Spatio-temporal Modelling of Remote-sensing Lake Surface Water Temperature Data

Remote-sensing technology is widely used in environmental monitoring. The coverage and resolution of satellite based data provide scientists with great opportunities to study and understand environmental change. However, the large volume and the missing observations in the remote-sensing data present challenges to statistical analysis. This paper investigates two approaches to the spatio-temporal modelling of remote-sensing lake surface water temperature data. Both methods use the state space framework, but with different parameterizations to reflect different aspects of the problem. The appropriateness of the methods for identifying spatial/temporal patterns in the data is discussed

Statistical methods for sparse image time series of remote-sensing lake environmental measurements

Remote-sensing technology is widely used in Earth observation, from everyday weather forecasting to long-term monitoring of the air, sea and land. The remarkable coverage and resolution of remote sensing data are extremely beneficial to the investigation of environmental problems, such as the state and function of lakes under climate change. However, the attractive features of remote-sensing data bring new challenges to statistical analysis. The wide coverage and high resolution means that data are usually of large volume. The orbit track of the satellite and the occasional obscuring of the instruments due to atmospheric factors could result in substantial missing observations. Applying conventional statistical methods to this type of data can be ineffective and computationally intensive due to its volume and dimensionality. Modifications to existing methods are often required in order to incorporate the missingness. There is a great need of novel statistical approaches to tackle these challenges. This thesis aims to investigate and develop statistical approaches that can be used in the analysis of the sparse remote-sensing image time series of environmental data. Specifically, three aspects of the data are considered, (a) the high dimensionality, which is associated with the volume and the dimension of data, (b) the sparsity, in the sense of high missing percentages and (c) the spatial/temporal structures, including the patterns and the correlations. Initially, methods for temporal and spatial modelling are explored and implemented with care, e.g. harmonic regression and bivariate spline regression with residual correlation structures. In recognizing the drawbacks of these methods, functional data analysis is employed as a general approach in this thesis. Specifically, functional principal component analysis (FPCA) is used to achieve the goal of dimension reduction. Bivariate basis functions are proposed to transform the satellite image data, which typically consists of thousands/millions of pixels, into functional data with low dimensional representations. This approach has the advantage of identifying spatial variation patterns through the principal component (PC) loadings, i.e. eigenfunctions. To overcome the high missing percentages that might invalidate the standard implementation of the FPCA, the mixed model FPCA (MM-FPCA) was investigated in Chapter 3. Through estimating the PCs using a mixed effect model, the influence of sparsity could be accounted for appropriately. Data imputation can be obtained from the fitted model using the (truncated) Karhunen-Loeve expansion. The method's applicability to sparse image series is examined through a simulation study. To incorporate the temporal dependence into the MM-FPCA, a novel spatio-temporal model consisting of a state space component and a FPCA component is proposed in Chapter 4. The model, referred to as SS-FPCA in the thesis, is developed based on the dynamic spatio-temporal model framework. The SS-FPCA exploits a flexible hierarchical design with (a) a data model consisting of a time varying mean function and random component for the common spatial variation patterns formulated as the FPCA, (b) a process model specifying the type of temporal dynamic of the mean function and (c) a parameter model ensuring the identifiability of the model components. A 2-cycle alternating expectation - conditional maximization (AECM) algorithm is proposed to estimate the SS-FPCA model. The AECM algorithm allows different data augmentations and parameter combinations in various cycles within an iteration, which in this case results in analytical solutions for all the MLEs of model parameters. The algorithm uses the Kalman filter/smoother to update the system states according to the data model and the process model. Model investigations are carried out in Chapter 5, including a simulation study on a 1-dimensional space to assess the performance of the model and the algorithm. This is accompanied by a brief summary of the asymptotic results of the EM-type algorithm, some of which can be used to approximate the standard errors of model estimates. Applications of the MM-FPCA and SS-FPCA to the remote-sensing lake surface water temperature and Chlorophyll data of Lake Victoria (obtained from the European Space Agency's Envisat mission) are presented at the end of Chapter 3 and 5. Remarks on the implications and limitations of these two methods are provided in Chapter 6, along with the potential future extensions of both methods. The Appendices provide some additional theorems, computation and derivation details of the methods investigated in the thesis

A changepoint approach to modelling non-stationary soil moisture dynamics

Soil moisture dynamics provide an indicator of soil health that scientists model via soil drydown curves. The typical modeling process requires the soil moisture time series to be manually separated into drydown segments and then exponential decay models are fitted to them independently. Sensor development over recent years means that experiments that were previously conducted over a few field campaigns can now be scaled to months or even years, often at a higher sampling rate. Manual identification of drydown segments is no longer practical. To better meet the challenge of increasing data size, this paper proposes a novel changepoint-based approach to automatically identify structural changes in the soil drying process, and estimate the parameters characterizing the drying processes simultaneously. A simulation study is carried out to assess the performance of the method. The results demonstrate its ability to identify structural changes and retrieve key parameters of interest to soil scientists. The method is applied to hourly soil moisture time series from the NEON data portal to investigate the temporal dynamics of soil moisture drydown. We recover known relationships previously identified manually, alongside delivering new insights into the temporal variability across soil types and locations.Comment: 19 pages for the main manuscript, 6 pages for the supplemental documen

State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality

Satellite remote sensing can provide indicative measures of environmental variables that are crucial to understanding the environment. The spatial and temporal coverage of satellite images allows scientists to investigate the changes in environmental variables in an unprecedented scale. However, identifying spatiotemporal patterns from such images is challenging due to the complexity of the data, which can be large in volume yet sparse within individual images. This paper proposes a new approach, state space functional principal components analysis (SS-FPCA), to identify the spatiotemporal patterns in processed satellite retrievals and simultaneously reduce the dimensionality of the data, through the use of functional principal components. Furthermore our approach can be used to produce interpolations over the sparse areas. An algorithm based on the alternating expectation–conditional maximisation framework is proposed to estimate the model. The uncertainty of the estimated parameters is investigated through a parametric bootstrap procedure. Lake chlorophyll-a data hold key information on water quality status. Such information is usually only available from limited in situ sampling locations or not at all for remote inaccessible lakes. In this paper, the SS-FPCA is used to investigate the spatiotemporal patterns in chlorophyll-a data of Taruo Lake on the Tibetan Plateau, observed by the European Space Agency MEdium Resolution Imaging Spectrometer

How is Baseflow Index (BFI) impacted by water resource management practices?

Water resource management (WRM) practices, such as abstractions and discharges, may impact baseflow. Here the CAMELS-GB large-sample hydrology dataset is used to assess the impacts of such practices on baseflow index (BFI) using statistical models of 429 catchments from Great Britain. Two complementary modelling schemes, multiple linear regression (LR) and machine learning (random forests, RF), are used to investigate the relationship between BFI and two sets of covariates (natural covariates only and a combined set of natural and WRM covariates). The LR and RF models show good agreement between explanatory covariates. In all models, the extent of fractured aquifers, clay soils, non-aquifers, and crop cover in catchments, catchment topography and aridity are significant or important natural covariates in explaining BFI. When WRM terms are included, groundwater abstraction is significant or the most important WRM covariate in both modelling schemes and discharge to rivers is also identified as significant or influential, although natural covariates still provide the main explanatory power of the models. Surface water abstraction is a significant covariate in the LR model but of only minor importance in the RF model. Reservoir storage covariates are not significant or are unimportant in both the LR and RF models for this large-sample analysis. Inclusion of WRM terms improves the performance of some models in specific catchments. The LR models of high BFI catchments with relatively high levels of groundwater abstraction show the greatest improvements, and there is some evidence of improvement in LR models of catchments with moderate to high discharges. However, there is no evidence that the inclusion of the WRM covariates improves the performance of LR models for catchments with high surface water abstraction or that they improve the performance of the RF models. These observations are used to formulate a conceptual framework for baseflow generation that incorporates WRM practices. It is recommended that information on WRM, particularly groundwater abstraction, should be included where possible in future large-sample hydrological data sets and in the analysis and prediction of BFI and other measures of baseflow

Assessment of suspended sediment export and dynamics using in‐line turbidity sensors and time series statistical models

The Coln is an ecologically sensitive river in a limestone dominated catchment with no major tributaries. Three in-line turbidity sensors were installed to monitor changes in the dynamics of suspended sediment transport from headwaters to the confluence. The aims were to (i) provide estimates of yield (t km−2 year−1) and likely drivers of suspended sediment over ~3 years and (ii) assess turbidity dynamics during storm events in different parts of the catchment. In addition, the sensor installation allowed a novel wavelet analysis based on identifying groups of turbidity peaks to estimate transport times of suspended sediment through the catchment. Yearly suspended sediment yields calculated for the upper catchment were typically less than 4 t ha−1 year−1 being similar to other UK limestone or chalk-based rivers. Time series autoregressive integrated moving average models including explanatory variable regression modelling indicated that river discharge, groundwater level and water temperature were all significant predictors of turbidity levels throughout the year. However, high model residuals demonstrate that the models failed to capture random turbidity events. Five parts of the time series data were used to examine sediment dynamics. Plots of scaled discharge verses turbidity demonstrated that in the upper catchment, after initial suspended sediment generation, sediment quickly became limited. In the lower catchment, hysteresis analysis suggested that sediment dilution occurred, due to increasing base flow. The novel wavelet analysis demonstrated that during winter ‘sediment events’ identified as groups of turbidity peaks, took ~18 h to pass from the first sensor in the upper catchment to the second sensor (10.3 km downstream of sensor 1) and 24 h to the third sensor (23.3 km from sensor 1). The work demonstrates the potential for using multiple turbidity sensors and time series statistical techniques in developing greater understanding of suspended sediment dynamics and associated poor water quality in ecologically sensitive rivers

Polygonatum cyrtonema polysaccharides reshape the gut microbiota to ameliorate dextran sodium sulfate-induced ulcerative colitis in mice

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized inflammatory imbalance, intestinal epithelial mucosal damage, and dysbiosis of the gut microbiota. Polygonatum cyrtonema polysaccharides (PCPs) can regulate gut microbiota and inflammation. Here, the different doses of PCPs were administered to dextran sodium sulfate-induced UC mice, and the effects of the whole PCPs were compared with those of the fractionated fractions PCP-1 (19.9 kDa) and PCP-2 (71.6 and 4.2 kDa). Additionally, an antibiotic cocktail was administered to UC mice to deplete the gut microbiota, and PCPs were subsequently administered to elucidate the potential role of the gut microbiota in these mice. The results revealed that PCP treatment significantly optimized the lost weight and shortened colon, restored the balance of inflammation, mitigated oxidative stress, and restored intestinal epithelial mucosal damage. And, the PCPs exhibited superior efficacy in ameliorating these symptoms compared with PCP-1 and PCP-2. However, depletion of the gut microbiota diminished the therapeutic effects of PCPs in UC mice. Furthermore, fecal transplantation from PCP-treated UC mice to new UC-afflicted mice produced therapeutic effects similar to PCP treatment. So, PCPs significantly ameliorated the symptoms, inflammation, oxidative stress, and intestinal mucosal damage in UC mice, and gut microbiota partially mediated these effects

Spatio-temporal Modelling of Remote-sensing Lake Surface Water Temperature Data

Remote-sensing technology is widely used in environmental monitoring. The coverage and resolution of satellite based data provide scientists with great opportunities to study and understand environmental change. However, the large volume and the missing observations in the remote-sensing data present challenges to statistical analysis. This paper investigates two approaches to the spatio-temporal modelling of remote-sensing lake surface water temperature data. Both methods use the state space framework, but with different parameterizations to reflect different aspects of the problem. The appropriateness of the methods for identifying spatial/temporal patterns in the data is discussed

A Novel Fuzzy Model Predictive Control of a Gas Turbine in the Combined Cycle Unit

The complex characteristics of the gas turbine in a combined cycle unit have brought great difficulties in its control process. Meanwhile, the increasing emphasis on the efficiency, safety, and cleanliness of the power generation process also makes it significantly important to put forward advanced control strategies to satisfy the desired control demands of the gas turbine system. Therefore, aiming at higher control performance of the gas turbine in the gas-steam combined cycle process, a novel fuzzy model predictive control (FMPC) strategy based on the fuzzy selection mechanism and simultaneous heat transfer search (SHTS) algorithm is presented in this paper. The objective function of rolling optimization in this novel FMPC consists of two parts which represent the state optimization and output optimization. In the weight coefficient selection of those two parts, the fuzzy selection mechanism is introduced to overcome the uncertainties existing in the system. Furthermore, on account of the rapidity of the control process, the SHTS algorithm is used to solve the optimization problem rather than the traditional quadratic programming method. The validity of the proposed method is confirmed through simulation experiments of the gas turbine in a combined power plant. The simulation results demonstrate the remarkable superiorities of the adopted algorithm with higher control precision and stronger disturbance rejection ability as well as less optimization time