Explore the vast range of titles available.

Our study focused on quantifying the alterations of streamflow at a weir site due to the construction of a mini-hydropower plant in the Gurugoda Oya (Sri Lanka), and evaluating the spatial responses of benthic macroinvertebrates to altered flow regime. The HEC–HMS 3.5 model was applied to the Gurugoda Oya sub-catchment to generate streamflows for the time period 1991–2013. Pre-weir flows were compared to post-weir flows with 32 Indicators of Hydrologic Alteration using the range of variability approach (RVA). Concurrently, six study sites were established upstream and downstream of the weir, and benthic macroinvertebrates were sampled monthly from May to November 2013 (during the wet season). The key water physico-chemical parameters were also determined. RVA analysis showed that environmental flow was not maintained below the weir. The mean rate of non-attainment was ~ 45% suggesting a moderate level of hydrologic alteration. Benthic macroinvertebrate communities significantly differed between the study sites located above and below the weir, with a richness reduction due to water diversion. The spatial distribution of zoobenthic fauna was governed by water depth, dissolved oxygen content and volume flow rate. Our work provides first evidence on the effects of small hydropower on river ecosystem in a largely understudied region. Studies like this are important to setting-up adequate e-flows.

The Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET’s low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used.

The CONtiguous United States scale (CONUS) Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope (FIM HF IRIS) dataset integrates satellite-derived global IRIS river slopes for 117,357 spatially corresponding main-stream reaches within National Oceanic and Atmospheric Administration (NOAA) Office of Water Prediction operational FIM forecasting system (OWP HAND-FIM). A spatial joining approach was first developed to align FIM HF and IRIS reaches, addressing differences in reach flowline sources. Original FIM HF slopes had an average bias of 76 ± 168% relative to IRIS slopes. Applying to OWP HAND-FIM, FIM HF IRIS improved FIM accuracy by average 31 ± 25% (CSI) across eight flood events compared to the FIM HF slopes. Using a common attribute, IRIS data were transferred from FIM HF IRIS to the CONUS Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. Leveraging a common attribute, the resulting datasets enable using SWOT vector data within OWP HAND-FIM and NextGen. The spatial joining approach enabling integrating the hydrofabrics with other hydrologic datasets via flowlines is provided.

Merging observations from multiple satellites is necessary to ensure that extreme hydrological events are consistently observed. Here, we evaluate the potential improvements to flood detectability afforded by combining data collected globally by Landsat, Sentinel-2, and Sentinel-1. The enhanced temporal sampling increased the number of floods with at least 1 useful image (≤20% clouds) from 7% for single sensors to up to 66% for a potential multi-sensor product. As dramatic as the increased coverage is, the socioeconomic impacts are even more tangible. In the pre-Sentinel era, only 22% of the total population displaced by flood events benefitted from having high-resolution images, whereas a potential multi-sensor product would serve 75% of the displaced population. Additionally, the merged dataset could observe up to 100% of floods caused by challenging drivers, e.g., tropical cyclones, tidal surges, including those rarely seen by single sensors, and thereby enable insights into governing mechanisms of these events.

Flood maps are needed for emergency response, research, and planning. The Height Above Nearest Drainage (HAND) technique is a low-complexity, terrain-based approach for inundation mapping using elevation data, discharge–height relationships, and streamflow inputs. The recent operational capacities of the NOAA National Water Model (NWM) and preprocessed HAND products from the University of Texas offer an operational framework for real-time and forecast flood guidance across the US. In this study, we evaluate the integrated National Water Model –Height Above Nearest Drainage (NWM–HAND) flood mapping approach using 28 remotely sensed inundation maps and 54 reach-level catchments. The results show the NWM–HAND method tends to underpredict inundated cells in 4th-order and lower-order reaches but does better with a slight tendency to overpredict in high-order reaches. An evaluation of the roughness coefficient used in the production of synthetic rating curves suggests it is the most important parameter for correcting these errors. Persistent inaccuracies do occur when NWM streamflow predictions are substantially biased (>60 % mean absolute error between NWM and observed streamflow) and in regions of low relief. Overall, the NWM–HAND method does not accurately capture inundated cells but is quite capable of highlighting regions likely to be at risk in 4th-order streams and higher. While NWM–HAND should be used with caution when identifying flood boundaries or making decisions of whether a cell is dry or wet, its applicability as a high-level guidance tool along larger rivers is noteworthy.

Remote sensing analysis is routinely used to map flooding extent either retrospectively or in near-real time. For flood emergency response, remote-sensing-based flood mapping is highly valuable as it can offer continued observational information about the flood extent over large geographical domains. Information about the floodwater depth across the inundated domain is important for damage assessment, rescue, and prioritizing of relief resource allocation, but cannot be readily estimated from remote sensing analysis. The Floodwater Depth Estimation Tool (FwDET) was developed to augment remote sensing analysis by calculating water depth based solely on an inundation map with an associated digital elevation model (DEM). The tool was shown to be accurate and was used in flood response activations by the Global Flood Partnership. Here we present a new version of the tool, FwDET v2.0, which enables water depth estimation for coastal flooding. FwDET v2.0 features a new flood boundary identification scheme which accounts for the lack of confinement of coastal flood domains at the shoreline. A new algorithm is used to calculate the local floodwater elevation for each cell, which improves the tool's runtime by a factor of 15 and alleviates inaccurate local boundary assignment across permanent water bodies. FwDET v2.0 is evaluated against physically based hydrodynamic simulations in both riverine and coastal case studies. The results show good correspondence, with an average difference of 0.18 and 0.31 m for the coastal (using a 1 m DEM) and riverine (using a 10 m DEM) case studies, respectively. A FwDET v2.0 application of using remote-sensing-derived flood maps is presented for three case studies. These case studies showcase FwDET v2.0 ability to efficiently provide a synoptic assessment of floodwater. Limitations include challenges in obtaining high-resolution DEMs and increases in uncertainty when applied for highly fragmented flood inundation domains.

Flood maps are needed for emergency response, research, and planning. The Height Above Nearest Drainage (HAND) technique is a low-complexity, terrain-based approach for inundation mapping using elevation data, discharge-height relationships, and streamflow inputs. The recent operational capacities of the NOAA National Water Model (NWM) and preprocessed HAND products from the University of Texas offer an operational framework for real-time and forecast flood guidance across the US. In this study, we evaluate the integrated National Water Model -Height Above Nearest Drainage (NWM-HAND) flood mapping approach using 28 remotely sensed inundation maps and 54 reach-level catchments. The results show the NWM-HAND method tends to underpredict inundated cells in 4th-order and lower-order reaches but does better with a slight tendency to overpredict in high-order reaches. An evaluation of the roughness coefficient used in the production of synthetic rating curves suggests it is the most important parameter for correcting these errors. Persistent inaccuracies do occur when NWM streamflow predictions are substantially biased (60 % mean absolute error between NWM and observed streamflow) and in regions of low relief. Overall, the NWM-HAND method does not accurately capture inundated cells but is quite capable of highlighting regions likely to be at risk in 4th-order streams and higher. While NWM-HAND should be used with caution when identifying flood boundaries or making decisions of whether a cell is dry or wet, its applicability as a high-level guidance tool along larger rivers is noteworthy.

River deltas, home to almost half a billion people around the world, are important coastal depositional systems. Valuable natural resources, fertile grounds, and convenient locations for trade have proven deltaic land to become hot spots for urbanization, industrialization, and food production over the last few decades. In this Dissertation the following research questions are investigated: (1) what remote sensing-based algorithms are most efficient in river delta shoreline detection? (2) what changes do we observe of shorelines of individual deltas historically? (3) how is human modification on river delta plains contributing to delta plain erosion? (4) are changes in fluvial sediment flux to the delta are directly linked to decadal changes in delta morphology? A novel multifaceted research approach is used that combines (1) remote sensing analysis of past delta morphology changes, (2) numerical modeling of fluvial sediment fluxes, and (3) GIS/Statistical analysis of shoreline migration rates to answer the intricacies of the aforesaid spatio-temporal questions. This study (a) provides recommendations on different shoreline extraction techniques and make the transfer of knowledge to lesser studied deltaic systems done informatively, (b) provides quantitative understandings of historical shoreline change rates of deltas, (c) quantitative understandings of delta plain erosion from humans having modified delta plains from their pristine conditions, and (d) how shoreline mobility is informed based on riverine fluvial sediment, overall, at a global scale. The outcomes of this study yield several novel insights and scientific advancements of delta morphology changes of the last four decades, and not only transforms our analytical capabilities for studying human influences on river deltas, globally, but also provide a predictive platform that could assist decision makers to make better informed decisions for long-term sustainability of deltas.

Hydrologic variability plays a major role in structuring the riparian vegetation within river ecosystems. This study evaluates the spatial and temporal response of riparian vegetation to altered flow regimes below 16 river dams across the contiguous United States using a combination of a holistic Environmental Flow Assessment approach and satellite remote sensing. River flows were characterized using thirty-three (33) different Indicators of Hydrologic Alteration (IHA) using the Range of Variability Approach (RVA). The alterations of riverflows were determined for post-dam scenarios comparing between the pre-dam and post-dam IHAs. Of the 16 locations assessed, 2 showed low levels, 11 moderate and 3 high levels of alteration. Change detection of riparian vegetation revealed an increase at majority of the sites (10 of the 16) immediately after the construction of the dam. Also, in a majority of the locations a decrease (10 of the 16) in vegetation was observed at the 1 year post-dam completion mark. Analyses show that vegetation change effects due to flow regime alterations below smaller dams occurred at shorter time spans (1-year post-completion) than larger dams (5-year post completion). It is inferred that categorizing dams based on capacity was successful in understanding effects on the vegetation extents better. In addition to the in-stream flow paradigm, regional climate and geomorphology are also identified as driving factors of riparian vegetation regulation. The need for a multi-factor model that drives annual changes in riparian zones is recognized to make better-informed decisions on sustainable dam operations.