconsistent for monthly/annual and daily timescales. At longer timescales, CHIRPS and IMERG_Final are better at representing the spatial and temporal pattern of the rainfall (spatial correlation of 0.75) and least percentage bias (PBIAS < 12%). At a daily timescale, IMERG_Final, ERA5, and PERSIANN_CDR stand out for probability of detection (POD) of rainfall, while all GPPs perform poorly in false alarm ratio (FAR). Although all GPPs have relatively high RMSE (6-14 mm/day), correlation (CC) with observed rainfall was high for IMERG_Final, ERA5, and MERRA_2 in most of the sub-basins. With elevation, the performance of all GPPs is reduced, as evidenced by higher PBIAS, and lower CC. Although there is plenty of room for improvements in rainfall estimation by GPPs, among the existing dataset, IMERG_Final scored best in the majority of the performance indicators and ranked first in five out of six sub-basins. It would be relatively the better choice in the data sparse Himalayan region when daily rainfall data is required. For applications that require monthly/annual rainfall, both CHIRPS and IMERG_Final are equally suitable. The method proposed in the study for assessing GPPs can be readily applied in other river basins and at sub-daily timescales.

Densidade e classificação da rede pluviométrica e análise espaço-temporal das chuvas na região do alto Rio Paraná, Brasil

ABSTRACT Water management aims to ensure the water availability necessary to meet the current and future demand for water resources. For which it is essential to implement monitoring networks that support the investigation of events that interfere with the rainfall regime of watersheds, such the phases of the ocean-atmospheric phenomenon El Niño – Southern Oscillation (ENSO). The classification of the rainfall network was proposed according to the World Meteorological Organization (WMO) for 14 management watersheds in the Upper Parana River (UPR) region, Brazil. On the other hand, the spatial and temporal variability of annual rainfall was analyzed using geostatistical techniques and confronted with ENSO data. To this purpose, data from 408 stations were collected via the Hidroweb portal, for the period from 1990 to 2020. The low representativeness of data in the region was verified from the observational network. The areas with the lowest and highest rainfall reduction were the north and northwest regions of the UPR, and the areas surrounding the Paraná River, respectively. The years 2019 and 2020 were identified as the most critical period of the last 3 decades with below-average rainfall (-13.21%) in 49.55% of the studied area, indicating a persistence in the drought scenario.

ERA5-Derived Precipitation: Insights from Historical Rainfall Networks in Southern Africa

Abstract Patterns of freshwater availability—its variability and distribution—are already shifting as a function of global climate change and climate variability. High-resolution global gridded reanalysis products present an important tool to understand the already observed changes and thereby improve future scenarios as the climate evolves. A historical 100-yr-long district rainfall dataset and a unique set of highly detailed rainfall data from the highveld of South Africa spanning a 10-yr period provide an opportunity to independently evaluate the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis product. Evaluation is challenged by the episodic nature of significant rainfall events of southern Africa as well as differences in spatial and temporal resolution between model output and surface precipitation data. Here we present a convergent methodology spanning annual to event time scales and regional to gauge-level spatial scales to identify the characteristics of systematic biases in variability and amount of rain as well as timing of events. We find that ERA5 is consistently wetter than observed in ways that affect the timing of individual events while performing well on metrics associated with large-scale trends and seasonal variability. Errors are associated with both stratiform and convective rainfall types, but the timing of onset of convective rainfall is a challenge that is critical in this summer-rainfall-dominated region. Significance Statement High-resolution gridded datasets are invaluable tools for gaining improved understanding of historical rainfall variations under the influence of climate change. In addition, these datasets provide consistent information for purposes such as water resources management. Quantification of dataset biases provides important guidance for robust decision-making as well as for the development of future climate scenarios. However, rainfall is an especially challenging quantity to assess. With the increasing incidence of drought and flood, methods that independently validate this high-resolution gridded data are needed to ensure high-quality knowledge support. This study demonstrates an approach using convergent streams of evidence to assess the European Centre for Medium-Range Weather Forecasts gridded rainfall dataset with the purpose of better understanding the evolving characteristics of rainfall in southern Africa.

Relationships between transmission of malaria in Africa and climate factors

The spread of malaria is related to climate change because temperature and rainfall are key parameters of climate change. Fluctuations in temperature affect the spread of malaria by lowering or speeding up its rate of transmission. The amount of rainfall also affects the transmission of malaria by offering a lot of sites suitable for mosquitoes to breed in. However, a high amount of rainfall does not have a great effect. Because of the high malaria incidence and the death rates in African regions, by using malaria incidence data, temperature data and rainfall data collected in 1901–2015, we construct and analyze climate networks to show how climate relates to the transmission of malaria in African countries. Malaria networks show a positive correlation with temperature and rainfall networks, except for the 1981–2015 period, in which the malaria network shows a negative correlation with rainfall.

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Precipitation is one of the most intrinsic resources for manifold industrial activities all over Western Australia; consequently, immaculate rainfall prediction is indispensable for flood mitigation as well as water resources management. This study investigated the performance of artificial neural networks (ANN) and Linear multiple regression (LMR) analysis to forecast long-term seasonal spring rainfall in Western Australia, using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential climatic phenomena. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm and subsequently LMR was used with statistical significance for future spring rainfall forecast. The total climatic dataset has been divided into calibration and testing phases to determine the efficacy of the developed models. Different statistical skill tests such as root mean square error (RMSE), mean absolute error (MAE), and Willmott index of agreement ‘d’ were used to assess the efficacy of LMR and ANN modelling. In general, LMR has lower MAE and RMSE values as compared to ANN for most of the stations during calibration and testing periods, whereas ANN models performed better than LMR models based on ‘d’ values. The overall statistical analysis paradigm suggests the efficacy of LMR over ANN models for rainfall forecasting using more climatic variables. As a result, the developed LMR model, incorporated with lagged global climate indices, will facilitate the adequate preparedness for the risks associated with potential droughts in the study region.

General circulation models for rainfall simulations: Performance assessment using complex networks

Reliable assessment of the impact of climate change on hydrology in a region requires proper selection of General Circulation Models (GCMs). The present study introduces the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall and selecting an ensemble of the best-performing GCMs. The performance of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating monthly rainfall over India is assessed. The observed (interpolated and gridded rainfall provided by the India Meteorological Department) rainfall data and GCM-simulated rainfall data over the period 1961–2014 at a spatial resolution of 1° x 1° (a total of 288 grids) are studied. The GCMs are evaluated for two cases: (1) Case 1 – whole year rainfall; and (2) Case 2 – summer monsoon rainfall (June–September). The clustering coefficient of the rainfall network is used as a network measure to evaluate the ability of the GCMs in simulating rainfall. For rainfall network construction, each grid is considered as a network. The phase space reconstruction concept is used to reconstruct the single-variable rainfall time series in a multi-dimensional phase space to represent the rainfall dynamics. The optimal dimension for reconstruction is determined using the false nearest neighbor (FNN) algorithm. For network construction, each reconstructed vector is considered as a node, and the connections between them, identified using a distance threshold for the reconstructed vectors in the phase space, serve as the links. For each of the 288 grids, the GCMs are ranked based on the difference in the clustering coefficient between the observed and GCM-simulated rainfall networks. The group decision-making (GDM) approach is employed to identify the ensemble of the best-performing GCMs for the entire study area considering all the grids. The results suggest different models perform well for the whole-year rainfall and summer monsoon rainfall. For the whole-year rainfall, the models CMCC-CM2-HR4, GFDL-ESM4, CMCC-ESM2, EC-Earth3-AerChem, and CMCC-CM2-SR5 rank as the top five. For the summer monsoon rainfall, FIO-ESM-2-0, E3SM-1-1, CESM2-FV2, CMCC-CM2-HR4, and CMCC-CM2-SR5 perform the best. Considering the rank of each model for both the cases, the best-performing models are identified to be CMCC-CM2-HR4, CMCC-CM2-SR5, CMCC-ESM2, FIOESM-2-0, and E3SM-1-1. The rainfall simulated from these models also show close resemblance to the observed rainfall, especially in terms of monthly mean rainfall. Therefore, even among the many GCMs that show good agreement with the monthly mean observed rainfall, the clustering coefficient-based analysis helps to narrow down the models for the ensemble of best-performing GCMs.

Rationalization Study of Rainfall Network Density Using The Kagan-Rodda Method in Sub Watershed of Bango

Management of water resources in watershed determined by avaibility of accurate rainfall data. The study of rationalization rainfall recorded density result the suggestion of amount and placement of rainfall recorded in watershed. The study was manage in Watershed of Bango with total area 246,4 km2 using Kagan-Rodda method and WMO Standard. The analysis of rainfall recorded using WMO Standard, 100-250 km2/rainfall recorded shown that none of the existing rainfall recorded in Bango Sub-Watershed are qualified. The result of Kagan-Rodda analysis giving recommendation the precise amount of rainfall recorded in Bango Sub-Watershed is Blimbing as point of reference station and form another rainfall recorded, C station. The coverage area of Blimbing is 133,74Â km2 and C Station is 112,61 km2.

Improving GSMaP V06 precipitation products over the Upper Tocantins River basin in the Brazilian Cerrado, based on local rain-gauge network

Gridded precipitation products from remote sensing are currently available and could potentially enhance the use of precipitation data in regions with sparse network of ground rain gauges. Thus, this study aimed to evaluate the performance and propose correction models for Global Satellite Mapping of Precipitation (GSMaP) use in the Upper Tocantins River basin, a key area for food and electricity production in central Brazil, but with sparse network of ground rain gauges, challenging water resources and agriculture decision makers. GSMaP data were compared with data from a rain gauge network between 2000 and 2019. Evaluations were made at daily and monthly temporal scales. In general, GSMaP products show an overestimate bias for drizzle (0.1 ~ 1 mm day−1) and underestimate for rainfalls above 10 mm day−1. The use of monthly scale data significantly reduces the bias observed in the daily scale, but with an underestimation trend of -28.3% and -39.7% for the dry and rainy periods, respectively. Categorical indices showed that the GSMaP system had better hit rates for rain detection in the rainy season (October–April) than in the dry season (May–September). For the studied region, the use of GSMaP data on daily and monthly scales should be preceded by a bias analysis as a function of rain gauge network data. The use of bias coefficient corrected observed rainfall data underestimation on daily and monthly scales, improved correlation between GSMaP and observed rainfall data and reduced errors associated with rainfall network data within the basin influence area. The findings of this study indicate how decision makers could adjust and apply GSMaP products to estimate rainfall for water resources, agriculture and drought management challenges in the Upper Tocantins River basin.

Assessing the Trustworthiness of Crowdsourced Rainfall Networks: A Reputation System Approach

AbstractHigh resolution and accurate rainfall information is essential to modeling and predicting hydrological processes. Crowdsourced personal weather stations (PWSs) have become increasingly popular in recent years and can provide dense spatial and temporal resolution in rainfall estimates. However, their usefulness could be limited due to less trust in crowdsourced data compared to traditional data sources. Using crowdsourced PWSs data without a robust evaluation of its trustworthiness can result in inaccurate rainfall estimates as PWSs are installed and maintained by non‐experts. In this study, we advance the Reputation System for Crowdsourced Rainfall Networks (RSCRN) to bridge this trust gap by assigning dynamic trust scores to PWSs. Based on rainfall data collected from 18 PWSs in two dense clusters in Houston, Texas, USA as a case study, we found that using RSCRN‐derived trust scores can increase the accuracy of 15‐min PWS rainfall estimates when compared to rainfall observations recorded at the city's high‐fidelity rainfall stations. Overall, RSCRN rainfall estimates improved for 77% (48 out of 62) of the analyzed storm events, with a median root‐mean‐square error (RMSE) improvement of 27.3%. Compared to an existing PWS quality control method, results showed that RSCRN improved rainfall estimates for 71% of the storm events (44 out of 62), with a median RMSE improvement of 18.7%. Using RSCRN‐derived trust scores can make the rapidly growing network of PWSs a more useful resource for hydrologic applications, greatly improving knowledge of rainfall patterns in areas with dense PWSs.

Mikrobiološka kvaliteta morske vode i dagnje (Mytilus galloprovincialis, Lamarck 1819) u priobalju Dubrovnika (jugoistočni Jadran)

This research was conducted between March 2016 and March 2017 as the first study on the water and mussel quality in the coastal area of Dubrovnik (Croatia). Bacteriological analyses were conducted simultaneously on wild mussels (Mytilus galloprovincialis) tissue and their ambient water. The total numbers of heterotrophic bacteria, total coliforms, faecal coliforms and faecal streptococci in the water were determined. Concentrations of Escherichia coli were measured in mussel. The investigated stations are located in coastal water, which is particularly influenced by climatic and anthropogenic factors interacting at the boundaries between the land and freshwater and seawater environments. Seasonal variables, such as rainfall and riverine network characteristics, determine the delivery of bacteria (including E.coli) to coastal water. Direct methods of assessing the density of heterotrophic bacteria at all sampling stations showed the characteristics of an oligotrophic area. The abundance of faecal coliform bacteria was in all cases higher within mussel tissue than in the surrounding water where levels were sometimes undetectable. Maximum E. coli concentrations in mussels and seawater were regularly recorded through the summer, with the exceptions of April 2016 and January 2017 at two stations following precipitation. Minimum faecal coliform concentrations were recorded in the winter.

An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data

Abstract Accurate and precise rainfall records are crucial for hydrological applications and water resources management. The accuracy and continuity of ground-based time series rely on the density and distribution of rain gauges over territories. In the context of a decline of rain gauge distribution, how to optimize and design optimal networks is still an unsolved issue. In this work, we present a method to optimize a ground-based rainfall network using satellite-based observations, maximizing the information content of the network. We combine Climate Prediction Center MORPhing technique (CMORPH) observations at ungauged locations with an existing rain gauge network in the Rio das Velhas catchment, in Brazil. We use a greedy ranking algorithm to rank the potential locations to place new sensors, based on their contribution to the joint entropy of the network. Results show that the most informative locations in the catchment correspond to those areas with the highest rainfall variability and that satellite observations can be successfully employed to optimize rainfall monitoring networks.

Design of Optimal Rainfall Monitoring Network Using Radar and Road Networks.

Uncertainty in the rainfall network can lead to mistakes in dam operation. Sudden increases in dam water levels due to rainfall uncertainty are a high disaster risk. In order to prevent these losses, it is necessary to configure an appropriate rainfall network that can effectively reflect the characteristics of the watershed. In this study, conditional entropy was used to calculate the uncertainty of the watershed using rainfall and radar data observed from 2018 to 2019 in the Goesan Dam and Hwacheon Dam watersheds. The results identified radar data suitable for the characteristics of the watershed and proposed a site for an additional rainfall gauge. It is also necessary to select the location of the additional rainfall gauged by limiting the points where smooth movement and installation, for example crossing national borders, are difficult. The proposed site emphasized accessibility and usability by leveraging road information and selecting a radar grid near the road. As a practice result, the uncertainty of precipitation in the Goesan and Hwacheon Dam watersheds could be decreased by 70.0% and 67.9%, respectively, when four and three additional gauge sites were installed without any restriction. When these were installed near to the road, with five and four additional gauge sites, the uncertainty in the Goesan Dam and Hwacheon Dam watersheds were reduced by up to 71.1%. Therefore, due to the high degree of uncertainty, it is necessary to measure precipitation. The operation of the rainfall gauge can provide a smooth site and configure an appropriate monitoring network.

Uncertainty-based rainfall network design using a fuzzy spatial interpolation method

The importance of optimization of rain gauge stations locations is critical given that rainfall data is central to various water-related studies. As rainfall data has vagueness in nature, fuzzy set theory can describe uncertainties existing in rainfall data. In this paper, we develop a framework to rainfall network design that combines fuzzy concepts and a deterministic spatial interpolation method known as Fuzzy Inverse Distance Weighted (FIDW). It addresses two important issues: (1) the assessment of two types of fuzzy mathematical approaches known as Fuzzy Standard IDW (FS-IDW) and Fuzzy Modified IDW (FM-IDW); (2) the comparison of the FIDW with spatial and spatiotemporal network designs using Ordinary Kriging (OK), known as OK-S and OK-ST, respectively. We consider four objective functions (OF): interval-based Estimation Error Variance Types 1 and 2 (EEVT1, EEVT2), Mean Square Error (MSE) and Coefficient of Determination (R2). Four scenarios of number of removed stations including 5, 10, 15 and 20 are also analysed via statistical indicators. Firstly, the FIDW parameters (power and radius) are optimized for each OF. Then, we resort to a Genetic Algorithm (GA) to solve these OFs. Percentage of similarity between optimal removed station in both FIDW methods (FS-IDW and FM-IDW) is higher than OK-ST method. Between FIDW methods, FM-IDW yields better results. Statistical results of four removed stations (5, 10, 15 and 20 rain gauge stations) show that the highest variation in estimation accuracy is from 5 to 20 removed stations which belongs to EEVT1 OF and is around 30%.

Application of complex networks for monthly rainfall dynamics over central Vietnam

Adequate understanding of the temporal connections in rainfall is important for reliable predictions of rainfall and, hence, for water resources planning and management. This research aims to study the temporal connections in rainfall using complex networks concepts. First, the single-variable rainfall time series is represented in a multi-dimensional phase space using delay embedding (i.e. phase-space reconstruction), where the appropriate delay time and optimal embedding dimension of the time series are determined by using average mutual information and false nearest neighbors methods, respectively. Then, this reconstructed phase space is treated as a ‘network,’ with the reconstructed vectors serving as ‘nodes’ and the connections between them serving as ‘links’. Finally, the strength of the nodes are calculated to identify some key properties of the temporal rainfall network. The approach is employed independently to monthly rainfall data observed over a period of 38 years (1979–2016) from 14 rain gauge stations in the Vu Gia Thu Bon River basin in central Vietnam. Moreover, entropy values of the original rainfall time series are calculated for obtaining additional information on the properties of the rainfall dynamics. The average node strengths are also examined in terms of the mean annual rainfall, entropy of the time series, and elevation of the rain gauge station. The results indicate that: (1) while some adjacent stations (i.e. networks) have somewhat similar strength (average node strength) values, several others that are geographically close show significantly different network strengths; (2) similar entropies for adjacent stations are found more frequently than similar average node strengths; (3) there is generally a positive and proportional relationship between average strengths of nodes and entropies; and (4) the average node strengths of different months have some distinct temporal patterns (3-month, 4-month, and 6-month patterns) in rainfall dynamics, depending upon the specific region of the study area. These results have important implications for prediction, interpolation, and extrapolation of rainfall data.