•DEM resolution and vertical accuracy are related to flood inundation maps.•DEM properties are linearly related to water surface elevation.•DEM properties are linearly related to flood inundation ...area.•Improved flood inundation maps can be obtained from coarser resolution DEM.
Topography plays a major role in determining the accuracy of flood inundation areas. However, many areas in the United States and around the world do not have access to high quality topographic data in the form of Digital Elevation Models (DEM). For such areas, an improved understanding of the effects of DEM properties such as horizontal resolution and vertical accuracy on flood inundation maps may eventually lead to improved flood inundation modeling and mapping. This study attempts to relate the errors arising from DEM properties such as spatial resolution and vertical accuracy to flood inundation maps, and then use this relationship to create improved flood inundation maps from coarser resolution DEMs with low accuracy. The results from the five stream reaches used in this study show that water surface elevations (WSE) along the stream and the flood inundation area have a linear relationship with both DEM resolution and accuracy. This linear relationship is then used to extrapolate the water surface elevations from coarser resolution DEMs to get water surface elevations corresponding to a finer resolution DEM. Application of this approach show that improved results can be obtained from flood modeling by using coarser and less accurate DEMs, including public domain datasets such as the National Elevation Dataset and Shuttle Radar Topography Mission (SRTM) DEMs. The improvement in the WSE and its application to obtain better flood inundation maps is dependent on the study reach characteristics such as land use, valley shape, reach length and width. Application of the approach presented in this study on more reaches may lead to development of guidelines for flood inundation mapping using coarser resolution and less accurate topographic datasets.
•An alternative DEM-based method for detection of floodplain areas is proposed.•An accurate method that considers the threshold variability in data-scarce regions.•Floodplain is related to watershed ...slope, stream slope and average elevation.•The proposed model performs well in inland watersheds.•The proposed model does not perform well in coastal and mountainous watersheds.
Binary threshold classifiers are a simple form of supervised classification methods that can be used in floodplain mapping. In these methods, a given watershed is examined as a grid of cells with a particular morphologic value. A reference map is a grid of cells labeled as flood and non-flood from hydraulic modeling or remote sensing observations. By using the reference map, a threshold on morphologic feature is determined to label the unknown cells as flood and non-flood (binary classification). The main limitation of these methods is the threshold transferability assumption in which a homogenous geomorphological and hydrological behavior is assumed for the entire region and the same threshold derived from the reference map (training area) is used for other locations (ungauged watersheds) inside the study area. In order to overcome this limitation and consider the threshold variability inside a large region, regression modeling is used in this paper to predict the threshold by relating it to the watershed characteristics. Application of this approach for North Carolina shows that the threshold is related to main stream slope, average watershed elevation, and average watershed slope. By using the Fitness (F) and Correct (C) criteria of C>0.9 and F>0.6, results show the threshold prediction and the corresponding floodplain for 100-year design flow are comparable to that from Federal Emergency Management Agency’s (FEMA) Flood Insurance Rate Maps (FIRMs) in the region. However, the floodplains from the proposed model are underpredicted and overpredicted in the flat (average watershed slope <1%) and mountainous regions (average watershed slope >20%). Overall, the proposed approach provides an alternative way of mapping floodplain in data-scarce regions.
•We quantified the relative amount of climate and human impacts on streamflow.•Human impact is higher at most stations in all states compared to climate impact.•Human activities should be given more ...attention when looking at long-term forecasts.
The objective of this study is to quantify the role of climate and human impacts on streamflow conditions by using historical streamflow records, in conjunction with trend analysis and hydrologic modeling. Four U.S. states, including Indiana, New York, Arizona and Georgia area used to represent various level of human activity based on population change and diverse climate conditions. The Mann–Kendall trend analysis is first used to examine the magnitude changes in precipitation, streamflow and potential evapotranspiration for the four states. Four hydrologic modeling methods, including linear regression, hydrologic simulation, annual balance, and Budyko analysis are then used to quantify the amount of climate and human impacts on streamflow. All four methods show that the human impact is higher on streamflow at most gauging stations in all four states compared to climate impact. Among the four methods used, the linear regression approach produced the best hydrologic output in terms of higher Nash–Sutcliffe coefficient. The methodology used in this study is also able to correctly highlight the areas with higher human impact such as the modified channelized reaches in the northwestern part of Indiana. The results from this study show that population alone cannot capture all the changes caused by human activities in a region. However, this approach provides a starting point towards understanding the role of individual human activities on streamflow changes.
Technological aspects of producing, delivering and updating of flood hazard maps in the US have has gone through a revolutionary change through Federal Emergency Management Agency’s Map Modernization ...program. In addition, the use of topographic information derived from Light Detection and Ranging (LIDAR) is enabling creation of relatively more accurate flood inundation maps. However, LIDAR is not available for the entire United States. Even for areas, where LIDAR data are available, the effect of other factors such as cross-section configuration in one-dimensional (1D) models, mesh resolution in two-dimensional models (2D), representation of river bathymetry, and modeling approach is not well studied or documented. The objective of this paper is to address some of these issues by comparing newly developed flood inundation maps from LIDAR data to maps that are developed using different topography, geometric description and modeling approach. The methodology involves use of six topographic datasets with different horizontal resolutions, vertical accuracies and bathymetry details. Each topographic dataset is used to create a flood inundation map for twelve different cross-section configurations using 1D HEC-RAS model, and two mesh resolutions using 2D FESWMS model. Comparison of resulting maps for two study areas (Strouds Creek in North Carolina and Brazos River in Texas) show that the flood inundation area reduces with improved horizontal resolution and vertical accuracy in the topographic data. This reduction is further enhanced by incorporating river bathymetry in topography data. Overall, the inundation extent predicted by FESWMS is smaller compared to prediction from HEC-RAS for the study areas, and that the variations in the flood inundation maps arising from different factors are smaller in FESWMS compared to HEC-RAS.
•Degree of local relevance is defined by the spatial resolution of stream network.•Simulation of real-life flood events across 26,000 streams in the Ohio River Basin.•Satellite images and another ...similar framework verify flood mapping accuracy.•Incorporating streamflow uncertainty in flood maps minimizes prediction bias.•Streamflow input in lower order streams is essential for accurate flood mapping.
Lack of geospecificity or local relevance is a major limitation in contemporary large-scale flood modeling frameworks. There is a little practical value for configuring a large-scale model if the model produces streamflow and/or inundation maps only along the large rivers while numerous lower order streams remain overlooked. This study fills the gap through a new flood prediction framework based on the loose coupling of a hydrologic model Soil and Water Assessment Tool (SWAT) and a 1D/2D hydrodynamic model LISFLOOD-FP (hence, SWAT-LISFP). The prototype SWAT-LISFP framework was configured with ~26,000 stream reaches across the ~500,000 km2 Ohio River Basin, United States. After being calibrated against 50 gauge stations across the basin, SWAT simulated streamflow outputs were fed as upstream boundary conditions in LISFLOOD-FP. The resultant flood inundation extents consistently captured 70–80% of the remotely sensed inundation, irrespective of the flood events or locations within the basin. This was also confirmed via cross-validation with an existing flood modeling framework AutoRAPID (Follum et al., 2017). Additional modeling experiments were conducted to facilitate two critical discussions – how simulated inundation extent is affected by the uncertainty in streamflow prediction and the density of streamflow boundary conditions. Taking into account the uncertainties in SWAT streamflow, LISFLOOD-FP showed a remarkable improvement with more than 95% of remotely sensed inundation captured within the simulated extent. While this approach produces a variable-area flood map (i.e., a range of areas likely to be inundated at a particular point of time), inundation in the lower order streams can still remain undetected. A solution to this problem was demonstrated by setting up streamflow boundary conditions across further lower order streams, which subsequently justified the need for high-resolution stream network, and hence, the essence of locally relevant flood inundation modeling. The new contributions of his study, particularly through introducing SWAT as a functional hydrologic alternative to supplement a hydrodynamic model such as LISFLOOD-FP and the series of experiments to draw insights on addressing lack of accuracy and local relevance, will enhance the global flood modeling initiatives.
The effect of climate change on precipitation intensity is well documented. However, findings regarding changes in spatial extent of extreme precipitation events are still ambiguous as previous ...studies focused on particular regions and time domains. This study addresses this ambiguity by investigating the pattern of changes in the spatial extent of short duration extreme precipitation events globally. A grid‐based indicator termed Spatial‐Homogeneity is proposed and used to assess the changes of spatial extent in Global Precipitation Measurement records. This study shows that (a) rising temperature causes significant shrinking of precipitation extent in tropics, but an expansion of precipitation extent in arid regions, (b) storms with higher precipitation intensity show a faster decrease in spatial extent, and (c) larger spatial extent storms are associated with higher total precipitable water. Results imply that in a warming climate, tropics may experience severe floods as storms may become more intense and spatially concentrated.
Plain Language Summary
Variation in extreme precipitation patterns can significantly impact flood risk, ecology as well as the efficacy of water supply and management strategies. With a changing climate, there is an overarching need to understand how alterations in climate changes precipitation patterns, particularly those corresponding to extreme precipitation events. Analyzing the intensity (amount of rainfall/hour) of precipitation, spatial extent of the precipitation event, duration of the precipitation event and total volume of precipitated water are key to understanding these extreme precipitation events. There is a clear consensus among the scientific community that higher temperatures result in more intense precipitation events, but the effect of temperature on spatial extent is still debated. This study uses a new Spatial‐Homogeneity metric to analyze the global changes in spatial extent of extreme precipitation storms. The study finds that a higher temperature results in smaller size extreme storms in the tropics, but larger size storms in the arid regions. It is also observed that more intense precipitation events have smaller spatial extent, implying that rising temperatures will result in spatially smaller and more intense extreme precipitation storms.
Key Points
A global trend of moisture accumulation toward the storm center as spatial extent decreases with a rise in temperatures
Rising temperature causes significant shrinking of precipitation extent in tropics, but an expansion in arid regions and central Europe
Storms with higher precipitation intensity show a faster decrease in spatial extent
The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project ...(CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Evaluation of the performance of flood models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as uncertainty bounds, Nash Sutcliffe ...efficiency, Kling Gupta efficiency, and the coefficient of determination, which are widely used in the model evaluation, the inherent properties and sampling uncertainty in these metrics are demonstrated. A comprehensive evaluation is conducted using an ensemble of one‐dimensional Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models, which account for the uncertainty associated with the channel roughness and upstream flow input, of six reaches located in Indiana and Texas of the United States. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high‐flow scenarios, and various types of measurement errors in observations on the evaluation metrics are investigated using bootstrapping. Results show that the model performances based on the uniform and normal priors are comparable. The statistical distributions of all the evaluation metrics in this study are significantly different under different high‐flow scenarios, thus suggesting that the metrics should be treated as “random” variables due to both aleatory and epistemic uncertainties and conditioned on the specific flow periods of interest. Additionally, the white‐noise error in observations has the least impact on the metrics.
Editorial on the Research Topic Groundwater systems worldwide Groundwater, with a total volume of 23.4 × 10 6 km 3 , represents 30% of the world's freshwater or 2.5% of the total global water ...storage. Thus, it is an important area of research and a valuable resource for humankind (Oki and Kanae, 2006). Groundwater is an essential component of the global hydrological and biogeochemical cycles and plays a major role in ecosystems sustainability (
AbstractThe process of creating flood inundation maps is affected by uncertainties in data, modeling approaches, parameters, and geoprocessing tools. Generalized likelihood uncertainty estimation ...(GLUE) is one of the popular techniques used to represent uncertainty in model predictions through Monte Carlo analysis coupled with Bayesian estimation. The objectives of this study are to (1) compare the uncertainty arising from multiple variables in flood inundation mapping using Monte Carlo simulations and GLUE and (2) investigate the role of subjective selection of the GLUE likelihood measure in quantifying uncertainty in flood inundation mapping. The role of the flow, topography, and roughness coefficient is investigated on the output of a one-dimensional Hydrologic Engineering Center–River Analysis System (HEC–RAS) model and flood inundation map for an observed flood event on East Fork White River near Seymour, Indiana (Seymour reach) and Strouds Creek in Orange County, North Carolina. Performance of GLUE is assessed by selecting three likelihood functions including the sum of absolute error (SAE) in water surface elevation and inundation width, sum of squared error (SSE) in water surface elevation and inundation width, and a statistic (F-statistic) on the basis of the area of observed and simulated flood inundation map. Results show that the uncertainty in topography, roughness and flow information created an uncertainty bound in the inundation area that ranged from 1.4 to 4.6% for Seymour reach and 4 to 29% for Strouds Creek of the base inundation areas. Additionally, flood inundation maps produced by applying GLUE have different uncertainty bounds depending on the selection of the likelihood functions. However, the overall difference in the flood inundation maps on the basis different likelihood functions is less than 2%, suggesting that the subjectivity involved in selecting the likelihood measure in GLUE did not create a significant effect on the overall uncertainty quantification in flood inundation mapping of the selected study areas.
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