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

A new indirect multi-step-ahead prediction model for a long-term hydrologic prediction

Author name used in this publication: Chun-Tian Cheng2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Hydroclimatology, modes of climatic variability and stream flow, lake and groundwater level variability

Hydroclimatology is an expansive discipline largely concerned with understanding the workings of the hydrological cycle in a climate context. Acknowledging this, and given the burgeoning interest in the relation between climate and water in the context of working towards an improved understanding of the impacts of climatic variability on water resources, this progress report turns its attention to the connection between large-scale modes of climatic variability and hydrological variability in streams, lakes and groundwater. A survey of the recent literature finds that a plethora of teleconnection indices have been employed in the analysis of hydrological variability. Indices representing modes of climatic variability such as El Niño Southern Oscillation, the North Atlantic Oscillation, the Pacific North America pattern, the Pacific Decadal Oscillation and Atlantic Meridional Oscillation dominate the literature on climatic and hydrological variability. While examples of discernible signals of modes of climatic variability in stream flow and lake and groundwater level time series abound, the associations between periodic to quasi-period oscillations in atmospheric/ocean circulation patterns and variability within the terrestrial branch of the hydrological are far from simple, being both monotonic (linear and non-linear) and non-monotonic and also conditional on period of analysis, season and geographic region. While there has been considerable progress over the last five years in revealing the climate mechanisms that underlie the links between climatic and hydrological variability, a bothering feature of the literature is how climatic and hydrological variability is often viewed through a purely statistical lens with little attention given to diagnosing the relationship in terms of atmosphere and ocean physics and dynamics. Consequently, significant progress remains to be made in obtaining a satisfactory hydroclimatological understanding of stream flow, lake and groundwater variability, especially if hydroclimatological knowledge is to be fully integrated into water resource management and planning

The Drought Risk Analysis, Forecasting, and Assessment under Climate Change

This Special Issue is a platform to fill the gaps in drought risk analysis with field experience and expertise. It covers (1) robust index development for effective drought monitoring; (2) risk analysis framework development and early warning systems; (3) impact investigations on hydrological and agricultural sectors; (4) environmental change impact analyses. The articles in the Special Issue cover a wide geographic range, across China, Taiwan, Korea, and the Indo-China peninsula, which covers many contrasting climate conditions. Hence, the results have global implications: the data, analysis/modeling, methodologies, and conclusions lay a solid foundation for enhancing our scientific knowledge of drought mechanisms and relationships to various environmental conditions

Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large résolution spatiale (25-50 km) n’en font pas un outil adéquat pour des applications hydrologiques à une échelle locale telles que la modélisation et la prévision hydrologiques. Dans de nombreuses études, une désagrégation d’échelle de l’humidité du sol des produits satellites micro-ondes est faite puis validée avec des mesures in-situ. Toutefois, l’utilisation de ces données issues d’une désagrégation d’échelle n’a pas encore été pleinement étudiée pour des applications en hydrologie. Ainsi, l’objectif de cette thèse est de proposer une méthode de désagrégation d’échelle de l’humidité du sol issue de données satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) à différentes résolutions spatiales afin d’évaluer leur apport sur l’amélioration potentielle des modélisations et prévisions hydrologiques. À partir d’un modèle de forêt aléatoire, une désagrégation d’échelle de l’humidité du sol de SMAP l’amène de 36-km de résolution initialement à des produits finaux à 9-, 3- et 1-km de résolution. Les prédicteurs utilisés sont à haute résolution spatiale et de sources différentes telles que Sentinel-1A, MODIS et SRTM. L'humidité du sol issue de cette désagrégation d’échelle est ensuite assimilée dans un modèle hydrologique distribué à base physique pour tenter d’améliorer les sorties de débit. Ces expériences sont menées sur les bassins versants des rivières Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situés aux États-Unis. De plus, le modèle assimile aussi des données d’humidité du sol en profondeur issue d’une extrapolation verticale des données SMAP. Par ailleurs, les données d’humidité du sol SMAP et les mesures in-situ sont combinées par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilé dans le modèle hydrologique pour tenter d’améliorer la prévision hydrologique sur le bassin versant Au Saumon situé au Québec. Les résultats montrent que l'utilisation de l’humidité du sol à fine résolution spatiale issue de la désagrégation d’échelle améliore la représentation de la variabilité spatiale de l’humidité du sol. En effet, le produit à 1- km de résolution fournit plus de détails que les produits à 3- et 9-km ou que le produit SMAP de base à 36-km de résolution. De même, l’utilisation du produit de fusion SMAP/ in-situ améliore la qualité et la représentation spatiale de l’humidité du sol. Sur le bassin versant Susquehanna, la modélisation hydrologique s’améliore avec l’assimilation du produit de désagrégation d’échelle à 9-km, sans avoir recours à des résolutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la résolution spatiale la plus fine à 1- km qui offre les meilleurs résultats de modélisation hydrologique. L’assimilation de l’humidité du sol en profondeur issue de l’extrapolation verticale des données SMAP n’améliore que peu la qualité du modèle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon améliore la qualité de la prévision du débit, même si celle-ci n’est pas très significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

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

Application of Entropy Theory to Multivariate Hydrologic Analysis. (Volumes I and II).

This dissertation discusses the multivariate hydrologic analysis by the entropy theory. It is divided into two major parts. The first part (Volume I) examines hydrologic frequency analysis, specifically rainfall-runoff modeling and the design of rainfall networks. The second part (Volume II) develops two flood forecasting models: the univariate streamflow model, and the bivariate rainfall-runoff model. The hydrologic frequency analysis focuses on three topics: multivariate normal distribution, multivariate exponential distribution and multivariate exponential distribution and multivariate mixed distributions. Many forms of univariate, bivariate and multivariate normal distributions are derived by using the principle of maximum entropy (POME), emphasizing the serial dependency of rainfall and runoff process, and the variable dependency among rainfall and runoff processes. The importance of the variables in the partial duration series model dependent on the cutoff level is examined by entropy and transinformation. Several entropy criteria exist in spacetime design of rainfall networks. Multivariate forms of the Marshall-Olkin exponential distributions are also derived using POME. The bivariate exponential distribution is compared with the bivariate normal distribution in the space design of rainfall networks. By combining exponential and discrete distributions, the multivariate mixed distributions are constructed. These distributions are tested on partial and annual duration series models. The flood forecasting models are developed by adjustment of equations from the maximum entropy spectral analysis. The univariate streamflow model for a long-term (monthly and seasonal) flood forecasting is developed and tested on five climatologically different watersheds, and then compared with the established time series models (ARIMA and state-space). The bivariate rainfall-runoff model, theoretically extending the univariate case, is developed for real-time forecasting, tested on five different climatological areas, and compared with the state-space model. Extensive parameter analyses for both models are given and some intriguing conceptual connections between developed models and time series models are established. Finally, comprehensive guidelines and recommendations for the future work are given

Variance Decomposition of Forecasted Water Budget and Sediment Processes under Changing Climate in Fluvial and Fluviokarst Systems

Variance decomposition is the partitioning of different factors affecting the variance structure of a response variable. The present research focuses on future streamflow and sediment transport processes projections as the response variables. The authors propose using numerous climate factors and hydrological modeling factors that can cause any response variable to vary from historic to future conditions in any given watershed system. The climate modeling factors include global climate model, downscaling method, emission scenario, project phase, bias correction. The hydrological modeling factor includes hydrological model parametrization, and meteorological variable inclusion in the analysis. This research uses a wide spectrum of data, including climate data of precipitation and temperature from GCM results, and observations of meteorological data, streamflow and spring flow data, and sediment yield data. This research focuses on employing an off-the-shelf hydrological model and developing different numerical models (using MATLAB) for simulating sediment transport processes and water movement in an epigenetic karst system. With regards to variance decomposition, the approach is to use a mixed statistical method of linear and nonlinear analysis by means of analysis of variance (ANOVA) and artificial neural networks (ANN) respectively. All the computational tools that will be used to perform the statistics are provided by SPSS software. Two study sites are considered in this work including South Elkhorn watershed and Cave Run watershed. South Elkhorn watershed is a fluvial system and is located in Lexington, Kentucky, USA. This system is characterized as a wet, temperate region in the central and eastern United States where a change in the climate is projected. The mean streamflow, extreme streamflow, and sediment processes forecast are investigated in this watershed. Royal Spring watershed is a fluviokarst system and is adjacent to the South Elkhorn watershed. In this watershed we investigate the water pathway connectivity as well as the impact of climate change on the mean annual spring flow and streamflow. Analysis of variance results indicate that the difference in forecast and hindcast mean streamflow predictions is a function of GCM type, climate model project phase, and downscaling approach. Predicted average monthly change in streamflow tends to follow precipitation changes and result in a net increase in the average annual precipitation and streamflow by 10% and 11%, respectively, when comparing historical period (1980-2000) to the future period (2045-2065). Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima method versus peak over threshold method. However, it was dependent all climate modeling factors. Ensemble projections forecast an increase of streamflow maxima of 51% for 100-year streamflow event. Hydrologic model parameterization was the greatest source of variance impacting forecasted sediment transport variables. Hydrologic inputs from climate change including forecasted precipitation, temperature, relative humidity, solar radiation and wind speed all impacted sediment transport. Ensemble average forecasts sediment yield to increase by 14% for the Upper South Elkhorn watershed. The numerical model of the Cave Run/ Royal Spring watershed suggests 30 to 45% of surface stream discharge originates from in-stream swallet reversal and hillside springs. Also, the hydrology of the floviokarst system might be altered by the impact of climate change where an increase in the surface flow and spring flow is projected to be 8.8% and 12.2%, respectively. The results show that the change in pathway connectivity is important on seasonal bases and follows the seasonal change in precipitations