1,721,014 research outputs found
From Impacts to Implementation: A Survey of Sand Dams in Sub-Saharan Africa
No full textInternational development projects are a massive business, with billions invested annually in the Global South. However, such projects have an unacceptably long record of high failure rates. The problem perpetuates, in part, due to the success factors by which international development projects are judged. Often, projects are assessed on the basis of donor-identified priorities that are not aligned with local impacts. One such international development project involves the construction of small-scale water harvesting structures known as sand dams. Non-governmental organizations (NGOs) continue to raise sufficient funds to build thousands of sand dams across sub-Saharan Africa, and yet 50% of sand dams are estimated to be non-functioning.Sand dams are small, reinforced concrete dams built across an impermeable streambed. Over time, sand settles behind the dam, creating an upstream sand reservoir that fills with rainwater and surface runoff. The sand helps filter the water, protects it from evapotranspiration, and can provide water to the local community for domestic and agricultural use during the dry season. Sand dams often fail due to poor construction, inadequate siting, and siltation.This dissertation explores methodologies for studying the regional and local impacts of sand dams and investigates the feasibility of developing model-based site selection guidelines for sand dams. Three objectives of this study are: (1) to develop a methodology to assess the ability of sand dams in improving the overall water availability in the region; (2) to examine claims made by non-scientific bodies about sand dam impacts by investigating how diverse sand dams influence macroinvertebrate habitat, vegetation, erosion, and local water availability; and (3) to create guidelines for siting new sand dams based on a fully integrated surface and groundwater flow model.For the first objective, two multiple regression models are developed to compare (1) water storage and (2) vegetation in an area with a high density of sand dams, termed the sand dam counties (SDC), to those in a control area. The models analyze remotely sensed datasets to assess whether evidence exists of significantly increased storage in the SDC relative to the control area. The results show that the remotely sensed water storage data is unable to consistently detect higher levels of water storage in the SDC. This is likely due to the low resolution of the dataset combined with the small magnitude of sand dams’ impact on regional water storage. The results of the vegetation model show that the sand dams have a consistent, positive impact on vegetation within the SDC relative to the control area. Because vegetation health and cover is often correlated with groundwater levels, these results likely indicate that the sand dams are also increasing local groundwater levels. Overall, this study shows that remotely sensed dataset can provide a useful basis to assess the impact of international development projects, particularly those that involve the natural environment.For the second objective, data relating to macroinvertebrates, vegetation, erosion, and water table elevations at three sand dams were collected and analyzed during a year-long field study in Tanzania
Drought policy development and assessment in East Africa using hydrologic and system dynamics modeling
No full textDrought is a natural disaster that affects millions of people across the globe. Lack of rainfall reduces crop yields and livestock productivity and in turn, food availability and income. In developing countries, these effects are even more detrimental. As droughts become more frequent, adaptation is a fundamental concern for countries and their policy makers. Hydrologic and system dynamics models were developed for a region in East Africa, focused on the Horn of Africa (i.e. a region bordering Kenya, Somalia, and Ethiopia), an area well-known for frequent droughts due to unpredictable rainfall and high temperatures. The models simulate the interdependencies between water availability, land degradation, food availability, socio-economic welfare and the impact new adaptation policies can have on the region over a 10 year simulation. It was found that a combination of increased hydraulic infrastructure and innovative agricultural practice policy can reduce domestic water deficits by 54–100% while increasing the income per capita up to 285% over the 10 years. By innovatively combining hydrologic and system dynamics modeling, realistic simulation of the effects water scarcity has on natural systems can be observed. Implementation of policies within the model aids the selection process by evaluating multiple options, quantifying the effectiveness the policies have on individual stakeholder livelihood, and analyzing the overall outcome to ensure equitable costs and benefits
Investigating the role of dem resolution and accuracy on flood inundation mapping
Get PDFSaksena, Siddharth. M.S.C.E., Purdue University, May 2014. Investigating the Role of DEM Resolution and Accuracy on Flood Inundation Mapping. Major Professor: Venkatesh Merwade. Topography plays an important role in determining the accuracy of flood inundation maps. A lot of the current flood inundation maps are created using topographic information derived from Light Detection and Ranging (LiDAR) data. Although LiDAR data is very accurate, it is expensive, computationally time consuming and not available in several areas across the United States and around the world. As a result, coarser resolution DEMs which are easily available but less accurate are used for flood modeling. It is essential to understand the properties of LiDAR data to create methods to modify coarser resolution DEMs and increase their accuracy. These properties can be used to understand how elevation errors propagate within a DEM and reduce the impact of errors in coarser resolution datasets. The first objective of this study is to quantify the errors arising from DEM properties such as resolution and accuracy on flood inundation maps. The results from these six study areas show that water surface elevations and flood inundation area have a linear relationship with the DEM resolution and accuracy. The second objective of this study is to use the linear relationship between hydraulic outputs and the DEM resolution or accuracy to create an approach for developing accurate flood inundation maps using less accurate DEMs by modeling the spatial distribution of DEM errors. Application of this new approach on USGS NED 30 m resolution DEMs and SRTM 90 m resolution DEMs shows significant increase in the accuracy of water surface elevations and improvement in predicted flood extents created from coarser resolution DEM when compared to results from high resolution accurate DEMs. A check on the applicability of this approach for different interpolation methods and river channel conditions is also made in this study. The new approach thus provides promising results in obtaining more accurate flood maps from less accurate topographic data
Downward model development of the soil moisture accounting loss method in HEC-HMS: Revelations concerning the soil profile
Get PDFDespite the fact that the soil profile is known to impact streamflow, most Curve Number (CN)-based models ignore subsurface processes. This study explores the influence of soil storage on peak flows. Two watersheds in flat, humid west-central Indiana were modeled using both the Natural Resources Conservation Service (NRCS) Curve Number and four versions of the Soil Moisture Accounting (SMA) loss methods in the United States Army Core of Engineers-developed (USACE) Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS). One watershed encompasses the Wabash and Tippecanoe Rivers\u27 confluence; the other contains an ephemeral stream, Plum Creek. The CN-based model was developed using standard practices, but for the SMA-based model, four increasingly sophisticated SMA loss method arrangements of the two study areas were included and analyzed for summer and winter seasons. All four arrangements contain identical surface characteristics but vary in the soil profile parameters included. The first arrangement includes unlimited soil storage, the second includes limited tension zone storage, the third limits soil storage and includes groundwater parameters, and finally, the fourth includes baseflow characteristics. Results show that the streamflow from the four arrangements differs little for much of the year. However, significant differences in model results are observed when the causative storm has relatively high maximum precipitation intensity. While these results do not necessarily coincide with the results of previous studies, the departure can be explained by the greater soil profile depth in the watersheds of interest. Comparison of streamflow from both the CN-based and SMA-based models with observed streamflow data show that these models do vary in their prediction of peak flow values
Assessment of Epistemic Uncertainty in Flood Inundation Modeling
Get PDFFlooding is one of the most devastating natural disasters in the world and it exacerbated during the past decades. In order to reduce the loss of lives and properties from repeating flooding events, reliable flood predictions are required. Currently, there exist a series of hydrodynamic models that have different model structures which solve different forms of governing equations in one- (1D), two- (2D) or three- (3D) dimensions, thus providing various possible predictions for decision makers to choose from. Even for the same model, depending on how the model is implemented for a specific parameter set, input data and channel geometry representation, the prediction is different. Therefore, investigating the reducible uncertainty (epistemic uncertainty) in flood inundation modeling and finding a proper way to generate robust predictions are very crucial for future modelers. In this dissertation, epistemic uncertainty sources from model structure, model parameter and model input are investigated and evaluated by using stream reaches ranging from reach scale to watershed scale in different geographical settings. The three objectives of this dissertation are to: (1) evaluate the impact of hydrodynamic model structure uncertainty on predicted water stages and inundation extents under different geophysical settings, and explore the influence of channel and floodplain roughness on model performance respectively, (2) investigate and apply a multi-model combing approach, Bayesian model averaging (BMA), to produce reliable predictions by considering four uncertainty sources including channel width, channel cross-sectional shape, channel roughness and flow forcing and (3) separate and prioritize different uncertainty sources, including DEM resolution, channel width, channel cross-sectional shape, channel roughness and flow forcing, based on their relative influences using hierarchical Bayesian model averaging (HBMA). In the first objective, the performance of four hydraulic models including HEC-RAS 1D, HEC-RAS 2D, LISFLOOD-FP diffusive and LISFLOOD-FP subgrid are evaluated at four rivers that have different geophysical settings in the United States. The results show that HEC-RAS 2D does not perform well at low channel roughness condition. However, at high channel roughness condition, the performance of HEC-RAS 2D and HEC-RAS 1D are comparable. The performance of the subgrid version of LISFLOOD-FP (LS) is more stable under different channel roughness conditions, and in general it performs better than the diffusive version (LD) in simulating floodplain inundation. Moreover, applying distributed floodplain roughness does not necessarily improve model performances. In the second objective, LISFLOOD-FP subgrid model is applied for a relatively large catchment-Black River watershed in Missouri and Arkansas considering four uncertainty sources using BMA approach. The results indicate that although BMA deterministic prediction may not always outperform all the model members in the ensemble, this approach is able to provide a relatively robust water stage prediction. Typically, BMA deterministic prediction behaves better than most of the member predictions in the ensemble and ensemble mean prediction. BMA has better performance than ensemble mean prediction for high-chance flood regions at Black River watershed. On the other hand, there is no significant difference between two types of probabilistic flood maps for low-chance flood regions. In the third objective, LISFLOOD-FP subgrid model is also set up in the Black River watershed to find out the relative influence of five different uncertainty sources. The results demonstrate that channel width and topographical data resolution have largest impact on the hydrodynamic model predictions. These two sources are followed by flow forcing, which has relatively greater influence than channel cross-sectional shape and model parameter. However, when model weights are taken into account, input (topography and input forcing) and model parameter (roughness) have larger impact on prediction variance than model structure (channel shape and width)
Comparative Analysis of Swat Cup and Swatshare for Calibrating Swat Models
No full textSoil and water assessment tool model (SWAT model) is a widely used model when dealing with large and complex watershed simulations. To correctly predict runoff of a watershed, autocalibration methods are applied. Among all the platforms, SWAT CUP is widely used in the SWAT model community. The new web-based calibration platform: SWATShare is also gaining its popularity due to the benefits of user-friendly interface, access to high-performance computing resources, and collaborative interface. While the algorithm implemented in SWAT CUP is Sequential Uncertainty Fitting version 2 (SUFI2), Sorting Genetic Algorithm II (NSGA-II) is the algorithm employed by SWATShare. There is a limited amount of research comparing the model performance between these two calibration algorithms and platforms.This study aims to examine whether the performances of calibrated models are providing equally reliable results. Thirty US watersheds are studied in this research, SWAT models were calibrated using seven years of rainfall data and outflow observations from 2001 to 2007, and then the models were validated using three years of historical records from 2008 to 2010. Inconsistency exists between different algorithms calibrated parameter sets, and the percentage difference between parameter values ranges from 8.7% to 331.5%. However, in two-thirds of the study basins, there is no significant difference between objective function values in two algorithms calibrated models. Correlations are examined using values of parameters and watershed features. Among all the features and parameters, Length of reach and GW_DELAY, CH_N2 and ALPHA_BF, climate zone and GWQMN, SFTMP and NSE have medium correlation exist in both SWATShare and SWAT CUP calibrated models among 30 watersheds. The correlation coefficient difference between them are less than 0.1. When visualizing results by Ecoregions, KGE and NSE are similar in calibrated models from both tools.The initial parameter range used for SWAT CUP calibration could lead to satisfactory results with greater than 0.5 objective function values. However, the parameter values of the calibrated model might not be presenting a real physical condition since they are out of the realistic range. The inaccurate parameter values might lead to lower objective function values in the validation. The objective function values can be improved by setting the range of parameter values to match the realistic values.By comparing two tools, SWATShare accurately calibrates parameter values to a realistic range using default range in most cases. For those models with an unsatisfactory result from SWATShare, the objective function values could be improved after specifying the parameters to the best-fit range given by SWAT CUP results. Also, for those watersheds which have similar satisfactory calibrated objective values from both tools, constraining the parameter to a reasonable range could generate a new calibrated model that performs as well as the original one. Using the approach to constrain parameter values to a realistic range gradually can exclude some statistically satisfactory but physically meaningless models. Comparing two auto-calibration software, SWATShare accurately calibrates parameter values to a realistic range using default range in most cases. Also, in some of the ecoregions, the best parameter sets in SWATShare fall in a more physically meaningful range. Overall, the newly emerged platform, SWATShare, is found to have the capability of conducting good SWAT model calibration
Floodplain mapping using soil survey geographic (SSURGO) database
Get PDFFloods are the most damaging of all natural disasters, adversely affecting millions of lives and causing financial losses worth billions of dollars every year across the globe. Flood inundation maps play a key role in assessment and mitigation of the potential flood hazards. However, there are several communities in the United States for which the flood risk maps have not been published yet, as the current flood inundation mapping methods are typically very expensive and time consuming. The objective of this study is to develop and examine an economical alternative approach to floodplain mapping using widely available soil survey data. In this study, floodplain maps were developed for the entire state of Indiana, and some counties in Washington, Minnesota, and Wisconsin by identifying the flood-prone soil map units based on their attributes recorded in the SSURGO database. For validation, the flood extents predicted by these maps were compared with the extents predicted by other floodplain maps viz.: Federal Emergency Management Agency (FEMA) issued Flood Insurance Rate Maps (FIRM), flood extents observed during past floods, and other floodmaps derived using Digital Elevation Models (DEMs). In general, SSURGO based floodplain maps were found to be largely in agreement with the other flood inundation maps. They were as effective as floodmaps derived using DEMs in their predictions of flood extents. Although there was comparatively greater agreement between the FEMA maps and the observed flood extents, SSURGO floodplain maps could predict most of the observed flood extents with a median overlap of 72% between the two flood extents. Thus, albeit with a slight loss in accuracy, SSURGO approach offers an economical and fast alternative for floodplain mapping. In particular, it has potentially high utility in areas where no detailed flood studies have been conducted
Floodplain Mapping in Data-Scarce Environments Using Regionalization Techniques
No full textFlooding is one of the most devastating and frequently occurring natural phenomena in the world. Due to the adverse impacts of floods on the life and property of humans, it is crucial to investigate the best flood modeling approaches for delineation of floodplain areas. Conventionally, different hydrodynamic models are used to identify the floodplain areas. However, the high computational cost, and the dependency of these models on detailed input datasets limit their application for large scale floodplain mapping in data-scarce regions. Recently, a new floodplain mapping method based on a hydrogeomorphic feature, named Height Above Nearest Drainage (HAND), has been proposed as a successful alternative for fast and efficient floodplain mapping at the large scale. The overall goal of this study is to improve the performance of HAND-based method by overcoming its current limitations. The main focus will be on extending the application of the HAND-based method to data-scarce environments. To achieve this goal, regionalization techniques are integrated with the floodplain models at the regional and continental scales. Considering these facts, four research objective are established to (1) Develop a regression model to create 100-year floodplain maps at a regional scale (2) Develop a classification framework for creating 100-year floodplain maps for the Contiguous United States (3) Develop a new version of the HAND-based method for creating probabilistic 100-year floodplain maps, and (4) Propose a general regionalization framework for transferring information from data-rich basins to datascarce environments.In the first objective, the state of North Carolina is selected as the study area, and a regression model is developed to regionalize the available 100-year Flood Insurance Rate Maps (FIRMs) to the data-scarce regions. The regression model is an exponential equation with three independent variables including the average slope, the average elevation, and the main stream slope of the watershed. The results show that the estimated floodplains are within the expected range of accuracy of C\u3e0.6 and F\u3e0.9 for majority of watersheds located in the mid-altitude regions, but it overpredicts and underpredicts in the flat and mountainous regions respectively.The second objective of this research extends the spatial application of the HAND-based method to the entire United States by proposing a new classification framework. The proposed framework classifies the watersheds into three groups by using seven watershed characteristics related to the topography, climate and land use. The validation results show that the average error of floodplain maps is around 14% which demonstrate the reliability and robustness of the proposed framework for continental floodplain mapping. In addition to the acceptable accuracy, the proposed framework creates the floodplain maps for any watershed within the United States.The HAND-based method is a deterministic modeling approach to floodplain mapping. In the third objective, the probabilistic version of this method is proposed. Using a probabilistic approach to floodplain mapping provides more informative maps. In this study, a flat watershed in the state of Kansas is selected as the case study, and the performance of four probabilistic functions for floodplain mapping is compared. The results show that a linear function with one parameter and a gamma function with two parameters are the best options for this study area. It is also shown that the proposed probabilistic approach can reduce the overpredictions and underpredictions made by the deterministic HAND-based approach.In the fourth objective, a new regionalization framework for transferring the calibrated environmental models to data-scarce regions is proposed. This framework aims to improve the current similarity-based regionalization methods by reducing the subjectivity that exists in the selection of basin descriptors. Using this framework for the probabilistic HAND-based method in the third objective, the floodplains are regionalized for a large set of watersheds in the Central United States. The results show that “vertical component of centroid (or latitude)” is the dominant descriptor of spatial variabilities in the probabilistic floodplain maps. This is an interesting finding which shows how a systematic approach can help to explore the hidden descriptors for regionalization. It is demonstrated that using common methods, such as correlation coefficient calculation, or stepwise regression analysis, will not reveal the critical role of latitude on the spatial variability of floodplains
Object-oriented hydrologic modeling with GIS
No full textA prototype geographic information system (GIS) based tightly coupled object oriented framework called GIS and Hydrologic Information System Modeling Object (GHISMO) is presented in this thesis. The proposed GHISMO framework is developed within ArcGIS environment such that geographic datasets can be treated as hydrologic objects that have properties and methods to simulate a hydrologic system. The overall GHISMO framework consists of HydroShed as a super class which is composed of six sub classes, namely, HydroGrid (for grid based data such as digital elevation model), ParameterGrid (for grid based parameters such as land use type), HydroArea (for polygon features such as lakes and reservoirs), HydroCatchment (for polygon features representing catchments and watersheds), HydroLine (for polyline features such as rivers) and HydroTable (for input and output tabular data). The GHISMO framework is applied to develop a modular hydrologic modeling system called the Storage Release based Distributed Hydrologic Model (STORE DHM). The storage–release concept uses the travel time within each grid cell to compute how much water is stored or discharged to the watershed outlet at each time step. The STORE DHM is tested by simulating multiple hydrologic events in three watersheds in Indiana. In addition, the GHISMO framework is tested for its flexibility to adopt additional modules by implementing three rainfall bias correction methods to provide accurate input for the STORE DHM. Application of STORE DHM to multiple hydrologic events in three different watersheds in Indiana show that the model is able to predict runoff hydrographs for different types of events in terms of storm duration, peak flow magnitude and time–to– peak. In addition, STORE DHM output is compared with outputs from two hydrologic models including Hydrologic Engineering Center’s Hydrologic Modeling System (HEC– HMS) and time variant Spatially Distributed Direct Hydrograph travel time method (SDDH). Results from these comparisons show that the STORE DHM outperforms both HEC–HMS and SDDH in terms of overall hydrograph shape and flow magnitude. The flexibility of GHISMO framework is tested by extending it to include a rainfall bias correction module. The rainfall bias correction module is then used to correct NEXRAD radar rainfall by implanting two non–uniform bias correction techniques. Results from STORE DHM simulations using the original NEXRAD rainfall and bias-corrected rainfall created in this study shows that the model response is dictated by rainfall variations in the study area. The performance of STORE DHM output is relatively better in a larger watershed with high variable rainfall compared to a smaller watershed with uniform rainfall pattern. The findings from this study are limited by the number of watersheds used, and the quality of the data. More testing of the GHISMO framework and its modules is needed to make the proposed framework applicable for different watersheds with varying scales
Comparing the Effectiveness of Rain Barrels and Detention Ponds on Peak Flow Reduction in a Semi-Urban Watershed
Get PDFRain barrels are useful for stormwater management where conditions of on-site space are limited for retrofitting techniques. This paper aims at determining the scope of the effectiveness of rain barrels on reduction of direct runoff peak flow and volume. The Sugar Creek Watershed in the northwest of Illinois is simulated with a historical storm and design storms of 2-, 10-, 25-, 50-, and 100-year return periods for three different scenarios: only rain barrels, only detention ponds, and the combination of these two. For a storm with the return period less than 3 years, harvesting all rainfall volume received by rooftops, rain barrels are sufficient for flood control. In individual design to achieve the same flood control goal, compared with detention ponds, desired number of rain barrels need to harvest 10% to 15% more of the runoff volume of 2- to 100-year storms, and occupy up to 0.22% larger surface area of the watershed in case of a 100-year storm and as low as 0.13% less for a 5-year storm, which are 0.18 km2 and 0.11 km2, respectively. In combined design of capacity number of rain barrels with detention ponds, the overall combined area is slightly larger than the area of detention ponds in individual design, but the area of detention ponds in combined design is greatly reduced by more than 67% compared with the area of detention ponds in individual design
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