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26 pages, 36263 KiB  
Article
Characteristics and Comparative Assessment of Flash Flood Hazard Evaluation Techniques: Insights from Wadi Haily Basin, Eastern Red Sea Coast, Saudi Arabia
by Bashar Bashir and Abdullah Alsalman
Water 2024, 16(24), 3634; https://doi.org/10.3390/w16243634 - 17 Dec 2024
Viewed by 230
Abstract
The Wadi Haily basin in southwest Saudi Arabia, which runs along the Red Sea coast, serves as an ideal natural laboratory for understanding flash flood dynamics in this region. However, limited morphometric and hydrological data are currently available in this area. This study [...] Read more.
The Wadi Haily basin in southwest Saudi Arabia, which runs along the Red Sea coast, serves as an ideal natural laboratory for understanding flash flood dynamics in this region. However, limited morphometric and hydrological data are currently available in this area. This study aims to analyze key morphometric effective parameters to examine and assess flash flood risk potential within the basin. Using remote sensing, GIS, geological, and topographical datasets, this research combines advanced modeling and GIS tools to produce detailed flood hazard maps and risk assessments. This study examines 15 sub-basins of varying sizes, characterized by primary stream orders ranging from 4th to 8th. Based on morphometric analysis, the basins are categorized by flood susceptibility: four basins have a low flood risk, five exhibit moderate risk, and six are highly susceptible to flooding. Key findings indicate that the study area features a vast drainage area, high grid cell values of the drainage frequency, moderate drainage density, elongated basin shapes, low infiltration rates, and long overland flow distances, all suggesting a heightened flood hazard. Additional indicators include high values in gradient ratios, slopes, ruggedness numbers, relief ratios, and basin relief, reinforcing the basin’s flash flood vulnerability. This study provides a comprehensive morphological framework that can support strategic flood management and hazard mitigation planning for the Wadi Haily region. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) A view of the Red Sea from Google Earth, highlighting the surrounding countries and different water bodies. (<b>b</b>) The inset red box in map a indicates the study basin’s location.</p>
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<p>Map displaying the distribution of the geological units of the study basin.</p>
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<p>General Wadi Haily basin geomorphological features: (<b>a</b>) a SRTM 30 m spatial resolution DEM map, (<b>b</b>) a slope map, (<b>c</b>) an aspect map, and (<b>d</b>) a contouring map.</p>
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<p>A flowchart that illustrates the used methodology.</p>
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<p>A hydrograph graphic showing how the morphometry of drainage basin affects peak discharges, falling limb (FL) and rib (RL); modified after references [<a href="#B4-water-16-03634" class="html-bibr">4</a>,<a href="#B23-water-16-03634" class="html-bibr">23</a>].</p>
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<p>(<b>a</b>) Stream orders of the wadi Haily basin (1–8); and (<b>b</b>) basins of the Wadi Haily basin (1–15).</p>
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<p>(<b>a</b>) Stream orders and mean stream length verses (<b>b</b>) stream orders and total stream length.</p>
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<p>Hydro-geomorphic diagram presenting the flash flood susceptibility due to (<b>a</b>) Rb versus Dd and (<b>b</b>) Rb versus Fs diagrams that have been established by Ref. [<a href="#B6-water-16-03634" class="html-bibr">6</a>].</p>
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<p>Flash flooding hazard maps obtained by (<b>a</b>) El-Shamy first diagram (Rb vs. Dd). (<b>b</b>) El-Shamy second diagram (Rb vs. Fs), (<b>c</b>) and the ranking method.</p>
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<p>Final integrated flash flooding hazard map. This map also shows the approximate higher signals of the soil erosion and water recharge of the entire Wadi Haily basin. Yellow numbers indicate the sub-basins numbers.</p>
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28 pages, 6728 KiB  
Article
Ice-Jam Flooding of the Peace–Athabasca Delta, Canada: Insights from Recent Notable Spring Breakup Events and Implications for Strategic Flow Releases from Upstream Dams
by Spyros Beltaos
Geosciences 2024, 14(12), 335; https://doi.org/10.3390/geosciences14120335 - 7 Dec 2024
Viewed by 382
Abstract
Ice jamming is the primary mechanism that can generate overland flooding and recharge the isolated basins of the Peace–Athabasca Delta (PAD), a valuable ecosystem of international importance and the ancient homeland of the Indigenous Peoples of the region. Focusing on the regulated Peace [...] Read more.
Ice jamming is the primary mechanism that can generate overland flooding and recharge the isolated basins of the Peace–Athabasca Delta (PAD), a valuable ecosystem of international importance and the ancient homeland of the Indigenous Peoples of the region. Focusing on the regulated Peace River and the Peace Sector of the delta, which has been experiencing a drying trend in between rare ice-jam floods over the last ~50 years, this study describes recent notable breakup events, associated observational data, and numerical applications to determine river discharge during the breakup events. Synthesis and interpretation of this material provide a new physical understanding that can inform the ongoing development of a protocol for strategic flow releases toward enhancing basin recharge in years when major ice jams are likely to form near the PAD. Additionally, several recommendations are made for future monitoring activities and improvements in proposed antecedent criteria for early identification of “promising” breakup events. Full article
(This article belongs to the Section Hydrogeology)
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Figure 1

Figure 1
<p>Plan view of the lower Peace River and Peace Sector of the Peace–Athabasca Delta. Common ice jam lodgment sites (or “toes”) are shown in the upper portion of the figure. Also shown are sites of Water Survey of Canada hydrometric gauges, of which the records have been used in this study.</p>
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<p>Plan view of Peace River and Peace–Athabasca Delta (showing only the northern portion of the Athabasca River). The river distance from the W.A.C. Bennett dam is marked at 100 km intervals. The Slave River begins at the MOP and flows in a generally northward direction (from [<a href="#B2-geosciences-14-00335" class="html-bibr">2</a>], with changes).</p>
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<p>Overview of the extent of 2014 flooding discernible during aerial monitoring in Wood Buffalo National Park. Adapted from [<a href="#B26-geosciences-14-00335" class="html-bibr">26</a>] with permission from Parks Canada.</p>
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<p>Views of the western end of Lake Athabasca on April 20 (<b>left</b>) and 25 (<b>right</b>), 2018, showing the development of an open lead and early melt-out in the upper reach of RdR (triple channel). See image attribution at <a href="https://www.openstreetmap.org/copyright" target="_blank">https://www.openstreetmap.org/copyright</a>—accessed 12 August 2024.</p>
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<p>Sequence of images from 1 May 2018 mobilization and run of the ice cover at PP. Time sequence: 2024 h (stationary ice), 2027 h, 2033 h, 2040 h, 2047 h, and 2120 h. Photo times can also be seen by zooming in to the upper left corner of each image. Flow direction: right to left.</p>
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<p>Schematic illustration of spatiotemporal variations in ice conditions in the lower Peace River during the 2018 pre-breakup and breakup seasons, as revealed by time-lapse cameras. Conditions during darkness (~2200 h to 0400 h) are estimated. The “ice run” icon does not differentiate between sheet ice and rubble, which typically follows moving ice sheets. The partial jam in the Slave River formed over a large eddy area near the right bank, but rubble kept moving farther out and closer to the left bank.</p>
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<p>Water level variation in the lower Peace and upper Slave Rivers, as captured by five pressure loggers and WSC gauges. The RdR logger was placed next to the WSC gauge on Rivière des Rochers, located ~600 m upstream from the MOP. The L. Athabasca stages are from the gauge at Fort Chipewyan. The flat logger segments signify that the logger was still above water and merely indicating its own elevation.</p>
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<p>Variation in PP discharge in early 2018 May, as estimated by different approaches. The WSC data points represent daily mean values and are plotted at noon each day. The local ice cover moved out in late 1 May, though backwater effects likely persisted during the following days. The blue arrow marks the last day with ice-related backwater, as assessed by the WSC.</p>
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<p>Mean November discharge at Hudson’s Hope and below Peace Canyon Dam, 1960 to 2023. The Hudson’s Hope WSC gauge operation was discontinued in August 2019. The Canyon Dam data points were derived from BC Hydro’s Station 001 daily flows and can be downloaded from <a href="https://rivers.alberta.ca/" target="_blank">https://rivers.alberta.ca/</a>—accessed 1 December 2024. Neither station was affected by ice.</p>
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<p>Variation in snow on the ground and mean air temperature at the Grand Prairie met station No. 3072921. Note that the snow depletion is coincident with mild weather spells in January and February; 7.1 mm of rain was recorded on 17 January, when the minimum/maximum temperatures amounted to −20/+0.4 °C.</p>
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<p>The appearance of highly deteriorated ice cover at the upstream end of Moose Island shortly before final breakup: 30 April 2018 (<b>left</b>, ice moved out later that day or in early 1 May); 4 May 2020 (<b>middle</b>, ice moved out on 5 May); and 4 May 2022 (<b>right</b>, ice moved out on 5 May). The Sentinel images have been enhanced using the B04 band. A similarly mottled ice surface appears on several 5 May photos at this and other sites within the PAD reach [<a href="#B24-geosciences-14-00335" class="html-bibr">24</a>]. See satellite image attribution at <a href="https://www.openstreetmap.org/copyright" target="_blank">https://www.openstreetmap.org/copyright</a>—accessed 12 August 2024.</p>
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<p>Variation in water level at the PP gauge (No. 07KC001) during the passage of javes on 5 May 2022. Unpublished WSC data, provided on request.</p>
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<p>End-of-winter ice thickness at PP versus Fort Chipewyan degree-days of frost, 1959–2022. Based on raw WSC data and assessed according to the procedure described in [<a href="#B22-geosciences-14-00335" class="html-bibr">22</a>]. Regulation commenced in 1968, and the reservoir was full in 1971.</p>
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<p>Time series of Fort Chipewyan DDF and PP HF (CGVD28), 1959–2022. The regulation commenced in 1968, and the reservoir was full in 1971. The red square markers indicate LIJFs.</p>
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<p>Average celerity of breakup front (CB) between ~Sunny Valley and ~MOP, plotted versus freezeup level at PP (<b>a</b>) and versus FC-DDF (<b>b</b>), for all years for which relevant data are available (promising events: 1996, 1997, 2003, 2007, 2014, 2018, 2020; unpromising events: 2004, 2015, 2016, 2017, 2019, 2021). Red square markers identify LIJFs. From [<a href="#B19-geosciences-14-00335" class="html-bibr">19</a>], with changes.</p>
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<p>Maximum daily mean breakup discharge at PP plotted versus Fort Chipewyan degree-days of Frost (<b>a</b>) and versus Grand Prairie Oct-Apr solid precipitation (<b>b</b>) for the regulation period 1972–2022 (reservoir filling years 1968–1971 are excluded). Pearson correlation coefficient <span class="html-italic">r</span>~0.63 for both graphs.</p>
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17 pages, 3863 KiB  
Article
One-Dimensional Numerical Cascade Model of Runoff and Soil Loss on Convergent and Divergent Plane Soil Surfaces: Laboratory Assessment and Numerical Simulations
by Babar Mujtaba, João L. M. P. de Lima and M. Isabel P. de Lima
Water 2024, 16(20), 2955; https://doi.org/10.3390/w16202955 - 17 Oct 2024
Viewed by 630
Abstract
A one-dimensional numerical overland flow model based on the cascade plane theory was developed to estimate rainfall-induced runoff and soil erosion on converging and diverging plane surfaces. The model includes three components: (i) soil infiltration using Horton’s infiltration equation, (ii) overland flow using [...] Read more.
A one-dimensional numerical overland flow model based on the cascade plane theory was developed to estimate rainfall-induced runoff and soil erosion on converging and diverging plane surfaces. The model includes three components: (i) soil infiltration using Horton’s infiltration equation, (ii) overland flow using the kinematic wave approximation of the one-dimensional Saint-Venant shallow water equations for a cascade of planes, and (iii) soil erosion based on the sediment transport continuity equation. The model’s performance was evaluated by comparing numerical results with laboratory data from experiments using a rainfall simulator and a soil flume. Four independent experiments were conducted on converging and diverging surfaces under varying slope and rainfall conditions. Overall, the numerically simulated hydrographs and sediment graphs closely matched the laboratory results, showing the efficiency of the model for the tested controlled laboratory conditions. The model was then used to numerically explore the impact of different plane soil surface geometries on runoff and soil loss. Seven geometries were studied: one rectangular, three diverging, and three converging. A constant soil surface area, the rainfall intensity, and the slope gradient were maintained in all simulations. Results showed that increasing convergence angles led to a higher peak and total soil loss, while decreasing divergence angles reduced them. Full article
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Figure 1

Figure 1
<p>Sketch and notation used for the cascade of <span class="html-italic">n</span>-planes representing converging (sloping to the left) or diverging (sloping to the right) cascades.</p>
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<p>(<b>a</b>) Laboratory setup used in the experiments, consisting of a rainfall simulator and a soil flume (top and bottom left); the flume has outlets on both ends for the converging plane (bottom middle) and diverging plane surface (bottom right) experiments. (<b>b</b>) Dashed lines represent contour lines relative to an arbitrary datum (ground level) for a 20% flume slope, while solid lines indicate the border wall of the flume’s geometry.</p>
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<p>On the <b>right</b>, a schematic sketch illustrates the approximate representation of the soil flume surface planar geometry using converging and diverging cascade planes, each with an equal length <math display="inline"><semantics> <mrow> <mi>L</mi> </mrow> </semantics></math> of 0.5 m, measured along the direction of the plane slope. The width <math display="inline"><semantics> <mrow> <mi>W</mi> </mrow> </semantics></math> of each plane is also shown, which is measured in the perpendicular direction. On the <b>left</b>, the variation in mean rainfall intensity across the planes of the converging and diverging cascades is shown.</p>
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<p>Seven plane soil surfaces: one rectangular, three converging with different convergence angles θ, and three diverging with different divergence angles θ. The schematic sketch also illustrates the approximate representation of the soil surface planar geometry using converging and diverging cascade planes of equal length <span class="html-italic">L</span> (1 m). The width <span class="html-italic">W</span> of each plane is also shown. The dimensions of the rectangular plane soil surface (4 m × 2 m) are provided as well.</p>
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<p>Observed and numerically simulated hydrographs for converging and diverging surfaces with different combinations of rainfall intensity (<span class="html-italic">I</span>) and slope (<span class="html-italic">S</span>). The Nash–Sutcliffe (<math display="inline"><semantics> <mrow> <mi>N</mi> <mi>S</mi> </mrow> </semantics></math>) coefficients are also shown.</p>
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<p>Observed and numerically simulated sediment graphs for converging and diverging surfaces with different combinations of rainfall intensity (<span class="html-italic">I</span>) and slope (<span class="html-italic">S</span>). Note that the Y-axis scale is not the same on the top-left figure. The Nash–Sutcliffe (<math display="inline"><semantics> <mrow> <mi>N</mi> <mi>S</mi> </mrow> </semantics></math>) coefficients are also shown.</p>
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<p>Simulated hydrographs for plane soil surfaces with different angles (θ) of convergence (<b>left</b>) and divergence (<b>right</b>). Section A represents a rectangular surface where θ = 0°.</p>
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<p>Runoff peaks for plane soil surfaces with different angles (θ) of convergence (<b>left</b>) and divergence (<b>right</b>). A rectangular surface is represented by θ = 0°. Trend lines fitted to the data are included for reference.</p>
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<p>Simulated sediment graphs for plane soil surfaces with different angles (θ) of convergence (<b>left</b>) and divergence (<b>right</b>). Section A represents a rectangular surface where θ = 0°. Note that the Y-axis scales are not the same.</p>
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<p>Peak soil loss for plane soil surfaces with different angles (θ) of convergence (<b>left</b>) and divergence (<b>right</b>). A rectangular surface is represented by θ = 0°. Trend lines fitted to the data are included for reference. Note that the Y-axis scales differ.</p>
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15 pages, 2753 KiB  
Article
Assessing Soil Physical Quality in a Layered Agricultural Soil: A Comprehensive Approach Using Infiltration Experiments and Time-Lapse Ground-Penetrating Radar Surveys
by Simone Di Prima, Gersende Fernandes, Maria Burguet, Ludmila Ribeiro Roder, Vittoria Giannini, Filippo Giadrossich, Laurent Lassabatere and Alessandro Comegna
Appl. Sci. 2024, 14(20), 9268; https://doi.org/10.3390/app14209268 - 11 Oct 2024
Cited by 1 | Viewed by 1072
Abstract
Time-lapse ground-penetrating radar (GPR) surveys, combined with automated infiltration experiments, provide a non-invasive approach for investigating the distribution of infiltrated water within the soil medium and creating three-dimensional images of the wetting bulb. This study developed and validated an experimental protocol aimed at [...] Read more.
Time-lapse ground-penetrating radar (GPR) surveys, combined with automated infiltration experiments, provide a non-invasive approach for investigating the distribution of infiltrated water within the soil medium and creating three-dimensional images of the wetting bulb. This study developed and validated an experimental protocol aimed at quantifying and visualizing water distribution fluxes in layered soils under both unsaturated and saturated conditions. The 3D images of the wetting bulb significantly enhanced the interpretation of infiltration data, enabling a detailed analysis of water movement through the layered system. We used the infiltrometer data and the Beerkan Estimation of Soil Transfer parameters (BEST) method to determine soil capacitive indicators and evaluate the physical quality of the upper soil layer. The field survey involved conducting time-lapse GPR surveys alongside infiltration experiments between GPR repetitions. These experiments included both tension and ponding tests, designed to sequentially activate the soil matrix and the full pore network. The results showed that the soil under study exhibited significant soil aeration and macroporosity (represented by AC and pMAC), while indicators related to microporosity (such as PAWC and RFC) were notably low. The RFC value of 0.55 m3 m−3 indicated the soil’s limited capacity to retain water relative to its total pore volume. The PAWC value of 0.10 m3 m−3 indicated a scarcity of micropores ranging from 0.2 to 30 μm in diameter, which typically hold water accessible to plant roots within the total porosity. The saturated soil hydraulic conductivity, Ks, values ranged from 192.2 to 1031.0 mm h−1, with a mean of 424.4 mm h−1, which was 7.9 times higher than the corresponding unsaturated hydraulic conductivity measured at a pressure head of h = −30 mm (K−30). The results indicated that the upper soil layer supports root proliferation and effectively drains excess water to the underlying limestone layer. However, this layer has limited capacity to store and supply water to plant roots and acts as a restrictive barrier, promoting non-uniform downward water movement, as revealed by the 3D GPR images. The observed difference in hydraulic conductivity between the two layers suggests that surface ponding and overland flow are generated through a saturation excess mechanism. Water percolating through the soil can accumulate above the limestone layer, creating a shallow perched water table. During extreme rainfall events, this water table may rise, leading to the complete saturation of the soil profile. Full article
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Figure 1

Figure 1
<p>Flowchart outlining the process to generate a 3D image of the wetting bulb. The arrow indicates the funneling flow path through the limestone layer.</p>
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<p>Three-dimensional representations of the wetting zones obtained from ground-penetrating radar surveys conducted before and after wetting, during (<b>a</b>) tension and (<b>e</b>) ponding infiltrometer experiments at the Ottava site. Panels (<b>b</b>,<b>f</b>) illustrate horizontal cross-sections taken from the 3D models at a depth of −0.1m from the soil surface. Panels (<b>c</b>,<b>g</b>) present vertical cross-sections oriented north–south with a view to the east, while panels (<b>d</b>,<b>h</b>) show vertical cross-sections oriented west–east within a view to the north. The red arrows highlight the detected flow channeling through the limestone layer (see <a href="#applsci-14-09268-f001" class="html-fig">Figure 1</a> for reference).</p>
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<p>Example of the procedure adopted for detecting flow impedance owing to the hydraulic resistance exerted by the underlying limestone layer. (<b>a</b>): Entire cumulative infiltration curve [<span class="html-italic">I</span>(<span class="html-italic">t</span>) vs. <span class="html-italic">t</span>]. (<b>b</b>): Data linearized according to the cumulative linearization (CL, Smiles and Knight, 1976) method (<span class="html-italic">I</span>√<span class="html-italic">t</span> vs. √<span class="html-italic">t</span>). The abscissa (√<span class="html-italic">t</span>) of the intersection point of the two straight lines splits the infiltration data into two subsets. (<b>c</b>): Cumulative infiltration data representative of the first stage when water infiltrates into the upper layer.</p>
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<p>θ<span class="html-italic"><sub>PWP</sub></span> [m<sup>3</sup> m<sup>−3</sup>] is the permanent wilting point soil water content, corresponding to <span class="html-italic">h</span> = −150 m. θ<span class="html-italic"><sub>FC</sub></span> [m<sup>3</sup> m<sup>−3</sup>] is the field capacity (gravity drained) soil water content, corresponding to <span class="html-italic">h</span> = −1 m. θ<span class="html-italic"><sub>m</sub></span> [m<sup>3</sup> m<sup>−3</sup>] is the saturated volumetric water content of the soil matrix, corresponding to <span class="html-italic">h</span> = −0.1 m. θ<span class="html-italic"><sub>TI</sub></span> [m<sup>3</sup> m<sup>−3</sup>] is the final volumetric water content at the end of the TI test (corresponding to <span class="html-italic">h</span> = −0.03 m), θ<span class="html-italic"><sub>s</sub></span> [m<sup>3</sup> m<sup>−3</sup>] is the saturated volumetric water content. <span class="html-italic">AC</span> [m<sup>3</sup> m<sup>−3</sup>] is the air capacity. <span class="html-italic">PAWC</span> [m<sup>3</sup> m<sup>−3</sup>] is the plant-available water capacity. <span class="html-italic">RFC</span> [−] is the relative field capacity. <span class="html-italic">p<sub>MAC</sub></span> [m<sup>3</sup> m<sup>−3</sup>] is the soil macroporosity. <sup>†</sup> Water content values determined from wet soil samples collected after the tension (θ<span class="html-italic"><sub>TI</sub></span>) and Beerkan (θ<span class="html-italic"><sub>s</sub></span>) infiltration tests.</p>
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16 pages, 8312 KiB  
Article
Impact of Thinning and Contour-Felled Logs on Overland Flow, Soil Erosion, and Litter Erosion in a Monoculture Japanese Cypress Forest Plantation
by Moein Farahnak, Takanori Sato, Nobuaki Tanaka, Anand Nainar, Ibtisam Mohd Ghaus and Koichiro Kuraji
Water 2024, 16(20), 2874; https://doi.org/10.3390/w16202874 - 10 Oct 2024
Viewed by 729
Abstract
This study investigated the impact of thinning and felled logs (random- and contour-felled logs) on overland flow, soil erosion, and litter erosion in a Japanese cypress forest plantation (2400 tree ha−1) with low ground cover, from 2018 to 2023 in central [...] Read more.
This study investigated the impact of thinning and felled logs (random- and contour-felled logs) on overland flow, soil erosion, and litter erosion in a Japanese cypress forest plantation (2400 tree ha−1) with low ground cover, from 2018 to 2023 in central Japan. Monthly measurements of overland flow and soil and litter erosion were carried out using small-sized traps across three plots (two treatments and one control). In early 2020, a 40% thinning (tree ha−1) was conducted in the two treatment plots. Overland flow increased in the plot with random-felled logs during the first year post-thinning (from 139.1 to 422.0 L m−1), while it remained stable in the plot with contour-felled logs (from 341.8 to 337.1 L m−1). A paired-plot analysis showed no change in overland flow in the contour-felled logs plot compared to the control plot from the pre- to post-thinning periods (pre-thinning Y = 0.41X − 0.69, post-thinning Y = 0.5X + 5.46, ANCOVA: p > 0.05). However, exposure to direct rainfall on uncovered ground areas post-thinning led to increased soil and litter erosion in both treatment plots. These findings suggest that thinning combined with contour-felled logs effectively stabilizes overland flow. Therefore, thinning with contour-felled logs can be considered a viable method for mitigating overland flow in monoculture plantations with low ground cover. Full article
(This article belongs to the Special Issue Forest Hydrology and Watershed Management)
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Figure 1

Figure 1
<p>Study sites (<b>a</b>,<b>b</b>), plot locations (<b>c</b>), and small-sized trap arrangements (<b>d</b>). OEF: Obora Experimental Forest, OMS: Obara Meteorological Station, TMS: Toyota Meteorological Station.</p>
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<p>Condition of treatment plots (<b>A</b>,<b>B</b>) and control plot (<b>C</b>) in the post-thinning period. In plot A, felled logs placed randomly after thinning and in plot B, felled logs aligned to contour lines.</p>
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<p>Dimensions (<b>a</b>), installation (<b>b</b>), and design (<b>c</b>) of small-sized trap.</p>
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<p>Monthly rainfall (mm), 1 h maximum rainfall (mm h<sup>−1</sup>, balck dots) (<b>a</b>), monthly overland flow (L m<sup>−1</sup>) (<b>b</b>), soil and litter erosion (g m<sup>−1</sup>) (<b>c</b>,<b>d</b>) in the study plots during monitoring periods. The grey area shows the thinning period, while the pre-thinning and post-thinning periods are indicated before and after the grey area, respectively.</p>
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<p>Paired-plot analysis results comparing overland flow (<b>a</b>,<b>b</b>), soil erosion (<b>c</b>,<b>d</b>), and litter erosion (<b>e</b>,<b>f</b>) between different study plots. Panels (<b>a</b>,<b>c</b>,<b>e</b>) represent comparisons between plot A and plot C, while (<b>b</b>,<b>d</b>,<b>f</b>) compare plot B and plot C. Asterisks indicate significant differences between the slopes of pre- and post-thinning regression lines based on the results of analysis of covariance (ANCOVA) followed by post hoc Tukey’s HSD test (* <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, *** <span class="html-italic">p</span>-value &lt; 0.001).</p>
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<p>Paired-trap analysis results. Asterisks define the significant differences between slope of pre- and post-thinning regression lines based on the results of analysis of covariance (ANCOVA) followed by post hoc Tukey’s HSD test. * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, *** <span class="html-italic">p</span>-value &lt; 0.001.</p>
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<p>Cumulative frequency distribution of overland flow (<b>a</b>,<b>b</b>), soil erosion (<b>c</b>,<b>d</b>), and litter erosion (<b>e</b>,<b>f</b>) for plot A (left column) and plot B (right column) during pre-thinning (solid lines) and post-thinning (dashed lines) periods.</p>
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<p>The relationship between overland flow and 1-h maximum rainfall (<b>a</b>,<b>b</b>), soil erosion and overland flow (<b>c</b>,<b>d</b>), and litter erosion and overland flow (<b>e</b>,<b>f</b>) for the study plots during pre-thinning (left column) and post-thinning (right column) periods.</p>
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22 pages, 3493 KiB  
Article
A Coupled River–Overland (1D-2D) Model for Fluvial Flooding Assessment with Cellular Automata
by Hsiang-Lin Yu, Tsang-Jung Chang, Chia-Ho Wang and Shyh-Yuan Maa
Water 2024, 16(18), 2703; https://doi.org/10.3390/w16182703 - 23 Sep 2024
Viewed by 1045
Abstract
To provide accurate and efficient forecasting of fluvial flooding assessment in the river basin, the present study links the well-known CA-based urban inundation modeling (2D-OFM-CA) with a one-dimensional river flow model (1D-RFM) as a coupled 1D-2D river–overland modeling. Rules to delineate the geometric [...] Read more.
To provide accurate and efficient forecasting of fluvial flooding assessment in the river basin, the present study links the well-known CA-based urban inundation modeling (2D-OFM-CA) with a one-dimensional river flow model (1D-RFM) as a coupled 1D-2D river–overland modeling. Rules to delineate the geometric linking between the 1D-RFM and 2D-OFM-CA along embankments are developed. The corresponding exchanged water volume across an embankment is then computed by using the free and submerged weir flow formulas. The applicability of the proposed coupled model on fluvial flooding assessment is then assessed and compared with a well-recognized commercial software (HEC-RAS model) through an idealized fluvial case and an extensively studied real-scale fluvial case in the Severn River Basin. Based on the simulated results concerning the numerical accuracy, the coupled model is found to give similar results in the aspects of the river flow and overland flow modeling in both two study cases, which demonstrates the effectiveness of the linking methodology between the 1D-RFM and 2D-OFM-CA. From the viewpoint of numerical efficiency, the coupled model is 47% and 41% faster than the HEC-RAS model in the two cases, respectively. The above results indicate that the coupled model can reach almost the same accuracy as the HEC-RAS model with an obvious reduction in its computational time. Hence, it is concluded that the coupled model has considerable potential to be an effective alternative for fluvial flooding assessment in the river basin. Full article
(This article belongs to the Special Issue Advances in Hydraulic and Water Resources Research (2nd Edition))
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<p>The schematic illustration of the geometric linking methodology between the 1D-RFM and 2D-OFM-CA.</p>
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<p>(<b>a</b>) The configuration of the idealized study case. The locations of the two measured stations in the river for inspecting water level and discharge hydrographs are plotted in the figure as well. (<b>b</b>) The input rainfall data on Floodplain A that introduces lateral surface runoffs into the river and subsequently causes overtopping discharges to Floodplain B.</p>
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<p>The comparison of the water level and discharge hydrographs between the two models. (<b>a</b>) The simulated water level hydrographs and (<b>b</b>) the simulated water discharge hydrographs at the measured station P1. (<b>c</b>) The simulated water level hydrographs and (<b>d</b>) the simulated water discharge hydrographs at the measured station P2.</p>
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<p>The comparison of the 1D–2D exchanged discharges between the HEC-RAS model and coupled model. (<b>a</b>) The discharge hydrographs from Floodplain A to the river. (<b>b</b>) The overtopping discharge hydrographs from the river to Floodplain B.</p>
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<p>The simulated flood maps of (<b>a</b>) the coupled model and (<b>b</b>) the HEC-RAS model in the idealized study case, respectively.</p>
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<p>The real-scale study case in the Severn River basin. (<b>a</b>) The map of the modeled reach of the River Severn and the three floodplains. The digital elevation model, locations of the forty-two cross-sections, three floodplains, and 16 m contour lines for defining the simulated boundaries of overland flow modeling are plotted for illustration. The four cross-sections for accuracy comparison (i.e., M015, M025, M035, and M045) are highlighted with four purple rectangles. The upstream and downstream sides of the river are also marked. (<b>b</b>) The locations of the left and right banks along the river and eighteen observed points for accuracy comparison in the overland flow modeling. (<b>c</b>) The inflow discharge series at the upstream start of the modeled river. (<b>d</b>) The rating curve prescribed as the boundary condition at the downstream end of the modeled river.</p>
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<p>The simulated water level hydrographs of the cross-sections (<b>a</b>) M015, (<b>b</b>) M025, (<b>c</b>) M035, and (<b>d</b>) M045, respectively, of the two models in the basin-scale study case.</p>
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<p>The simulated water level hydrographs at the observed points (<b>a</b>) O1, (<b>b</b>) O2, (<b>c</b>) O6, (<b>d</b>) O8, (<b>e</b>) O9, (<b>f</b>) O11, (<b>g</b>) O12, (<b>h</b>) O14, and (<b>i</b>) O17, respectively, in the basin-scale study case.</p>
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<p>The simulated flood extents of the (<b>a</b>) coupled model and (<b>b</b>) HEC-RAS model, respectively, in the basin-scale study case.</p>
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21 pages, 7799 KiB  
Article
Identification and Characterization of Reclaimed and Underclaimed Mine Features Using Lidar and Temporal Remote Sensing Methods within the Coastal Plain Uranium Mining Region of Texas
by Victoria G. Stengel, Tanya J. Gallegos, Bernard E. Hubbard, Steven M. Cahan and David S. Wallace
Remote Sens. 2024, 16(18), 3519; https://doi.org/10.3390/rs16183519 - 22 Sep 2024
Viewed by 912
Abstract
We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic [...] Read more.
We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic modifications to the landscape. The Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features. Visual interpretation of Landsat imagery and time-series analysis augmented the Lidar analysis revealing the temporal life cycle of mining. The combination of bare earth texture with time-lapse and time-series analyses revealed areas of disturbance for reclaimed mines. The spatiotemporal mapping approach proved to be most useful in identifying and characterizing the known mine pit and pile features, reclamation status, and areas of disturbance due to mining. Two mine waste volume estimation methods resulted in a 21% difference indicating that although the approach helps to map mine features and areas of mining disturbance for the purposes of mine land inventory, additional information is needed to improve the estimation of buried mine waste at reclaimed mine sites. Full article
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<p>Study area with calibration and validation areas (<b>A</b>) at the intersection of Karnes, Atascosa, and Live Oak Counties within the Texas Coastal Plain uranium mining region (<b>B</b>), and stratigraphy within the study area (<b>C</b>), modified from Hall et al. (2017) [<a href="#B1-remotesensing-16-03519" class="html-bibr">1</a>] (* denotes units not discussed in the text and are shown for stratigraphic context).</p>
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<p>Flowchart describing how the multiple lines of evidence from the (Lidar and multispectral temporal) analyses are integrated into the spatiotemporal mapping classification system to identify, characterize, and evaluate mine features and areas of mining disturbance.</p>
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<p>An example of the Lidar point cloud cross-section revealing (<b>A</b>) the Boso-Hackney mine pit from underneath the vegetation cover otherwise not apparent in (<b>B</b>) multispectral imagery true-color visualization. The bare earth elevation surface is extracted from the (<b>C</b>) Lidar point cloud.</p>
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<p>Examples of Lidar derivative products generated at the Boso-Hackney mine site: (<b>A</b>) bare earth elevation; (<b>B</b>) slope; (<b>C</b>) elevation contours; (<b>D</b>) hydrographic stream vectors.</p>
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<p>An example of how texture was implemented to interpret mine reclamation status. Cross-section <b>A</b> presents a consistent smooth texture across the reclaimed mine pile; in contrast, cross-section <b>B</b> provides an example of the erosion features present on the slope of an unreclaimed mine pile.</p>
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<p>An example of how Landsat multispectral imagery time-lapse analysis was implemented to reveal LCLU changes associated with mining activities, 1972–2015: (<b>A</b>) active mining; (<b>B</b>) pre-reclamation; (<b>C</b>) reclamation activities; (<b>D</b>) post-reclamation land cover.</p>
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<p>An example of time-series analysis comparing (<b>A</b>) shifts in spectral indices (tasseled cap wetness, tasseled cap brightness, and tasseled cap greenness) indicative of the mine reclamation sequence (pit de-watering, earthwork, and revegetation) to (<b>B</b>) no change in spectral vectors over time, indicative of no change in the LCLU for the period analyzed at the mine sites.</p>
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<p>Map of abandoned and reclaimed mine features and areas of mine disturbance.</p>
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23 pages, 4613 KiB  
Article
Flash Floods Hazard to the Settlement Network versus Land Use Planning (Lublin Upland, East Poland)
by Leszek Gawrysiak, Bogusława Baran-Zgłobicka and Wojciech Zgłobicki
Appl. Sci. 2024, 14(18), 8425; https://doi.org/10.3390/app14188425 - 19 Sep 2024
Viewed by 650
Abstract
There has been an increase in the frequency of hazards associated with meteorological and hydrological phenomena. One of them is flash floods occurring episodically in areas of concentrated runoff—valleys without permanent drainage. In the opinion of residents and local authorities, these are potentially [...] Read more.
There has been an increase in the frequency of hazards associated with meteorological and hydrological phenomena. One of them is flash floods occurring episodically in areas of concentrated runoff—valleys without permanent drainage. In the opinion of residents and local authorities, these are potentially safe areas—they are not threatened by floods and are therefore often occupied by buildings. The importance of addressing flash floods in land use planning is essential for sustainable development and disaster risk reduction. The objective of this research was to assess the level of the hazard and to evaluate its presence in land use planning activities. This manuscript fills a research gap, as to date flash flood threats have not been analyzed for individual buildings located in catchments of dry valleys in temperate climates. More than 12,000 first-order catchments were analyzed. The study covered an upland area located in East Poland, which is characterized by high population density and dispersed rural settlement. Within the 10 municipalities, buildings located on potential episodic runoff lines were identified. Qualitative assessment was applied to ascertain the susceptibility of catchments to flash floods. Such criteria as slopes, size, shape of the catchment area, and land cover, among others, were used. Between 10 and 20% of the buildings were located on episodic runoff lines, and about 900 sub-catchments were highly or very highly susceptible to flash floods. The way to reduce the negative effects of these phenomena is to undertake proper land use planning based on knowledge of geohazards, including flash floods. However, an analysis of available planning documents shows that phenomena of this type are not completely taken into account in spatial management processes. Full article
(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)
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<p>Location of studied municipalities (black polygons). Red outline—border of Lublin Upland.</p>
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<p>Workflow model of data processing and calculations.</p>
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<p>Example of geomorphon map, part of Krasnobród municipality. Borders of catchments drawn with black lines; red dots indicate pour points of catchments.</p>
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<p>Scheme of watersheds merging during delimitation of buildings threatened by flash floods. Colors indicate different watersheds linked to individual buildings (left side) and borders of final watersheds included in analysis (right). Colors of outlines of watersheds express the degree of susceptibility to flash floods.</p>
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<p>Classified watersheds in analyzed communes (susceptibility classes according to <a href="#applsci-14-08425-t003" class="html-table">Table 3</a>).</p>
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19 pages, 11199 KiB  
Article
Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network
by Leon S. Besseling, Anouk Bomers and Suzanne J. M. H. Hulscher
Hydrology 2024, 11(9), 152; https://doi.org/10.3390/hydrology11090152 - 12 Sep 2024
Viewed by 1558
Abstract
Hydrodynamic models are often used to obtain insights into potential dike breaches, because dike breaches can have severe consequences. However, their high computational cost makes them unsuitable for real-time flood forecasting. Machine learning models are a promising alternative, as they offer reasonable accuracy [...] Read more.
Hydrodynamic models are often used to obtain insights into potential dike breaches, because dike breaches can have severe consequences. However, their high computational cost makes them unsuitable for real-time flood forecasting. Machine learning models are a promising alternative, as they offer reasonable accuracy at a significant reduction in computation time. In this study, we explore the effectiveness of a Long Short-Term Memory (LSTM) neural network in fast flood modelling for a dike breach in the Netherlands, using training data from a 1D–2D hydrodynamic model. The LSTM uses the outflow hydrograph of the dike breach as input and produces water depths on all grid cells in the hinterland for all time steps as output. The results show that the LSTM accurately reflects the behaviour of overland flow: from fast rising and high water depths near the breach to slowly rising and lower water depths further away. The water depth prediction is very accurate (MAE = 0.045 m, RMSE = 0.13 m), and the inundation extent closely matches that of the hydrodynamic model throughout the flood event (Critical Success Index = 94%). We conclude that machine learning techniques are suitable for fast modelling of the complex dynamics of dike breach floods. Full article
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<p>The structure of an LSTM neural network, showing three data streams and four internal neural nodes functioning as the forget, input and output gates, here displayed with an input dimension of 3 and output dimension of 2. Reproduced from Karim [<a href="#B34-hydrology-11-00152" class="html-bibr">34</a>].</p>
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<p>The study area of the Rhine river entering the Netherlands and bifurcating, and its representation in the 1D–2D hydrodynamic model of Bomers [<a href="#B27-hydrology-11-00152" class="html-bibr">27</a>].</p>
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<p>(<b>a</b>) Examples of river discharge waves at the boundary that lead to a breach downstream. The breach at the IJssel breach location occurs when the discharge in the Rhine reaches around 16,000 m<sup>3</sup>/s, marked with x on the three shown discharge waves. (<b>b</b>) The overland flow patterns computed by the hydrodynamic model of Bomers [<a href="#B27-hydrology-11-00152" class="html-bibr">27</a>] during the black river discharge scenario of panel A at various times after the breach. Starting from the breach (at the red X) and spreading towards the north-east, the largest inundation extent is reached about 48 h after the breach.</p>
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<p>The variability of the data set from Bomers [<a href="#B27-hydrology-11-00152" class="html-bibr">27</a>]. (<b>a</b>) The input breach outflow hydrograph scenarios used for training and testing, with the hydrographs leading to the maximum and minimum inundation extents shown in blue and red, respectively. <a href="#hydrology-11-00152-f003" class="html-fig">Figure 3</a>a shows the corresponding river discharge at the upstream boundary in blue and red. (<b>b</b>) Map of the maximum and minimum inundation extents of the flood scenarios, and the difference in water depth between them per grid cell. Breach location indicated with a red X. (<b>c</b>) The water depths at locations A, B and C of panel B for all scenarios, with the max and min inundation scenarios indicated in blue and red, respectively.</p>
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<p>Water depth predictions of LSTM model compared to hydrodynamic model for four time steps in the simulation of 1 of the 15 test flood events. Breach indicated with a red X.</p>
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<p>Water depth prediction error of two LSTM models compared to the hydrodynamic model for a test data set flood. (<b>a</b>–<b>c</b>) During the flood propagation phase (shown with the first time step after the breach), the end-of-flood LSTM model is less accurate than the LSTM model for flood propagation compared to the hydrodynamic model. (<b>d</b>–<b>f</b>) After 5 days, the end-of-flood LSTM model is more accurate than the flood propagation LSTM model compared to the hydrodynamic model. Breach indicated with a red X.</p>
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<p>Results for the Mean Absolute Error (MAE) for the test flood events. (<b>b</b>) A map of spatial variation in total MAE per grid cell, averaging all time steps and all 15 test flood events. Breach indicated with a red X. (<b>a</b>) Outflow hydrographs of two specific test flood events, one with quickly diminishing breach outflow and one with longer continuation of the flood event. (<b>c</b>) The water depths in grid cells A–D of panel B for the short and long test flood events show that the LSTM is able to capture the difference in flood behaviour.</p>
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<p>(<b>a</b>) A scatter plot of LSTM prediction against hydrodynamic model water depths for all grid cells and time steps during the 15 test flood events. Most of the 48 million data points are along the 1:1 solid line of perfect prediction, while only a few are scattered around. Some points concentrate along a less steep dashed line, which corresponds to the first time step delay between the LSTM and hydrodynamic model results. (<b>b</b>,<b>c</b>) The temporal variation of CSI and MAE/RMSE, averaged over all flooded grid cells for different wet–dry thresholds.</p>
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19 pages, 5902 KiB  
Article
Fire-Induced Changes in Geochemical Elements of Forest Floor in Southern Siberia
by Olga A. Shapchenkova, Elena A. Kukavskaya and Pavel Y. Groisman
Fire 2024, 7(7), 243; https://doi.org/10.3390/fire7070243 - 11 Jul 2024
Viewed by 1193
Abstract
Wildfires significantly influence the environmental distribution of various elements through their fire-induced input and mobilization, yet little is known about their effects on the forest floor in Siberian forests. The present study evaluated the effects of spring wildfires of various severities on the [...] Read more.
Wildfires significantly influence the environmental distribution of various elements through their fire-induced input and mobilization, yet little is known about their effects on the forest floor in Siberian forests. The present study evaluated the effects of spring wildfires of various severities on the levels of major and minor (Ca, Al, Fe, S, Mg, K, Na, Mn, P, Ti, Ba, and Sr) trace and ultra-trace (B, Co, Cr, Cu, Ni, Se, V, Zn, Pb, As, La, Sn, Sc, Sb, Be, Bi, Hg, Li, Mo, and Cd) elements in the forest floors of Siberian forests. The forest floor (Oi layer) samples were collected immediately following wildfires in Scots pine (Pinus sylvestris L.), larch (Larix sibirica Ledeb.), spruce (Picea obovata Ledeb.), and birch (Betula pendula Roth) forests. Total concentrations of elements were determined using inductively coupled plasma–optical emission spectroscopy. All fires resulted in a decrease in organic matter content and an increase in mineral material content and pH values in the forest floor. The concentrations of most elements studied in a burned layer of forest floor were statistically significantly higher than in unburned precursors. Sb and Sn showed no statistically significant changes. The forest floor in the birch forest showed a higher increase in mineral material content after the fire and higher levels of most elements studied than the burned coniferous forest floors. Ca was a predominant element in both unburned and burned samples in all forests studied. Our study highlighted the role of wildfires in Siberia in enhancing the levels of geochemical elements in forest floor and the effect of forest type and fire severity on ash characteristics. The increased concentrations of elements represent a potential source of surface water contamination with toxic and eutrophying elements if wildfire ash is transported with overland flow. Full article
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<p>Location of study area (<b>I</b>) and view of burned sites (<b>II</b>) in Scots pine (Plot 1), larch (Plot 2), spruce (Plot 3), and birch (Plot 4) forests.</p>
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<p>Mineral residue content (<b>a</b>), organic matter content (<b>b</b>), and pH (<b>c</b>) in unburned and burned forest floor at Scots pine, larch, spruce, and birch, forests (n = 5 for each site).</p>
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<p>Photo (<b>a</b>) of burned Scots pine forest (Plot 1); SEM micrographs and EDX spectra of wildfire ash produced across the site (<b>b</b>,<b>d</b>) and near tree stem (<b>c</b>,<b>e</b>) where intense burning was observed.</p>
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<p>Total concentrations of major and minor elements in forest floor. Mean and associated standard deviation are given. Asterisks indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between burned and unburned samples (n = 5 for each site).</p>
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<p>Total concentrations of trace and ultra-trace elements in forest floor. Mean and associated standard deviation are given. Asterisks indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between burned and unburned samples (n = 5 for each site).</p>
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<p>Pearson’s correlation matrix among element concentrations for unburned (<b>a</b>) and burned (<b>b</b>) samples. Blue and red colors indicate positive and negative correlations, respectively. The darker the tone, the more significant the corresponding correlation (at <span class="html-italic">p</span> &lt; 0.05).</p>
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13 pages, 15386 KiB  
Article
Impact of Human Development on the Phenomenon of Surface Runoff Crossing Adjacent Watershed Boundaries
by WeiCheng Lo, Chang-Mien Wang, Chih-Tsung Huang and Meng-Hsuan Wu
Water 2024, 16(13), 1831; https://doi.org/10.3390/w16131831 - 27 Jun 2024
Viewed by 758
Abstract
The concept of watersheds, also called catchments, is fundamental to both flood mitigation and water resource management, as it greatly aids in the calculation of overland flow attributes. Watershed boundaries are typically determined by elevation, as water adheres to the geological characteristics of [...] Read more.
The concept of watersheds, also called catchments, is fundamental to both flood mitigation and water resource management, as it greatly aids in the calculation of overland flow attributes. Watershed boundaries are typically determined by elevation, as water adheres to the geological characteristics of watersheds under natural circumstances and does not cross watershed boundaries. However, advances in human development have caused elevation and land usage changes, and boundaries between adjacent watersheds in downstream areas with flat terrain have become unclear and unstable. This study chose the Kaoping River watershed and Donggang River watershed as the study area, to investigate the cross-watershed runoff phenomenon under different return period rainfall. Based on land use surveys of the study area, the area in proximity to the boundary between the two watersheds was highly developed, with land primarily used for agriculture, buildings, and transportation. As the study area was highly developed, cross-watershed runoff was observed, even in the 2-year return period rainfall simulation case. The size and depth of the areas where cross-watershed runoff occurred became stable in the simulation cases, with return periods of 25 years or greater due to the surrounding high-elevation terrain obstructing further surface runoff development. Thus, when planning for flood mitigation, cross-watershed runoff from adjacent watersheds must also be considered, in addition to normal surface runoff. Full article
(This article belongs to the Special Issue Watershed Hydrology and Management under Changing Climate)
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<p>Elevation in the study area.</p>
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<p>Elevation of the boundary between the two watersheds and of the surrounding area.</p>
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<p>Land use distribution in the study area in different periods.</p>
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<p>Computational cells.</p>
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<p>A comparison between observed and simulated water levels of the five water level gauging stations (<b>A</b><b>E</b>) in <a href="#water-16-01831-f004" class="html-fig">Figure 4</a> during the 2016 Megi typhoon.</p>
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<p>Histogram on the precipitation for each return period.</p>
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<p>Maximum inundation depth distribution for each return period.</p>
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<p>Overlay of land use and maximum inundation depth distribution for the 25-year return period in the study area.</p>
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14 pages, 3577 KiB  
Article
Infiltration-Based Variability of Soil Erodibility Parameters Evaluated with the Jet Erosion Test
by Aaron A. Akin, Gia Nguyen and Aleksey Y. Sheshukov
Water 2024, 16(7), 981; https://doi.org/10.3390/w16070981 - 28 Mar 2024
Viewed by 1188
Abstract
Soil erosion by water on agricultural hillslopes leads to numerous environmental problems including reservoir sedimentation, loss of agricultural land, declines in drinking water quality, and requires deep understanding of underlying physical processes for better mitigation. It is imperative to accurately predict soil erosion [...] Read more.
Soil erosion by water on agricultural hillslopes leads to numerous environmental problems including reservoir sedimentation, loss of agricultural land, declines in drinking water quality, and requires deep understanding of underlying physical processes for better mitigation. It is imperative to accurately predict soil erosion caused by overland flow processes so that soil conservation efforts can be undertaken proactively before large-scale sedimentation problems arise. Soil detachment is often described by the excess shear stress equation that contains two physical soil erodibility parameters, erodibility coefficient, and critical shear stress. These parameters are normally assumed to be constant but can change across varying soil texture classes as well as during surface runoff events due to changes in soil cohesion and potential dependency on soil moisture content. These changes may significantly affect soil erosion rates at the field and watershed scale. In this study, the erodibility parameters of three soil types (sandy loam, clay loam, and silty clay loam) were analyzed using a laboratory mini-Jet Erosion Test (JET) to determine the effect of soil sample infiltration and moisture condition. Results from the experiments depicted a dynamic relationship between the soil erodibility parameters and amount of infiltrated mass of water. Data analysis displayed that for soils of different texture critical shear stress exhibited local minimum with higher values for very dry and saturated soils, while erodibility coefficient tended to increase with the increase of mass of soil water. Utilizing these dynamic soil erodibility parameters did not result in a significant difference in soil erosion rates when compared to using the averaged soil erodibility parameters taken from the experiment but the range of potential erosion rates increases with the increase of applied sheer stress to soil surface. The erosion rates with the experiment-based coefficients were found to be higher than with the baseline WEPP-based coefficients. These results highlight the importance of evaluating the effect of intrastorm dependent factors during surface runoff events, such as antecedent soil moisture content, time to peak from the start of runoff, soil cohesion, etc., on soil erodibility parameters to accurately calculate erosion rates, especially for initially dry soils or during earlier stages of surface runoff when critical shear stresses were highly affected. Further assessment of such factors with JET or other laboratory and field tests is recommended. Full article
(This article belongs to the Section Soil and Water)
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<p>Soil sample before and after the infiltration experiment: (<b>a</b>) standard mold filled with compacted soil, (<b>b</b>) soil specimen after soil core extraction, and (<b>c</b>) soil moisture in a core after the infiltration experiment.</p>
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<p>Laboratory setup for (<b>a</b>) infiltration test and (<b>b</b>) JET erosion experiment.</p>
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<p>Average soil moisture profiles with depth at each tested infiltrated mass of water for (<b>a</b>) soil I, (<b>b</b>) soil II, and (<b>c</b>) soil III.</p>
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<p>Scour depth with time for JET experiments for three soils: (<b>a</b>) soil I, (<b>b</b>) soil II, and (<b>c</b>) soil III. Open circles represent scour depth readings for each JET experiment, and solid curves show best-fit lines for each group of mass of infiltrated water.</p>
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<p>Soil erodibility parameters (<math display="inline"><semantics> <msub> <mi>k</mi> <mi>d</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>c</mi> </msub> </semantics></math>) versus mass of infiltrated water derived from JET experiments for three tested soils: (<b>a</b>) soil I, (<b>b</b>) soil II, and (<b>c</b>) soil III.</p>
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<p>Soil erosion rates <math display="inline"><semantics> <mi>ε</mi> </semantics></math> versus hydraulic shear stress <math display="inline"><semantics> <mi>τ</mi> </semantics></math> calculated with Equation (<a href="#FD1-water-16-00981" class="html-disp-formula">1</a>) and erodibility parameters <math display="inline"><semantics> <msub> <mi>k</mi> <mi>d</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>c</mi> </msub> </semantics></math> using three approaches: (i) dynamic as function of mass <span class="html-italic">M</span> of infiltrated water (gray band), (ii) average from JET experiments (red line), and (iii) WEPP-based (blue line), for three soils: (<b>a</b>) soil I, (<b>b</b>) soil II, and (<b>c</b>) soil III. The ranges of <math display="inline"><semantics> <msub> <mi>k</mi> <mi>d</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>c</mi> </msub> </semantics></math> values for the dynamic approach were exported from Equation (<a href="#FD8-water-16-00981" class="html-disp-formula">8</a>) with the coefficients <span class="html-italic">b</span>, <span class="html-italic">c</span>, and <span class="html-italic">d</span> from <a href="#water-16-00981-t002" class="html-table">Table 2</a>.</p>
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25 pages, 8405 KiB  
Article
Improving Flash Flood Hydrodynamic Simulations by Integrating Leaf Litter and Interception Processes in Steep-Sloped Natural Watersheds
by Gergely Ámon, Katalin Bene, Richard Ray, Zoltán Gribovszki and Péter Kalicz
Water 2024, 16(5), 750; https://doi.org/10.3390/w16050750 - 1 Mar 2024
Viewed by 1288
Abstract
More frequent high-intensity, short-duration rainfall events increase the risk of flash floods on steeply sloped watersheds. Where measured data are unavailable, numerical models emerge as valuable tools for predicting flash floods. Recent applications of various hydrological and hydrodynamic models to predict overland flow [...] Read more.
More frequent high-intensity, short-duration rainfall events increase the risk of flash floods on steeply sloped watersheds. Where measured data are unavailable, numerical models emerge as valuable tools for predicting flash floods. Recent applications of various hydrological and hydrodynamic models to predict overland flow have highlighted the need for improved representations of the complex flow processes that are inherent in flash floods. This study aimed to identify an optimal modeling approach for characterizing leaf litter losses during flash floods. At a gauged watershed in the Hidegvíz Valley in Hungary, a physical-based model was calibrated using two distinct rainfall–runoff events. Two modeling methodologies were implemented, integrating canopy interception and leaf litter storage, to understand their contributions during flash flood events. The results from the model’s calibration demonstrated this approach’s effectiveness in determining the impact of leaf litter on steep-sloped watersheds. Soil parameters can estimate the behavior of leaf litter during flash flood events. In this study, hydraulic conductivity and initial water content emerged as critical factors for effective parametrization. The findings underscore the potential of a hydrodynamic model to explore the relationship between leaf litter and flash flood events, providing a framework for future studies in watershed management and risk-mitigation strategies. Full article
(This article belongs to the Special Issue Advanced Research on Hydraulic Engineering and Hydrological Modelling)
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<p>Forest rainfall–runoff process.</p>
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<p>Rainfall–runoff processes.</p>
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<p>Location of the Hidegvíz Valley watershed.</p>
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<p>Land use distribution on the watershed.</p>
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<p>Distribution of near surface soil types.</p>
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<p>Event 1, between 15 and 16 July 2010.</p>
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<p>Event 2 between 6 and 7 July 2012.</p>
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<p>Flow event occurrence before Event 2.</p>
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<p>Leaf litter storage capacity for different leaf types on a 60-minute rainfall event with 10, 50, and 100 mm/h intensities.</p>
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<p>Leaf litter storage (mm) vs. time (day) [<a href="#B46-water-16-00750" class="html-bibr">46</a>].</p>
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<p>Modeling approaches.</p>
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<p>The adaptive grid mesh.</p>
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<p>Event 1 effect of interceptions, Method A (<b>a</b>) and Method B (<b>b</b>).</p>
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<p>Event 2 effect of interceptions, Method A (<b>a</b>) and Method B (<b>b</b>).</p>
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<p>Modeled soils (green—woodland (leaf litter as soil); dark blue—Magasbérc soil (cutting areas); magenta—Brennberg soil (meadows, creeks)).</p>
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<p>Prediction results for Event 1, for Methods A and B.</p>
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<p>State of modeled overland flow at jump #1 in both methods around the culverts (location 1) and the lower Rák Creek junction area (location 2) during Event 1.</p>
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<p>Prediction results for Event 2, for Methods A and B.</p>
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18 pages, 3122 KiB  
Article
Responses of Soil and Ammonia Nitrogen Loss Rates to Hydraulic Parameters under Different Slope Gradients and Rainfall Intensities
by Hao Yang, Chenchen Wei, Guanghui Sun, Xueqing Tao, Yitong Wang and Weimin Xing
Water 2024, 16(2), 230; https://doi.org/10.3390/w16020230 - 9 Jan 2024
Cited by 2 | Viewed by 1303
Abstract
Soil erosion and the consequent loss of nutrients have consistently been significant factors contributing to land degradation and nonpoint source pollution. While runoff serves as the primary carrier for nutrient loss, the hydraulic processes governing the mechanisms of nutrient loss remain not entirely [...] Read more.
Soil erosion and the consequent loss of nutrients have consistently been significant factors contributing to land degradation and nonpoint source pollution. While runoff serves as the primary carrier for nutrient loss, the hydraulic processes governing the mechanisms of nutrient loss remain not entirely clear. This paper aims to investigate the impacts of rainfall intensity and the slope gradient on hydraulic parameters, soil loss rates, and ammonia nitrogen loss rates, with the objective of determining the optimal hydraulic parameters for more accurate predictions of soil erosion and nutrient loss rates. A series of simulated rainfall experiments with three rainfall intensities (25, 50, and 75 mm min−1) and four slope gradients (8.7%, 17.6%, 26.8%, and 36.4%) were conducted on a 5 m × 10 m slope. The results indicated that the flow velocity, shear stress, stream power, unit stream power, and unit energy all increased with the increase in slope gradient or rainfall intensity. The water depth decreased with an increase in the slope gradient but increased with an increase in the rainfall intensity. Laminar flow occurred in all experiments (Reynolds number < 500). Only the overland flow under a 25 mm h−1 rainfall intensity and 8.7% slope gradient was subcritical flow (Froude number < 1). Hydraulic parameters, the soil loss rate, and ammonia nitrogen loss rate could be all expressed as the product of rainfall intensity and slope power function, with R2 ranging from 0.949 to 0.997. The average soil loss rate and process soil erosion rate could both be fitted using the power function of hydraulic parameters, with the optimal fitting parameter being stream power (R2 = 0.980 and 0.909). The average ammonia nitrogen loss rate exhibited a linear relationship with the hydraulic parameters, and the optimal fitting parameter was also stream power (R2 = 0.933). However, there were relatively low correlations between hydrodynamic parameters and the ammonia nitrogen loss rate (R2 = 0.450–0587). Our results contribute to a deeper understanding of the hydraulic processes involved in nutrient loss. Full article
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<p>Sketch of the slope and simulated rainfall system.</p>
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<p>Responses of average flow velocity and flow depth to the slope gradient and rainfall intensity: (<b>a</b>) flow velocity; (<b>b</b>) flow depth.</p>
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<p>Responses of Reynolds number and Froude number to the slope gradient and rainfall intensity, (<b>a</b>): Reynolds number; (<b>b</b>): Froude number.</p>
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<p>Responses of average shear stress, stream power, unit stream power, and unit energy to the slope gradient and rainfall intensity: (<b>a</b>) shear stress; (<b>b</b>) stream power; (<b>c</b>) unit stream power; (<b>d</b>) unit energy.</p>
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<p>Responses of soil loss rate and ammonia nitrogen loss rate to the slope gradient and rainfall intensity: (<b>a</b>) soil loss rate; (<b>b</b>) ammonia nitrogen loss rate; (<b>c</b>) total sediment losses; (<b>d</b>) total ammonia losses.</p>
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<p>Average soil loss rate and soil loss rate as a function of the flow hydraulic parameters: (<b>a</b>,<b>b</b>) shear stress; (<b>c</b>,<b>d</b>) stream power; (<b>e</b>,<b>f</b>) unit stream power; (<b>g</b>,<b>h</b>) unit energy.</p>
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<p>Average ammonia nitrogen loss rate and ammonia nitrogen loss rate as a function of the flow hydraulic parameters: (<b>a</b>,<b>b</b>) shear stress; (<b>c</b>,<b>d</b>) stream power; (<b>e</b>,<b>f</b>) unit stream power; (<b>g</b>,<b>h</b>) unit energy.</p>
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20 pages, 36217 KiB  
Article
Morpho-Hydrological Analysis and Preliminary Flash Flood Hazard Mapping of Neom City, Northwestern Saudi Arabia, Using Geospatial Techniques
by Bashar Bashir and Abdullah Alsalman
Sustainability 2024, 16(1), 23; https://doi.org/10.3390/su16010023 - 19 Dec 2023
Cited by 8 | Viewed by 1608
Abstract
Neom city is a unique cross-border city connecting Saudi Arabia, Jordan, and Egypt. Although Neom city is of great and critical importance for Saudi Arabia, few hydrological, natural hazard, and geomorphological studies have been undertaken on this region. This work aims to investigate [...] Read more.
Neom city is a unique cross-border city connecting Saudi Arabia, Jordan, and Egypt. Although Neom city is of great and critical importance for Saudi Arabia, few hydrological, natural hazard, and geomorphological studies have been undertaken on this region. This work aims to investigate the hydro-geomorphological characteristics and assess the flash flood hazards in Neom city by investigating several valuable morphometric parameters. The Shutter Radar Topography Mission (SRTM) digital elevation model and hydrological and geological data were analyzed in this study using ArcGIS software. Based on the morphometric parameter results, total stream lengths and stream orders were relatively high (17,956.03 km and 5, respectively), whereas the average bifurcation ratio was recorded to be low at 3.54. Basins 10, 12, 17, 30, 31, 32, and 34 were described as large basins, coarse-textured, elongated, with a medium drainage density, low infiltration values, long overland flows, and high values of constant maintenance. Additionally, the El-Shamy approach for flood hazard assessment was applied side by side with the morphometric analysis, which indicated that the possibility of an intense flood hazard is very low. In general, this study suggests that most of the studied basins cover similar and resistant rocks and soils. They have minimal conditions for flooding events and suitable conditions for underground and surface water resources. Therefore, they display high signals of susceptibility to erosion. The morphometric analysis and flash flood assessment techniques applied in this study were time- and cost-effective for the morphometric characterization of landforms. This text deals with the analysis of several environmental characteristics including hydro-morphological characteristics, drainage topography and lithology, soil erosion, groundwater recharge impact, and flash flood signals. Excellent sustainability plans should be reliant on extensive and varied information about the environment. Thus, integrated analyses incorporating environmental characteristics and flood hazard assessment play an important role in adjusting and adapting the suitable socioeconomic and scientific sustainability of the development of the study city. They build up the basic and essential information required to help decision-makers and sustainability managers design and adjust the most suitable sustainability plans for the study city over the long term. Full article
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<p>(<b>a</b>) Google Earth map of the Red Sea region showing the main countries and water bodies. The inset map and white dashed box show the location of the study and Neom city (<b>b</b>), respectively.</p>
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<p>General geomorphological characteristics of Neom city: (<b>a</b>) Digital elevation model map (SRTM 30 spatial resolution), (<b>b</b>) Slope map. (<b>c</b>) Digital contouring map showing constant elevation, and (<b>d</b>) Stream order map presenting the river networks of the study city.</p>
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<p>Simple flowchart showing the pre-processing steps.</p>
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<p>Map of the study city showing the distribution of basins from 1 to 52 delineated by ArcGIS.</p>
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<p>Map of the study city presenting basic geometries of the study basins. The colors white, red, yellow, and blue indicate the area (km<sup>2</sup>), perimeter (km), basin length (km), and basin relief (m), respectively.</p>
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<p>Map of the study city presenting stream orders of the study basins. Yellow numbers indicate stream orders from I to V for all basins; respectively and black numbers show the basin numbers.</p>
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<p>(<b>a</b>) Total stream length against stream order of Neom city, and (<b>b</b>) is an example from Kabite and Gessesse [<a href="#B6-sustainability-16-00023" class="html-bibr">6</a>].</p>
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<p>Flood susceptibility: (<b>a</b>) bifurcation ratio against drainage density, (<b>b</b>) bifurcation ratio against stream frequency. The A, B, and C symbols indicate Class A, Class B, and Class C, respectively.</p>
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<p>Flood hazard map based on the El-Shamy approach method.</p>
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