<div>With increasing focus on large scale planning and allocation of resources for protection against future flood risk, it is necessary to analyze and improve the deficiencies in the conventional flood modeling approach through a better understanding of the interactions between river hydrodynamics and subsurface processes. Recent studies have shown that it is possible to improve the flood inundation modeling and mapping using physically-based integrated models that incorporate observable data through assimilation and simulate hydrologic fluxes using the fundamental laws of conservation of mass at multiple spatiotemporal scales. However, despite the significance of integrated modeling in hydrology, it has received relatively less attention within the context of flood hazard. The overall aim of this dissertation is to study the heterogeneity in complex physical processes that govern the watershed response during flooding and incorporate these effects in integrated models across large scales for improved flood risk estimation. Specifically, this dissertation addresses the following questions: (1) Can physical process incorporation using integrated models improve the characterization of antecedent conditions and increase the accuracy of the watershed response to flood events? (2) What factors need to be considered for characterizing scale-dependent physical processes in integrated models across large watersheds? (3) How can the computational efficiency and process representation be improved for modeling flood events at large scales? (4) Can the applicability of integrated models be improved for capturing the hydrodynamics of unprecedented flood events in complex urban systems?</div><div><br></div><div>To understand the combined effect of surface-subsurface hydrology and hydrodynamics on streamflow generation and subsequent inundation during floods, the first objective incorporates an integrated surface water-groundwater (SW-GW) modeling approach for simulating flood conditions. The results suggest that an integrated model provides a more realistic simulation of flood hydrodynamics for different antecedent soil conditions. Overall, the findings suggest that the current practice of simulating floods which assumes an impervious surface may not be providing realistic estimates of flood inundation, and that an integrated approach incorporating all the hydrologic and hydraulic processes in the river system must be adopted.</div><div><br></div><div>The second objective focuses on providing solutions to better characterize scale-dependent processes in integrated models by comparing two model structures across two spatial scales and analyzing the changes in flood responses. The results indicate that since the characteristic length scales of GW processes are larger than SW processes, the intrinsic scale (or resolution) of GW in integrated models should be coarser when compared to SW. The results also highlight the degradation of streamflow prediction using a single channel roughness when the stream length scales are increased. A distributed channel roughness variable along the stream length improves the modeled basin response. Further, the results highlight the ability of a dimensionless parameter 𝜂1, representing the ratio of the reach length in the study region to maximum length of the single stream draining at that point, for identifying which streams may require a distributed channel roughness.</div><div><br></div><div>The third objective presents a hybrid flood modeling approach that incorporates the advantages of both loosely-coupled (‘downward’) and integrated (‘upward’) modeling approaches by coupling empirically-based and physically-based approaches within a watershed. The computational efficiency and accuracy of the proposed hybrid modeling approach is tested across three watersheds in Indiana using multiple flood events and comparing the results with fully- integrated models. Overall, the hybrid modeling approach results in a performance comparable to a fully-integrated approach but at a much higher computational efficiency, while at the same time, providing objective-oriented flexibility to the modeler.</div><div><br></div><div>The fourth objective presents a physically-based but computationally-efficient approach for modeling unprecedented flood events at large scales in complex urban systems. The application of the proposed approach results in accurate simulation of large scale flood hydrodynamics which is shown using Hurricane Harvey as the test case. The results also suggest that the ability to control the mesh development using the proposed flexible model structure for incorporating important physical and hydraulic features is as important as integration of distributed hydrology and hydrodynamics.</div>

Reliable estimates of design flood quantiles are often necessary at sparsely gauged/ungauged target locations in river basins for various applications in water resources engineering. Development of effective methods for use in this task has been a long-standing challenge in hydrology for over five decades.. Hydrologists often consider various regional flood frequency analysis (RFFA) approaches that involve (i) use of regionalization approach to delineate a homogeneous group of watersheds resembling watershed of the target location, and (ii) use of a regional frequency analysis (RFA) approach to transfer peak flow related information from gauged watersheds in the group to the target location, and considering the information as the basis to estimate flood quantile(s) for the target site. The work presented in the thesis is motivated to address various shortcomings/issues associated with widely used regionalization and RFA approaches. Regionalization approaches often determine regions by grouping data points in multidimensional space of attributes depicting watershed’s hydrology, climatology, topography, land-use/land-cover and soils. There are no universally established procedures to identify appropriate attributes, and modelers use subjective procedures to choose a set of attributes that is considered common for the entire study area. This practice may not be meaningful, as different sets of attributes could influence extreme flow generation mechanism in watersheds located in different parts of the study area. Another issue is that practitioners usually give equal importance (weight) to all the attributes in regionalization, though some attributes could be more important than others in influencing peak flows. To address this issue, a two-stage clustering approach is developed in the thesis. It facilitates identification of appropriate attributes and their associated weights for use in regionalization of watersheds in the context of flood frequency analysis. Effectiveness of the approach is demonstrated through a case study on Indiana watersheds. Conventional regionalization approaches could prove effective for delineating regions when data points (depicting watersheds) in watershed related attribute space can be segregated into disjoint groups using straight lines or linear planes. They prove ineffective when (i) data points are not linearly separable, (ii) the number of attributes and watersheds is large, (iii) there are outliers in the attribute space, and (iv) most watersheds resemble each other in terms of their attributes. In real world scenario, most watersheds resemble each other, and regions may not always be segregated using straight lines or linear planes, and dealing with outliers and high-dimensional data is inevitable in regionalization. To address this, a fuzzy support vector clustering approach is proposed in the thesis and its effectiveness over commonly used region-of-influence approach, and different cluster analysis based regionalization methods is demonstrated through a case study on Indiana watersheds. For the purpose of regional frequency analysis (RFA), index-flood approach is widely used over the past five decades. Conventional index-flood (CIF) approach assumes that values of scale and shape parameters of frequency distribution are identical across all the sites in a homogeneous region. In real world scenario, this assumption may not be valid even if a region is statistically homogeneous. Logarithmic index-flood (LIF) and population index-flood (PIF) methodologies were proposed to address the problem, but even those methodologies make unrealistic assumptions. PIF method assumes that the ratio of scale to location parameters is a constant for all the sites in a region. On the other hand, LIF method assumes that appropriate frequency distribution to fit peak flows could be found in log-space, but in reality the distribution of peak flows in log space may not be closer to any of the known theoretical distributions. To address this issue, a new mathematical approach to RFA is proposed in L-moment and LH-moment frameworks that can overcome shortcomings of the CIF approach and its related LIF and PIF methods that make various assumptions but cannot ensure their validity in RFA. For use with the proposed approach, transformation mechanisms are proposed for five commonly used three-parameter frequency distributions (GLO, GEV, GPA, GNO and PE3) to map the random variable being analyzed from the original space to a dimensionless space where distribution of the random variable does not change, and deviations of regional estimates of all the distribution’s parameters (location, scale, shape) with respect to their population values as well as at-site estimates are minimal. The proposed approach ensures validity of all the assumptions of CIF approach in the dimensionless space, and this makes it perform better than CIF approach and related LIF and PIF methods. Monte-Carlo simulation experiments revealed that the proposed approach is effective even when the form of regional frequency distribution is mis-specified. Case study on watersheds in conterminous United States indicated that the proposed approach outperforms methods based on index-flood approach in real world scenario. In recent decades, fuzzy clustering approach gained recognition for regionalization of watersheds, as it can account for partial resemblance of several watersheds in watershed related attribute space. In working with this approach, formation of regions and quantile estimation requires discerning information from fuzzy-membership matrix. But, currently there are no effective procedures available for discerning the information. Practitioners often defuzzify the matrix to form disjoint clusters (regions) and use them as the basis for quantile estimation. The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds. The lost information cannot be utilized in quantile estimation, owing to which the estimates could have significant error. To avert the loss of information, a threshold strategy (TS) was considered in some prior studies, but it results in under-prediction of quantiles. To address this, a mathematical approach is proposed in the thesis that allows discerning information from fuzzy-membership matrix derived using fuzzy clustering approach for effective quantile estimation. Effectiveness of the approach in estimating flood quantiles relative to DFA and TS was demonstrated through Monte-Carlo simulation experiments and case study on mid-Atlantic water resources region, USA. Another issue with index flood approach and its related RFA methodologies is that they assume linear relationship between each of the statistical raw moments (SMs) of peak flows and watershed related attributes in a region. Those relationships form the basis to arrive at estimates of SMs for the target ungauged/sparsely gauged site, which are then utilized to estimate parameters of flood frequency distribution and quantiles corresponding to target return periods. In reality, non-linear relationships could exist between SMs and watershed related attributes. To address this, simple-scaling and multi-scaling methodologies have been proposed in literature, which assume that scaling (power law) relationship exists between each of the SMs of peak flows at sites in a region and drainage areas of watersheds corresponding to those sites. In real world scenario, drainage area alone may not completely describe watershed’s flood response. Therefore flood quantile estimates based on the scaling relationships can have large errors. To address this, a recursive multi-scaling (RMS) approach is proposed that facilitates construction of scaling (power law) relationship between each of the SMs of peak flows and a set of site’s region-specific watershed related attributes chosen/identified in a recursive manner. The approach is shown to outperform index-flood based region-of-influence approach, simple-and multi-scaling approaches, and a multiple linear regression method through leave-one-out cross validation experiment on watersheds in and around Indiana State, USA. The conventional approaches to flood frequency analysis (FFA) are based on the assumption that peak flows at the target site represent a sample of independent and identically distributed realization drawn from a stationary homogeneous stochastic process. This assumption is not valid when flows are affected by changes in climate and/or land use/land cover, and regulation of rivers through dams, reservoirs and other artificial diversions/storages. In situations where evidence of non-stationarity in peak flows is strong, it is not appropriate to use quantile estimates obtained based on the conventional FFA approaches for hydrologic designs and other applications. Downscaling is one of the options to arrive at future projections of flows at target sites in a river basin for use in FFA. Conventional downscaling methods attempt to downscale General Circulation Model (GCM) simulated climate variables to streamflow at target sites. In real world scenario, correlation structure exists between records of streamflow at sites in a study area. An effective downscaling model must be parsimonious, and it should ensure preservation of the correlation structure in downscaled flows to a reasonable extent, though exact reproduction/mimicking of the structure may not be necessary in a climate change (non-stationary) scenario. A few recent studies attempted to address this issue based on the assumption of spatiotemporal covariance stationarity. However, there is dearth of meaningful efforts especially for multisite downscaling of flows. To address this, multivariate support vector regression (MSVR) based methodology is proposed to arrive at flood return levels (quantile estimates) for target locations in a river basin corresponding to different return periods in a climate change scenario. The approach involves (i) use of MSVR relationships to downscale GCM simulated large scale atmospheric variables (LSAVs) to monthly time series of streamflow at multiple locations in a river basin, (ii) disaggregation of the downscaled streamflows corresponding to each site from monthly to daily time scale using k-nearest neighbor disaggregation methodology, (iii) fitting time varying generalized extreme value (GEV) distribution to annual maximum flows extracted from the daily streamflows and estimating flood return levels for different target locations in the river basin corresponding to different return periods.

<p>In developing countries water access is not always available. In many locations around the world, people lack sufficient access of water for both drinking and domestic purposes and use unsafe water sources. Particularly in sub-Saharan Africa, women and children walk great distances to obtain access to water. People must have equitable and affordable access to safe and sufficient water that is palatable and in sufficient quantity for both drinking and domestic purposes before any other long-term economic development or social improvement can occur. This research seeks to increase access to subsurface water by improving the driving capabilities of the Well-Driver PUP (Purdue Utility Project) vehicle. The Well-Driver PUP is a low-volume manufactured utility vehicle with a hydraulic post driver mated to it in order to mechanize tube well installation. </p> <p>Worldwide, there are many locations where the water table depth is less than 23 meters, specifically in the 10-20 meters range. These areas include sub-Saharan Africa, the Caribbean, South America, northern India, Asia, and parts of the Asia Pacific Islands. These locations are places where the Well-Driver PUP could potentially be utilized, if sufficient reliability and depth can be demonstrated on a repeatable basis. This would increase the number of locations throughout the world that the vehicle could be used to access ground water for those with limited to no current water access. Ghana is one of the many countries located within sub-Saharan Africa where the Well-Driver PUP could have a positive impact.</p> <p>The author has had significant professional experience working in Ghana on various international development projects related to agriculture, water, sanitation, and hygiene (WASH). She has been part of international development projects in Ghana, Tanzania, and Haiti, with experience working cross-culturally since 2014. She has worked on projects specifically in Ghana for more than 9 years and has been part of more than 32 different water resource projects within the country. Therefore, consideration is specifically given to the appropriateness of the Well-Driver PUP as first piloted in Ghana. For this work, a cost analysis of using the Well-Driver PUP per depth and comparison to current driven wells in Ghana was carried-out. </p> <p>A review of the literature was conducted. Four research questions and experiments were established. Experiment 1 carried-out three different pipe stack numerical loading studies that were simulated in Fusion 360® (Autodesk, San Rafael, CA). Load models were examined of a centered hit, a non-centered hit, and a well point only. It was shown that the average dynamic impact force applied by the driving ram was calculated to be 39 kN. FEA analysis was conducted in Fusion 360®, and it included Von Mises, safety factor, and displacement results. The average dynamic impact force that the Well-Driver PUP applies was less than both the yield stress and ultimate tensile strength of ASTM A53 steel, indicating that no deformation or breakage of the well point should be expected. </p> <p>Experiment 2 included increasing the weight of the driving ram, through the addition of weight plates. A series of wooden fence post installations using these new weight additions was conducted. This experiment allowed for a regression model to be developed predicting the impact of weight added to the driving ram, the drop height of the ram, and the soil moisture content, on the driving depth of the vehicle. The MLR model included the penetration depth (Y), weight added (X₁), drop height (X₂), and soil moisture content (X₃). The model coefficient estimates were determined, and the predictor variables were all found to be significant at p < 0.01.</p> <p>Experiment 3 focused on improved reliability and finding the maximum depth capabilities of the Well-Driver PUP with new weight additions added to the driving ram. Two attempts were made to determine the driving depth capabilities of the vehicle. Both well installations were conducted in Montgomery County Indiana. Water was struck at both locations. At the first location, final well depth was 2.1 m with a 0.76 m of water within the column. The driver encountered a layer of blue-gray clay that it was unable to pass through. </p> <p>A second driving attempt was made to install a deeper well. The final well depth was 5.0 m with 1.67 m of water within the column. At this location, it is believed that a layer of limestone, shale, or siltstone was encountered. Comparing the compressive strength of limestone, sandstone, and shale, the Well-Driver PUP was not capable of driving through such materials. Therefore, at both well locations, the maximum driving depth capabilities of the driver were achieved. At both installation locations, the wells were formally developed. Both sets of water quality samples were submitted to the Montgomery County Health Department and received satisfactory ratings. </p> <p>Experiment 4 resulted in the fabrication and design of a 4” well point. The fabricated well point was installed to create a completed well at a depth of 2.7 m in Linden IN. There was 0.1 m of water within the pipe column. The well was formally developed, and the water quality results received a satisfactory rating. A cost analysis of a 4” well by depth was conducted. The total cost to fabricate one well point totaled $661.42. Of the total cost, 81% of the costs came from the 4” base pipe and the specialty pre-perforated screen used to create the secondary screen. The completion of these experiments provides a better understanding of the driving capabilities of the Well-Driver Pup. Improving the driving depth capabilities of the Well-Driver Pup will help to push this low-cost alternative technology closer to release in the developing world.</p> <p><br></p>

<p dir="ltr">Tropical Cyclones (TCs) are intense low-pressure weather systems that acts as a meteorological monster causing severe rainfall and widespread freshwater flooding, leading to extensive damage and disruption. Quantitative precipitation estimates (QPEs) are crucial for accurately understanding and evaluating the impacts of TCs. However, QPEs derived from various modalities, such as rain gauges, ground-based merged radars, and satellites, can differ significantly and require thorough comparison. Understanding the limitations/advantages of using each QPE is essential to simulate a hydrological model especially to estimate extreme events like TCs. The objective of the dissertation is to 1) characterize the tropical cyclone precipitation (TCP) using three gridded products, 2) characterize the impact of using different QPEs in estimation of hydrological variables using a hydrology model, and 3) understand the usability of satellite-derived QPEs for eight cases of TC and its impact on the estimate of hydrological variables. The QPEs include near real-time and post-processed satellite data from NASA’s Global Precipitation Mission-Integrated Multi-sensor Retrievals for GPM Rainfall Product (IMERG), merged ground radar observations (Stage IV) from the National Centers for Environmental Prediction (NCEP), and interpolated gauge observations from the National Weather Service Cooperative Observer Program (GCOOP). The study quantifies how differences in rainfall intensity and location, as derived from these gridded precipitation datasets, impact surface hydrology. The Variable Infiltration Capacity (VIC) model and the geographic information system (GIS) routing assess the propagation of bias in the daily rainfall rate to total runoff, evapotranspiration, and flooding. The analysis covers eight tropical cyclones, including Hurricane Charley (2004), Hurricane Frances (2004), Hurricane Jeanne (2004), Tropical Storm Fay (2008), Tropical Storm Beryl (2012), Tropical Storm Debby (2012), Hurricane Irma (2017) and Hurricane Michael (2018) focusing on different regions in South-Atlantic Gulf region and land uses. The findings indicate that IMERG underpredicts precipitation at higher quantiles but aligns closely with ground-based and radar-based products at lower quantiles. IMERG reliably estimates total runoff and evapotranspiration in 90% of TC scenarios along the track and in agricultural and forested regions. There is substantial overlap ~ 70% between IMERG and GCOOP/Stage IV for the 90th percentile rainfall spatially for the case of TC Beryl 2012. Despite previous perceptions of underestimation, the study suggests that satellite-derived rainfall products can be valuable in simulating streamflow, particularly in data-scarce regions where ground estimates are lacking. The relative error in estimation is 12% and 22% when using IMERG instead of Stage IV and GCOOP rainfall data. The findings contribute to a broader perspective on usability of IMERG in estimating near real-time hydrological characteristics, paving the way for further research in this area. This analysis demonstrates that IMERG can be a reliable data product for hydrological studies even in the extreme events like landfalling TCs. This will be helpful in improving the preparedness of vulnerable communities and infrastructure against TC-induced flooding in data scare regions.</p>