Learning recurrent transfer functions from data: From climate variability to high river discharge

2020 ◽  
Author(s):  
Mateusz Norel ◽  
Krzysztof Krawiec ◽  
Zbigniew Kundzewicz
Keyword(s):  
Time Series ◽  
River Discharge ◽  
Short Term Memory ◽  
Transfer Functions ◽  
Southern Oscillation ◽  
Flood Hazard ◽  
Atlantic Multidecadal Oscillation ◽  
Statistical Characteristics ◽  
Learning Tools ◽  
Discharge Data

<p>Interpretation of flood hazard and its variability remains a major challenge for climatologists, hydrologists and water management experts. This study investigates the existence of links between variability in high river discharge, worldwide, and inter-annual and inter-decadal climate oscillation indices: El Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Interdecadal Oscillation, and Atlantic Multidecadal Oscillation. Global river discharge data used here stem from the ERA-20CM-R reconstruction at 0.5 degrees resolution and form a multidimensional time series, with each observation being a spatial matrix of estimated discharge volume. Elements of matrices aligned spatially form time series which were used to induce dedicated predictive models using machine learning tools, including multivariate regression (e.g. ARMA) and recurrent neural networks (RNNs), in particular the Long Short Term Memory model (LSTM) that proved to be effective in many other application areas. The models are thoroughly tested and juxtaposed in hindcasting mode on a separate test set and scrutinized with respect to their statistical characteristics. We hope to be able to contribute to improvement of interpretation of variability of flood hazard and reduction of uncertainty.</p>

scholarly journals Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest

2020 ◽  
Vol 12 (6) ◽  
pp. 907 ◽  
Author(s):  
Teodoro Semeraro ◽  
Andrea Luvisi ◽  
Antonio O. Lillo ◽  
Roberta Aretano ◽  
Riccardo Buccolieri ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Southern Oscillation ◽  
Vegetation Indices ◽  
Atlantic Multidecadal Oscillation ◽  
Enso Event ◽  
Amazon Forest ◽  
Ecosystem Response ◽  
Water Index

Forests are important in sequestering CO2 and therefore play a significant role in climate change. However, the CO2 cycle is conditioned by drought events that alter the rate of photosynthesis, which is the principal physiological action of plants in transforming CO2 into biological energy. This study applied recurrence quantification analysis (RQA) to describe the evolution of photosynthesis-related indices to highlight disturbance alterations produced by the Atlantic Multidecadal Oscillation (AMO, years 2005 and 2010) and the El Niño-Southern Oscillation (ENSO, year 2015) in the Amazon forest. The analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) images to build time series of the enhanced vegetation index (EVI), the normalized difference water index (NDWI), and the land surface temperature (LST) covering the period 2001–2018. The results did not show significant variations produced by AMO throughout the study area, while a disruption due to the global warming phase linked to the extreme ENSO event occurred, and the forest was able to recover. In addition, spatial differences in the response of the forest to the ENSO event were found. These findings show that the application of RQA to the time series of vegetation indices supports the evaluation of the forest ecosystem response to disruptive events. This approach provides information on the capacity of the forest to recover after a disruptive event and, therefore is useful to estimate the resilience of this particular ecosystem.

scholarly journals Dynamic wavelet correlation analysis for multivariate climate time series

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Josué M. Polanco-Martínez ◽  
Javier Fernández-Macho ◽  
Martín Medina-Elizalde
Keyword(s):  
Time Series ◽  
Tropical Cyclone ◽  
North Atlantic ◽  
Large Scale ◽  
Southern Oscillation ◽  
Atlantic Multidecadal Oscillation ◽  
Last Millennium ◽  
Climate Data ◽  
The North ◽  
Climate Time Series

AbstractThe wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.

Discharge estimation and monitoring extreme events by satellite altimetry

2020 ◽  
Author(s):  
Rossella Belloni ◽  
Stefania Camici ◽  
Angelica Tarpanelli
Keyword(s):  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Satellite Altimetry ◽  
Global Scale ◽  
Discharge Time ◽  
Discharge Data ◽  
The World ◽  
Daily Discharge ◽  
Rating Curves

<p>In view of recent dramatic floods and drought events, the detection of trends in the frequency and magnitude of long time series of flood data is of scientific interest and practical importance. It is essential in many fields, from climate change impact assessment to water resources management, from flood forecasting to drought monitoring, for the planning of future water resources and flood protection systems. <br>To detect long-term changes in river discharge a dense, in space and time, network of monitoring stations is required. However, ground hydro-meteorological monitoring networks are often missing or inadequate in many parts of the world and the global supply of the available river discharge data is often restricted, preventing to identify trends over large areas.  <br>The most direct method of deriving such information on a global scale involves satellite earth observation. Over the last two decades, the growing availability of satellite sensors, and the results so far obtained in the estimation of river discharge from the monitoring of the water level through satellite radar altimetry has fostered the interest on this subject.  <br>Therefore, in the attempt to overcome the lack of long continuous observed time series, in this study satellite altimetry water level data are used to set-up a consistent, continuous and up-to-date daily discharge dataset for different sites across the world. Satellite-derived water levels provided by publicly available datasets (Podaac, Dahiti, River& Lake, Hydroweb and Theia) are used along with available ground observed river discharges to estimate rating curves. Once validated, the rating curves are used to fill and extrapolate discharge data over the whole period of altimetry water level observations. The advantage of using water level observations provided by the various datasets allowed to obtain discharge time series with improved spatio-temporal coverages and resolutions, enabling to extend the study on a global scale and to efficiently perform the analysis even for small to medium-sized basins.  <br>Long continuous discharge time series so obtained are used to perform a global trend analysis on extreme flood and drought events. Specifically, annual maximum discharge and peak-over threshold values are extracted from the simulated daily discharge time series, as proxy variables of independent flood events. For flood and drought events, a trend analysis is carried out to identify changes in the frequency and magnitude of extreme events through the Mann-Kendall (M-K) test and a linear regression model between time and the flood magnitude.  <br>The analysis has permitted to identify areas of the world prone to floods and drought, so that appropriate actions for disaster risk mitigation and continuous improvement in disaster preparedness, response, and recovery practices can be adopted. </p>

ADHI: The African Database of Hydrometric Indices (1950-2018)

2021 ◽  
Author(s):  
Nathalie Rouché ◽  
Yves Tramblay ◽  
Jean-Emmanuel Paturel ◽  
Gil Mahé ◽  
Jean-François Boyer ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Data ◽  
River Discharge ◽  
Data Availability ◽  
Eastern Africa ◽  
African Continent ◽  
Discharge Data ◽  
Daily Discharge ◽  
Time Periods ◽  
Different Sources

<p>The African continent is probably the one with the lowest density of hydrometric stations currently measuring river discharge, despite the fact that the number of stations was quite important until the 70s. In addition, there is a major issue of data availability, since the different existing datasets are scattered across vast regions, heterogeneous and often with a large amount of missing data in the time series. The aim of this African Dataset of Hydrometric Indices (ADHI) is to provide a set of hydrometric indices computed from an unprecedented large set of daily discharge data in Africa. The ADHI database is based on a new streamflow dataset of 1466 gauging stations with an average record length of 33 years and for over 100 stations complete records are available over 50 years. ADHI is compiling data from different sources carefully checked, based on the historical databases of ORSTOM / IRD and the GRDC, including also other contributions from different countries and basin agencies. The criterion for a station to be included in ADHI is to have a minimum of 10 full years of daily discharge data between 1950 and 2018 with less than 5% missing data. Some time series originating from different sources were concatenated, after making sure the rating curves applied on the different time periods to compute river discharge were similar. Data records were scrutinized to identify suspicious discharge records and time periods where gap-filling methods have been applied to the original records. The selected stations are spread across the whole African continent, with the highest density in Western and Southern Africa and the lowest density in Eastern Africa. They are representative of most of the climate zones of Africa according the Köppen-Geiger climate classification. From this dataset, a large range of hydrological indices and flow signatures have been computed and made available to the scientific community (https://doi.org/10.23708/LXGXQ9). They are representing mean flow characteristics and extremes (low flows and floods) but also catchment characteristics, allowing to study the long-term evolution of hydrology in Africa and support the modelling efforts that aim at reducing the vulnerability of African countries to hydro-climatic variability.</p>

From climate variability to heavy precipitation – Learning transfer functions from data

2020 ◽  
Author(s):  
Michał Kałczyński ◽  
Krzysztof Krawiec ◽  
Zbigniew Kundzewicz
Keyword(s):  
Climate Variability ◽  
Transfer Functions ◽  
Southern Oscillation ◽  
Dominant Role ◽  
Heavy Precipitation ◽  
Statistical Characteristics ◽  
Decadal Climate Variability ◽  
Full Data ◽  
Data Set ◽  
Climate Oscillation

<p>The contribution deals with spatial extremes of intense precipitation at the global scale, with the help of data-driven modelling. We ask whether the inter-annual and inter-decadal climate variability track plays a dominant role in the interpretation of the variability of heavy precipitation, globally. The study aims at discovering spatially and temporally organized links between climate oscillation indices, such as El Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Interdecadal Oscillation, Atlantic Multidecadal Oscillation and heavy precipitation. To this aim, we induce a range of machine-learning models, primarily recurrent neural networks, from multiple sources of global observations, including E-OBS data set from the UERRA project, GPCC Full Data Daily, and climate variability indices. The models are thoroughly tested and juxtaposed in hindcasting mode on a separate test set and scrutinized with respect to their statistical characteristics. We expect to identify climate-oscillation drivers for spatial dependence of heavy precipitation.</p>

scholarly journals Application of Vector Auto Regression Model for Rainfall-River Discharge Analysis

2015 ◽  
Vol 29 (1) ◽  
Author(s):  
Sri Hartini ◽  
Muhammad Pramono Hadi ◽  
Sudibyakto Sudibyakto ◽  
Aris Poniman
Keyword(s):  
Time Series ◽  
River Discharge ◽  
Time Series Data ◽  
Series Data ◽  
Var Model ◽  
Time Interval ◽  
Discharge Data ◽  
Vector Auto Regression ◽  
The Relationship ◽  
Auto Regression

River discharge quantity is highly depended on rainfall and initial condition of river discharge; hence, the river discharge has auto-correlation relationships. This study used Vector Auto Regression (VAR) model for analysing the relationship between rainfall and river discharge variables. VAR model was selected by considering the nature of the relationship between rainfall and river discharge as well as the types of rainfall and discharge data, which are in form of time series data. This research was conducted by using daily rainfall and river discharge data obtained from three weirs, namely Sojomerto and Juwero, in Kendal Regency and Glapan in Demak Regency, Central Java Province. Result of the causality tests shows significant relationship of both variables, those on the influence of rainfall to river discharge as well as the influence of river discharge to rainfall variables. The significance relationships of river discharge to rainfall indicate that the rainfall in this area has moved downstream. In addition, the form of VAR model could explain the variety of the relationships ranging between 6.4% - 70.1%. These analyses could be improved by using rainfall and river discharge time series data measured in shorter time interval but in longer period.

scholarly journals A New and Simplified Approach for Estimating the Daily River Discharge of the Tibetan Plateau Using Satellite Precipitation: An Initial Study on the Upper Brahmaputra River

2020 ◽  
Vol 12 (13) ◽  
pp. 2103
Author(s):  
Tian Zeng ◽  
Lei Wang ◽  
Xiuping Li ◽  
Lei Song ◽  
Xiaotao Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Real Time ◽  
River Discharge ◽  
The Tibetan Plateau ◽  
Time Lags ◽  
Precipitation Data ◽  
Precipitation Time Series ◽  
Discharge Data ◽  
Satellite Precipitation

Collecting in situ observations from remote, high mountain rivers presents major challenges, yet real-time, high temporal resolution (e.g., daily) discharge data are critical for flood hazard mitigation and river management. In this study, we propose a method for estimating daily river discharge (RD) based on free, operational remote sensing precipitation data (Tropical Rainfall Measuring Mission (TRMM), since 2001). In this method, an exponential filter was implemented to produce a new precipitation time series from daily basin-averaged precipitation data to model the time lag of precipitation in supplying RD, and a linear-regression relationship was constructed between the filtered precipitation time series and observed discharge records. Because of different time lags in the wet season (rainfall-dominant) and dry season (snowfall-dominant), the precipitation data were processed in a segmented way (from June to October and from November to May). The method was evaluated at two hydrological gauging stations in the Upper Brahmaputra (UB) river basin, where Nash–Sutcliffe Efficiency (NSE) coefficients for Nuxia (>0.85) and Yangcun (>0.80) indicate good performance. By using the degree-day method to estimate the snowmelt and acquire the time series of new active precipitation (rainfall plus snowmelt) in the target basins, the discharge estimations were improved (NSE > 0.9 for Nuxia) compared to the original data. This makes the method applicable for most rivers on the Tibetan Plateau, which are fed mainly by precipitation (including snowfall) and are subject to limited human interference. The method also performs well for reanalysis precipitation data (Chinese Meteorological Forcing Dataset (CMFD), 1980–2000). The real-time or historical discharges can be derived from satellite precipitation data (or reanalysis data for earlier historical years) by using our method.

scholarly journals Measuring compound flood potential from river discharge and storm surge extremes at the global scale

2020 ◽  
Vol 20 (2) ◽  
pp. 489-504 ◽  
Author(s):  
Anaïs Couasnon ◽  
Dirk Eilander ◽  
Sanne Muis ◽  
Ted I. E. Veldkamp ◽  
Ivan D. Haigh ◽  
...  
Keyword(s):  
Storm Surge ◽  
River Discharge ◽  
Flood Hazard ◽  
Joint Probability ◽  
Global Scale ◽  
Statistical Dependence ◽  
Dependence Structure ◽  
Meteorological Forcing ◽  
Continental Scale ◽  
Bivariate Dependence

Abstract. The interaction between physical drivers from oceanographic, hydrological, and meteorological processes in coastal areas can result in compound flooding. Compound flood events, like Cyclone Idai and Hurricane Harvey, have revealed the devastating consequences of the co-occurrence of coastal and river floods. A number of studies have recently investigated the likelihood of compound flooding at the continental scale based on simulated variables of flood drivers, such as storm surge, precipitation, and river discharges. At the global scale, this has only been performed based on observations, thereby excluding a large extent of the global coastline. The purpose of this study is to fill this gap and identify regions with a high compound flooding potential from river discharge and storm surge extremes in river mouths globally. To do so, we use daily time series of river discharge and storm surge from state-of-the-art global models driven with consistent meteorological forcing from reanalysis datasets. We measure the compound flood potential by analysing both variables with respect to their timing, joint statistical dependence, and joint return period. Our analysis indicates many regions that deviate from statistical independence and could not be identified in previous global studies based on observations alone, such as Madagascar, northern Morocco, Vietnam, and Taiwan. We report possible causal mechanisms for the observed spatial patterns based on existing literature. Finally, we provide preliminary insights on the implications of the bivariate dependence behaviour on the flood hazard characterisation using Madagascar as a case study. Our global and local analyses show that the dependence structure between flood drivers can be complex and can significantly impact the joint probability of discharge and storm surge extremes. These emphasise the need to refine global flood risk assessments and emergency planning to account for these potential interactions.

scholarly journals The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004–2019

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
N. D. B. Ehelepola ◽  
Kusalika Ariyaratne ◽  
A. M. S. M. C. M. Aththanayake ◽  
Kamalanath Samarakoon ◽  
H. M. Arjuna Thilakarathna
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Wavelet Analysis ◽  
Southern Oscillation ◽  
Population Data ◽  
Enso Modoki ◽  
Cross Correlation Analysis ◽  
Teleconnection Indices ◽  
Weather Parameters ◽  
Time Lagged

Abstract Background Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI. Methods We acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected. Results Wavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods. Conclusions Results of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

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