Characterization and uncertainty analysis of siliciclastic aquifer-fault system

The complex siliciclastic aquifer system underneath the Baton Rouge area, Louisiana, USA, is fluvial in origin. The east-west trending Baton Rouge fault and Denham Springs-Scotlandville fault cut across East Baton Rouge Parish and play an important role in groundwater flow and aquifer salinization. To better understand the salinization underneath Baton Rouge, it is imperative to study the hydrofacies architecture and the groundwater flow field of the Baton Rogue aquifer-fault system. This is done through developing multiple detailed hydrofacies architecture models and multiple groundwater flow models of the aquifer-fault system, representing various uncertain model propositions. The hydrofacies architecture models focus on the Miocene-Pliocene depth interval that consists of the “1,200-foot” sand, “1,500-foot” sand, “1,700-foot” sand and the “2,000-foot” sand, as these aquifer units are classified and named by their approximate depth below ground level. The groundwater flow models focus only on the “2,000-foot” sand. The study reveals the complexity of the Baton Rouge aquifer-fault system where the sand deposition is non-uniform, different sand units are interconnected, the sand unit displacement on the faults is significant, and the spatial distribution of flow pathways through the faults is sporadic. The identified locations of flow pathways through the Baton Rouge fault provide useful information on possible windows for saltwater intrusion from the south. From the results we learn that the “1,200-foot” sand, “1,500-foot” sand and the “1,700-foot” sand should not be modeled separately since they are very well connected near the Baton Rouge fault, while the “2,000-foot” sand between the two faults is a separate unit. Results suggest that at the “2,000-foot” sand the Denham Springs-Scotlandville fault has much lower permeability in comparison to the Baton Rouge fault, and that the Baton Rouge fault plays an important role in the aquifer salinization

Integrated High-Resolution Modeling for Operational Hydrologic Forecasting

Current advances in Earth-sensing technologies, physically-based modeling, and computational processing, offer the promise of a major revolution in hydrologic forecasting—with profound implications for the management of water resources and protection from related disasters. However, access to the necessary capabilities for managing information from heterogeneous sources, and for its deployment in robust-enough modeling engines, remains the province of large governmental agencies. Moreover, even within this type of centralized operations, success is still challenged by the sheer computational complexity associated with overcoming uncertainty in the estimation of parameters and initial conditions in large-scale or high-resolution models. In this dissertation we seek to facilitate the access to hydrometeorological data products from various U.S. agencies and to advanced watershed modeling tools through the implementation of a lightweight GIS-based software package. Accessible data products currently include gauge, radar, and satellite precipitation; stream discharge; distributed soil moisture and snow cover; and multi-resolution weather forecasts. Additionally, we introduce a suite of open-source methods aimed at the efficient parameterization and initialization of complex geophysical models in contexts of high uncertainty, scarce information, and limited computational resources. The developed products in this suite include: 1) model calibration based on state of the art ensemble evolutionary Pareto optimization, 2) automatic parameter estimation boosted through the incorporation of expert criteria, 3) data assimilation that hybridizes particle smoothing and variational strategies, 4) model state compression by means of optimized clustering, 5) high-dimensional stochastic approximation of watershed conditions through a novel lightweight Gaussian graphical model, and 6) simultaneous estimation of model parameters and states for hydrologic forecasting applications. Each of these methods was tested using established distributed physically-based hydrologic modeling engines (VIC and the DHSVM) that were applied to watersheds in the U.S. of different sizes—from a small highly-instrumented catchment in Pennsylvania, to the basin of the Blue River in Oklahoma. A series of experiments was able to demonstrate statistically-significant improvements in the predictive accuracy of the proposed methods in contrast with traditional approaches. Taken together, these accessible and efficient tools can therefore be integrated within various model-based workflows for complex operational applications in water resources and beyond

Automated calibration of a Carbon dynamic model for lakes and reservoirs : (calibração automática de um modelo de dinâmica de Carbono em lagos e reservatórios)

Orientador : Michael MannichCoorientador : Cristóvão Vicente Scapulatempo FernandesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Recursos Hídricos e Ambiental. Defesa: Curitiba, 16/03/2017Inclui referências e apêndicesResumo: A carência de medidas de fluxos de gases de efeito estufa (GEE), junto com as incertezas referentes às extrapolações de emissões pontuais para emissões totais, resultam em conclusões imprecisas referente a participação de reservatórios no clima global. O modelo matemático CICLAR é usado para simular fluxos de CO2 e CH4 por 45 anos no reservatório de Capivari, Paraná, Brasil. O modelo é estruturado em compartimentos de diferentes formas de carbono, como o carbono inorgânico dissolvido (CID) e o carbono orgânico particulado vivo (COPL). Processos químicos de transferência de massa entre compartimentos são modelados como reações de primeira ordem e de saturação que são controladas por parâmetros numéricos. O valor destes parâmetros são calibrados através da minimização de diferenças entre dados observados e modelados através de algoritmos de calibração. O algoritmo metaheuristico de Otimização Multi-objetivo por Enxame de Particulas Combinada de Pareto (CPMOPSO), que combina técnicas de seleção de líderes, mutações e subenxames, foi desenvolvido e aplicado como método de otimização. O algoritmo de calibração automática utiliza dados provenientes da calibração manual. Quatro cenários foram analisados: o avaliativo, que usa os primeiros 30 e os últimos 15 anos de dados do reservatório para calibrar e validar o modelo; e o retrospective, o prospectivo e o ideal, que usam 9 anos de dados, distribuídos de maneiras diferentes, para calibrar o modelo. A qualidade dos resultados da calibração foi positivamente considerada através do uso do cenário avaliativo. Os resultados da calibração sob os cenários retrospectivo e prospectivo mostraram que o algoritmo tende a superestimar emissões de metano se dados mal distribuídos são utilizados. A otimização sob o cenário ideal obteve melhores resultados e mostrou que a disposição dos dados tem maior impacto do que a quantidade sobre a calibração. Todas as soluções sob todos os cenários obtiveram soluções com coeficientes de Nash-Sutcliffe superiores a 0.95 para o período de calibração. As distribuições acumuladas das médias dos Potenciais de Aquecimento Global (GWP) mostraram que a maioria das soluções calibradas classificam o reservatório como um sumidouro de dióxido de carbono equivalente, absorvendo até 90 Gg de CO2 eq. Estimativas alternativas de estoque de carbono foram utilizadas para calibrar o modelo em um escopo em que nenhuma solução prévia é conhecida. São feitas considerações adicionas referentes a aplicação de métodos de análise de incertezas e agregação Bayesiana para melhor aferir múltiplos conjuntos de parâmetros. Palavras-chaves: Modelagem matemática. Dinâmica do carbono. Gases de efeito estufa. Potencial de aquecimento global. Enxame de partículas. Dominância de Pareto.Abstract: The low availability of measured greenhouse gas (GHG) fluxes for lakes and reservoirs, coupled with uncertainties regarding extrapolating total reservoir emissions from point measurements, result in inaccurate conclusions regarding the role of reservoirs in the global climate. The Carbon Cycle in Lakes and Reservoirs (CICLAR) model is used to study potential contributions, through carbon dioxide (CO2) and methane (CH4) emissions, of the Capivari reservoir, Brazil, since its construction in 1970. The model is structured in compartments for different carbon forms, such as dissolved inorganic carbon (DIC) and live particulate organic carbon (POCL), and model chemical processes as first order reactions controlled by numerical parameters. The values of these parameters are calibrated by minimizing differences between original and modeled data through an optimization algorithm. The Combined Pareto Multi-objective Particle Swarm Optimization (CPMOPSO) metaheuristic algorithm, which combines leader selection, mutation and subswarm techniques, is developed and successfully used as the optimization technique. The automated calibration algorithm uses data originated from the manual calibration. Four calibration scenarios are used to analyze the impact of data disposition in the calibration results: the evaluative scenario that has the initial 30 years to calibrate and the final 15 to validate the model; and the retrospective, prospective and ideal scenarios, that uses 9 years of data differently distributed. The evaluative data scenario is used to assess the quality of the calibration results, which successfully fit the validation data. The retrospective and prospective scenario are used to analyze the performance of the calibration under unevenly spread data, and the results show that the model had a bias to overestimate methane emissions. The calibration under the ideal scenario is used to show that having evenly spread data has a bigger impact on calibration results than having larger amounts of data. All calibrated solutions for all scenarios present Nash-Sutcliffe coefficient values higher than 0.95 for the calibration period. The cumulative distribution of average Global Warming Potential (GWP) indexes shows that most calibrated solutions estimated that the Capivari reservoir is a sinkhole for equivalent carbon dioxide and that it can absorb up to 90 Gg of equivalent CO2. Alternative carbon stock estimations are used to calibrate the model under a framework in which the results cannot be validated due to no previous solutions being known. Further consideration are drawn regarding the application of uncertainty analysis and Bayesian aggregation methods to better assess the combination of multiple set of parameters. Keywords: Mathematical modeling. Carbon dynamics. Greenhouse gases. Global warming potential. Particle swarm optimization. Pareto dominance

A combined modelling approach for simulating channel–wetland exchanges in large African river basins

In Africa, many large and extensive wetlands are hydrologically connected to rivers, and their environmental integrity, as well as their influence on downstream flow regimes, depends on the prevailing channel–wetland exchange processes. These processes are inherently complex and vary spatially and temporally. Understanding channel–wetland exchanges is therefore, indispensable for the effective management of wetlands and the associated river basins. However, this information is limited in most of the river basins containing large wetlands in Africa. Furthermore, it is important to understand the links between upstream and downstream flow regimes and the wetland dynamics themselves, specifically where there are water resource developments that may affect these links (upstream developments), or be affected by them (downstream developments). Hydrological modelling of the entire basin using basin-scale models that include wetland components in their structures can be used to provide the information required to manage water resources in such basins. However, the level of detail of wetland processes included in many basin-scale models is typically very low and the lack of understanding of the wetland dynamics makes it difficult to quantify the relevant parameters. Detailed hydraulic models represent the channel-wetland exchanges in a much more explicit manner, but require relatively more data and time resources to establish than coarser scale hydrological models. The main objective of this study was, therefore, to investigate the use of a detailed hydraulic wetland model to provide a better understanding of channel–wetland exchanges and wetland dynamics, and to use the results to improve the parameterisation of a basin-scale model. The study focused on improving the water resource assessments modelling of three data-scarce African river basins that contain large wetlands: the floodplains of the Luangwa and Upper Zambezi River basins and the Usangu wetland in the Upper Great Ruaha River basin. The overall objective was achieved through a combined modelling approach that uses a detailed high-resolution LISFLOOD-FP hydraulic model to inform the structure and parameters of the GW Pitman monthly hydrological model. The results from the LISFLOOD-FP were used to improve the understanding of the channel–wetland exchange dynamics and to establish the wetland parameters required in the GW Pitman model. While some wetland parameters were directly quantified from the LISFLOOD-FP model results, others, which are highly empirical, were estimated by manually calibrating the GW Pitman wetland sub-model implemented in excel spreadsheets containing the LISFLOOD-FP model results. Finally, the GW Pitman model with the inclusion of the estimated wetland parameters was applied for each basin and the results compared to the available downstream observed flow data. The two models have been successfully applied in southern Africa, with the GW Pitman model being one of the most widely applied hydrological models in this region. To address the issue of data scarcity, during setup of these models, the study mainly relied on the global datasets which clearly adds to the overall uncertainty of the modelling approach. However, this is a typical situation for most of the data scarce regions of the continent. A number of challenges were, however, faced during the setup of the LISFLOOD-FP, mainly due to the limitations of the data inputs. Some of the LISFLOOD-FP data inputs include boundary conditions (upstream and downstream), channel cross-sections and wetland topography. In the absence of observed daily flows to quantify the wetland upstream boundary conditions, monthly flow volumes simulated using the GW Pitman monthly model (without including the wetland sub-model) were disaggregated into daily flows using a disaggregation sub-model. The simulated wetland inflows were evaluated using the observed flow data for downstream gauging stations that include the wetland effects. The results highlighted that it is important to understand the possible impacts of each wetland on the downstream flow regime during the evaluations of the model simulation results. Although the disaggregation approach cannot be validated due to a lack of observed data, it at least enables the simulated monthly flows to be used in the daily time step hydraulic model. One of the recommendations is that improvements are required in gauging station networks to provide more observed information for the main river and the larger tributary inflows into these large and important wetland systems. Even a limited amount of newly observed data would be helpful to reduce some of the uncertainties in the combined modelling approach. The SRTM 90 m DEM (used to represent wetland topography) was filtered to reduce local variations and noise effects (mainly vegetation bias), but there were some pixels that falsely affect the inundation results, and the recently released vegetation-corrected DEMs are suggested to improve the simulation results. Channel cross-section values derived from global datasets should be examined because some widths estimated from the Andreadis et al. (2013) dataset were found to be over-generalised and did not reflect widths measured using high-resolution Google Earth in many places. There is an indication that channel cross-sections digitised from Google Earth images can be successfully used in the model setup except in densely vegetated swamps where the values are difficult to estimate, and in such situations, field measured cross-section data are required. Small channels such as those found in the Usangu wetland could play major role in the exchange dynamics, but digitising them all was not straightforward and only key ones were included in the model setup. Clearly, this inevitably introduced uncertainties in the simulated results, and future studies should consider applying methods that simplify extractions of most of these channels from high-resolution images to improve the simulated results. The study demonstrated that the wetland and channel physical characteristics, as well as the seasonal flow magnitude, largely influence the channel–wetland exchanges and wetland dynamics. The inundation results indicated that the area–storage and storage–inflow relationships form hysteretic curves, but the shape of these curves vary with flood magnitude and wetland type. Anticlockwise hysteresis curves were observed in both relationships for the floodplains (Luangwa and Barotse), whereas there appears to be no dominant curve type for the Usangu wetlands. The lack of well-defined hysteretic relationships in the Usangu could be related to some of the difficulties (and resulting uncertainties) that were experienced in setting up the model for this wetland. The storage–inflow relationships in all wetlands have quite complex rising limbs due to multiple flow peaks during the main wet season. The largest inundation area and storage volume for the Barotse and Usangu wetlands occurred after the peak discharge of the wet season, a result that is clearly related to the degree of connectivity between the main channel and those areas of the wetlands that are furthest away from the channel. Hysteresis effects were found to increase with an increase in flood magnitudes and temporal variations in the wetland inflows. Overall, hysteresis behaviour is common in large wetlands and it is recommended that hysteresis curves should be reflected in basin-scale modelling of large river basins with substantial wetland areas. At a daily time scale, inflow–outflow relationships showed a significant peak reduction and a delayed time to peak of several weeks in the Barotse and Usangu wetlands, whereas the attenuation effects of the Luangwa floodplain are minimal. To a large extent, the LISFLOOD-FP results provided useful information to establish wetland parameters and assess the structure of Pitman wetland sub-model. The simple spreadsheet used to estimate wetland parameters did not account for the wetland water transfers from the upstream to the next section downstream (the condition that is included in the LISFLOOD-FP model) for the case when the wetlands were distributed across more than one sub-basin. It is recommended that a method that allows for the upstream wetland inflows and the channel inflows should be included in the spreadsheet. The same is true to the Pitman model structure, and a downstream transfer of water can be modelled through return flows to the channel. The structure of the wetland sub-model was modified to allow an option for the return flows to occur at any time during the simulation period to provide for types of wetlands (e.g. the Luangwa) where spills from the channel and drainage back to the channel occur simultaneously. The setup of the GW Pitman model with the inclusion of wetland parameters improved the simulation results. However, the results for the Usangu wetlands were not very satisfactory and the collection of additional field data related to exchange dynamics is recommended to achieve improvements. The impacts of the Luangwa floodplain on the flow regime of the Luangwa River are very small at the monthly time scale, whereas the Barotse floodplain system and the Usangu wetlands extensively regulate flows of the Zambezi River and the Great Ruaha River, respectively. The results highlighted the possibilities of regionalising some wetland parameters using an understanding of wetland physical characteristics and their water exchange dynamics. However, some parameters remain difficult to quantify in the absence of site-specific information about the water exchange dynamics. The overall conclusion is that the approach implemented in this study presents an important step towards the improvements of water resource assessments modelling for research and practical purposes in data-scarce river basins. This approach is not restricted to the two used models, as it can be applied using different model combinations to achieve similar study purpose