Global Sensitivity Analysis (GSA) approach helps to identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. The effects of 14 parameters and one input (forcing data) of the Sacramento Soil Moisture Accounting (SAC-SMA) model are analyzed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. The main parameter sensitivities (first-order) and the interactions sensitivities (second-order) are evaluated in this study. Our results show that some hydrological processes are highly affected by the simulation length. In other words, some parameters reveal importance during the short period simulation (e.g. one-year) while other parameters are effective in the long period simulations (e.g. four-year and seven-year). Moreover, the reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show that the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. This study confirms that the Sobol' and FAST methods are reliable GSA methods that can be applied in different scientific applications. Finally, as a future work, we suggest to study the uncertainty associated with the sensitivity analysis approach regarding the reliability of evaluating different sensitivity analysis methods.

One of the most important purposes of surface water resource management is to develop predictive models to assist in identifying and evaluating operational and structural measures for improving water quality. To better understand the effects of external and internal nutrient and organic loading and the effects of reservoir operation, a model is often developed, calibrated, and used for sensitivity and management simulations. The importance of modeling and simulation in the scientific community has drawn interest towards methods for automated calibration. This study addresses using an automatic technique to calibrate the water quality model CE-QUAL-W2 (Cole and Wells, 2013). CE-QUAL-W2 is a two-dimensional (2D) longitudinal/vertical hydrodynamic and water quality model for surface water bodies, modeling eutrophication processes such as temperature-nutrient-algae-dissolved oxygen-organic matter and sediment relationships. The numerical method used for calibration in this study is the particle swarm optimization method developed by Kennedy and Eberhart (1995) and inspired by the paradigm of birds flocking. The objective of this calibration procedure is to choose model parameters and coefficients affecting temperature, chlorophyll a, dissolved oxygen, and nutrients (such as NH4, NO3, and PO4). A case study is presented for the Karkheh Reservoir in Iran with a capacity of more than 5 billion cubic meters that is the largest dam in Iran with both agricultural and drinking water usages. This algorithm is shown to perform very well for determining model parameters for the reservoir water quality and hydrodynamic model. Implications of the use of this procedure for other water quality models are also shown.

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The very concept of sustainable development calls for a holistic understanding of socio-economic and natural systems in order to help achieve greater sustainability. The complexity characterizing such systems, however, makes it likely impossible for quantified approaches of even isolated problems to account for all relevant factors in a single, robust and deterministic representation of reality – an inherent feature which largely motivates the use of statistical models applied to empirical data. On three independent examples with significant socio-economic and environmental importance, the present dissertation illustrates how econometrical models applied to real-life environmental data can be fruitfully deployed to facilitate the identification and motivation of innovative policies to achieve greater sustainability. Specifically, the first chapter explores the extent to which large-scale irrigation affects local climate by inducing cooler temperatures in areas located downwind from irrigated land, an externality with positive economic consequences quantified in terms of improved crop yields and reduced human mortality. The second chapter illustrates the benefits offered by a family of new differencing estimators (as theoretically derived from a generalization of existing techniques found in the literature) on the example of the nonparametric estimation of error variance in streamflow measurements - a step that is critical for the accurate prediction by hydrological models of extreme flood events. The third chapter investigates the joint effect of traffic speed and acceleration on urban air quality in order to help anticipate the consequences of innovative traffic regulation on the concentration of key air pollutants with detrimental consequences on human health and the economy.

Hydrological models play a crucial role in forecasting future water resource availability and water-related risks. It's essential that they realistically represent and simulate the processes of interest. However, which model structure is most suitable for a given task, catchment and data situation is often difficult to determine. There are only few tangible guidelines for model structure selection, and comparing multiple models simply to choose one to use in further work is a cumbersome process. It is therefore not surprising that the hydrological community has spent considerable effort on improving model parameter estimation, which can be treated as an automatized process, but the selection of a suitable model structure (i.e., the specific set of equations describing catchment function) has received comparatively little attention. To facilitate easier testing of different model structures, this thesis introduces an approach for Automatic Model Structure Identification (AMSI), which allows for the simultaneous calibration of model structural choices and model parameters. Model structural choices are treated as integer decision variables while model parameters are treated as continuous model variables in this approach. Through combining the modular modelling framework Raven with the mixed-integer optimization algorithm DDS, the testing of different structural hypotheses can thus be automated. AMSI then allows to effectively search a vast number of model structure and parameter choices to identify the most suitable model structures for a specific objective function. This thesis uses four experiments to test and benchmark AMSI's performance and capabilities. First, a synthetic experiment generates “observations” with known model structures and tests AMSI’s ability to re-identify these same structures. Second, AMSI is used in a real-world application on twelve diverse MOPEX catchments to test the feasibility of the approach. Third, a comprehensive benchmark study explores how reliably AMSI searches the available model space by comparing AMSI’s outcomes to a brute force approach that calibrates all feasible model structures in the available model search space. Fourth, the model space AMSI searches was compared to a much wider model hypothesis space, as defined by 45 diverse and commonly used model structures taken from the MARRMoT-Toolbox. This evaluation of AMSI’s performance is based on mathematical accuracy (tested via statistical metric performance) and hydrological adequacy (tested via the performance on several hydrological signatures) to assess the advantages and limitations of the method. The re-identification experiments showed that process choices that show little impact on the hydrograph are difficult to re-identify due to near equivalent diagnostic measures. The real-world experiment showed that AMSI is capable of identifying feasible and avoiding infeasible model structures for the twelve tested MOPEX catchments. The performance of the identified models was compared to that of eight other models configured for the MOPEX catchments. AMSI's performance is in the top half of the performance range found by these eight, partially more complex, models, and is therefore considered satisfactory. However, the high variance in the identified model structures with comparable objective function values reflects substantial model equifinality. This was also seen in the benchmark study. While AMSI reliably identifies the most accurate model structures in a given model hypothesis space, the equifinality in model choice as measured through an aggregated metric such as KGE is considerable. In some catchments up to 30\% of the tested model choices obtain comparable KGE scores. These models, however, show significantly different behaviour in their internal storages, showing that a wide range of simulated hydrologic conditions can lead to comparable efficiency scores and therefore a wide ensemble of different model structures may appear suitable. Using AMSI with aggregated statistical metrics therefore provides only limited insights into which models are most suitable for the given catchment. Further investigations showed that the large number of identified mathematically accurate models (as measured through good metric performance) could hardly ever also be considered hydrologically adequate models (as measured through good signature performance). In nine out of twelve catchments none of the accurate models was also considered to be adequate, while only between one (0.1\%) and 49 (0.7\%) of all tested model structures met the defined adequacy requirements in the other three catchments. This glaring disconnect between mathematical accuracy and hydrological adequacy applies to all model selection approaches tested in the benchmark experiments. Neither AMSI, nor the brute force search, nor the MARRMoT models are able to provide accurate as well as adequate model structures when calibrated for the aggregated statistical metric KGE. Therefore, no distinct advantages of commonly used, expert-developed conceptual model structures could be identified over the data-derived AMSI models, as long as model performance is assessed only with aggregated efficiency scores. This has relevant implications for all modelling studies, as despite many papers suggesting to do otherwise, assessing model performance only through mathematical accuracy (i.e., with scores such as NSE or KGE) has remained the standard practice. The great empirical evidence of the inherent constraints of aggregated metrics such as KGE provided in this thesis may help to convey the message that relying only on these scores simply cannot guarantee hydrologically adequate model structures due to the equifinality in the combined model and parameter selection problem. The results also indicate that the AMSI method is able to identify model structures that are just as mathematically accurate and hydrologically inadequate as previously developed methods for model selection yield, but at a reduced work load to the modeller. Multi-variate datasets and better model performance metrics are often mentioned as ways to reduce equifinality. If such improved methods are implemented in the calibration procedure, AMSI's ability to discriminate between more granular process equations will increase. AMSI could then be a promising way forward to reduce the subjectivity in model selection, and to explore the connections between suitable model structures and catchment characteristics.

This work introduces diagnostic methods for land surface model (LSM) evaluation that enable developers to identify structural shortcomings in model parameterizations by evaluating model 'signatures' (characteristic temporal and spatial patterns of behavior) in feature, cost-function, and parameter spaces. The ensemble-based methods allow researchers to draw conclusions about hypotheses and model realism that are independent of parameter choice. I compare the performance and physical realism of three versions of Noah LSM (a benchmark standard version [STD], a dynamic-vegetation enhanced version [DV], and a groundwater-enabled one [GW]) in simulating high-frequency near-surface states and land-to-atmosphere fluxes in-situ and over a catchment at high-resolution in the U.S. Southern Great Plains, a transition zone between humid and arid climates. Only at more humid sites do the more conceptually realistic, hydrologically enhanced LSMs (DV and GW) ameliorate biases in the estimation of root-zone moisture change and evaporative fraction. Although the improved simulations support the hypothesis that groundwater and vegetation processes shape fluxes in transition zones, further assessment of the timing and partitioning of the energy and water cycles indicates improvements to the movement of water within the soil column are needed. Distributed STD and GW underestimate the contribution of baseflow and simulate too-flashy streamflow. This work challenges common practices and assumptions in LSM development and offers researchers more stringent model evaluation methods. I show that, because of equifinality, ad-hoc evaluation using single parameter sets provides insufficient information for choosing among competing parameterizations, for addressing hypotheses under uncertainty, or for guiding model development. Posterior distributions of physically meaningful parameters differ between models and sites, and relationships between parameters themselves change. 'Plug and play' of modules and partial calibration likely introduce error and should be re-examined. Even though LSMs are 'physically based,' model parameters are effective and scale-, site- and model-dependent. Parameters are not functions of soil or vegetation type alone: they likely depend in part on climate and cannot be assumed to be transferable between sites with similar physical characteristics. By helping bridge the gap between the model identification and model development, this research contributes to the continued improvement of our understanding and modeling of environmental processes. / text

Flash flood is among the most hazardous natural disasters, and it can cause severe damages to the environment and human life. Flash floods are mainly caused by intense rainfall and due to their rapid onset (within six hours of rainfall), very limited opportunity can be left for effective response. Understanding the socio-economic characteristics involving natural hazards potential, vulnerability, and resilience is necessary to address the damages to economy and casualties from extreme natural hazards. The vulnerability to flash floods is dependent on both biophysical and socio-economic factors. This study provides a comprehensive assessment of socio-economic vulnerability to flash flood alongside a novel framework for flash flood early warning system. A socio-economic vulnerability index was developed for each state and county in the Contiguous United States (CONUS). For this purpose, extensive ensembles of social and economic variables from US Census and the Bureau of Economic Analysis were assessed. The coincidence of socio-economic vulnerability and flash flood events were investigated to diagnose the critical and non-critical regions. In addition, a data-analytic approach is developed to assess the interaction between flash flood characteristics and the hydroclimatic variables, which is then applied as the foundation of the flash flood warning system. A novel framework based on the D-vine copula quantile regression algorithm is developed to detect the most significant hydroclimatic variables that describe the flash flood magnitude and duration as response variables and estimate the conditional quantiles of the flash flood characteristics. This study can help mitigate flash flood risks and improve recovery planning, and it can be useful for reducing flash flood impacts on vulnerable regions and population.

Bibliography: p. 474-514.

A comprehensive hydrological study of large watersheds in Africa e.g. the Congo basin and the Nile basin has not been vigorously pursued for various reasons. One of the major reasons is the lack of adequate modeling tools that would not be very demanding in terms of input data needs and yet inclusive enough to cover such wide extents (over 3 million square kilometers for the Congo basin). Using a coupled run of the Community Atmospheric model (CAM3) and Community Land Model (CLM3) components of the Community Climate System of Models (CCSM), this study looks into the spatial and temporal variation of precipitation and river runoff in the Congo basin in the light of increasing trends in deforestation of the tropical forests. The effect of deforestation on precipitation and runoff is investigated by changing the land cover-type from the current configuration of broadleaf evergreen/deciduous, non-Artic grass and corn to a mostly grass type of vegetation. Discharge simulation for the river Congo is centered at the point of entrance to the Atlantic Ocean. Although the CLM3 does not presently simulate the observed river runoff to within at least one standard deviation it gives an opportunity to iteratively improve on the land surface parameterization with a possibility of future accurate prediction of mean monthly river runoffs under varying climate scenarios and land use practices. When forced with the National Center for Environment and Prediction (NCEP) re-analysis data the CLM3 runoff simulation results are relatively more stable and much closer to the observed. An improved CLM3 when coupled to CAM3 or other Global Climate Models is definitely a better tool for investigative studies on the regional hydrological cycle in comparison to the traditional methods. There was a slight reduction in rainfall in the first experiment which mimicked a severe form of deforestation and a slight increase in rainfall following low level of deforestation. These changes in rainfall were however statistically insignificant when compared to the control simulation. There was notable heterogeneity in the spatial distribution of the changes in rainfall following deforestation.

A study was undertaken to evaluate the suitability of the SLURP hydrological model for simulating the hydrology of the Saint Esprit watershed (26 km 2) in Quebec. Climatic data and other input were made available through a monitoring program set up in the watershed from 1994 to 1998. GIS was used to store, analyze and export the watershed information into the model. The continuous semi-distributed model SLURP was calibrated using three years of data (1994--1996). Parameter calibration, except that of snowpack melt-rate, was done through an automatic optimization technique. The model was validated using graphical outputs, the Nash/Sutcliffe (R2) coefficient of performance for daily runoff, and the percent difference of predicted versus computed runoff on a monthly, seasonal and annual basis. Additionally, the evapotranspiration (ET) component of the model was compared with an ET estimated using the Baier & Robertson model (BR) calibrated for the region. (Abstract shortened by UMI.)