scholarly journals Analysing the temporal dynamics of model performance for hydrological models

2008 ◽  
Vol 5 (6) ◽  
pp. 3169-3211 ◽  
Author(s):  
D. E. Reusser ◽  
T. Blume ◽  
B. Schaefli ◽  
E. Zehe
Keyword(s):  
Time Series ◽  
Performance Assessment ◽  
Performance Measures ◽  
Hydrological Model ◽  
Temporal Dynamics ◽  
Model Performance ◽  
High Dimensional ◽  
High Temporal Resolution ◽  
Headwater Catchment

Abstract. The temporal dynamics of hydrological model performance gives insights into errors that cannot be obtained from global performance measures assigning a single number to the fit of a simulated time series to an observed reference series. These errors can include errors in data, model parameters, or model structure. Dealing with a set of performance measures evaluated at a high temporal resolution implies analyzing and interpreting a high dimensional data set. This paper presents a method for such a hydrological model performance assessment with a high temporal resolution and illustrates its application for two very different rainfall-runoff modeling case studies. The first is the Wilde Weisseritz case study, a headwater catchment in the eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The second is the Malalcahuello case study, a headwater catchment in the Chilean Andes, simulated with the physics-based model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of classically used performance measures for a moving window. The key of the developed approach is a data-reduction method based on self-organizing maps (SOMs) and cluster analysis to classify the high-dimensional performance matrix. Synthetic peak errors are used to interpret the resulting error classes. The final outcome of the proposed method is a time series of the occurrence of dominant error types. For the two case studies analyzed here, 6 such error types have been identified. They show clear temporal patterns which can lead to the identification of model structural errors.

scholarly journals Analysing the temporal dynamics of model performance for hydrological models

2009 ◽  
Vol 13 (7) ◽  
pp. 999-1018 ◽  
Author(s):  
D. E. Reusser ◽  
T. Blume ◽  
B. Schaefli ◽  
E. Zehe
Keyword(s):  
Time Series ◽  
Performance Assessment ◽  
Performance Measures ◽  
Hydrological Model ◽  
Temporal Dynamics ◽  
Model Performance ◽  
High Dimensional ◽  
High Temporal Resolution ◽  
Headwater Catchment

Abstract. The temporal dynamics of hydrological model performance gives insights into errors that cannot be obtained from global performance measures assigning a single number to the fit of a simulated time series to an observed reference series. These errors can include errors in data, model parameters, or model structure. Dealing with a set of performance measures evaluated at a high temporal resolution implies analyzing and interpreting a high dimensional data set. This paper presents a method for such a hydrological model performance assessment with a high temporal resolution and illustrates its application for two very different rainfall-runoff modeling case studies. The first is the Wilde Weisseritz case study, a headwater catchment in the eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The second is the Malalcahuello case study, a headwater catchment in the Chilean Andes, simulated with the physics-based model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of classically used performance measures for a moving window. The key of the developed approach is a data-reduction method based on self-organizing maps (SOMs) and cluster analysis to classify the high-dimensional performance matrix. Synthetic peak errors are used to interpret the resulting error classes. The final outcome of the proposed method is a time series of the occurrence of dominant error types. For the two case studies analyzed here, 6 such error types have been identified. They show clear temporal patterns, which can lead to the identification of model structural errors.

scholarly journals Estimating parameter values of a socio-hydrological flood model

2018 ◽  
Vol 379 ◽  
pp. 193-198
Author(s):  
Marlies Holkje Barendrecht ◽  
Alberto Viglione ◽  
Heidi Kreibich ◽  
Sergiy Vorogushyn ◽  
Bruno Merz ◽  
...  
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Hydrological Model ◽  
Promising Tool ◽  
Flood Model ◽  
Modelling Studies ◽  
Data Points ◽  
Few Data ◽  
Parameter Values

Abstract. Socio-hydrological modelling studies that have been published so far show that dynamic coupled human-flood models are a promising tool to represent the phenomena and the feedbacks in human-flood systems. So far these models are mostly generic and have not been developed and calibrated to represent specific case studies. We believe that applying and calibrating these type of models to real world case studies can help us to further develop our understanding about the phenomena that occur in these systems. In this paper we propose a method to estimate the parameter values of a socio-hydrological model and we test it by applying it to an artificial case study. We postulate a model that describes the feedbacks between floods, awareness and preparedness. After simulating hypothetical time series with a given combination of parameters, we sample few data points for our variables and try to estimate the parameters given these data points using Bayesian Inference. The results show that, if we are able to collect data for our case study, we would, in theory, be able to estimate the parameter values for our socio-hydrological flood model.

scholarly journals Hydrological model performance and parameter estimation in the wavelet-domain

2009 ◽  
Vol 6 (2) ◽  
pp. 2451-2498 ◽  
Author(s):  
B. Schaefli ◽  
E. Zehe
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Time Domain ◽  
Model Performance ◽  
Time Varying ◽  
Wavelet Domain ◽  
Frequency Content ◽  
Time Varying Frequency ◽  
The Time Domain

Abstract. This paper proposes a method for rainfall-runoff model calibration and performance analysis in the wavelet-domain by fitting the estimated wavelet-power spectrum (a representation of the time-varying frequency content of a time series) of a simulated discharge series to the one of the corresponding observed time series. As discussed in this paper, calibrating hydrological models so as to reproduce the time-varying frequency content of the observed signal can lead to different results than parameter estimation in the time-domain. Therefore, wavelet-domain parameter estimation has the potential to give new insights into model performance and to reveal model structural deficiencies. We apply the proposed method to synthetic case studies and a real-world discharge modeling case study and discuss how model diagnosis can benefit from an analysis in the wavelet-domain. The results show that for the real-world case study of precipitation – runoff modeling for a high alpine catchment, the calibrated discharge simulation captures the dynamics of the observed time series better than the results obtained through calibration in the time-domain. In addition, the wavelet-domain performance assessment of this case study highlights which frequencies are not well reproduced by the model, which gives specific indications about how to improve the model structure.

scholarly journals Supplier performance assessment

2019 ◽  
Vol 27 (2) ◽  
pp. 817-838 ◽  
Author(s):  
Kristina Romule ◽  
Ozlem Bak ◽  
Claudia Colicchia ◽  
Sarah Shaw
Keyword(s):  
Performance Assessment ◽  
Performance Measures ◽  
Content Type ◽  
Implementation Challenges ◽  
Academic Literature ◽  
Delivery Performance ◽  
Supplier Performance ◽  
Manufacturing Company ◽  
Internal Business Process

Purpose The discussion of supplier performance assessment and implementation challenges has been evidenced well in the academic literature. However, the analysis of supplier performance assessment has been limited in terms of inclusion of suppliers’ perspective, especially in terms of what key performance indicators they deem to be relevant and aligned with their goals. Therefore, the purpose of this paper is to shed light on supplier performance assessment, taking into account both manufacturing company’s and suppliers’ perspective, to evaluate to what extent the utilised performance measures are beneficial to all parties. Design/methodology/approach Based on literature review on supplier performance assessment, ten categories of performance measures were established and explored in a case study involving a UK manufacturing company and its suppliers. A questionnaire was distributed to the manufacturing company and their 30 suppliers, resulting with a total of 41 responses. Findings From the established ten categories only five categories were highly rated which were: net profits, flexibility and responsiveness, delivery performance and time and cycle time, product quality and availability, which were aligned to financial and internal business process categories. Research limitations/implications This study focused on a UK-based company and its relationship with its suppliers and how performance measures were assessed within this context. A further study needs to be conducted in terms of comparing the results of the study to other companies’ supplier performance assessment. Originality/value The research on the topic of supplier performance assessment often relates to measurement and highlights measures for assessing suppliers’ performance to a particular industry or area of performance measurement. Hence, this study embeds three distinctive angles including the academic literature on supplier performance assessment, suppliers’ and the manufacturing company’s perspectives.

Model adequacy tests for improving predictions in ungauged basins

2020 ◽  
Author(s):  
Cristina Prieto ◽  
Nataliya Le Vine ◽  
Dmitri Kavetski ◽  
César Álvarez ◽  
Raúl Medina
Keyword(s):  
Time Series ◽  
Hydrological Model ◽  
Flow Time ◽  
Flow Index ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Ungauged Catchments ◽  
Subsequent Work ◽  
Study Results

<p>Flow prediction in ungauged catchments is a major unresolved challenge in scientific and engineering hydrology. Meeting this challenge is made difficult by the uncertainty in the “regionalization” model used to transpose hydrological data (e.g., flow indices) from gauged to ungauged basins, and by the uncertainty in the hydrological model used to predict streamflow in the ungauged basin. This study combines recent advances in flow index selection, regionalization via machine learning methods, and a Bayesian inference framework. In addition, it proposes two new statistical metrics, “DistanceTest” and “InfoTest”, to assess the adequacy of a model before estimating its parameters. “DistanceTest” quantifies whether a model (hydrological or regionalization) is likely to reproduce the available hydrological information in a catchment. “InfoTest” is based on Bayes Factors and quantifies the information added by a model (hydrological or regionalization) over prior knowledge about the available hydrological information in a catchment). The proposed adequacy tests can be seen as a prerequisite for a model (hydrological or regionalization) being considered capable of providing meaningful and high quality flow time series predictions in ungauged catchments. If a model is found inadequate a priori and rejected, the modeler is spared the effort in estimating the model parameters, which can be a substantial saving.</p><p>The proposed regionalization approach is applied to 92 northern Spain catchments, with 16 catchments treated as ungauged. It is found that (1) a small number of PCs capture approximately 87% of variability in the flow indices, and (2) adequacy tests with respect to regionalized information are indicative of (but do not guarantee) the ability of a hydrological model to predict flow time series. The adequacy tests identify the regionalization of flow index PCs as adequate in 12 of 16 catchments but the hydrological model as adequate in only 1 of 16 catchments. In addition, the case study results suggest that the hydrological model is the main source of uncertainty in comparison to the regionalization model, and hence should receive the main priority in subsequent work at the case study catchments.</p>

scholarly journals Information content of stream level class data for hydrological model calibration

2017 ◽  
Vol 21 (9) ◽  
pp. 4895-4905 ◽  
Author(s):  
H. J. Ilja van Meerveld ◽  
Marc J. P. Vis ◽  
Jan Seibert
Keyword(s):  
Time Series ◽  
Citizen Science ◽  
Model Calibration ◽  
Hydrological Model ◽  
Model Performance ◽  
Added Value ◽  
Distributed Data ◽  
Vertical Resolution ◽  
Level Data ◽  
Spatially Distributed

Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and are likely also observed more easily by citizen scientists than streamflow. However, the challenge with crowd based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data to stream level classes. A bucket-type hydrological model was calibrated with these hypothetical stream level class data and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes, but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model based streamflow time series for previously ungauged basins.

scholarly journals Information content of stream level class data for hydrological model calibration

2017 ◽  
Author(s):  
Ilja van Meerveld ◽  
Marc Vis ◽  
Jan Seibert
Keyword(s):  
Time Series ◽  
Citizen Science ◽  
Model Calibration ◽  
Hydrological Model ◽  
Model Performance ◽  
Added Value ◽  
Data Sets ◽  
Distributed Data ◽  
Vertical Resolution ◽  
Level Data

Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and can be observed more easily by citizen scientists. However, the challenge with crowd-based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd-based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data into stream level classes. A bucket-type hydrological model was calibrated with these hypothetical data sets and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model-based streamflow time series for previously ungauged basins.

scholarly journals A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
I. Creus-Martí ◽  
A. Moya ◽  
F. J. Santonja
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Autoregressive Model ◽  
Time Series Data ◽  
Series Data ◽  
High Dimensional ◽  
Time Varying ◽  
Proposed Model ◽  
Time Varying Parameters

Growing interest in understanding microbiota dynamics has motivated the development of different strategies to model microbiota time series data. However, all of them must tackle the fact that the available data are high-dimensional, posing strong statistical and computational challenges. In order to address this challenge, we propose a Dirichlet autoregressive model with time-varying parameters, which can be directly adapted to explain the effect of groups of taxa, thus reducing the number of parameters estimated by maximum likelihood. A strategy has been implemented which speeds up this estimation. The usefulness of the proposed model is illustrated by application to a case study.

scholarly journals Snow depth estimation by time-lapse photography: Finnish and Italian case studies

2019 ◽  
Author(s):  
Marco Bongio ◽  
Ali Nadir Arslan ◽  
Cemal Melih Tanis ◽  
Carlo De Michele
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Temporal Resolution ◽  
Optical Sensors ◽  
Snow Depth ◽  
Time Lapse ◽  
High Temporal Resolution ◽  
Mean Square Errors ◽  
Time Lapse Photography

Abstract. We explored the potentiality of time-lapse photography method to estimate the snow depth in boreal forested and alpine regions. Historically, the snow depth has been measured manually by rulers or snowboards, with a temporal resolution of once per day, and a time-consuming activity. In the last decades, ultrasonic and/or optical sensors have been developed to obtain automatic measurements with higher temporal resolution and accuracy, defining a network of sensors within each country. The Finnish Meteorological Institute Image processing tool (FMIPROT) is used to retrieve the snow depth from images of a snow stake on the ground collected by cameras. An “ad-hoc” algorithm based on the brightness difference between snowpack and stake’s markers has been developed. We illustrated three case studies (case study 1-Sodankylä Peatland, case study 2-Gressoney la Trinitè Dejola, and case study 3-Careser dam) to highlight potentialities and pitfalls of the method. The proposed method provides, respect to the existing methods, new possibilities and advantages in the estimation of snow depth, which can be summarized as follows: 1) retrieving the snow depth at high temporal resolution, and an accuracy comparable to the most common method (manual measurements); 2) errors or misclassifications can be identified simply with a visual observation of the images; 3) estimating the spatial variability of snow depth by placing more than one snow stake on the camera’s view; 4) concerning the well-known under catch problem of instrumental pluviometer, occurring especially in mountain regions, the snow water equivalent can be corrected using high-temporal digital images; 5) the method enables retrieval of snow depth in avalanche, dangerous and inaccessible sites, where there is in general a lack of data; 6) the method is cheap, reliable, flexible and easily extendible in different environments and applications. We analyzed cases in which this method can fail due to poor visibility conditions or obstruction on the camera’s view. Defining a simple procedure based on ensemble of simulations and a post processing correction we can reproduce a snow depth time series without biases. Root Mean Square Errors (RMSE) and Nash Sutcliffe Efficiency (NSE) are calculated for all three case studies comparing with both estimates from the FMIPROT and visual observations of images. For the case studies, we found NSE = 0.917 , 0.963, 0.916 respectively for Sodankylä, Gressoney and Careser. In terms of accuracy, the first case study gave better results (RMSE equal to 3.951 · 10−2 m, 5.242 · 10−2 m, 10.78 · 10−2 m, respectively). The worst performances occurred at Careser dam located at 2600 m a.s.l. where extreme weather conditions occur, strongly affecting the clarity of the images. For Sodankylä case study, we showed that the proposed method can improve the measurements obtained by a Campbell snow depth ultrasonic sensor. According to results, we provided also useful information about the proper geometrical configuration stake-camera and the related parameters, which allow to retrieve reliable snow depth time series.

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