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

A state-space and clustering approach for analysing the water quality in a river basin

The aim of this contribution is to apply the state-space models to identify homogeneous groups of water quality monitoring sites based on compar- ison of temporal dynamics of the concentration of pollutants in the surface water of a river basin. This comparison is performed using the Kullback information, adapting the approach used in Bengtsson and Cavanaugh (2007). The purpose of our study is to identify spatial and temporal patterns

Combining Statistical Methodologies in Water Quality Monitoring in a Hydrological Basin - Space and Time Approaches

In this work are discussed some statistical approaches that combine multivariate statistical techniques and time series analysis in order to describe and model spatial patterns and temporal evolution by observing hydrological series of water quality variables recorded in time and space. These approaches are illustrated with a data set collected in the River Ave hydrological basin located in the Northwest region of Portugal

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