scholarly journals Sub-daily runoff simulations with parameters inferred at the daily time scale

2015 ◽  
Vol 12 (8) ◽  
pp. 7437-7467 ◽  
Author(s):  
J. E. Reynolds ◽  
S. Halldin ◽  
C. Y. Xu ◽  
J. Seibert ◽  
A. Kauffeldt
Keyword(s):  
Time Series ◽  
Numerical Method ◽  
Time Scales ◽  
Time Scale ◽  
Input Data ◽  
Flood Forecasting ◽  
Euler Method ◽  
Time Stepping ◽  
Parameter Values ◽  
Daily Time

Abstract. Concentration times in small and medium-sized watersheds (~ 100–1000 km2) are commonly less than 24 h. Flood-forecasting models then require data at sub-daily time scales, but time-series of input and runoff data with sufficient lengths are often only available at the daily time scale, especially in developing countries. This has led to a search for time-scale relationships to infer parameter values at the time scales where they are needed from the time scales where they are available. In this study, time-scale dependencies in the HBV-light conceptual hydrological model were assessed within the generalized likelihood uncertainty estimation (GLUE) approach. It was hypothesised that the existence of such dependencies is a result of the numerical method or time-stepping scheme used in the models rather than a real time-scale-data dependence. Parameter values inferred showed a clear dependence on time scale when the explicit Euler method was used for modelling at the same time steps as the time scale of the input data (1–24 h). However, the dependence almost fully disappeared when the explicit Euler method was used for modelling in 1 h time steps internally irrespectively of the time scale of the input data. In other words, it was found that when an adequate time-stepping scheme was implemented, parameter sets inferred at one time scale (e.g., daily) could be used directly for runoff simulations at other time scales (e.g., 3 or 6 h) without any time scaling and this approach only resulted in a small (if any) model performance decrease, in terms of Nash–Sutcliffe and volume-error efficiencies. The overall results of this study indicated that as soon as sub-daily driving data can be secured, flood forecasting in watersheds with sub-daily concentration times is possible with model-parameter values inferred from long time series of daily data, as long as an appropriate numerical method is used.

scholarly journals Locally and Remotely Forced Subtropical AMOC Variability: A Matter of Time Scales

2020 ◽  
Vol 33 (12) ◽  
pp. 5155-5172
Author(s):  
Quentin Jamet ◽  
William K. Dewar ◽  
Nicolas Wienders ◽  
Bruno Deremble ◽  
Sally Close ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
North Atlantic ◽  
Time Scale ◽  
Low Frequency ◽  
Meridional Overturning Circulation ◽  
Open Boundary ◽  
Overturning Circulation ◽  
Decadal Scale ◽  
Meridional Overturning

AbstractMechanisms driving the North Atlantic meridional overturning circulation (AMOC) variability at low frequency are of central interest for accurate climate predictions. Although the subpolar gyre region has been identified as a preferred place for generating climate time-scale signals, their southward propagation remains under consideration, complicating the interpretation of the observed time series provided by the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing from signals of remote origin for the subtropical low-frequency AMOC variability. We analyze for this a set of four ensembles of a regional (20°S–55°N), eddy-resolving (1/12°) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals. Their analysis reveals the predominance of local, atmospherically forced signal at interannual time scales (2–10 years), whereas signals imposed by the boundaries are responsible for the decadal (10–30 years) part of the spectrum. Due to this marked time-scale separation, we show that, although the intergyre region exhibits peculiarities, most of the subtropical AMOC variability can be understood as a linear superposition of these two signals. Finally, we find that the decadal-scale, boundary-forced AMOC variability has both northern and southern origins, although the former dominates over the latter, including at the site of the RAPID array (26.5°N).

scholarly journals Methods of Constructing Time Series for Predicting Local Time Scales by Means of a GMDH-Type Neural Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5615
Author(s):  
Łukasz Sobolewski ◽  
Wiesław Miczulski
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Local Time ◽  
Time Scales ◽  
Time Scale ◽  
Prediction Accuracy ◽  
Prediction Horizon

Ensuring the best possible stability of UTC(k) (local time scale) and its compliance with the UTC scale (Universal Coordinated Time) forces predicting the [UTC-UTC(k)] deviations, the article presents the results of work on two methods of constructing time series (TS) for a neural network (NN), increasing the accuracy of UTC(k) prediction. In the first method, two prepared TSs are based on the deviations determined according to the UTC scale with a 5-day interval. In order to improve the accuracy of predicting the deviations, the PCHIP interpolating function is used in subsequent TSs, obtaining TS elements with a 1-day interval. A limitation in the improvement of prediction accuracy for these TS has been a too large prediction horizon. The introduction in 2012 of the additional UTC Rapid scale by BIPM makes it possible to shorten the prediction horizon, and the building of two TSs has been proposed according to the second method. Each of them consists of two subsets. The first subset is based on deviations determined according to the UTC scale, the second on the UTC Rapid scale. The research of the proposed TS in the field of predicting deviations for the Polish Timescale by means of GMDH-type NN shows that the best accuracy of predicting the deviations has been achieved for TS built according to the second method.

scholarly journals Development and Integration of Sub-Daily Flood Modelling Capability within the SWAT Model and a Comparison with XAJ Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1263 ◽  
Author(s):  
Dachen Li ◽  
Simin Qu ◽  
Peng Shi ◽  
Xueqiu Chen ◽  
Feng Xue ◽  
...  
Keyword(s):  
Time Scale ◽  
Flood Control ◽  
Assessment Tool ◽  
Spatial Scales ◽  
Swat Model ◽  
Flood Forecasting ◽  
Base Flow ◽  
Flood Simulation ◽  
Promising Tool ◽  
Daily Time

To date, floods have become one of the most severe natural disasters on Earth. Flood forecasting with hydrological models is an important non-engineering measure for flood control and disaster reduction. The Xin’anjiang (XAJ) model is the most widely used hydrological model in China for flood forecasting, while the Soil and Water Assessment Tool (SWAT) model is widely applied for daily and monthly simulation and has shown its potential for flood simulation. The objective of this paper is to evaluate the performance of the SWAT model in simulating floods at a sub-daily time-scale in a slightly larger basin and compare that with the XAJ model. Taking Qilijie Basin (southeast of China) as a study area, this paper developed the XAJ model and SWAT model at a sub-daily time-scale. The results showed that the XAJ model had a better performance than the sub-daily SWAT model regarding relative runoff error (RRE) but the SWAT model performed well according to relative peak discharge error (RPE) and error of occurrence time of peak flow (PTE). The SWAT model performed unsatisfactorily in simulating low flows due to the daily calculation of base flow but behaved quite well in simulating high flows. We also evaluated the effect of spatial scale on the SWAT model. The results showed that the SWAT model had a good applicability at different spatial scales. In conclusion, the sub-daily SWAT model is a promising tool for flood simulation though more improvements remain to be studied further.

Data-driven stochastic model for cross-interacting processes with different time scales

2021 ◽  
Author(s):  
Andrey Gavrilov ◽  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Alexander Feigin
Keyword(s):  
Time Series ◽  
Stochastic Model ◽  
Time Scales ◽  
Time Scale ◽  
Sampling Interval ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Middle Pleistocene ◽  
Model Based ◽  
Different Time Scales

<p>The problem of modeling interaction between processes with different time scales is very important in geoscience. In this report, we propose a new form of empirical evolution operator model based on the analysis of multiple time series representing processes with different time scales. We assume that the time series are given on the same time interval.</p><p>To construct the model, we extend the previously developed general form of nonlinear stochastic model based on artificial neural networks and designed for the case of time series with constant sampling interval [1]. This sampling interval is related to the main time scale of the process under consideration, which is described by the deterministic component of the model, while the faster time scales are modeled by its stochastic component, possibly depending on the system’s state. This model also includes slower processes in the form of weak time-dependence, as well as external forcing. The structure of the model is optimized using Bayesian approach [1]. The model has proven its efficiency in a number of applications [2-4].</p><p>The idea of modeling time series with different time scales is to formulate the above-described model individually for each time scale, and then to include the parameterized influence of the other time scales in it. Particularly, the influence of “slower” time series is included in the form of parameter trends, and the influence of “faster” time series is included by time-averaging their statistics. The algorithm and first results of comparison between the new model and the model without cross-interactions will be discussed.</p><p>The work was supported by the Russian Science Foundation (Grant No. 20-62-46056).</p><p>1. Gavrilov, A., Loskutov, E., & Mukhin, D. (2017). Bayesian optimization of empirical model with state-dependent stochastic forcing. Chaos, Solitons & Fractals, 104, 327–337. http://doi.org/10.1016/j.chaos.2017.08.032</p><p>2. Mukhin, D., Kondrashov, D., Loskutov, E., Gavrilov, A., Feigin, A., & Ghil, M. (2015). Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models. Journal of Climate, 28(5), 1962–1976. http://doi.org/10.1175/JCLI-D-14-00240.1</p><p>3. Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., & Kurths, J. (2019). Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics, 52(3–4), 2199–2216. http://doi.org/10.1007/s00382-018-4255-7</p><p>4. Mukhin, D., Gavrilov, A., Loskutov, E., Kurths, J., & Feigin, A. (2019). Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition. Scientific Reports, 9(1), 7328. http://doi.org/10.1038/s41598-019-43867-3</p>

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

2021 ◽  
Vol 12 (3) ◽  
pp. 2427-2435
Author(s):  
Adib Mashuri Et.al
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Water Level ◽  
Time Scale ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Daily Time

This study focused on chaotic analysis of water level data in different elevations located in the highland and lowland areas. This research was conducted considering the uncertain water level caused by the river flow from highland to lowland areas. The analysis was conducted using the data collected from the four area stations along Pahang River on different time scales which were hourly and daily time series data. The resulted findings were relevant to be used by the local authorities in water resource management in these areas. Two methods were used for the analysis process which included Cao method and phase space plot. Both methods are based on phase space reconstruction that is referring to reconstruction of one dimensional data (water level data) to d-dimensional phase space in order to determine the dynamics of the system. The combination of parameters  and d is required in phase space reconstruction. Results showed that (i) the combination of phase space reconstruction’s parameters gave a higher value of parameters by using hourly time scale compared to daily time scale for different elevation; (ii) different elevation gave impact on the values of phase space reconstructions’ parameters; (iii) chaotic dynamics existed using Cao method and phase space plot for different elevation and time scale. Hence, water level data with different time scale from different elevation in Pahang River can be used in the development of prediction model based on chaos approach.

scholarly journals Multi-scale event synchronization analysis for unravelling climate processes: A wavelet-based approach

2017 ◽  
Author(s):  
Ankit Agarwal ◽  
Norbert Marwan ◽  
Maheswaran Rathinasamy ◽  
Bruno Merz ◽  
Jürgen Kurths
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Time Scale ◽  
Temporal Dynamics ◽  
Multiple Scales ◽  
Nonlinear Interactions ◽  
Reference Time ◽  
Multi Scale ◽  
Spatio Temporal ◽  
Different Time Scales

Abstract. The temporal dynamics of climate processes are spread across different time scales and, as such, the study of these processes only at one selected time scale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. For capturing the nonlinear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analyse the time series at one reference time scale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, wavelet based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various time scales. The proposed method allows the study of spatio-temporal patterns across different time scales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different time scales.

Determinants of calcite flux in planktonic foraminifera on seasonal and interannual time scales

2020 ◽  
Author(s):  
Peter Kiss ◽  
Lukas Jonkers ◽  
Natália Hudáčková ◽  
Runa Turid Reuter ◽  
Michal Kučera
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Time Scale ◽  
Planktonic Foraminifera ◽  
Sediment Traps ◽  
Shell Size ◽  
Global Estimate ◽  
Export Flux ◽  
Species Specific ◽  
Annual Time

<p>Planktonic foraminifera precipitate calcareous shells, which after the death of the organisms are exported from the sea surface to the sea floor, where they are preserved on geologically relevant timescales. The export flux of planktonic foraminifera shells constitutes globally up to a half, and in the studied region off Cap Blanc (Atlantic Ocean) about one third, of the marine pelagic calcite flux. Given their importance for the marine calcite budget and for the pelagic carbonate counter pump, which counteracts the biological pump in terms of oceanic capacity for intake of CO<sub>2</sub>, it is crucial to gain an understanding of factors modulating the export flux of planktonic foraminifera calcite. In principle, variability in the export flux of planktonic foraminifera calcite could depend within one species on i) shell flux, ii) shell size and iii) calcification intensity, and where shell size and calcification intensity differ among species also on the species composition of the deposited assemblage. To assess the importance of these aspects in modulating the export flux of planktonic foraminifera calcite, we investigated two annual time series (from 1990-1991 and 2007-2008) from sediment traps moored in the Cap Blanc upwelling area. We assessed the predictability of foraminifera calcite flux variability on seasonal and interannual time scales, by determining the variability in species-specific shell fluxes, shell sizes and weights with bi-weekly resolution. We find a remarkable discrepancy in the contribution of the controlling factors between seasonal and interannual scales. On the seasonal time scale, 80% of the variability of the calcite flux is explained by shell flux. On the inter-annual time scale, on the other hand, variations in shell size and calcification intensity are key to explain the calcite flux, since the time series from 2007-2008 yielded 58% larger and 11% heavier specimens. These results imply that for the global estimate of planktonic foraminifera calcite flux, shell flux is likely the most relevant predictor. However, a prediction of the temporal evolution of the calcite flux will likely require estimates of changes in shell size and calcification intensity of the involved foraminifera species.</p>

scholarly journals A neuro-fuzzy model to predict the inflow to the guardialfiera multipurpose dam (Southern Italy) at medium-long time scales

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
L.F. Termite ◽  
F. Todisco ◽  
L. Vergni ◽  
F. Mannocchi
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Input Data ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Computational Technique ◽  
Inference System ◽  
The Past ◽  
Neuro Fuzzy ◽  
Multipurpose Dam

Intelligent computing tools based on fuzzy logic and artificial neural networks have been successfully applied in various problems with superior performances. A new approach of combining these two powerful tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Few studies have been undertaken to evaluate their performances in hydrologic modeling. Specifically are available rainfall-runoff modeling typically at very short time scales (hourly, daily or event for the real-time forecasting of floods) with in input precipitation and past runoff (i.e. inflow rate) and in few cases models for the prediction of the monthly inflows to a dam using the past inflows as input. This study presents an application of an Adaptive Network-based Fuzzy Inference System (ANFIS), as a neuro-fuzzy-computational technique, in the forecasting of the inflow to the Guardialfiera multipurpose dam (CB, Italy) at the weekly and monthly time scale. The latter has been performed both directly at monthly scale (monthly input data) and iterating the weekly model. Twenty-nine years of rainfall, temperature, water level in the reservoir and releases to the different uses were available. In all simulations meteorological input data were used and in some cases also the past inflows. The performance of the defined ANFIS models were established by different efficiency and correlation indices. The results at the weekly time scale can be considered good, with a Nash- Sutcliffe efficiency index E = 0.724 in the testing phase. At the monthly time scale, satisfactory results were obtained with the iteration of the weekly model for the prediction of the incoming volume up to 3 weeks ahead (E = 0.574), while the direct simulation of monthly inflows gave barely satisfactory results (E = 0.502). The greatest difficulties encountered in the analysis were related to the reliability of the available data. The results of this study demonstrate the promising potential of ANFIS in the forecasting of the short term inflows to a reservoir and in the simulation of different scenarios for the water resources management in the longer term.

scholarly journals Issues on Applying One- and Multi-Step Numerical Methods to Chaotic Oscillators for FPGA Implementation

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 151
Author(s):  
Omar Guillén-Fernández ◽  
María Fernanda Moreno-López ◽  
Esteban Tlelo-Cuautle
Keyword(s):  
Time Series ◽  
Numerical Methods ◽  
Numerical Method ◽  
Euler Method ◽  
Fpga Implementation ◽  
Chaotic Time Series ◽  
Time Step ◽  
Chaotic Oscillators ◽  
One Step ◽  
Selection Of

Chaotic oscillators have been designed with embedded systems like field-programmable gate arrays (FPGAs), and applied in different engineering areas. However, the majority of works do not detail the issues when choosing a numerical method and the associated electronic implementation. In this manner, we show the FPGA implementation of chaotic and hyper-chaotic oscillators from the selection of a one-step or multi-step numerical method. We highlight that one challenge is the selection of the time-step h to increase the frequency of operation. The case studies include the application of three one-step and three multi-step numerical methods to simulate three chaotic and two hyper-chaotic oscillators. The numerical methods provide similar chaotic time-series, which are used within a time-series analyzer (TISEAN) to evaluate the Lyapunov exponents and Kaplan–Yorke dimension (DKY) of the (hyper-)chaotic oscillators. The oscillators providing higher exponents and DKY are chosen because higher values mean that the chaotic time series may be more random to find applications in chaotic secure communications. In addition, we choose representative numerical methods to perform their FPGA implementation, which hardware resources are described and counted. It is highlighted that the Forward Euler method requires the lowest hardware resources, but it has lower stability and exactness compared to other one-step and multi-step methods.

scholarly journals Improvement of the SWAT model for event-based flood forecasting on a sub-daily time scale

2017 ◽  
Author(s):  
Dan Yu ◽  
Ping Xie ◽  
Xiaohua Dong ◽  
Xiaonong Hu ◽  
Ji Liu ◽  
...  
Keyword(s):  
Time Scale ◽  
Swat Model ◽  
Flood Forecasting ◽  
Flood Events ◽  
Calibration And Validation ◽  
Event Model ◽  
Simulation Results ◽  
Event Based ◽  
Daily Time

Abstract. Flooding represents one of the most severe natural disasters threatening the development of human society. Flood forecasting systems imbedded with hydrological models are some of the most important non-engineering measures for flood defense. The Soil and Water Assessment Tool (SWAT) is a well-designed hydrological model that is widely applied for runoff and water quality modeling. The original SWAT model is a long-term yield model. However, a daily simulation time step and continuous time marching limit the use of the SWAT model for detailed, event-based flood forecasting. In addition, SWAT uses a uniform parameter set to parameterize the Unit Hydrograph (UH) for all sub-basins, thereby ignoring the heterogeneity among the sub-basins. This paper developed a method to perform event-based flood forecasting on a sub-daily time scale based on SWAT2005. First, model programs for surface runoff and water routing were modified for a sub-daily time scale. Subsequently, the entire loop structure was broken into discrete flood events in order to obtain a SWAT-EVENT model in which antecedent soil moisture and antecedent reach storage could be obtained from daily simulations of the original SWAT model. Finally, the original lumped UH parameters were refined into distributed parameters to reflect the spatial variability of the studied area. The modified SWAT-EVENT model was used in the Wangjiaba catchment located in the upper reaches of the Huaihe River in China. Daily calibration and validation procedures were first performed for the SWAT model with long-term flow data from 1990 to 2010, after which sub-daily (Δt = 2 h) calibration and validation in the SWAT-EVENT model were conducted with 24 flood events originating primarily during the flood seasons within the same time span. Daily simulation results demonstrated acceptable model performances with Nash-Sutcliffe efficiency coefficient (ENS) values of 0.77 and 0.78 for the calibration and the validation, respectively. Event-based flood simulation results indicated reliable performances, with ENS values varying from 0.66 to 0.95. The SWAT-EVENT model, compared to the SWAT model, also improved the simulation accuracies of the flood peaks. The application of distributed UH parameters within the SWAT-EVENT model can more effectively depict the spatial variability within the study area, resulting in higher qualification ratios of the relative peak discharge error (ERP), relative peak time error (ERPT) and relative runoff volume error (ERR) relative to the application of lumped parameters.

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