Interval Flood Research Articles

Hydrogeomorphic response of steep streams following severe wildfire in the Western cascades, Oregon

AbstractSevere wildfire may alter steep mountain streams by increasing peak discharges, elevating sediment and wood inputs into channels, and increasing susceptibility to landslides and debris flows. In the Pacific Northwest, where mean annual precipitation is high and mean fire‐return intervals range from decades to centuries, understanding of steep stream response to fire is limited. We evaluate the hydrologic and geomorphic response of ~100‐m‐long steep stream reaches to the large‐scale and severe 2020 fires in the Western Cascade Range, Oregon. In the two runoff seasons after the fires, peak flows in burned reaches were below the 2‐year recurrence interval flood, a level sufficient to mobilize the median grain size of bed material, but not large enough to mobilize coarser material and reorganize channel morphology. Sediment inputs to study streams consisted of two road‐fill failure landslides, slumps, sheetwash, and minor bank erosion; precipitation thresholds to trigger debris flows were not exceeded in our sites. There was a 50% increase in the number of large wood pieces in burned reaches after the fires. Changes in fluxes of water, sediment, and wood induced shifts in the balance of sediment supply to transport capacity, initiating a sequence of sediment aggradation and bed‐material fining followed by erosion and bed‐material coarsening. Gross channel form showed resilience to change, and an unburned reference reach exhibited little morphologic change. Post‐fire recruitment of large wood will likely have long‐term implications for channel morphology and habitat heterogeneity. Below‐average precipitation during the study period, combined with an absence of extreme precipitation events, was an important control on channel responses. Climate change may have a complex effect on stream response to wildfire by increasing the propensity for both drought and extreme rain events and by altering vegetation recovery patterns.

Quantifying form resistance is essential for estimating summer low and bankfull flow from stream survey channel morphology

Reliable estimates of low flow and flood discharge at ungaged locations are required for evaluating stream flow alteration, designing culverts and stream crossings, and interpreting regional surveys of habitat and biotic condition. Very few stream gaging stations are located on small, remote streams, which typically have complex channel morphology. Adequate gaging is also lacking on larger streams that are remote, smaller than those typically gaged, or have channel morphology not conducive to installation of gages. Complex channels typically contain large scale hydraulic roughness elements that dominate flow patterns (i.e., form roughness), making it difficult to measure channel cross-section area and water velocity, or to measure channel volume even where discharge is known. In channels with large channel form roughness, it is equally difficult to estimate discharge using commonly applied equations based on slope and channel dimensions or basin area. We employed a novel approach that explicitly accounts for hydraulic resistance from large wood and riffle-pool morphology (form roughness) in calculating low flow and bankfull discharge from stream and river physical habitat data collected from 4229 stream and river sites in the conterminous US (CONUS) sampled in 2008–9 and 2013–14 as part of the US Environmental Protection Agency's National Rivers and Streams Assessment (NRSA). Hydraulic resistance derived from form roughness clearly dominated resistance derived from bed particles (particle resistance) during summer low flows in wadeable streams across the spectrum of channel slopes and substrate sizes smaller than boulders. Under bankfull conditions, the influence of form resistance relative to particle resistance was diminished, but form resistance still dominated except in large low gradient rivers lacking complex channels, and in streams or rivers with boulder-size bed particles. We validated our hydraulic resistance estimates by comparing measured discharges with calculated discharges that used those hydraulic resistance estimates along with measured NRSA channel morphology data. Morphology-based summer discharge (low flow) estimates and direct field measurements of discharge in 2333 wadeable CONUS streams showed reasonable agreement (median difference < 2.5×) for discharges ranging from 3.6 × 10−5 to 123 m3/s and drainage areas of 0.12 to 171,000 km2. In a subset of 759 of NRSA's larger wadeable stream and non-wadeable river sites where nearby U.S. Geological Survey (USGS) gage data were available and adequate, our morphology-based summer discharge estimates agreed fairly well (median difference < 2.0×) with USGS 20-yr average August mean flows ranging from 0.003 to 16,000 m3/s. Similarly, morphology-based estimates of bankfull flow ranging from 0.3 to 100,000 m3/s agreed reasonably well with the 1.5-yr recurrence interval flood in these gaged sites (median deviation <2.2×). These findings demonstrate the importance of quantifying flow resistance from large-scale form roughness features in natural channels and provide a novel approach for estimating discharge from widely available survey data. This will allow examination of discharge and its ecological influence across the full range of stream and river sizes sampled by NRSA or other synoptic surveys where comprehensive measures of biota, physical habitat, and chemistry are also made. Although these morphology-based estimates exhibit some variability, they are adequate for examining regional patterns in discharge and flow alteration and their association with instream biota and anthropogenic disturbances, providing summer low and bankfull flow information where reliable estimates are lacking.

Enhancing resilience: Integrating future flood modeling and socio-economic analysis in the face of climate change impacts

As climate change intensifies, future floods will become more severe in some areas with geographic variation, necessitating that local and regional governments implement systems to provide information for climate adaptation, particularly for vulnerable populations. Therefore, we aimed to develop a methodology to identify areas that are at an increased risk from future floods and independently socially vulnerable. In this study, 100-year recurrence interval flood extents and depths were estimated using an ensemble of six independent Coupled Model Intercomparison Project Phase 6 climate models for a past and future period under the highest-emissions climate scenario. The flood inundation results were related to social vulnerability for two selected study areas in the Mississippi River Basin. The range of flood extents and depths for both time periods were estimated, and differences were evaluated to determine the effects from climate change. To identify at-risk areas, the relationship between the spatial distribution of flood depths and vulnerability was then assessed. Finally, an analysis of the current and future damages on infrastructure from flooding on residential housing was performed to determine whether damages are correlated with higher vulnerability areas. Results show in every flooding scenario, flood extents and depths are increasing in the future compared with the past, ranging from an increase of 6 to 76 km2 in extent across both locations. A statistically significant relationship between spatial clusters of flooding and of vulnerability was found. The infrastructure analysis found that residential structures in the most vulnerable census tracts are 6 to 59 times more likely to experience moderate damage compared with the least vulnerable tracts depending on scenario. Overall, a framework was established to holistically understand the hydrologic and socioeconomic impacts of climate change, and a methodology was developed to use for allocating resources at the local scale.

Linking Nutrient Dynamics with Urbanization Degree and Flood Control Reservoirs on the Bahlui River

This work analyzed the nutrient dynamics (2011–2022) and discharge (2005–2022) for the Bahlui River at four distinctive locations: Parcovaci—a dam-protected area that has been untouched by agriculture or urbanization; Belcesti—a primarily agricultural area, also dam-protected; Podu Iloaiei—a region influenced by agriculture and urbanization; and Holboca—placed after a heavily urbanized area. The analysis focused on determining a series of statistical indicators using the Minitab 21.2 software. Two drought intervals and one flood interval were analyzed to highlight daily discharge evolution during the selected period, showing that the constructed reservoirs successfully control the streamflow. For the entire period, the evolution of mean and median values of the streamflow is consistent, considering the locations’ positions from the source to the river’s end. The total nitrogen and total phosphorus were selected as representative quality indicators. The study follows the influence of the analyzed areas’ characteristics and reservoirs’ presence on nutrient dynamics. The results showed that the most influential factor that impacts nutrient dynamics is the reservoirs’ presence, which controls the discharge, creates wetlands and swamps, and implicitly impacts nutrient concentration.

Accounting for the power of nature: Using flume and field studies to compare the capacities of bio‐energy and fluvial energy to move surficial gravels

AbstractRiver channels, riparian and floodplain forms and dynamics are all influenced strongly by biological processes. However, the influence of macroinvertebrates on entrainment and transport of river sediments remains poorly understood. We use an energy‐based approach to explore the capacity of benthic animals to move surficial, gravel‐bed particles in field and laboratory settings and use the results to assess the relative significance of biological and physical benthic processes. Our results showed that in 11 British gravel‐bed rivers, the maximum energy content (i.e., calorific content) of macroinvertebrate communities generally matched the flow energy associated with median discharges and, at multiple sites, exceeded that of the 10‐year return interval flood. A series of laboratory experiments used to estimate the minimum energy expended by signal crayfish (Pacifastacus leniusculus) when performing geomorphic work established that crayfish move gravel particles at energy levels below that expected of the flow, complicating direct comparisons of the capacity for macroinvertebrates and fluvial flows to influence bed mobility. Our findings suggest that the influence of macroinvertebrate communities in either promoting or suppressing, the mobilisation of the bed may be large compared to equivalent values of fluvial energy. Based on these findings, we conclude that in the gravel‐bed rivers studied, the macroinvertebrate community's potential to perform geomorphic work matches or exceeds the stream power during most of the year. Although our study examined biological and fluvial energy systems separately, it is important to recognise that in nature, these systems are highly interactive. It follows that utilising the energy framework presented in this paper could lead to rapid advances in both fluvial biogeomorphology and river management and restoration.

Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients

Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients

Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation

&lt;abstract&gt; &lt;p&gt;Flood time series forecasting stands a critical challenge in precise predictive models and reliable error estimation methods. A novel approach utilizing a hybrid deep learning model for both point and interval flood prediction is presented, enhanced by improved kernel density estimation (KDE) for prediction comparison and error simulation. Firstly, an optimized gated recurrent unit-time convolutional network (GRU-TCN) is constructed by tuning the internal structure of the TCN, the activation function, the L2 regularization, and the optimizer. Then, Pearson Correlation is used for feature selection, and the hyperparameters of the improved GRU-TCN are optimized by the subtraction-average-based optimizer (SABO). To further assess the prediction uncertainty, interval predictions are provided via Non-parametric KDE, with an optimized bandwidth setting for accurate error distribution simulation. Experimental comparisons are made on 5-year hydro-meteorological daily data from two stations along the Yangtze River. The proposed model surpasses long short-term memory network (LSTM), TCN, GRU, TCN-LSTM, and GRU-TCN, with a reduction of more than 13% in root mean square error (RMSE) and approximately 15% in mean absolute error (MAE), resulting in better interval estimation and error control. The improved kernel density estimation curves for the errors are closer to the mean value of the confidence intervals, better reflecting the trend of the error distribution. This research enhances the accuracy and reliability of flood predictions and improves the capacity of humans to cope with climate and environmental changes.&lt;/p&gt; &lt;/abstract&gt;

Widespread wood placement and regrading drive lateral connectivity and reworking of the channel and floodplain in a valley bottom reset to Stage 0

Valley bottom process reset, or the excavation of high surfaces and fill of incised channels combined with large wood addition, is a new method for creating multi-channel river-wetland corridors (also referred to as Stage 0 valley bottoms). Valley bottom process reset seeks to increase lateral flow and sediment connectivity, retain water and sediment, and kickstart geomorphic processes that may sustain aquatic and riparian habitat. This study uses this anthropogenic intervention to examine relationships among wood-induced hydraulic roughness, valley bottom topography, and geomorphic processes such as overbank flow and sediment transport, avulsion, sediment retention, and pool scour. Here, I present a 6-year case study of a two-phase valley bottom process reset along Deer Creek, Oregon. Kickstarting the floodplain reshaping processes required a substantial increase in roughness and hydrologic connectivity. The first phase of construction enhanced hydrologic but not sediment connectivity, failing to kickstart avulsion and floodplain reworking even during a likely 2- to 5-year recurrence interval flood. A second, more intensive phase of construction substantially reduced the threshold discharge necessary for overbank flow and floodplain reworking, as evidenced by the occurrence of these processes after only a <2-year recurrence interval flood. During this flood, a spatially distributed wood lattice rearranged into discrete jams that scoured pools, retained sediment, and drove geomorphically effective overbank flows. While valley bottom regrading likely contributed to these geomorphic effects, the spatial correlation between newly incised floodplain channels and areas of wood aggregation indicates wood's important role in driving floodplain reworking. I derive from these findings a conceptual model that details how wood drove floodplain reworking via two mechanisms: In-channel wood jams backwatered and constricted flow into the floodplain, and small wood jams formed in the floodplain forest further constricted flow through nascent channels to facilitate channel incision.

Facies analysis and depositional environment of a late Cambrian mixed carbonate-siliciclastic ramp from the Zard Kuh Mountain (Zagros Basin, south-western Iran)

Mixed carbonate-siliciclastic rocks were particularly widespread during the Furongian (late Cambrian), a critical time interval marked by biotic turnovers, extinction events and carbon cycle perturbations. In this context, the Zagros Basin (SW Iran) is a key area for the study of this time interval due to the well-exposed mixed carbonate-siliciclastic successions. Nevertheless, these are still poorly constrained from a sedimentological point of view. The current study aims to contribute to the palaeoenvironmental reconstruction of the Zagros Basin during the Furongian through high-resolution facies and microfacies analysis of the Tang-e-Ilbeyk section. The latter includes, in ascending order, the upper Mila C Member and Ilbeyk Formation. The facies and microfacies analysis recognized six facies associations, representing different settings of a mixed carbonate-siliciclastic ramp at the north-eastern Gondwanan margin. The vertical stacking pattern of these facies associations revealed five medium-scale Transgressive-Regressive cycles, superimposed by larger-scale sequences. In particular, during the latest Miaolingian (formerly late middle Cambrian) to early Furongian, a climate change from arid to humid conditions caused a major regressive event with the progradation of clastic shorelines and the following pollution and drowning of the Mila C Member's carbonate factory. The latter was replaced by a storm-dominated siliciclastic shelf (Ilbeyk Formation) that, throughout middle/late Furongian, experienced a large-scale transgressive event, culminating with a maximum flooding interval. A significant forced regression occurred in the latest Furongian when shallow water conditions were re-established (uppermost Ilbeyk Formation). The palaeoenvironmental reconstruction of this study allowed correlation with other coeval depositional events at regional (Arabian plate) and global (Laurentia and Baltica) scales, contributing to new constraints for the lower Palaeozoic evolution of north-eastern Gondwana.

The Ordovician record of North and West Africa: unravelling sea-level variations, Gondwana tectonics, and the glacial impact

Abstract The Ordovician of North and West Africa comprises three main transgressive–regressive sequences understood as ‘second-order’ cycles of 10–15 myr duration. Tide- to wave-dominated shallow-marine clastic successions, preserving incidental bryozoan carbonates to the north, include fluvial deposits over the most proximal southern stretches of the platform. The boundary with Cambrian strata remains unclear but the latter are progressively less represented to the south in the undifferentiated ‘Cambro-Ordovician’. To the north, graptolites, brachiopods and trilobites combined with palynomorphs provide a robust biostratigraphic frame. Maximum flooding intervals occurred in the early to middle Tremadocian, middle Darriwilian and middle to late Katian. Two events interfered with an overall long-term transgressive trend. The ‘intra-Arenig’ (late Floian?) tectonic event highlighted palaeohighs coinciding with Paleoproterozoic basements. Gondwanan drainage basins were reorganized, which had an impact on sediment sourcing and distribution of detrital material (e.g. zircons) feeding the pre-Variscan Europe. The second event is the end-Ordovician glaciation. The domain supported the greatest part of the Hirnantian glaciers and may also have preserved pre-Hirnantian glacial archives. It is not until the very latest Ordovician that offshore conditions developed far inland; it is however suspected that this inundation benefited from a transient postglacial isostatic flexure.

Prairie stream metabolism recovery varies based on antecedent hydrology across a stream network after a bank‐full flood

ABSTRACTIntermittent streams are characterized by significant periods of low to no flow, yet are also frequently subjected to flashy, high floods. Floods alter ecosystem function and result in variable successional patterns across the stream network. Yet, the timing of restored function after floods in intermittent stream networks is relatively unexplored. We measured recovery of stream ecosystem function using rates of gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and the primary production to respiration ratio (P/R) across eight locations in the Kings Creek drainage basin with differing preflood conditions (previously dry [intermittent] or flowing [perennial]) over a 30‐d period following a 2‐yr return interval flood. We found that all metabolic rates (GPP, ER, NEP, P/R) varied primarily by time (days since flood) and antecedent flow, but not spatial network position (i.e., drainage area). Intermittent sites exhibited high rates of ER (0.17–3.33 g dissolved oxygen [DO] m−2 d−1) following rewetting compared to perennial sites (0.03–1.17 g DO m−2 d−1), while GPP, NEP, and P/R were slower to recover and varied less between sites of differing preflood conditions. Metabolic rates were not strongly influenced by other environmental conditions. A large proportion of variation was explained by the random effect of location. Our results suggest that metabolism is temporally asynchronous and highly heterogenous across intermittent watersheds and that antecedent hydrology (drying prior to rewetting) stimulates heterotrophic activity, likely dependent on terrestrially derived organic matter and nutrient subsidies.

Optimization of Reservoir Flood Control Operation Based on Multialgorithm Deep Learning

With the rapid development of China's social economy, it is the most important task for the water conservancy industry to make use of the existing water conservancy engineering measures to carry out the research on river basin flood control dispatching. Large-scale joint operation of river basins usually needs to consider meteorological and hydrological conditions, historical flood data, multireservoir engineering conditions, and multiple flood control targets, which is a complex decision-making problem. Therefore, electing the optimal operation model of reservoir flood control optimization is very important. In this paper, Luanhe River Basin is taken as the research area, and three kinds of constraints, namely, water balance constraint, reservoir flood control capacity constraint, and water release decision constraint, are set to construct the flood control optimization model. Taking the minimum square of the sum of reservoir discharge and interval flood discharge as the objective function, genetic algorithm (GA), particle swarm optimization (PSO), Spider swarm optimization (SSO), and grey wolf optimization (GWO) are introduced into flood control optimal operation to seek the minimum value of objective function, and the results are compared and analyzed. Through the analysis of optimization results, the optimization ability and convergence effect of grey wolf optimization algorithm are better than those of genetic algorithm and particle algorithm, and the results are more stable than those of spider swarm algorithm. It has a good model structure and can make full use of the results of three wolf groups for optimization. Through the analysis of scheduling results, the results of genetic algorithm and particle swarm optimization algorithm are similar, while those of spider swarm optimization algorithm and grey wolf optimization algorithm are similar and slightly better than those of the first two. Moreover, the search range of grey wolf optimization algorithm for solving long sequence problems is wider and the calculation time is shorter. Therefore, the grey wolf optimization algorithm can be applied to solve the flood control operation optimization model of Panjiakou Reservoir Group.

Resilience of art cities to flood risk: A quantitative model based on depth‐idleness correlation

AbstractCultural heritage (CH) is threatened by floods; however, the understanding of exposure and vulnerability is challenging and makes risk and resilience assessment rarely practiced. CH is crucial for post‐disaster resilience, especially when the local economy is based on tourism. The work presents a novel framework for evaluating flood resilience, indirect impacts, and associated risk in art cities. The exposure of CH is estimated using the number of visitors as a proxy variable for the social appreciation. A new depth‐idleness vulnerability function assigning a reopening time to flood depth is developed from post‐event reports. A resilience model is conceived to (i) describe the recovery dynamics, (ii) estimate the indirect impacts in terms of lost visitors to CH for different probabilistic scenarios, (iii) calculate risk, and (iv) identify mitigation actions. The application of the model to the art city of Florence (Italy), a UNESCO site visited by approximately 10 million people a year, shows that a medium recurrence interval flood requires a recovery time of 351 days and causes a loss of 10.5 million visitors. The annual average number of lost visitors is 88,000 approximately. Resilience can be increased by accelerating the reopening and by reinforcing the attractivity of the city.

Influence of agricultural managed aquifer recharge on nitrate transport: The role of soil texture and flooding frequency

AbstractAgricultural managed aquifer recharge (Ag‐MAR) is a concept in which farmland is flooded during the winter using excess surface water to recharge the underlying groundwater. In this study, we show how different recharge practices affect NO3− leaching and mineralization–denitrification processes in different soil systems. Two contrasting soil textures (sand and fine sandy loam) from the Central Valley, California, were repeatedly flooded with 15 cm of water at varying time intervals in field and soil column experiments. Nitrogen species (NO3–, NH4+, total N), total C, dissolved O2, and moisture content were measured throughout the experiments. Results show that when flooding occurs at longer intervals (every 1–2 wk), N mineralization increases, leading to an increase of mobile NO3− in the upper root zone and leaching of significant quantities of NO3− from both soil textures (137.3 ± 6.6% [sand] and 145.7 ± 5.8% [fine sandy loam] of initial residual soil NO3−) during subsequent flooding events. Laboratory mineralization incubations show that long flooding intervals promote mineralization and production of excess NO3− at rates of 0.11–3.93 mg N kg–1 wk–1 (sand) and 0.08–3.41 mg N kg–1 wk–1 (fine sandy loam). Decreasing the flooding frequency to 72 h reduces potential mineralization, decreasing the amount of NO3− leached during flooding events (31.7 ± 3.8% [sand] and 64.7 ± 10.4% [fine sandy loam] of initial residual soil NO3–). The results indicate that implementing recharge as repeated events over a long (multiple‐week) time horizon might increase the total amount of NO3− potentially available for leaching to groundwater.

An Ecogeomorphic Framework Coupling Sediment Modeling With Invasive Riparian Vegetation Dynamics

AbstractFeedbacks between geomorphic processes and riparian vegetation in river systems are an important control on fluvial morphodynamics and on vegetation composition and distribution. Invasion by nonnative riparian species alters these feedbacks and drives management and restoration along many rivers, highlighting a need for ecogeomorphic models to assist with understanding feedbacks between plants and fluvial processes, and with restoration planning. In this study, we coupled a network‐scale sediment model (Sediment Routing and Floodplain Exchange; SeRFE) that simulates bank erosion and sediment transport in a spatially explicit manner with a recruitment potential analysis for a species of riparian vegetation (Arundo donax) that has invaded river systems and wetlands in Mediterranean climates worldwide. We used the resulting ecogeomorphic framework to understand both network‐scale sediment balances and the spread and recruitment of A. donax in the Santa Clara River watershed of Southern California. In the coupled model, we simulated a 1‐year time period during which a 5‐year recurrence interval flood occurred in the mainstem Santa Clara River. Outputs identify key areas acting as sources of A. donax rhizomes, which are subsequently transported by flood flows and deposited in reaches downstream. These results were validated in three study reaches, where we assessed postflood geomorphic and vegetation changes. The analysis demonstrates how a coupled model approach is able to highlight basin‐scale ecogeomorphic dynamics in a manner that is useful for restoration planning and prioritization and can be adapted to analogous ecogeomorphic questions in other watersheds.

A Forecast-Skill-Based Dynamic Pre-Storm Level Control for Reservoir Flood-Control Operation

The design and operation of reservoirs based on conventional flood-limited water levels (FLWL) implicitly adopts the assumption of hydrological stationarity. As such, historical-record-based FLWL may not be the best choice for flood-control operations due to the inherent non-stationarity of rainfall inputs. With maturing flood forecasts, this study focuses on establishing linkage between FLWL and skill of forecast, thus developing a “dynamic pre-storm level” approach for reservoir flood-control operations. The approach utilizes forecast flood magnitude, forecast skill and exceedance probability of forecast error to determine the pre-storm reservoir storage for each flood event. The exceedance probability of forecast error for each incoming flood is used as the reservoir flood control standard instead of the probability of a static return interval flood. This approach is demonstrated in a hypothetical situation in the Three Gorges Reservoir in China. The results show that under zero-forecast-skill conditions, the proposed dynamic pre-storm level matches well with the Three Gorges Reservoir-designed FLWL; and, as the forecast accuracy/skill increase, the proposed approach can make better use of the increased forecast accuracy, thereby maximizing floodwater utilization and reservoir storage. In this way, coupling the new approach with FLWL allows for more efficient and economic day-to-day reservoir operations without adding any flood risk. This study validates the usefulness of dynamic water level control during flood season, considering the improvement of flood forecast accuracy.

Cross Section Intervals of Flood Intervals of Flood Inundation Mapping at Ungauged Area

Digital Elevation Models (DEMs) are essential to present the continuous surface elevation and is used for flood mapping. The use of different cross-section intervals obtained from the various spatial resolution of DEMs will affect the flood depth and inundation area estimation. Therefore, a comparison study is carried out to investigate the effect of cross-section intervals on flood expansion and flood depth which is simulated using one dimensional (1D) HEC-RAS model at Padang Terap River, Malaysia. Two digital elevation models (DEMs) imageries, Interferometry Synthetic Aperture Radar (IFSAR) and Shuttle Radar Topography Mission (SRTM) are used in this study. The result was evaluated using likelihood measures (F-statistics, root mean square error (RMSE), and mean absolute error). The findings reveal the IFSAR DEM with cross-section interval 50 m has higher F-statistics of 70% on flood inundation estimation. By proposing the methodology, flood mapping can be provided accurately by considering the error that exist in the Geographical Information System (GIS) spatial context.

Flood dynamics dictate distributions of Elaeagnus angustifolia L. (Russian olive) on a riverine floodplain

North American invasions of Elaeagnus angustifolia L. (Russian olive) are still expanding, and hydrochory could be increasing invasion impacts along rivers. Spatial association between riverine flood-inundation patterns and E. angustifolia distributions suggests a link between seed movement potential and invasion patterns in time and space. Using output from hydraulic inundation models, we mapped inundation zones associated with successive flood return intervals within 10.81 km2 of the estimated 500-year floodplain of the Yellowstone River, Montana, USA. We then evaluated spatial relationships among inundation zones and E. angustifolia distribution. Elaeagnus angustifolia was highly spatially associated with frequent flood return intervals: 80% of E. angustifolia exists within a subset of the floodplain predicted to flood every 10 years or less, with the highest occupancy in the 1.5- to 5-year return zones. Within frequently inundated areas, floodplain occupancy of E. angustifolia was distributed in patterns consistent with likely areas of sediment deposition. Thus, existing predictions of invasion timelines are likely to improve with explicit consideration of dispersal opportunities associated with hydrochory during flooding. In particular, if hydrochory is a significant secondary dispersal mechanism, then seed immigration at flood intervals of 5 years or less could lead to substantially shorter invasion lags than currently predicted.

An Alternative for Estimating the Design Flood Interval of Agricultural Reservoirs under Climate Change Using a Non-Parametric Resampling Technique

Agricultural reservoirs play such a central role in supplying water to rural areas that it is essential to properly estimate the design flood for agricultural reservoirs under climate change. The objective of this study was to estimate the inflow design flood interval using a non-parametric resampling technique for agricultural reservoirs under climate change. This study suggested an alternative method to point estimation using insufficient past data by providing the interval of the inflow design flood under the representative concentration pathway. To estimate the interval of the inflow design flood, we employed the bootstrap technique, which estimated the confidence interval corresponding to the 95% confidence level. This study covered a spatial range of 30 agricultural reservoirs in South Korea and a temporal range of past and three future representative periods: the base period (2015s: 1986–2015) and future periods (2040s: 2011–2040, 2070s: 2041–2070, 2100s: 2071–2100). We analyzed the results of a 200-year return period and 24-hour duration as a representative case. For the 97.5th bias-corrected and accelerated percentile value, the overall inflow design floods were larger than the base period value (2015s) with the safety factor applied. The northern and midwestern regions of South Korea showed relatively greater changes than the southeastern region. Some agricultural reservoirs showed a decrease in the design flood during the 2040s but generally increased after the 2070s. Through the non-parametric resampling technique, the interval estimation was provided considering the uncertainty of the inflow design flood. By presenting the results for three periods, we can provide policymakers with information to select according to the target period. The findings may provide an essential step in replacing a safety factor used for determining the design flood of agricultural reservoirs with the confidence interval calculated in accordance with statistical characteristics.

An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.