Improvement of surface water quality variables modelling that incorporates a hydro-meteorological factor: a state-space approach

In this work it is constructed a hydro-meteorological factor to improve the adjustment of statistical time series models, such as state space models, of water quality variables by observing hydrological series (recorded in time and space) in a River basin. The hydro-meteorological factor is incorporated as a covariate in multivariate state space models fitted to homogeneous groups of monitoring sites. Additionally, in the modelling process it is considered a latent variable that allows incorporating a structural component, such as seasonality, in a dynamic way

Application of Change-Point Detection to a Structural Component of Water Quality Variables

In this study, methodologies were developed in statistical time series models, such as multivariate state-space models, to be applied to water quality variables in a river basin. In the modelling process it is considered a latent variable that allows incorporating a structural component, such as seasonality, in a dynamic way and a change-point detection method is applied to the structural component in order to identify possible changes in the water quality variables in consideration

Using udometric network data to estimate an environmental covariate

Manyhydrologicalandecologicalstudiesrecognizetheimportanceofcharacterizingthetemporalandspatialvari- ability of precipitation. In this study, geostatistical methodologies were developed in order to estimate a hydro-meteorological factor by (re)building the space-time distribution of the precipitation associated to monthly averages in a certain hydrological river basin that will be used in the modelling of surface water quality. A hydro-meteorological factor is constructed for each water quality monitoring site (WQMS), based on the analysis of the space-time behaviour of the precipitation observed in an udometric network located in a Portuguese river basin

Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering

This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predic- tors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration in a network of water quality monitoring sites. The hydro-meteorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section

Change-point analysis in environmental time series

Change-points are present in many environmental time series. Time variations in environmental data are complex and they can hinder the identification of the so-called change-points when traditional models are applied to this type of problems. In this study, it is proposed an alternative approach for the application of the change-point analysis by taking into account this data structure (seasonality and autocorrelation) based on the Schwarz Information Criterion (SIC). The approach was applied to time series of surface water quality variables measured at eight monitoring site

Kalman filtering approach in the calibration of radar rainfall data: a comparative analysis of state space representations

In this chapter it is presented a comparative study of some methods to estimate radar rainfall in real time. This work in- tends to discuss and compare different state space formulations based on a same data set; for instance, the comparison between the mode- ling of the mean field radar rainfall logarithmic bias (Chumchean et al., 2006), a linear radar-rain gauge calibration model (Alpuim & Barbosa, 1999; Costa & Alpuim, 2011) and a power law model (Brown et al., 2001)

Forecasting temperature time series for irrigation planning problems

Climate change is a reality and efficient use of scarce resources is vital. The challenge of this project is to study the behaviour of humidity in the soil by mathematical/statistical modeling in order to find optimal solutions to improve the efficiency of daily water use in irrigation systems. For that, it is necessary to estimate and forecast weather variables, in this particular case daily maximum and minimum air temperature. These time series present strong trend and high-frequency seasonality. This way, we perform a state space modeling framework using exponential smoothing by incorporating Box-Cox transformations, ARMA residuals, Trend and Seasonality.This research was partially financed by Portuguese funds by the Center for Research and Development in Mathematics and Applications (CIDMA) and the Portuguese Foundation for Science and Technology (”Fundação para a Ciência e a Tecnologia” - FCT), within project UID/MAT/04106 2019. This research was partially financed by Portuguese funds through Portuguese Foundation for Science and Tech nology (”Funda¸c˜ao para a Ciˆencia e a Tecnologia” - FCT), within project UID/MAT/00013/2013. FEDER/ COMPETE/- NORTE2020/ POCI/FCT funds through grants PTDC-EEI-AUT-2933-2014116858-TOCCATA and To CHAIR - POCI-01-0145-FEDER-028247 Financial support from the Portuguese Foundation for Science and Technology (FCT) within the frame work of Strategic Financing UIDIFIS/04650/2013 is also acknowledge

Kalman filtering approach in the calibration of radar rainfall data

This work presents a comparative study of some models to estimate radar rainfall in real time using the Kalman filtering approach. This comparison adresses the parameters estimation, the assessment of the accuracy estimates obtained by each model and the impact of the number of rain gauges used in the improvement of radar calibration estimates

Information criterion on the change-point detection in water quality data

The number of studies using change-point methods to detect shifts has been increasing. The Schwarz Information Criterion SIC is used in this study to search for the change-point in time series of water quality variables

Time series analysis by state space models applied to a water quality data in Portugal

Time series analysis by state space models provide a very flexible tool for analysing dynamic phenomena and evolving systems, and have significantly contributed to extending the classical domains of application of statistical time series analysis. In this study, in the context of a surface water quality monitoring problem in a river basin, it is proposed an approach for the structural time series analysis based on the state space models associated to the Kalman filter. The main goals are to analyse and evaluate the temporal evolution of the environmental time series, and to identify trends or possible changes in the water quality on a dynamic monitoring procedure. The data concerns the River Ave's hydrological basin located in the Northwest of Portugal, where monitoring has become a priority in water quality planning and management because its water has been in a state of obvious environmental degradation for many years. As a result, the watershed is now monitored by seven monitoring sites distributed along the River Ave and its main streams. For the modeling process we consider the monthly dissolved oxygen concentration dataset between January 1999 and January 2014. The framework of the state space models shows versatility to incorporate unobserved components, such as trends, cycles and seasonals, that have a natural interpretation and represent the salient features of the environmental time series under investigation. From the environmental point of view, the proposed approach allows to obtain pertinent findings concerning water surface quality interpretation and change point, thus highlighting the potential value of this type of analysis, and it is also relevant to identify unanticipated changes that are important in the management process and for the assessment of water quality.A. Manuela Gonc¸alves was supported by the Research Centre of Mathematics of the University of Minho with the Portuguese Funds from the FCT-Fundac¸ao para a Ciência e aTecnologia, through the Project PEstOE/MAT/UI0013/2014. Marco Costa was supported by Portuguese funds through the CIDMA-Centre for Research and Development in Mathematics and Applications, and the Portuguese Foundation for Science and Technology ”FCT-Fundac¸ao para a Ciência e a Tecnologia”, within project UID/MAT/04106/2013