Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application

Various time variant non-stationary signals need to be pre-processed properly in hydrological time series forecasting in real world, for example, predictions of water level. Decomposition method is a good candidate and widely used in such a pre-processing problem. However, decomposition methods with an inappropriate sampling technique may introduce future data which is not available in practical applications, and result in incorrect decomposition-based forecasting models. In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. This sampling technique with decomposition methods, such as Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is applied to predict water level time series in three different stations of Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique. In the meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1% and 1.1% respectively. We conclude that the newly developed FSDB sampling technique can be used to enhance the performance of decomposition-based hybrid model in water level time series forecasting in real world

Streamflow forecasting using functional regression

Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented

Precipitation Forecasting With Wavelet-Based Empirical Orthogonal Function And Artificial Neural Network (WEOF-ANN) Model

Western drought began since 2000 caused sharp decrease by about 100 feet in the largest reservoir of North America, Lake Mead due to the precipitation pattern shift in the upstream lower Virgin River Basin. Oceans play an important role on earth’s climate via oceanic-atmospheric interactions known as climate teleconnections, which deeply affect the terrestrial precipitation patterns. This issue signifies the necessity of developing a modern hydroinformatics tool - precipitation forecasting model - to account for teleconnection signals from climate change and mitigate drought hazards impact on lake water, quantitatively and qualitatively, which cannot be achieved by using traditional Global Circulation Model. Therefore, understanding the relationship between precipitation and teleconnection patterns could initial step for precipitation forecasting. However, highly nonlinear and non-stationary nature of teleconnection patterns result in large uncertainties in estimates, since simple linear analyses failed to capture underlying trends at sub-continental scales. For this purpose, the integrated high-resolution remote sensing imagery, spectral analysis techniques, and wavelet analysis were integrated to explore the nonstationary and nonlinear behavior of teleconnection signals between the Pacific and Atlantic sea surface temperature (SST) on a long-term basis (30 years) from which the precipitation pattern shift in the lower Virgin River Basin can be elucidated. These processes lead to the creation of linear-lagged correlation maps which specify index regions within the Atlantic and Pacific Oceans where SST anomaly can be statistically significant in correlation with terrestrial precipitation. These indexed regions delivering some kind of memory effects of SST were extracted to be inputs into an Artificial Neural Network (ANN). Advances in wavelet-based ANN (WEOF-ANN) model for rainfall prediction assists in local water management agencies to mitigate the drought impact and obtain sustainable development strategies 3-6 months ahead of the time in urban drinking water infrastructure assessment plan around Lake Mead area

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Estudio comparativo completo de varios métodos basados en datos para la gestión de los recursos hídricos en ambientes mediterráneos a través de diferentes escalas temporales

Since the beginning of time, there has been innovation in the knowledge and technology of water and the hydraulic systems, to achieve an efficient and upgrade management of them. In this project, as an opening hypothesis, we will apply computational techniques and Artificial Intelligence concepts. Given that the primary asset of these studies is data, we have preferred to use the term ”Data-Driven”, as the term Artificial Intelligence can cause confusion in non-experts. This is an expanding field in all aspects of science and life, where the computing and processing powers are increasing periodic, so does the generation of information. There we have 5G technology, or the Internet of things, where the exponential build up in the volume of data utilised, pushes us to set up frameworks for the treatment and analysis of the information.Data-Driven techniques offers enormous potential to transform our perception to understand,monitor and predict the states of hydro-meteorological variables. Its application provides benefits, however, performing these exercises requires practice and explicit knowledge. Therefore, a deeper understanding of the capabilities and limitations of novel computational techniques within our field of knowledge is needed. Hence, it is essential to carry out ”hydro-informatics” experiences under this assumption. For the development of these models, we identify which points are the most relevant and need to be taken into account in regional conditions or frameworks. In consequence, we will work with the time series collected in the different monitoring networks, selecting the hydrological points of interest, in order to further develop hydrological frameworks that are useful for water management and optimisation. Here, we are interested in seeing the practical applicability to hydro-meteorology under Mediterranean conditions, where data are sometimes scarce, by selecting two hydrographic basins in south-east Andalusia: the Guadalhorce river (Málaga) and the Guadalfeo river (Granada). In chapter 1, an introduction to the doctoral thesis is made. Likewise, we establish the general and the specific objectives, and the motivation of the thesis. Afterwards, we describe the three fundamental exercises to be carried out in the research work: Regression, Classification and Optimisation. Ultimately, we carry out a brief review of previous works under Mediterranean climatic conditions and similar assumptions. Chapter 2 presents the study areas, analysing the spatial and temporal characteristics of two Andalusian Mediterranean basins in south-east Spain: Guadalhorce (GH) and Guadalfeo (GF). These are hydrographic basins with highly variable/heterogeneous spacetime patterns. The first hydrological system, GH, contains an area of socio-economic importance, such is the city of M´alaga. The second, GF, to the north has the Sierra Nevada National Park, crowned by the Mulhac´en peak and flowing in a few kilometres into the area of Motril. In this particular water system, we find large gradients of the geophysical agents. Both systems have regulation structures of great interest for the development and study of their optimisation. We also review the monitoring networks available in these basins, and which environmental agents and/or processes should be taken into account to meet the objectives of this work. We carry out a bibliographic review of the most relevant historical floods, listing the factors associated with these extreme events. In the data analysis stage of this chapter, we focus on the spatialtemporal evolution of the risk of flooding in the two mouths of the Guadalhorce and Guadalfeo Rivers into the Albor´an Sea. We quantify that had stepped up in recent years, noting that dangerous practices have increased the risk of flooding because of the intrusion of land uses with high-costs. This chapter also analyses collected data within the monitoring networks, to understand the occurrence of floods in the river GH related to upstream discharges. We found that this basin has limitations in regulation and cannot mitigate costs downstream. The results got, were part of the work presented in Egüen et al. (2015). These analyses allow us to identify in which parts of the flood management of this hydrological system need a more precise optimisation. Finally, a summary of another important hydrological risk is carried out, such as droughts, and how these water deficits can be represented by standardised indices, both in rainfall and the flow rates. The various approaches and methodologies for hydro-meteorological time series modelling are discussed in the chapter 3. The contrasting concepts are exposed antagonistically, to focus on the different design choices that we need to make: black box vs. grey box vs. white box, parametric vs. non-parametric, static vs. dynamic, linear vs. non-linear, frequency vs. Bayesian, single vs. multiple, among others..., detailing the advantages and disadvantages of each approach. We presented some ideas that emerged in this part of the research in Herrero et al. (2014). The partition, management and data transformation steps for the correct application of these experimental methods are also discussed. This is of great importance, since part of the hard work in the application of these methods comes from the transformation of the data. So that, the algorithms and transfer functions work correctly. Finally, we focus on how to test and validate the deterministic and probabilistic behaviours through evaluative coefficients to avoid coefficients that mask the results, and therefore focus on the behaviours of our interest, in our case precision and predictability. We have also taken parsimony into account in models based on neural networks, since they can easily fall into over-parameterisation. In chapter 4, we present the experimental work, where seven short-term, six daily and one hourly rainfall-runoff regressions are performed. The case studies correspond to various points of interest within the study areas with important implications for hydrological management. On an hourly scale, we analyse the efficiency and predictive capacities of the MLR and BNN at ten time horizons for the level of the Guadalhorce River in Cártama. We found that, for closer predictive horizons, a simpler approach such as linear (MLR) can outperform other with a priori higher capabilities, such as non-linear (BNN). This finding could simplify greatly its development and application. At a daily scale, we establish a comparative framework between the two previous models and a complete Bayesian method such as the Gaussian Processes. This DD computational technique, allows us to apply different transfer functions under a single model. This is an advantage over the other two DD models, since the results show that they work well in one domain, but do not work well in the other. During the construction of the models, we do the selection of the input variables in a progressive way, through a trial-and-error method, where the significant improvements with respect to the last predictor structure are taken into account preserving the principle of parsimony. Here, we have used different types of data: real data collected in the monitoring networks, and data generated in parallel from physically based hydrological modelling (WiMMed). The results are robust, where the major limitation is the high computational cost by the recurrent and iterative method used. Some results of this chapter, were presented in Gulliver et al. (2014). In chapter 5 three medium-term time scale prediction experiments are performed. We base the first modelling experiment on a quarterly scale, where a hydrological time scheme determines the cumulative flow for specific time horizons. We start the scheme according to the relevant dates where hydrological planning takes place. It is validated that the forecasts are more prosperous after have been consumed the first six months of the hydrological year. Instead of the three months in which we carry out the evaluations. The observed input variables quantified in the water system are: cumulative stream flow, cumulative rainfall, cumulative snowfall values and atmospheric oscillations (AO). At the level of modelling with DD, this experience has shown the importance of combining mixed regression classification models instead of only regression models within static frameworks. In this manner, we reduce and narrow the space of possible solutions and, therefore, we optimised the predictive behaviour of the DD model. During the development of this exercise, we have also carried out a classification practice comparing three DD classifiers: Probabilistic Neural Network (PNN), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). We see that the SVM behaves better than the others with our data. However, more research is still needed on classifiers in hydro-meteorological frameworks like ours, because of their variability. We showed this part of the doctoral thesis in Gulliver et al. (2016). In the second section of this chapter (Sec. 5.3), we carry out a rain forecast exercise on a monthly scale. To do so, we use BNN following the same construction method of the SVI model exposed in the previous chapter (Sec. Ref. Chapter 4), thus validating it in another time scale. However, the results in predictive terms are poor for this hydro-meteorological variable. This confirms the difficulty of predicting this variable from historical data and without the incorporation of dynamic tools. Thus, the need for complex hydrodynamic modelling for the prediction of this important variable is confirmed. On the other hand, this case serves to empirically infer the causality of the most relevant atmospheric oscillations in the points of study. From multiple simulations with the model-based approach it has been possible to establish which indices have a greater influence. In the last section of this chapter (Section 5.4), an exercise was carried out to predict the deviation or anomaly of rainfall and runoff indices for four time series representative of different locations within the Guadalfeo BR. In this case, we verified the suitability of seven statistical distributions to characterize the anomalies/deviations under Mediterranean conditions. Under this hypothesis, the indices that passed the Shapiro-Wilk normality test were modelled to analyse the capabilities of BNN to predict these indices at various time horizons. Here, predictions of negative phases (droughts or deficit periods) have been poor, and the behaviour of the models for positive phases (wet periods) has been more successful. Regarding the causal inference of IC and its possible influence on the study area, we found out how NAO and WEMO help forecasts for shorter time horizons, while MOI helps for longer cumulative time horizons/times. We have analysed the relevance of these atmospheric variables in each case where sometimes their introduction was convenient and sometimes not, following the rules of construction and detailing them in each case study. Throughout the work, the usefulness of mixed modelling approaches has been verified, using models based on observed data from the different monitoring networks with physical modelling for the reproduction of essential hydrological processes. With the proposed methodology, a positive influence of atmospheric oscillations has been observed for medium-term prediction within the study regions, finding no evidence for short-term predictions (daily scale). The final conclusions and the most important points for future work are presented in the chapter 6. Applications of this type of methods are currently necessary. They help us to establish relationships based on measured hydro-meteorological data and thus ”based on real data”, without hypothesizing any assumptions. These data-based experiences are very useful for limiting future uncertainty and optimizing water resources. The establishment of temporal relationships between different environmental agents allows us, through supervised methods, to establish causal relationships. From here a physical inference exercise is necessary to add coherence and establish a robust scientific exercise. The results obtained in this work, reaffirm the practicality of implementing this Data- Driven frameworks, in both the public and private spheres, being a good starting point for technology transfer. Most of the routines and models provided in this thesis, could be directly applied in Hydro-meteorological Services, or Decision Support Systems for water officials. This includes potential users as varied as public administrations and basin organisations, reservoir managers, energy companies that manage hydroelectric generation, irrigation communities, water bottling plants,... etc. The establishment of iterative and automatic frameworks for data processing and modelling, needs to be implemented, to make the most of the data collected in the water systems.Desde el inicio de los tiempos, se innova en el conocimiento y la tecnología de los sistemas hídricos e hidráulicos con el fin de conseguir una eficiente y correcta gestión de los mismos. En este proyecto, como hipótesis de partida, se van a aplicar diversas técnicas computacionales y conceptos de Inteligencia Artificial. Dado que el principal activo de estas aplicaciones son los datos, optamos por el término ”Data-Driven” (DD), ya que el término de Inteligencia Artificial puede causar confusión en los no expertos. Este es un campo en expansión en todos los aspectos de la ciencia y de la vida, donde al tiempo que se incrementan las capacidades de computación y de procesamiento, se incrementa la generación de datos. Ahí tenemos la tecnología 5G, o el internet de las cosas, donde el incremento exponencial del volumen de datos que se utilizan nos obliga a desarrollar marcos para el tratamiento y el análisis de los mismos. Los métodos DD tienen un enorme potencial para transformar nuestra habilidad de establecer un seguimiento supervisado y predecir estados de variables hidro-meteorológicas. Su aplicación provee claramente de beneficios, sin embargo realizar estos ejercicios requiere una práctica y un conocimiento específico. Por ello, es necesario un entendimiento más profundo de las capacidades y de las limitaciones de estas técnicas computacionales, dentro de nuestro campo de conocimiento y casos específicos. Por estos motivos, es esencial realizar experiencias ”hidro-informáticas” bajo este supuesto, identificando así que puntos son los más relevantes y a tener en cuenta en el desarrollo y la validación de estos modelos en condiciones o marcos más regionales. Para ello, trabajaremos con las series temporales recogidas en las diferentes redes de monitorización, con series resultantes de modelado hidro-meteorológico y con series de las oscilaciones atmosféricas más relevantes en la zona de estudio. El objetivo principal de este trabajo es el desarrollo y la validación de marcos metodológicos basados en datos. Para ello, se seleccionan puntos de interés, con el fin de desarrollar marcos hidro-meteorológicos ´útiles en la gestión y optimización de los recursos hídricos. En este supuesto, nos interesa ver la aplicabilidad práctica de estas herramientas de aprendizaje automático, machine learning, en condiciones mediterráneas y locales, donde los datos a veces son escasos o de baja calidad. En el primer capítulo (Cap.1) se realiza una introducción a la tesis doctoral, estableciendo los objetivos tanto generales como específicos, y la motivación de la tesis. Seguidamente se realiza a modo introductorio una descripción de los tres ejercicios fundamentales a realizar en el trabajo de investigación: Regresión, Clasificación y Optimización. Finalmente, se realiza una revisión del estado del arte de trabajos previos bajo condiciones climáticas mediterráneas y similares. El capítulo 2 presenta las zonas de estudio, analizando las características espacio-temporales de dos cuencas mediterráneas andaluzas situadas en el sureste español: río Guadalhorce (GH) y río Guadalfeo (GF). Son cuencas hidrográficas con unos patrones espaciotemporales altamente variables/heterogéneos. El primer sistema hidrológico, GH, contiene una zona de gran importancia socio-económica como es la ciudad de Málaga. El segundo, GF, al norte tiene situado el Parque Nacional de Sierra Nevada, coronado por el pico Mulhacén y desemboca a pocos kilómetros en la costa de Motril. Esto hace que este sea un sistema con grandes gradientes geo-morfológicos e hidro-meteorológicos. En ambas cuencas existen estructuras de regulación de gran interés para el desarrollo y estudio de su optimización. También se revisan las redes de monitorización disponibles en estas cuencas, y que agentes deben ser tenidos en cuenta para la consecución de los objetivos del presente trabajo. En la etapa de análisis de datos de este capítulo, nos centramos en la evolución espacio temporal del riesgo frente a las inundaciones en las desembocaduras de ambos sistemas hidrológicos al mar de Alborán. Se cuantifica el aumento del riesgo frente a inundaciones ante la intrusión de usos del suelo con altos costes en las zonas potencialmente inundables en estos ´últimos años, constatando así una mala práctica en la planificación del territorio dentro de la zona de estudio. También, en este capítulo se analizan los datos registrados con el fin de comprender la ocurrencia de avenidas en el río GH y su relación con los desembalses aguas arriba. En este análisis se pudo identificar, como ante algunos eventos pluviométricos extremos (> 100mm/24h), esta cuenca tiene limitaciones en la regulación, no pudiendo así mitigar los costes aguas abajo. Parte de los resultados obtenidos formaron parte del trabajo presentado en Egüen et al. (2015). Estos análisis nos permiten identificar la necesidad de una optimización temporal más precisa en la gestión de avenidas en este sistema hidrológico. Finalmente, realizamos un análisis de otro riesgo hidrológico importante como son las sequías, y cómo podemos representar este déficit hídrico mediante índices estandarizados, tanto para la pluviometría como para la escorrentía. En el capítulo 3 se analizan los diversos enfoques y metodologías para el modelado de series temporales hidro-meteorológicas. Los enfoques se exponen de forma antagonista entre las diferentes opciones de modelado que tenemos: caja negra vs. caja gris vs. caja blanca, paramétricos vs. no-paramétricos, estático vs. dinámico, lineal vs. no-lineal, frecuentista vs. bayesiano, único vs múltiple, entre otros..., enumerando las ventajas e inconvenientes de cada enfoque. Algunas ideas surgidas en esta parte de la investigación fueron expuestas en Herrero et al. (2014). Por otro lado, también se discuten los pasos de partición, gestión y transformación de los datos para una correcta aplicación de este tipo de métodos experimentales. Esto es de gran importancia, ya que parte del trabajo duro en la aplicación de este tipo de metodologías, proviene de la transformación de los datos para que los algoritmos y las funciones de transferencia funcionen correctamente. En la parte final de este capítulo, nos centramos en cómo evaluar y validar el comportamiento determinista y probabilístico mediante coeficientes evaluativos. En este punto, prestamos especial atención en evitar la utilización de coeficientes que enmascaren los resultados o muy generalistas, y por lo tanto nos centramos en aquellos que evalúan las capacidades predictivas y de precisión de los modelos. También se ha tenido en cuenta la parsimonia para los modelos basados en redes neuronales, ya que pueden caer fácilmente en una sobre-parametrización. El capítulo 4 expone trabajo puramente experimental, donde se realizan siete regresiones lluvia escorrentía a corto plazo, seis diarias y una horaria. Los casos de estudio corresponden a diversos puntos de interés dentro de las zonas de estudio, con importantes implicaciones en la gestión hidrológica. A escala horaria se analiza las capacidades de eficiencia y predictivas de la Regresión Lineal Múltiple (MLR) y Redes Neuronales Bayesianas (BNN) a diez horizontes temporales para el nivel del río Guadalhorce en el puente de Cártama. Se encontró que, para horizontes predictivos más cercanos, un enfoque más sencillo como puede ser el lineal (MLR), puede superar a uno con mayores capacidades predictivas a priori, como pueden ser uno no lineal (BNN). Simplificando así, el desarrollo y la implementación de este tipo de técnicas computacionales bajo este tipo de marcos hidrológicos. Por otro lado, a escala diaria se establece un marco comparativo entre los dos modelos anteriores, MLR y BNN, y un método bayesiano completo: Procesos Gaussianos (GP). Esta técnica computacional, nos permite aplicar funciones de transferencia de diferente naturaleza bajo un único modelo. Esto es una ventaja con respecto a los otros dos modelos computacionales, ya que los resultados nos indican que a veces funcionan bien en un dominio, pero no funcionan bien en el contrario. Durante la construcción de los modelos, la selección de las variables de entrada se realiza de forma progresiva, mediante un método de prueba y error, donde se tienen en cuenta las mejoras significativas con respecto a la última estructura de predictores preservando el principio de parsimonia. Se han utilizado datos de diferente naturaleza: datos reales recogidos en las redes de monitorización y datos generados paralelamente de modalización hidrológica con base física (WiMMed). Los resultados son robustos donde la principal limitación es el alto coste computacional por el método recurrente e iterativo. Resultados de este capítulo fueron presentados en Gulliver et al. (2014). En el capítulo 5 se realizan tres

Sustainable Reservoir Management Approaches under Impacts of Climate Change - A Case Study of Mangla Reservoir, Pakistan

Reservoir sedimentation is a major issue for water resource management around the world. It has serious economic, environmental, and social consequences, such as reduced water storage capacity, increased flooding risk, decreased hydropower generation, and deteriorated water quality. Increased rainfall intensity, higher temperatures, and more extreme weather events due to climate change are expected to exacerbate the problem of reservoir sedimentation. As a result, sedimentation must be managed to ensure the long-term viability of reservoirs and their associated infrastructure. Effective reservoir sedimentation management in the face of climate change necessitates an understanding of the sedimentation process and the factors that influence it, such as land use practices, erosion, and climate. Monitoring and modelling sedimentation rates are also useful tools for forecasting future impacts and making management decisions. The goal of this research is to create long-term reservoir management strategies in the face of climate change by simulating the effects of various reservoir-operating strategies on reservoir sedimentation and sediment delta movement at Mangla Reservoir in Pakistan (the second-largest dam in the country). In order to assess the impact of the Mangla Reservoir's sedimentation and reservoir life, a framework was developed. This framework incorporates both hydrological and morphodynamic models and various soft computing models. In addition to taking climate change uncertainty into consideration, the proposed framework also incorporates sediment source, sediment delivery, and reservoir morphology changes. Furthermore, the purpose of this study is to provide a practical methodology based on the limited data available. In the first phase of this study, it was investigated how to accurately quantify the missing suspended sediment load (SSL) data in rivers by utilizing various techniques, such as sediment rating curves (SRC) and soft computing models (SCMs), including local linear regression (LLR), artificial neural networks (ANN) and wavelet-cum-ANN (WANN). Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for SCMs development. Based on an evaluation of the outcomes of all leading models for SSL estimation, it can be concluded that SCMs are more effective than SRC approaches. Additionally, the results also indicated that the WANN model was the most accurate model for reconstructing the SSL time series because it is capable of identifying the salient characteristics in a data series. The second phase of this study examined the feasibility of using four satellite precipitation datasets (SPDs) which included GPM, PERSIANN_CDR, CHIRPS, and CMORPH to predict streamflow and sediment loads (SL) within a poorly gauged mountainous catchment, by employing the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANN), random forests (SWAT-RF), and support vector regression (SWAT-SVR). SCMs were developed using the outputs of un-calibrated SWAT hydrological models to improve the predictions. The results indicate that during the entire simulation, the GPM shows the best performance in both schemes, while PERSIAN_CDR and CHIRPS also perform well, whereas CMORPH predicts streamflow for the Upper Jhelum River Basin (UJRB) with relatively poor performance. Among the best GPM-based models, SWAT-RF offered the best performance to simulate the entire streamflow, while SWAT-ANN excelled at simulating the SL. Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating streamflow and SL, particularly in complex terrain where gauge network density is low or uneven. The third and last phase of this study investigated the impact of different reservoir operating strategies on Mangla reservoir sedimentation using a 1D sediment transport model. To improve the accuracy of the model, more accurate boundary conditions for flow and sediment load were incorporated into the numerical model (derived from the first and second phases of this study) so that the successive morphodynamic model could precisely predict bed level changes under given climate conditions. Further, in order to assess the long-term effect of a changing climate, a Global Climate Model (GCM) under Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 for the 21st century is used. The long-term modelling results showed that a gradual increase in the reservoir minimum operating level (MOL) slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of sustainable reservoir use for societal benefit. In summary, this study provides comprehensive insights into reservoir sedimentation phenomena and recommends an operational strategy that is both feasible and sustainable over the long term under the impact of climate change, especially in cases where a lack of data exists. Basically, it is very important to improve the accuracy of sediment load estimates, which are essential in the design and operation of reservoir structures and operating plans in response to incoming sediment loads, ensuring accurate reservoir lifespan predictions. Furthermore, the production of highly accurate streamflow forecasts, particularly when on-site data is limited, is important and can be achieved by the use of satellite-based precipitation data in conjunction with hydrological and soft computing models. Ultimately, the use of soft computing methods produces significantly improved input data for sediment load and discharge, enabling the application of one-dimensional hydro-morphodynamic numerical models to evaluate sediment dynamics and reservoir useful life under the influence of climate change at various operating conditions in a way that is adequate for evaluating sediment dynamics.:Chapter 1: Introduction Chapter 2:Reconstruction of Sediment Load Data in Rivers Chapter 3:Assessment of The Hydrological and Coupled Soft Computing Models, Based on Different Satellite Precipitation Datasets, To Simulate Streamflow and Sediment Load in A Mountainous Catchment Chapter 4:Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan Chapter 5:Conclusions and Recommendation

Application of machine learning in operational flood forecasting and mapping

Considering the computational effort and expertise required to simulate 2D hydrodynamic models, it is widely understood that it is practically impossible to run these types of models during a real-time flood event. To allow for real-time flood forecasting and mapping, an automated, computationally efficient and robust data driven modelling engine - as an alternative to the traditional 2D hydraulic models - has been proposed. The concept of computationally efficient model relies heavily on replacing time consuming 2D hydrodynamic software packages with a simplified model structure that is fast, reliable and can robustly retains sufficient accuracy for applications in real-time flood forecasting, mapping and sequential updating. This thesis presents a novel data-driven modelling framework that uses rainfall data from meteorological stations to forecast flood inundation maps. The proposed framework takes advantage of the highly efficient machine learning (ML) algorithms and also utilities the state-of-the-art hydraulic models as a system component. The aim of this research has been to develop an integrated system, where a data-driven rainfall-streamflow forecasting model sets up the upstream boundary conditions for the machine learning based classifiers, which then maps out multi-step ahead flood extents during an extreme flood event. To achieve the aim and objectives of this research, firstly, a comprehensive investigation was undertaken to search for a robust ML-based multi-step ahead rainfall-streamflow forecasting model. Three potential models were tested (Support Vector Regression (SVR), Deep Belief Network (DBN) and Wavelet decomposed Artificial Neural Network (WANN)). The analysis revealed that SVR-based models perform most efficiently in forecasting streamflow for shorter lead time. This study also tested the portability of model parameters and performance deterioration rates. Secondly, multiple ML-based models (SVR, Random Forest (RF) and Multi-layer Perceptron (MLP)) were deployed to simulate flood inundation extents. These models were trained and tested for two geomorphologically distinct case study areas. In the first case of study, of the models trained using the outputs from LISFLOOD-FP hydraulic model and upstream flow data for a large rural catchment (Niger Inland Delta, Mali). For the second case of study similar approach was adopted, though 2D Flood Modeller software package was used to generate target data for the machine learning algorithms and to model inundation extent for a semi-urban floodplain (Upton-Upon-Severn, UK). In both cases, machine learning algorithms performed comparatively in simulating seasonal and event based fluvial flooding. Finally, a framework was developed to generate flood extent maps from rainfall data using the knowledge learned from the case studies. The research activity focused on the town of Upton-Upon-Severn and the analysis time frame covers the flooding event of October-November 2000. RF-based models were trained to forecast the upstream boundary conditions, which were systematically fed into MLP-based classifiers. The classifiers detected states (wet/dry) of the randomly selected locations within a floodplain at every time step (e.g. one hour in this study). The forecasted states of the sampled locations were then spatially interpolated using regression kriging method to produce high resolution probabilistic inundation (9m) maps. Results show that the proposed data centric modelling engine can efficiently emulate the outcomes of the hydraulic model with considerably high accuracy, measured in terms of flood arrival time error, and classification accuracy during flood growing, peak, and receding periods. The key feature of the proposed modelling framework is that, it can substantially reduce computational time, i.e. ~14 seconds for generating flood maps for a flood plain of ~4 km2 at 9m spatial resolution (which is significantly low compared to a fully 2D hydrodynamic model run time)

Multi-Resolution Spatio-Temporal Change Analyses of Hydro-Climatological Variables in Association with Large-Scale Oceanic-Atmospheric Climate Signals

The primary objective of the work presented in this dissertation was to evaluate the change patterns, i.e., a gradual change known as the trend, and an abrupt change known as the shift, of multiple hydro-climatological variables, namely, streamflow, snow water equivalent (SWE), temperature, precipitation, and potential evapotranspiration (PET), in association with the large-scale oceanic-atmospheric climate signals. Moreover, both observed datasets and modeled simulations were used to evaluate such change patterns to assess the efficacy of the modeled datasets in emulating the observed trends and shifts under the influence of uncertainties and inconsistencies. A secondary objective of this study was to utilize the detected change patterns in designing data-driven prediction models, e.g., artificial neural networks (ANNs), support vector machines (SVMs), and Gaussian process regression (GPR) models, coupled with data pre-processing techniques, e.g., principal component analysis (PCA) and wavelet transforms (WTs). The study was not solely limited to the hydrologic regions of the conterminous United States (U.S.); rather it was extended to include an analysis of northern India to appraise the differences in the spatiotemporal variation on a broader scale. A task was designed to investigate the significant spatiotemporal variations in continental US streamflow patterns as a response to large-scale climate signals across multiple spectral bands (SBs). Using non-parametric (long-term) trend and (abrupt) shift detection tests, coupled with discrete wavelet transform, 237 unimpaired streamflow stations were analyzed over a study period of 62 years (1951 to 2012), looking at the water year and seasonal data, along with three discrete SBs of two, four, and eight years. Wavelet coherence analysis, derived from continuous wavelet transform, determined the association between the regional streamflow patterns and three large-scale climate signals, i.e., El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multi-decadal Oscillation (AMO), across continuous SBs ranging from two to 16 years. The results indicated significant positive (negative) trends and shifts in the northeastern and north-central (northwestern) regions with an increase in the number of stations as the SB durations increased. The spatiotemporal association between regional streamflow and climate signals varied significantly (from no correlation, Rn2 ~ 0, to perfect correlation, Rn2 ~ 1.0) even amongst adjacent regions. Among the climate signals, ENSO showed the highest association (Rn2 ~ 1.0), having a consistent phase relationship with regional streamflow patterns, especially in the higher SBs. PDO (with the least influence among the three signals) and AMO showed stronger associations, in the lower SBs. These results may help explain the teleconnections between the climate signals and the US streamflow variations across multiple SBs, which may lead to improved regional flow regulations. The comparison among several data-driven models, e.g., ANN, SVM, and GPR models, preceded by PCA and WT, produced comparable results with significant accuracy (with R2 above 0.90) in short-term prediction of streamflow. Later, the correlations between the western U.S. snow water equivalent (SWE) and the two major oceanic-atmospheric indices originating from the Pacific Ocean, namely, ENSO and PDO, were evaluated using continuous wavelet transform and its derivatives. Snow Telemetry (SNOTEL) data for 1 April SWE from 323 sites (out of which 258 are in six hydrologic regions) were obtained for a study period of 56 years (1961–2016). The results showed that ENSO had a much higher influence than PDO throughout the western U.S. SWE across the study period. Both ENSO and PDO showed a higher correlation with SWE at multiple timescale bands across different time intervals, although significant intervals in the higher timescales were of longer duration. ENSO showed a higher correlation in the 10-to-16-year band across the entire study period as well as in the lower timescales. PDO showed a higher correlation below the 4-year band. The relative phase relationship suggested that ENSO led SWE, with certain lags, while both were moving in the same direction in many instances. The lag-response behavior of SWE and PDO was not found to be uniform. Regional analyses, based on the western U.S. hydrologic regions, suggested significant variation across adjacent regions in terms of their correlation with ENSO/PDO. Association with ENSO was also observed to be higher compared to PDO among the regions. Regions close to the ocean and at lower elevation showed higher correlation compared to the inland regions with higher elevation. The influence of ENSO on the north Indian temperature, precipitation, and PET change patterns was evaluated during the monsoon season across the last century. Trends and shifts in 146 districts were assessed using non-parametric statistical tests. To quantify their temporal variation, the concept of apportionment entropy was applied to both the annual and seasonal scales. Results suggest that the El Niño years played a greater role in causing hydro-climatological changes compared to the La Niña or neutral years. El Niño was more influential in causing shifts compared to trends. For certain districts, a phase change in ENSO reversed the trend/shift direction. The all-year (century-wide) analysis suggested that the vast majority of the districts experienced significant decreasing trends/shifts in temperature and PET. However, precipitation experienced both increasing and decreasing trends/shifts based on the location of the districts. Entropy results suggested a lower apportionment of precipitation compared to the other variables, indicating an intermittent deviation of precipitation pattern from the generic trend. The findings may help understand the effects of ENSO on hydro-climatological variables during the monsoon season. Practitioners may find the results useful, as monsoon, among the Indian seasons, experience the largest climate extremes. A final task was designed that evaluated Coupled Model Intercomparison Project 5 (CMIP5) simulation models’ ability to capture the observed trends under the influence of shifts and persistence in their data distributions. A total of 41 temperature and 25 precipitation CMIP5 simulation models across 22 grid cells (2.5° x 2.5° squares) within the Colorado River Basin were analyzed and compared against the Climate Research Unit Time Series (CRU-TS) observed datasets over a study period of 104 years (from 1901 to 2004). Both the model simulations and observations were tested for shifts, and the time series before and after the shifts were analyzed separately for trend detection and quantification. Effects of several types of persistence were accounted for prior to both the trend and shift detection tests. The mean significant shift points (SPs) of the CMIP5 temperature models across the grid cells were found to be within a narrower range (between 1960 and 1970) compared to the CRU-TS observed SPs (between 1930 and 1980). Precipitation time series, especially the CRU-TS dataset, had a lack of significant SPs, which led to an inconsistency between the models and observations since the numbers of grid cells with a significant SP were not comparable. The modeled CMIP5 temperature trends, under the influence of shifts and persistence, were able to match the observed trends quite satisfactorily (within the same order and consistent direction). Unlike the temperature models, the CMIP5 precipitation models detected the SPs earlier than the observed SPs found in the CRU-TS data. The direction (as well as the magnitude) of trends, before and after significant shifts, were found to be inconsistent between the modeled simulations and observed precipitation data. Shifts, based on their direction, were found to either strengthen or neutralize pre-existing trends both in the model simulations as well as in the observations. The results also suggest that the temperature and precipitation data distributions were sensitive to different types of persistence. Such sensitivity was found to be consistent between the modeled and observed datasets. The study detected certain biases in the CMIP5 models in detecting the SPs (a tendency of detecting shifts earlier or later than the observed shifts) and also in quantifying the trends (overestimating the trend slopes). Such insights may be helpful in evaluating the efficacy of the simulation models in capturing observed trends under uncertainties and natural variabilities

Selected data exploration methods in hydroclimatology

The volumes of climatological data are rapidly growing due to development of new acquisition platforms and advances in data storage technologies. Such advances provide new challenging problems for data analysis methods. As a result, there is an increasing interest in application and development of new machine learning and data mining methods in climatological data analysis. This dissertation contributes to the field of data analysis by evaluation of selected methods and developing new techniques with application to hydro-climatological datasets. It is shown that data pre-processing with the decimated wavelet discrete transform can cause false predictive accuracy in regression machine learning algorithms. A general result is obtained that a decimated wavelet discrete transform based on a pyramidal algorithm requires utilizing some future values of the time series concerned. When the discrete wavelet transform is utilized as a pre-processing step for forecasting the time series, the necessary independence of calibration and validation data is compromised. This in turn translates into over-optimistic forecasting accuracy or even giving the illusion of forecasting skill when there in none. The obtained result is general and has wide implications in any discipline where the discrete wavelet transform is utilised in forecasting frameworks. In addition, a general framework for creating simple predictive models is presented, based on LASSO regularised regression. The method is illustrated for time series modelling with external event forcing but the approach has general applicability. As a contribution to association discovery, two tests of bivariate association are developed. The first method is designed for detecting threshold-like associations in a scatter plot with particular reference to testing the significance of the extent of a data-sparse region within a scatter plot. The second method is a more general test of non-random associations. Both methods utilise significance testing based on randomizations. Finally, LASSO regularized regression is investigated as a tool for discovering informative large scale climatogical predictors of local hydrological processes. A cross-validation scheme is proposed, which is related to practical forecasts of the next time interval to come, while at the same time maximising use of available information. The proposed methodology was applied to a case study predicting next-season river discharges in the Upper Waitaki River in New Zealand. The proposed forecasting methodology and cross-validation frameworks are applicable for similar hydroclimatological forecasting situations. The physical aspect of this part of the study included discovering the influence of the Interdecadal Pacific Oscillation on winter discharges in the upper Waitaki catchment