Flood Magnitude Research Articles

Drivers of Geomorphic Heterogeneity in Unconfined Non‐Perennial River Corridors

AbstractRiver corridors along non‐perennial stream networks provide diverse physical and ecological functions that are thought to be related to the spatial and temporal variability of geomorphic units, also known as geomorphic heterogeneity. While studies on the characteristics and drivers of geomorphic heterogeneity have been developed in perennial streams, similar studies in ephemeral streams are lacking. Given the ubiquity of non‐perennial streams globally, we aim to answer questions regarding the magnitude and drivers of geomorphic heterogeneity in ephemeral river corridors as well as how geomorphic unit assemblages reflect processes related to flood disturbance. Geomorphic units were mapped in 30 unconfined river corridors within six non‐perennial watersheds in Utah and Arizona, USA. Landscape heterogeneity metrics—Shannon's Diversity Index, Shannon's Evenness Index, and patch density—were used to quantify geomorphic heterogeneity within each reach. Additionally, variables that potentially constrain or drive heterogeneity were quantified, including floodplain shape, grain size, large wood abundance, and proxies for flood disturbance. While heterogeneity positively correlated with metrics for morphology and disturbance, statistical models suggest that morphologic context, particularly river corridor width, was a more important predictor for estimating geomorphic heterogeneity. Still, geomorphic units reflected aggradation processes indicative of a range of flood energies, suggesting a strong tie between heterogeneity and disturbance. Results suggest that geomorphic heterogeneity may be resilient to changes in flood disturbance frequency or magnitude, but future studies investigating long‐term temporal heterogeneity are needed.

An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States

Abstract. Floods are among the most destructive natural hazards in the world, posing numerous risks to societies and economies globally. Accurately understanding and modeling floods driven by extreme rainfall events has long been a challenging task in the domains of hydrologic science and engineering. Unusual catchment responses to flooding cause great difficulty in predicting the variability and magnitude of floods, as well as proposing solutions to manage large volumes of overland flow. The usage of nature-based solutions (NBSs) has proved to be effective in the mitigation of flood peak rate and volume in urban or coastal areas, yet it is still not widely implemented due to limited knowledge and testing compared to traditional engineering solutions. This research examined an integrated hydrological and hydraulic modeling system to understand the response of an at-risk watershed system to flooding and evaluate the efficacy of NBS measures. Using the Hydrologic Engineering Center Hydrologic Modeling System and River Analysis System (HEC-HMS and HEC-RAS) software, an integrated hydrologic–hydraulic model was developed for Hurricane Matthew- (2016) and Florence-driven (2018) floods across the Little Pee Dee–Lumber River watershed, North and South Carolina (the Carolinas), US. The focus was on Nichols, a small town that has disproportionately been impacted by flooding during these two hurricane events. The present article proposes a methodology for selecting, modeling, and evaluating the performance of NBS measures within a catchment, which can be extended to other case studies. Different NBS measures, including flood storage ponds, riparian reforestation, and afforestation in croplands, were designed, modeled, and evaluated. Hurricane Matthew's flooding event was used for evaluating the NBS scenarios given its high simulation accuracy in flood inundation compared to the less accurate results obtained for Hurricane Florence. The scenario comparison evidenced that large-scale natural interventions, such as afforestation in croplands, can reduce the inundated area in Nichols by 8 % to 18 %. On the contrary, the smaller-scale interventions such as riparian reforestation and flood storage ponds showed a negligible effect of only 1 % on flood mitigation.

River science: an educational resource for understanding and learning to survive flooding and environmental change

ABSTRACT Integrated river science provides decision-making life-skills for individuals and communities facing environmental change; it is a variant of critical physical geography through its socio-political dimensions. As an example, the frequency and magnitude of river flooding are amplified by climate change: society needs to understand how to cope through learning, adaptative behaviour and planning. Choices should be informed by trusted science that can only be delivered by education, to avoid “knee jerk” post-hazard reactions. We examine curricula and resources in physical geography for England to judge if they make river science more pervasive and persuasive. Scales of causation and impact remain a central geographical focus. The concept of “place” is revitalized: environmental processes harnessed to improve resilience. Literacy and numeracy skills to communicate and predict need improving, to ensure that students understand problems sufficiently well to identify the best long-term, rigorously assessed options. Higher education has a hub role for empowering lay individuals, communities and professionals but the core learning principles need to be set at school. We conclude the current physical geography river science content is not fit for future needs, notably at A-Level. Weaknesses include naivety about river processes (water and sediment movement), arcane jargon, absence of a catchment-wide approach to river management, and inadequate links between disciplined fieldwork and databases. Contemporary community dimensions of social learning, argument and countering myths are also weakly developed.

Characterizing the 2019 Midwest Flood: A Hydrologic and Socioeconomic Perspective

Abstract During the spring of 2019, severe flooding across the U.S. Midwest caused widespread damage to communities in the Missouri and Mississippi River basins. While it is known that flood magnitude and economic damage are often related, little work exists to examine these factors simultaneously. In this study, we analyze both the hydrologic and socioeconomic characteristics of the 2019 Midwest flood to gain a comprehensive understanding of impacts to individuals, households, and communities. We examine flood magnitude, duration, and probability of occurrence in tandem with claim and grant applications from federal disaster recovery programs, such as the National Flood Insurance Program (NFIP) and the Individual and Households Program (IHP). Overall, we find that many areas, particularly in Nebraska and Iowa, experienced moderate or major flooding due to historic discharge magnitudes. In these states, NFIP claims totaled more than $31 million and IHP applications exceeded $42 million in reported damages. In most cases, counties that reported a high density of insurance claims or grant applications overlapped with regions with significant flooding. We also identify the economic advantages to NFIP policyholders for flood recovery in terms of aid eligibility and financial aid amounts.

Intelligent flood forecasting and warning: a survey

Accurately predicting the magnitude and timing of floods is an extremely challenging problem for watershed management, as it aims to provide early warning and save lives. Artificial intelligence for forecasting has become an emerging research field over the past two decades, as computer technology and related areas have been developed in depth. In this paper, three typical machine learning algorithms for flood forecasting are reviewed: supervised learning, unsupervised learning, and semi-supervised learning. Special attention is given to deep learning approaches due to their better performance in various prediction tasks. Deep learning networks can represent flood behavior as powerful and beneficial tools. In addition, a detailed comparison and analysis of the multidimensional performance of different prediction models for flood prediction are presented. Deep learning has extensively promoted the development of real-time accurate flood forecasting techniques for early warning systems. Furthermore, the paper discusses the current challenges and future prospects for intelligent flood forecasting.

Projection and Analysis of Floods in the Upper Heihe River Basin under Climate Change

The projection of future hydrological processes can provide insights into the risks associated with potential hydrological events in a changing environment and help develop strategies to cope with and prevent them. The Heihe River basin in Northwest China is crucial for providing water resources to water-scarce regions. Thus, understanding the future runoff trends in the context of climate change can optimize water allocation, alleviate water shortages, and mitigate flood risks in the region. In this study, we use meteorological data from 10 general circulation models under two future scenarios to drive the Soil and Water Assessment Tool (SWAT) model and project hydrological processes in the upper Heihe River basin from 2026 to 2100. After examining the future changes in total runoff in the basin, we assess the magnitude, frequency, and timing of daily flood events in the future. The results of the multi-model ensemble averaging (MMEA) method show that the change in the multi-year average annual runoff is −4.5% (2026–2050), −1.8% (2051–2075), and +2.0% (2076–2100) under the SSP245 scenario and −1.0% (2026–2050), +0.4% (2051–2075), and +0.2% (2076–2100) under the SSP585 scenario compared to the historical period. The analysis of flood magnitudes indicates that the basin will experience higher-magnitude floods in the future, with the largest increase rates of 61.9% and 66.4% for the 1-day maximum flows under the SSP245 and SSP585 scenarios, respectively. The flood return period is projected to be shorter in the future, and the 1-day maximum flows of a 100-year flood are expected to increase by 44.7% and 63.7% under the SSP245 and SSP585 scenarios, respectively. Furthermore, a significant shift in the flood timing is expected, with the highest frequency moving from July to August, representing a one-month lag compared to the historical period. Our findings suggest that the hydrological characteristics of the upper Heihe River basin may be significantly altered in the future due to the effects of climate change, resulting in floods with higher magnitudes and frequencies and different timings. Therefore, it is imperative to consider these changes carefully when developing risk prevention measures.

Exploring the relationship between urban flood risk and resilience at a high-resolution grid cell scale

The assessment of flood risk and resilience has become increasingly important in recent years for effective urban flood management. While flood resilience and risk are two distinct concepts with unique assessment metrics, there is lack of quantitative analysis and understanding of the relationship between them. This study aims to investigate this relationship at the grid cell level in urban areas. To assess flood resilience for high-resolution grid cells, this study proposes a performance-based flood resilience metric, which is calculated using the system performance curve based on flood duration and magnitude. Flood risk is calculated as the product of maximum flood depth and probability, considering multiple storm events. The case study of Waterloo in London, UK is analyzed using a two-dimensional cellular automata-based model CADDIES, which consists of 2.7 million grid cells (5 m × 5 m). The results indicate that over 2 % of grid cells have risk values exceeding 1. Furthermore, there is a 5 % difference in resilience values below 0.8 between the 200-year and 2000-year design rainfall events, specifically 4 % for the former and 9 % for the latter. Additionally, the results reveal a complex relationship between flood risk and resilience, though decreasing flood resilience generally leads to increasing flood risk. However, this relationship varies depending on the land cover type, with building, green land, and water body cells showing higher resilience for the same level of flood risk compared to other land uses such as roads and railways. Classifying urban areas into four categories, including high risk vs. low resilience, high risk vs. high resilience, low risk vs. low resilience, and low risk vs. high resilience, is crucial in identifying flood hotspots for intervention development. In conclusion, this study provides an in-depth understanding of the relationship between risk and resilience in urban flooding, which could help improve urban flood management. The proposed performance-based flood resilience metric and the findings from the case study of Waterloo in London could be valuable for decision-makers in developing effective flood management strategies in urban areas.

Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches

The Karnali River Basin (KRB) comprises the longest river in Nepal, located south of the Himalayas. Despite its high susceptibility to floods, the basin lacks detailed studies. Proper floodplain management is essential to reduce the impacts due to rising flood frequency, magnitude, and severity aggravated by climate change. This research applies three machine learning techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), to flood event data from the KRB. Ten flood conditioning factors; Aspect, curvature, distance to a river (DTR), normalized difference vegetation index (NDVI), elevation, slope, rainfall, soil, stream power index (SPI), and topographical wetness index (TWI) were selected based on the multicollinearity test. The parameter performance was evaluated using the Cohen Kappa Score, with NDVI having the greatest influence, followed by elevation, DTR, curvature, and TWI. Based on the Area Under the Curve of Receiver Operating Characteristics (AUROC), SVM outperformed RF, ANN, and ANN for FSM. The area of very high flood susceptible areas ranges from 0.8 to 2.5% of the basin area, most of them located in the south with low slopes and elevations. The results of this study suggest the use of SVM for FSM to help with proper floodplain management.

Rhine flood stories: Spatio‐temporal analysis of historic and projected flood genesis in the Rhine River basin

AbstractThe genesis of floods in large river basins often is complex. Streamflow originating from precipitation and snowmelt and different tributaries can superimpose and cause high water levels, threatening cities and communities along the riverbanks. For better understanding the mechanisms (origin and composition) of flood events in large and complex basins, we capture and share the story behind major historic and projected streamflow peaks in the Rhine River basin. Our analysis is based on hydrological simulations with the mesoscale Hydrological Model forced with both meteorological observations and an ensemble of climate projections. The spatio‐temporal analysis of the flood events includes the assessment and mapping of antecedent liquid precipitation, snow cover changes, generated and routed runoff, areal extents of events, and the above‐average runoff from major sub‐basins up to 10 days before a streamflow peak. We introduce and assess the analytical setup by presenting the flood genesis of the two well‐known Rhine floods that occurred in January 1995 and May 1999. We share our extensive collection of event‐based Rhine River flood genesis, which can be used in‐ and outside the scientific community to explore the complexity and diversity of historic and projected flood genesis in the Rhine basin. An interactive web‐based viewer provides easy access to all major historic and projected streamflow peaks at four locations along the Rhine. The comparison of peak flow genesis depending on different warming levels elucidates the role of changes in snow cover and precipitation characteristics in the (pre‐)Alps for flood hazards along the entire channel of the Rhine. Furthermore, our results suggest a positive correlation between flood magnitudes and areal extents of an event. Further hydro‐climatological research is required to improve the understanding of the climatic impact on the Rhine and beyond.

Leaky dams augment afforestation to mitigate catchment scale flooding

AbstractDespite calls for large‐scale afforestation to alleviate flooding, the effectiveness of such action remains unclear. Simulations with the SHETRAN hydrological model are therefore carried out for the 335‐km2 Irthing catchment and its 1‐10‐km2 headwater catchments in northwest England to determine: (a) whether forests can reduce flood peak discharges in a large catchment; (b) the proportion of the catchment that requires afforestation to be effective; and (c) the extent to which a combination of afforestation and natural flood management features (leaky dams) improves upon afforestation on its own. Four‐year simulations were run with a range of forest covers and extents of leaky dam installation, the latter modelled as a channel hydraulic resistance. Hydrograph, flood frequency and peak discharge magnitude responses to forest cover simulated (and observed) in the headwater catchments are replicated in simulations at the full scale. Afforestation on its own can reduce the frequency of given flood magnitudes but has a variable impact on individual peak discharge magnitudes. For the Irthing, a 76% forest cover reduces the mean discharge of 20 peaks in a partial duration flood series by 10% relative to the current 21% forest cover but reduces the largest peak by only 2.5%. Accompanying adverse effects include 17.5% reduction in long‐term runoff and loss of agricultural land. By contrast, leaky dams mitigate flood frequencies and peak discharges effectively, over a range of discharge magnitudes, with no reduction of annual runoff. The dam installation required for a 10% mean peak discharge reduction reduces the largest peak by 22%. The flow resistance increases by which the dams are simulated have still to be translated into specific dam designs. Nevertheless, and considering the figures indicatively, dam installation in 20% of the Irthing streams with Strahler orders of 1–3 achieves a 10% peak discharge reduction with only 35% forest cover and 7.5% runoff reduction. The study illustrates the potential for a dense network of leaky dams to augment the impact of afforestation on flood mitigation, especially at the largest discharges, while minimizing adverse impacts on water resources and food security.

Scenario-based modelling of changes in chemical intake fraction in Sweden and the Baltic Sea under global change

The climate in Europe is warming twice as fast as it is across the rest of the globe, and in Sweden annual mean temperatures are forecast to increase by up to 3–6 °C by 2100, with increasing frequency and magnitude of floods, heatwaves, and other extreme weather. These climate change-related environmental factors and the response of humans at the individual and collective level will affect the mobilization and transport of and human exposure to chemical pollutants in the environment. We conducted a literature review of possible future impacts of global change in response to a changing climate on chemical pollutants in the environment and human exposure, with a focus on drivers of change in exposure of the Swedish population to chemicals in the indoor and outdoor environment. Based on the literature review, we formulated three alternative exposure scenarios that are inspired by three of the shared socioeconomic pathways (SSPs). We then conducted scenario-based exposure modelling of the >3000 organic chemicals in the USEtox® 2.0 chemical library, and further selected three chemicals (terbuthylazine, benzo[a]pyrene, PCB-155) from the USEtox library that are archetypical pollutants of drinking water and food as illustrative examples. We focus our modelling on changes in the population intake fraction of chemicals, which is calculated as the fraction of a chemical emitted to the environment that is ingested via food uptake or inhaled by the Swedish population. Our results demonstrate that changes of intake fractions of chemicals are possible by up to twofold increases or decreases under different development scenarios. Changes in intake fraction in the most optimistic SSP1 scenario are mostly attributable to a shift by the population towards a more plant-based diet, while changes in the pessimistic SSP5 scenario are driven by environmental changes such as rain fall and runoff rates.

Rooftop water harvesting for managed aquifer recharge and flood mitigation in tropical cities: Towards a strategy of co-benefit evaluations in João Pessoa, northeast Brazil

Intense urbanisation in many coastal areas has led to intensification of groundwater consumption, while reducing permeable areas and increasing the frequency and magnitude of flooding. Among the potential strategies to compensate for these adverse effects, which are expected to become worse as a result of climate change, rooftop rainwater harvesting (RWH) in combination with managed aquifer recharge (MAR), may be indicated. This work investigated the performance of different configurations of such a system, tested as a twofold sustainable stormwater and domestic water management tool in a tropical metropole (João Pessoa, Brazil). This area located over a sedimentary aquifer system illustrates the water security challenges of densely urbanised areas in southern cities. To that end, several configurations of rooftop catchments and storage volumes were evaluated, by simulating a MAR-RWH system connected to the regional unconfined aquifer (Barreiras Formation) through a 6″ diameter injection well. Rainfall-runoff-recharge processes and water balances were simulated using monitored high-temporal resolution rainfall data. The results showed that catchments ranging from 180 to 810 m2, connected to tanks from 0.5 to 30.0 m³, are the optimal solutions in terms of efficient rainwater retention and peak flow reduction. These solutions provided mean annual estimates of aquifer recharge between 57 and 255 m³/yr from 2004 to 2019. The results of this study highlight the opportunity for MAR schemes to reconcile stormwater management and water supply goals.

Nexus between climate change and environmental social work in Africa

Disasters indiscriminately cause huge global economic losses worth tens of billions of dollars and often result in deaths of millions of people annually. As such, the on-going climate change is the world’s biggest disaster, especially for the most vulnerable African countries that are less economically developed, dependent largely on primary industries, which are predominantly reliant on climate-controlled ecosystems as well as depend on rain-fed agro-based livelihoods characterised by abject poverty. Meanwhile, the social work profession caters for individuals, groups, and communities in responding to fast onset disasters affecting especially the marginalised, poor, and vulnerable. However, social work practice (SWP) seems to be more preoccupied with the outcomes of fast-onset disasters specifically centred on identifying beneficiaries in a bid to provide food relief, social safety nets, safe shelters, and providing psycho-social support to victims and survivors of these disasters than slow onset ones. The SWP seems to be silent about climate change despite corroborated scientific models confirming the increased frequency, magnitude, and severity of future tropical cyclones, floods, and droughts in the future. However, being more curative rather than preventive, contemporary SWP responds primarily to present crises whilst ignoring more serious future disasters. To connect SWP to climate change discourse, a literature review of previous sources of authority was utilised.
 How to reference using ASWNet style:
 Pedzisai E, Charamba S. and Mukurazhizha R. (2023). Nexus between climate change and environmental social work in Africa. African Journal of Social Work, 13(2), 87-99. https://dx.doi.org/10.4314/ajsw.v13i2.5
 Visit journal website: https://ajsw.africasocialwork.net

Climate change and hydrological dam safety: a stochastic methodology based on climate projections

ABSTRACT Climate change will likely increase the frequency and magnitude of extreme precipitation events and floods, increasing design peak flows that could lead to underestimates in current spillway capacity. Therefore, new methodologies for hydrological dam safety assessment considering climate change are required. This study presents a methodology that considers the impact of climate change on both inflow hydrographs and initial reservoir water levels. Moreover, the uncertainty in the procedure is assessed. The methodology is applied to the Eugui Dam in the River Arga catchment (Spain). An ensemble of 12 climate models is used. The results show an increase in the maximum reservoir water level during flood events and in the overtopping probability in the Representative Concentration Pathway 8.5 (RCP 8.5 scenario), especially in the 2071–2100 time window. The proposed methodology can be useful to assess future hydrological dam safety, fulfilling the requirements of recent regulations to consider the impact of climate change on dams.

Flood risk and shelter suitability mapping using geospatial technique for sustainable urban flood management: a case study in Palembang city, South Sumatera, Indonesia

ABSTRACT The populous city of Palembang is one of the most flood-prone cities in the Indonesian region. After some decades, the magnitude, duration, and frequency of floods have increased. Thus, this study aimed to develop flood risk and flood shelter suitability maps using the Analytic Hierarchy Process (AHP) and Geographical Information System (GIS) integration. Several flood-related factors that used in this study such as elevation, population, slope, land cover, distance from a river, drainage density, distance to a road, distance to a settlement, and soil type. Results found that the flood risk map of the study area was divided into three classes; 30.3% of the area was at high flood risk, while 60.5% was at moderate risk, and 9.2% of the area was at low flood risk. Moreover, flood shelter suitability assessments revealed that approximately 4.1% of flood shelters were highly suitable, 19.4% were moderately suitable, 60.5% were lowly suitable, and 16.1% were very lowly suitable. The highest suitability areas were predominantly found on the northwest and north sides of study area which were higher in elevation (ranging 13–41 m) and farther from the river. They could be assumed to be good choices for flood shelters.

Chronology and paleoclimatic implications of the upper Ganga catchment floods since Marine Isotopic Stage-2

The present study aims at understanding the chronology and pattern of extreme flood events in the upper Ganga catchment. Towards this, we explored relict alluvial sequences, particularly the sand bodies overlying the fluvial gravels and underlain by the source proximal alluvial fans and debris flows, supported by geochemistry, and optical chronology. Based on the accretionary pattern of sand bodies, the progressive increase in the flood magnitude since the beginning of Marine Isotopic Phase-2 (MIS-2) has been identified. The oldest flood is dated to 30 ± 4 ka and the subsequent floods are dated to the post-glacial transitional climate (16 ± 4 ka) and during the early Holocene (between 14 ± 2 ka and 12 ± 2 ka), and the late mid-Holocene (7 ± 1 ka). Based on the temporal changes in the major element geochemistry (detrital and weathering proxies) the study indicate that during early to mid-MIS-2, the upper Ganga catchment witnessed high frequency-low magnitude floods. Further, the absence of flood sediment, the development of calcrete, and a sharp decrease in the detrital proxies, a significant reduction in high-magnitude floods during the Last Glacial Maximum (LGM) is inferred. The isotopic fingerprinting, based on 87Sr/86Sr and ɛNd(0), indicates that the majority of the floods during the MIS-2 originated from the Higher Himalayan lithologies, whereas the inner Lesser Himalayan lithology dominates the mid to late Holocene floods. Climatically, the floods were triggered neither during too-wet nor too-dry climatic conditions, but instead during the transitional climate caused due to the mesoscale atmospheric coupling between Indian Summer Monsoon (ISM) and mid-latitude westerlies.

Flood Frequency Analysis of Jiadhal River Basin, India using Log Pearson Type III Distribution Method

Flood is considered as the most widespread and disastrous natural calamity in the world which adversely affects the socio-economic condition and lives of the people living within the floodplain areas of the world. The Jiadhal River basin of Dhemaji district, Assam experiences a high magnitude of flood almost every year, leaving the people of that area in jeopardy. In this regard, flood frequency analysis is considered an important step for proper planning and managing the region in terms of floods. Flood frequency analysis (FFA) is one of the utmost important and necessary measures taken to develop any floodplain region. In this present study, a FFA of the Jiadhal River Basin is carried out using the Log Pearson Type III (LP3) probability distribution method which is used to predict future flood events based on historical records. The data used in the study was for a period of 46 years. The method was used to predict the flood discharge estimations at 2, 10, 25, 50 and 100 years recurrence period. The result says that after 10 years the return period will surpass the water-carrying capacity of the Jiadhal River.

Probabilistic Conditioning and Recalibration of an Event-Based Flood Forecasting Model Using Real-Time Streamflow Observations

Flood warnings provide information about the timing and magnitude of impending floods, which can help mitigate the adverse impacts of flooding. Flood forecasts are highly influenced by uncertainty associated with rainfall forecasts as well as initial catchment wetness. Event-based models are simple and parsimonious and are widely favored by practitioners for flood estimation. However, these models require loss parameters to be manually specified for each simulated event, and this represents an additional source of uncertainty that needs to be considered along with errors in observations and rainfall forecasts. Little attention has been given to the coupling of updating techniques with event-based models to reduce the uncertainty associated with catchment wetness. To this end, we devised a sequential recalibration scheme to characterize uncertainty in ensemble forecasts derived using an event-based flood model. This scheme uses information on both observation and model errors to filter and update catchment loss estimates to improve the accuracy of the forecasts. Analysis of flood forecasts for 22 events showed that although initially, there was low skill in forecasts derived solely from external estimates of catchment wetness, the reliability and accuracy of the forecasts improved rapidly once the flood event commenced and the flood model was coupled with an updating scheme. Compared with forecasts made without any updating scheme, the conditioning and recalibration steps progressively improved the accuracy of the forecasts as measured by Nash-Sutcliffe efficiency from −0.14 to 0.88, bias was reduced by 78%, and root-mean square error reduced by 67%. The use of such schemes thus reinforces the advantages of using parsimonious models that have long been favored by practitioners for design and other purposes.

Flexible and consistent Flood–Duration–Frequency modeling: A Bayesian approach

Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Many hydrologic applications where flood retention is important, e.g. floodplain management and reservoir design, need design flood values for different durations. Flood–Duration–Frequency (QDF) models extend the standard statistical flood frequency analysis framework to multiple flood durations and are analogous to intensity–duration–frequency models for precipitation. Implementations of QDF models commonly assume simple scaling, where only the magnitude of the index flood is assumed to change with duration, despite empirical analyses showing a more complex dependence structure. We propose a multiscaling extension to existing QDF models where the magnitude of the index flood and the slope of the growth curve may scale independently with duration. In an application to 12 locations in Norway, we assess how three different QDF models capture relationships between floods of different duration. Incorporating duration dependency independently in both the index flood and the growth curve (extended QDF model) improves modeling of both short-duration events and events with long return periods. This model extension further expands the models’ ability to simultaneously model a wide range of durations. As measured by the integrated quadratic distance, the extended QDF model performs better than the original QDF model in 83% of the out of sample subdaily durations studied. Additionally, we find that the choice of durations used to fit QDF models is a highly influential aspect of the modeling process.

Large wood loads in channels and on floodplains after a 500-year flood using UAV imagery in Mark Twain National Forest, Ozark Highlands, Missouri

Anthropogenic climate change has increased the frequency of large floods in rivers draining the Ozark Highlands. This study assesses the effects of a > 500-yr flood in spring 2017 on riparian forests and large wood loads in the North Fork of the White River watershed, Missouri, for six stream reaches with drainage areas from 5 to 124 km2. Standing trees and large wood (LW) were assessed using unmanned aerial vehicle (UAV) imagery and calibrated by field surveys. Scaled flood magnitude (flood stage/bankfull depth) correlated with percent urban and agricultural land above each reach suggesting that land use may have contributed to forest damage. Canopy loss on the valley floor ranged from 7 to 63 % by reach and correlated with mean and cross-sectional stream power (p < 0.01). Standing tree density after the flood ranged from 50 to 243 trees/ha. The density of LW pieces ranged from 25 to 147 trees/ha. Most LW was aligned with stream flow, not in jams, and located on floodplains below riffles or bar heads, along channel bends, or in chutes. Wood loads on the valley floor increased downstream from 12 to 45 m3/ha. Channel loads were < 30 m3/ha while floodplain, terrace, and chute loads were > 30 m3/ha at drainage areas >50 km2. Channel LW loads increased with flood magnitude and in narrow valleys (p < 0.02), but not drainage area. Increased wood storage occurred on floodplains and terraces, but it is not clear if the stored wood will be available for downstream transport by future floods.