Hydrography90m: a new high-resolution global hydrographic dataset

The geographic distribution of streams and rivers drives a multitude of patterns and processes in hydrology, geomorphology, geography, and ecology. Therefore, a hydrographic network that accurately delineates both small streams and large rivers, along with their topographic and topological properties, with equal precision would be indispensable in the earth sciences. Currently, available global hydrographies do not feature small headwater streams in great detail. However, these headwaters are vital because they are estimated to contribute to more than 70 % of overall stream length. We aimed to fill this gap by using the MERIT Hydro digital elevation model at 3 arcsec (∼90 m at the Equator) to derive a globally seamless, standardised hydrographic network, the “Hydrography90m”, with corresponding stream topographic and topological information. A central feature of the network is the minimal upstream contributing area, i.e. flow accumulation, of 0.05 km2 (or 5 ha) to initiate a stream channel, which allowed us to extract headwater stream channels in great detail. By employing a suite of GRASS GIS hydrological modules, we calculated the range-wide upstream flow accumulation and flow direction to delineate a total of 1.6 million drainage basins and extracted globally a total of 726 million unique stream segments with their corresponding sub-catchments. In addition, we computed stream topographic variables comprising stream slope, gradient, length, and curvature attributes as well as stream topological variables to allow for network routing and various stream order classifications. We validated the spatial accuracy and flow accumulation of Hydrography90m against NHDPlus HR, an independent, national high-resolution hydrographic network dataset of the United States. Our validation shows that the newly developed Hydrography90m has the highest spatial precision and contains more headwater stream channels compared to three other global hydrographic datasets. This comprehensive approach provides a vital and long-overdue baseline for assessing actual streamflow in headwaters and opens new research avenues for high-resolution studies of surface water worldwide. Hydrography90m thus offers significant potential to facilitate the assessment of freshwater quantity and quality, inundation risk, biodiversity, conservation, and resource management objectives in a globally comprehensive and standardised manner. The Hydrography90m layers are available at https://doi.org/10.18728/igb-fred-762.1 (Amatulli et al., 2022a), and while they can be used directly in standard GIS applications, we recommend the seamless integration with hydrological modules in open-source QGIS and GRASS GIS software to further customise the data and derive optimal utility from it

Spatial prediction of flood susceptible areas using machine learning approach: a focus on west african region

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe constant change in the environment due to increasing urbanization and climate change has led to recurrent flood occurrences with a devastating impact on lives and properties. Therefore, it is essential to identify the factors that drive flood occurrences, and flood locations prone to flooding which can be achieved through the performance of Flood Susceptibility Modelling (FSM) utilizing stand-alone and hybrid machine learning models to attain accurate and sustainable results which can instigate mitigation measures and flood risk control. In this research, novel hybridizations of Index of Entropy (IOE) with Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) was performed and equally as stand-alone models in Flood Susceptibility Modelling (FSM) and results of each model compared. First, feature selection and multi-collinearity analysis were performed to identify the predictive ability and the inter-relationship among the factors. Subsequently, IOE was performed as bivariate and multivariate statistical analysis to assess the correlation among the flood influencing factor’s classes with flooding and the overall influence (weight) of each factor on flooding. Subsequently, the weight generated was used in training the machine learning models. The performance of the proposed models was assessed using the popular Area Under Curve (AUC) and statistical metrics. Percentagewise, results attained reveals that DT-IOE hybrid model had the highest prediction accuracy of 87.1% while the DT had the lowest prediction performance of 77.0%. Among the other models, the result attained highlight that the proposed hybrid of machine learning and statistical models had a higher performance than the stand-alone models which reflect the detailed assessment performed by the hybrid models. The final susceptibility maps derived revealed that about 21% of the study area are highly prone to flooding and it is revealed that human-induced factors do have a huge influence on flooding in the region

Quantitative Assessment of Water Security Using a Hydrological Modeling Framework

Water scarcity and drought are major threats to water security. Quantifying and defining boundaries between these threats are necessary to properly assess water security of a region. A comprehensive assessment of water security in terms of water scarcity, water vulnerability and drought can address water policy issues related to hydrological conditions and their interactions with societal and ecosystem functioning. Therefore, study of water security can provide useful information to multiple stakeholders. The overarching goal of this thesis is to improve water security in river basins around the world. To demonstrate our proposed methods, we selected Savannah River Basin (SRB) as a case study. In addition to water security assessment of SRB, we also explored the combined as well as individual roles of climate, anthropogenic (e.g., urbanization, agriculture, water demand) and ecological elements on various aspects of water security. Realizing the importance of water security impacts on society and ecosystem, the following objectives are formulated: 1) To investigate the blue and green water security of Savannah River Basin by applying the water footprint concept. 2) To quantify the influence of climate variability and land use change on streamflow, ecosystem services, and water scarcity. 3) To assess the climate, catchment, and morphological variables control over hydrological drought of a river basin. To summarize, the results obtained from first objective shows that our proposed modeling framework can be applied to investigate spatio-temporal pattern of blue and green water footprints as well as water security at a county scale for SRB, thereby locating the emerging hot spots within the river basin. The results obtained from second objective indicate that the land use change and climate variability have a key influence (either concomitant or independent) in altering the blue (green) water and related water security over the basin. The results based on third objective demonstrate that in addition to climate variables, catchment and morphological properties significantly control short, medium and long-term duration of hydrological droughts in SRB. An integrated modeling framework was developed to achieve these objectives and additional findings are explained in detail through the following chapters

Watershed Delineation in the Field: A New Approach for Mobile Applications Using LiDAR Elevation Data

With the advancement of mobile devices, opportunities to take watershed management tasks out of the office and into the field can be realized. In turn, field workers can utilize these technologies to expedite the decision-making process so that they may focus on meeting with clients and addressing agricultural watershed management issues. High-resolution (∼1.5 m postspacing) elevation data gathered by light detection and ranging (LiDAR) provides the topographic detail necessary to model hydrology at the field-scale (∼1 km2). Non-artifactual surface depressions lead to erroneous surface flow patterns when using existing algorithms. So a sequential depression-filling algorithm (SDFA) has been developed to address topographies that contain these types of features. Given a rainfall amount, water distributed across the landscape accumulates and fills only those depressions as necessary, halting the filling process when the only depressions that remain require additional rainfall. After the filling process is completed, the watershed contributing area draining to any particular point of interest may be identified and in the future this may be used as input to hydrologic models. Methods have also been developed to implement subsurface drainage features such as culverts and tile-inlets as well as soil infiltration such that the dynamics of how water is shed from a given landscape can be better represented. Tile inlets and drainage features may be identified via user input and assigned a drainage rate while infiltration may be implemented by assigning a drainage rate to each grid cell in the DEM based on their soil-type. The combination of the sequential depression-filling algorithm and this drainage feature implementation provides the tools to model localized drainage patterns that will match user\u27s field observations at the scale of hundreds of hectares. The flow routing, depression identification, and filling procedures of the SDFA were compared to similar functions in the ArcGIS Hydrology Toolset under conditions where all depressions were filled in order to validate that those components of the algorithm are identical as intended. Furthermore, several digital elevation models (DEMs) were analyzed to determine the variability in hydrologic connectivity across these landscapes as a function of rainfall and as a function of DEM size. In addition to depression storage, the impacts of infiltration on hydrologic connectivity over these landscapes were also analyzed using the SCS Curve Number Method. The assumptions made by existing algorithms that require complete hydrologic connectivity do not hold up in all landscapes, even more so when considering the effects of infiltration. In these landscapes, surface hydrologic connectivity varies noticeably with rainfall excess and it is inaccurate to assume that the watershed should be modeled as a monotonically descending 14 surface. In an applicability study of DEM size, depression features began to be captured around the 1 km 2 scale while it is recommended to use DEMs larger than 2 km 2 to ensure that the depressional features and their contributing areas are completely captured within the DEM extent so that the SDFA may account for those features correctly. The SDFA algorithm was ported from Matlab to an Android application for mobile phones and tablets. The Watershed Delineation app is free and publicly available through the Google Play Store. Users may view DEMs on a Google Map, use the sequential depression-filling algorithm to fill depressions, and delineate watersheds. It was found that the performance of this algorithm is a function of the number of depressions in the DEM which increases with DEM resolution (due to signal-noise effects). At a 3-meter resolution, the ideal DEM dimensions suitable for use of the SDFA on a Google Nexus 4 phone are about 500 x 500 (225 hectares), which took 68 seconds to run. At DEM sizes much greater than this, performance is drastically reduced. As DEM resolution increases, noise effects in the data (which vary based on the raw LiDAR data) result in a high amount of depression features causing an excessive number of iterations of the filling procedure within the algorithm

Random Forest Modeling Approach to Predict Streamflow Intermittence of Tennessee Headwaters using Flow Conditioned Parameter Grids

Water quality impairment in small tributaries due to soil erosion and stream degradation of West Tennessee is an ongoing problem. A method to model streamflow permanence can assist stream restoration work by supplementing ground monitoring and providing better targeting of conservation attempts in the most vulnerable areas. This project applied a random forest model by incorporating climatic and landcover data as predictors to create streamflow permanence data for the West Tennessee tributaries (Lower Mississippi-Hatchie Hydrologic Unit, HUC 4-801). Specifically, the applicability of the Flow Conditioned Parameter Grids (FCPG) process is tested to study if the process improves prediction results compared to raw predictor results. In addition, the model’s ability to capture the effect of headwater lakes in increasing the probability of streamflow permanence in downstream reaches is investigated using two pairs of streams from the Tennessee Department of Environmental Conservation (TDEC) database of watershed water quality assessments. With the various predictor variable configurations tested in the model, an average of 25 percent Mean Squared Error (MSE) accuracy is acquired in the prediction of the streamflow permanence status of west Tennessee streams. The results showed processing FCPG layers did not provide an increase in prediction accuracy for this study. Validation of the model results using the test stream pairs was inconclusive. While the model did predict streamflow permanence downstream of one headwater lake and intermittent streamflow in the other stream of the pair. it predicted perennial flow for both streams in the second pair, regardless of the presence of a headwater lake. This project provides scalable and replicable methods using machine learning and remote sensing data to predict streamflow permanence at a 25% MSE rate

Regular Hierarchical Surface Models: A conceptual model of scale variation in a GIS and its application to hydrological geomorphometry

Environmental and geographical process models inevitably involve parameters that vary spatially. One example is hydrological modelling, where parameters derived from the shape of the ground such as flow direction and flow accumulation are used to describe the spatial complexity of drainage networks. One way of handling such parameters is by using a Digital Elevation Model (DEM), such modelling is the basis of the science of geomorphometry. A frequently ignored but inescapable challenge when modellers work with DEMs is the effect of scale and geometry on the model outputs. Many parameters vary with scale as much as they vary with position. Modelling variability with scale is necessary to simplify and generalise surfaces, and desirable to accurately reconcile model components that are measured at different scales. This thesis develops a surface model that is optimised to represent scale in environmental models. A Regular Hierarchical Surface Model (RHSM) is developed that employs a regular tessellation of space and scale that forms a self-similar regular hierarchy, and incorporates Level Of Detail (LOD) ideas from computer graphics. Following convention from systems science, the proposed model is described in its conceptual, mathematical, and computational forms. The RHSM development was informed by a categorisation of Geographical Information Science (GISc) surfaces within a cohesive framework of geometry, structure, interpolation, and data model. The positioning of the RHSM within this broader framework made it easier to adapt algorithms designed for other surface models to conform to the new model. The RHSM has an implicit data model that utilises a variation of Middleton and Sivaswamy (2001)’s intrinsically hierarchical Hexagonal Image Processing referencing system, which is here generalised for rectangular and triangular geometries. The RHSM provides a simple framework to form a pyramid of coarser values in a process characterised as a scaling function. In addition, variable density realisations of the hierarchical representation can be generated by defining an error value and decision rule to select the coarsest appropriate scale for a given region to satisfy the modeller’s intentions. The RHSM is assessed using adaptions of the geomorphometric algorithms flow direction and flow accumulation. The effects of scale and geometry on the anistropy and accuracy of model results are analysed on dispersive and concentrative cones, and Light Detection And Ranging (LiDAR) derived surfaces of the urban area of Dunedin, New Zealand. The RHSM modelling process revealed aspects of the algorithms not obvious within a single geometry, such as, the influence of node geometry on flow direction results, and a conceptual weakness of flow accumulation algorithms on dispersive surfaces that causes asymmetrical results. In addition, comparison of algorithm behaviour between geometries undermined the hypothesis that variance of cell cross section with direction is important for conversion of cell accumulations to point values. The ability to analyse algorithms for scale and geometry and adapt algorithms within a cohesive conceptual framework offers deeper insight into algorithm behaviour than previously achieved. The deconstruction of algorithms into geometry neutral forms and the application of scaling functions are important contributions to the understanding of spatial parameters within GISc

Geo-Spatial Analysis in Hydrology

Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale