Large Flood Events Research Articles

Understanding the implications of climate change for Australia’s surface water resources: Challenges and future directions

Climate change is altering Australia’s streamflow and water resources due to a complex interplay of shifts in temperature, rainfall, evaporation, vegetation dynamics, soil moisture, and rainfall-runoff partitioning. Meanwhile increases in human activity and urbanisation continue to alter the landscape, further affecting streamflow and flood volumes. Understanding the nature of these recent changes is critical for evaluating future risks and ensuring planning decisions are sustainable. Positively, our knowledge of changes to Australia’s hydrologic regimes has greatly advanced in the last decade. Here we review our current understanding of historical hydrologic changes, focusing first on the drivers of change and then observed changes in streamflow and flooding, before turning our attention to the role that historical observations can play in anticipating future climate change impacts. We conclude with recommendations to help better understand and plan for an unknown future.Increases in extreme rainfalls have increased the risk of large flood events. However, declines in mean rainfall across large parts of the continent, increases in diversions, and increasing evaporate demand have led to an overall drying. Significant advances in our understanding have been made through analysing trends in the observational record and investigating the associated drivers. While these learnings may not reliably inform the potential effects of future change, they can still inform process understanding ultimately helping accurately model and project future impacts. Projecting climate change impacts on water resources in Australia remains challenging, and progress will require improved instrumentation to observe and understand catchment responses together with methods and management techniques that incorporate this additional uncertainty. We argue it is through process-informed models together with multi-scale observational data that this gap can be breached.

A comparative study of optical and size properties of dissolved organic matter in the lower Mississippi River and Pearl River

Monthly water samples were collected from the lower Mississippi and Pearl Rivers between January 2009 and August 2011 to investigate the heterogeneity in the dynamic variations of dissolved organic carbon (DOC), colloidal organic carbon, chromophoric and fluorescence dissolved organic matter (CDOM and FDOM), PARAFAC-derived fluorescent components, and other optical properties including spectral slope, specific UV absorbance (SUVA), and fluorescence indices between the two contrasting river systems. The lower Mississippi River exhibits relatively lower concentrations of DOC (306 ± 41 μM C) and CDOM (27.9 ± 5.7 m−1 at 254 nm), featuring lower aromaticity (indicated by SUVA254) and apparent molecular weight (or higher spectral slope) with weak seasonal variability. The Pearl River, in contrast, has elevated levels of DOC (537 ± 212 μM C) and CDOM (66.4 ± 31.4 m−1), characterized by higher aromaticity, higher molecular weight, and significant seasonality, primarily originating from soil-derived allochthonous sources. The abundance of the >1 kDa colloidal DOC was, on average, 58 ± 3 % of the bulk DOC in the lower Mississippi River and 68 ± 6 % in the lower Pearl River. The >1 kDa high-molecular weight DOM (HMW-DOM) consistently had lower spectral slope and biological index (BIX) values, but higher humification index (HIX) values compared to both bulk DOM and low-molecular-weight DOM (LMW-DOM) counterparts. These trends could be representative of other similar large and small rivers. Four PARAFAC-derived fluorescent components (three humic-like and one protein-like) were identified for both rivers. A positive correlation between discharge and terrestrial humic-like fluorescent components indicated their dominant allochthonous sources, while the protein-like component decreased with increasing discharge, consistent with its autochthonic source and a dilution effect during high flow seasons. The occurrence of large flood events during the sampling period contributed to large DOC pulses, with DOM of higher aromaticity and HMW-DOM. This has important implications for coastal ocean ecosystems, which are increasingly impacted by river flooding events under changing climate conditions. Our results also provide an improved understanding of DOM dynamics in two representative rivers and establish a baseline dataset for future studies to assess changes in sources and composition of DOM and their impacts on the coastal ocean in response to climate, hydrological, and anthropogenic influences.

The Role of Talus Pile Mobility in Valley Widening Processes and the Development of Wide Bedrock Valleys, Buffalo River, AR

AbstractValley width is largely controlled by lithology and upstream drainage area, but little work has focused on identifying the processes through which valleys widen. Bedrock valleys widen by first laterally eroding bedrock valley walls, followed by the collapse of overlying bedrock material that must then be transported away from the valley wall before the valley can continue widening. We hypothesize that talus piles that cannot be transported by the river protect the valley wall and slow valley widening, while talus piles that are rapidly transported allow for uninterrupted valley widening. We used field measurements from 40 locations in both wide and narrow valleys along the Buffalo River, AR to test this hypothesis. Our data show that wide valleys tend to have fewer talus piles and smaller talus grain sizes, whereas talus in narrow valleys is larger in size and more continuous along valley walls. We calculated potential talus block entrainment at each site location and found that talus blocks in wide valleys are potentially entrained and moved away from valley walls during moderate and large flood events, whereas talus blocks in narrow valleys are very rarely moved. Our results show that the potential transport of talus piles protecting bedrock valley walls from widening is controlled by the block size of collapsed bedrock wall material relative to stream competency. Our results also suggest that persistence versus mobility of collapsed talus piles is an important process in the development of wide bedrock valleys.

What controls river widening? Comparing large and extreme flood events

AbstractExtreme (i.e., centennial‐scale) floods are by definition rare and can therefore be difficult to study. As a result, case studies of response to extreme floods can provide unique insights. On 15 November 2021, the Nicola River in British Columbia, Canada, experienced a 200‐year flood that increased the average width of the Nicola River by more than 50% (35 m), leaving the only highway in the region impassable for nearly 12 months. This research assesses the spatial variability of erosion along a 71‐km‐long segment of the Nicola River during a period with a large flood (2015–2018) and a second period containing the extreme flood in 2021 (2018–2021). We use a random forest statistical analysis to explore the most important valley and channel characteristics affecting relative widening during both periods. Unit stream power, gradient and valley confinement were the primary determinants of erosion during the extreme flood, whereas vegetation cover, channel pattern and surficial material were less important. Erosion during the large flood event was not related to these variables, with channel pattern providing a better indication of widening potential. As the Nicola River is flanked by erodible glacial deposits, this work provides important insight into river response in the paraglacial environments that are common throughout much of Canada and the northern United States, as well as Europe and Asia, but less intensely studied. In these settings, the geomorphic response to large floods may not be representative of the potential erosion (and infrastructure impacts) that can occur during extreme events, which destabilize glacial terraces in confined reaches. Historical observations should therefore be used with caution in paraglacial settings as extreme events may not be captured in available data, creating a perception of stability in confined reaches that may have the potential to erode dramatically during extreme events.

Temporal and spatial water quality impacts of point-source versus catchment-derived nitrogen loads in an urbanised estuary

The Brisbane River estuary is an anthropogenically-impacted waterway in southeast Queensland, Australia. The estuary is over 80 km long and flows through an urbanised region. It receives over 500 t per year of total nitrogen (N) from direct point-source discharges in addition to sporadic flood loads of N from an agriculturally impacted upper catchment. Comprehensive water quality monitoring data for the estuary have been collected from at least 2001. This monitoring data includes ambient nutrient concentrations in the estuary, nutrient concentration and volume of the catchment inflows, and nutrient concentration and volume of point source discharges. This long-term data from a range of sources was used to determine temporal and spatial variations in concentrations, forms, stores and loads of N along the estuary for the period 2001 to 2022. Results showed that, during low-flow periods, the store of N in the mid-upper estuary (33–81 km upstream) is significantly determined by point-source discharges to this reach, and therefore the store of N can be modelled. Model parameters are the daily point source loads, a point source load decay factor, and a background constant store. In the lower estuary (0–33 km upstream) N store can be accurately determined based on dilution with seawater, with point sources not having significant influence on total N in the reach. Total N from large flood events was found to largely pass through the estuary without detectable removal processes, delivering catchment derived N directly to coastal waters. This work informs potential application of nutrient offsets in the estuary, guiding where and when offset options will be effective to mitigate the water quality impacts of point-source nutrients.

Historical catastrophic floods at the southern edge of the Atacama Desert: A multi-archive reconstruction of the Copiapó river extreme events

The last hydrometeorological extreme event that caused large floods in the southern Atacama Desert in March 2015 raised concern about how little was known about the fluvial dynamic of these arid basins. Understanding the response of intermittent and ephemeral rivers in drylands to the present context of global change is critical to preserve the ecological and human systems they support, to sustainably manage their scarce water resources and to develop flood risk management plans. We have studied the instrumental and historical record and explored the potential of the Copiapó River geological record in the comprehension of how extraordinary the 2015 flood was and how its fluvial dynamic relates with global climate oscillations. We have identified 36 flood events that have occurred in the last 400 years: 22 of them have been classified as ordinary rises of the river flow (discharges <30 m3/s), 11 as extraordinary floods in which the damage is confined to areas adjacent to the river (discharges 30–180 m3/s), and only 3 as catastrophic floods (discharges >180 m3/s), including the 2015 flood event. The incorporation of the historical and palaeohydrological data into the flood frequency analysis results in an increase of the magnitude of the flood quantiles in which large flood events occur with an average recurrence interval of 120 years. Most of the flood events were caused by heavy rains that are largely linked to the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation with a superimposed effect of the ENSO. Discharges >30 m3/s, i.e., extraordinary and catastrophic floods, occur with positive phases of the PDO and the ENSO. Further exploration of the fluvial geological record of the Copiapó River will help lengthening to thousands of years the flood record what will help improving communities' resilience by anticipating flood hazards in the current global change context, in which stronger rainfall events modulated by ENSO and ENSO-like conditions are expected.

Multi-objective calibration and uncertainty analysis for the event-based modelling of flash floods

ABSTRACT This study investigates the best approach to calibrate an event-based conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model, comparing different trials of single-objective, single-event multi-objective (SEMO), and multi-event-multi-objective (MEMO) model calibrations using root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), and Bias as objective functions. Model performance was validated for several peak events via 90% confidence interval (CI)-based output uncertainty quantification of relative error of discharges. Multi-objective optimization yielded more accurate and robust solutions compared to single-objective calibrations. Ensembles of Pareto solutions from the multi-objective calibrations better characterized the flood peaks within the uncertainty intervals. MEMO calibration exhibited lower uncertainties and better prediction of peak events versus SEMO calibration. Moreover, the MEMO_6D (six-dimensional) approach outperformed the SEMO_3D and MEMO_3D in capturing the larger peak events. This study suggests that the MEMO_6D is the best approach for predicting large flood events with lower model output uncertainties when the calibration is performed with a better combination of peak events.

Morphologic Adjustment of a River Reach with Groynes to Channel Bypassing

AbstractThis article is focused on the investigation of the spatio-temporal variability of the Danube River reach’s vertical accretion thickness due to the response of the Danube River reach to bypassing. Five groyne-induced benches (GIBs) of the bypassed channel developed after water diversion in 1992 was studied by changes in topography for three-time spans (for the original gravel surface, for the surface before the 2013 flood and for the surface after the 2013 flood). The allostratigraphic approach was applied to 548 drilling probes at all GIBs and toptop, supra-platform, tail and backchannel geomorphic units have been identified at each GIB. The main to side-channel system connectivity increase sedimentation rates and the accretion was controlled by large flood events. The 100-year flood in 2013 contributed to the total volume by almost 26%. During study period 1992–2017, totally 1,146,589 m3 was accreted on five GIBs, of this 209,752 m3 during flood event in 2013 and 267,700 m3 in post flood period 2014–2017. The top geomorphic unit exhibits the highest median values of vertical accretion and for all GIBs accretion thickness are not related to the height above the mean channel water level. The thickness of accretion varied, likely because the variability of the vegetation cover caused variable hydraulic conditions and accretion rate span from 3.8 cm.year−1 to 5.3 cm.year−1. The investigation of the sediment thickness over long time spans 24 years and a large flood event) allowed us to conclude that the thickness is spatially variable for individual GIBs; however, its trend over time remains constants depending on the intake of sediments during large floodsd events. This article also provides a methodological template for the detailed investigation of river channel adjustment due to bypassing.

Flood prediction using nonlinear instantaneous unit hydrograph and deep learning: A MATLAB program

In this study, we developed a MATLAB program for flood prediction in a watershed. The program consists of three modules. The instantaneous unit hydrograph (IUH) generation module utilizes a power-law based interpolation method to generate IUHs. The generated IUH is a function of the rainfall excess intensity and therefore considers nonlinearity. The long short-term memory (LSTM) module employs “lstmLayer” from the MATLAB deep learning toolbox to predict total rainfall excess; this is then used to estimate the curve number (CN) value for each flood event. The LSTM module uses a land surface modeling dataset and rainfall-runoff data as inputs. The flood hydrograph generation module calculates effective rainfall hyetographs and then predicts flood hydrographs using a convolution integration. A detailed description of the program is provided along with an application example for real watersheds. The application results demonstrated that our program can be effectively used for flood prediction in practice, particularly for large flood events.

Enhancing flood event predictions: Multi-objective calibration using gauge and satellite data

Rainfall-runoff models are widely used for predicting watershed runoff. The accuracy of the predictions heavily relies on effective model calibration, which is majorly influenced by the employed objective functions that measure the agreement between predictions and real-world observations. In this study, we propose a novel multi-objective calibration approach with the aim of improving the flood event predictions of a conceptual rainfall-runoff model applied to a catchment in the Austrian Alps. To achieve this, we include a total of five carefully selected objective functions into calibration. Three of these objective functions can be regarded as more classical and are roughly based on comparing absolute deviations, square of the residuals, as well as bias, correlation and variability of the flow. Another objective function relies on remote observations of the snow covered area, for which the MODIS Terra (MOD10A1.061) and Aqua (MYD10A1.061) datasets are used. The final objective function measures the differences in peak magnitude and timing for the largest events in the observation period. To showcase the proposed approach’s effectiveness, we compare the calibration and validation results with those obtained by a model that was only calibrated for two runoff objective functions and to carefully selected benchmark cases. The presented study reveals that our approach enhances the model’s capacity to depict snow cover dynamics, a crucial factor in accurately predicting snow-driven flooding events. Additionally, the results underscore that incorporating an error metric targeting magnitude and timing errors of large flood events can enhance the model’s ability to predict peak runoff. The results of this study further suggest that in general a more robust model can be obtained by incorporating additional error metrics that specifically target physical states and individual parts of the runoff curve into model calibration.

Interannual controls on riparian plant health in a dryland river

AbstractRiparian zones in drylands provide important refugia for plants but depend on groundwater and thus are subject to local temporal and spatial variability in abiotic controls. In lieu of costly field‐based sampling, we used readily available data to establish site–scale interannual relationships among riparian plant health and the abiotic factors that control their water balance for a historically persistent wetland adjoining the Santa Clara River in southern California, USA. Non‐linear generalized additive model (GAM) analysis of plant health, represented using the normalized difference vegetation index (NDVI), confirmed robust relationships among plant health and various geomorphological and hydrological factors over multi‐decadal timeframes, including years since last high‐flow event, intra‐year groundwater elevation changes and magnitude of 2‐year cumulative surface water inflows. Geomorphic controls are related to years with high flows that cause extensive scour and deposition that re‐set riparian plant communities. Relationships with dry‐season groundwater declines reflect direct plant access to sub‐surface moisture. Hydrological dependence via cumulative inflow magnitude indicates the dependency of groundwater elevations on sufficient winter recharge to prevent precipitous groundwater decline. GAMs‐based inflection point analysis of surface water inflows versus groundwater elevations confirmed that the cumulative magnitude of multi‐year inflows is critical in avoiding catastrophic groundwater declines and that large flood events drive groundwater recovery. We show that abiotic controls on plant health can be derived from readily available data and that non‐linear analysis better represents the complexity of these scalar controls. Our analysis has relevance for ecosystem management of human‐altered rivers and climate change adaptation.

Flood hazard zonation using GIS-based multi-parametric Analytical Hierarchy Process

Flood is considered to be a serious environmental hazard, especially in the tropical countries owing to its devastating consequence on human life. Tripura, a small hilly state of northeast India has faced large scale flood events over the last few decades. The present study is an attempt to identify flood hazard zones along the lower course of the Dhalai River flowing through the Dhalai district of Tripura State. An integrated approach of remote sensing and GIS coupled together with Analytical Hierarchy Process (AHP) was applied to identify the flood hazard zones of the study area and nine parameters were selected for this purpose. Thematic maps of the parameters were reclassified after assigning ranks to different classes. A pair-wise comparison matrix among all the parameters was prepared using AHP to determine the relative weight of each parameter. Finally, flood hazard zoning (FHZ) map of the study area was prepared by multiplying the reclassified values with the weighted values using raster calculator of Arc GIS 10.1. The outcome of the study revealed that 109.69 km2 (27.65%) of the study area fall under low flood risk category. At the same time, around 114.46 km2 (28.85%) and 90.43 km2 (22.80%) areas fall under moderate and high flood risk zone respectively. The study also disclosed that the high risk zone has maximum concentration of agricultural land (68.63%) and settled area (9.77%) in comparison to the other two zones which has increased vulnerability of flood hazard. The results validated the efficiency of AHP technique in generating fast and cost effective information regarding flood hazard assessment, especially in no data regions. Hence, the derived information could be very much helpful for the planners to prepare proper strategies to reduce the vulnerability of this hazard.

Recovery of a subtropical headwater fish community following a large flood, Klaserie River, Limpopo River System, South Africa

AbstractHeadwaters are important refuges for threatened fishes and play an important role in their conservation. The effects of large flooding events on headwater fish assemblages are under studied in southern Africa. In January 2012, heavy rainfall resulted in a large flood in the upper Klaserie River, Olifants River, and Limpopo River System, South Africa. This flood had an estimated return level of 225 years and caused significant ecological and economic damage. This study aimed to explore the impact of a large flood on the fish assemblage and substrate in the headwaters of a subtropical stream. The fish communities and selected substrate variables were collected at 10 sites. Sites were sampled at six monthly intervals thrice before and thrice after the flood. Fish were collected by electrofishing and substrate quantified visually. Changes in habitat resulted from the flood included the scouring of gravel sand and mud, greater exposure of bedrock and boulder substrates, increased stream width, and decreased stream depth in all zones. The fish community showed an increase in abundance for all but three species, colonisation of upstream sites, and colonisation of the study area by six species. The majority of fish species have opportunistic life‐history traits, which could explain the rapid colonisation and increase in abundance. The flood occurred in the middle of the breeding season for most the fish species, and this resulted in an increase of abundance for these species and provided opportunities for upstream colonisation. The impact of late season and aseasonal large floods requires further study.

Global flood extent segmentation in optical satellite images

Floods are among the most destructive extreme events that exist, being the main cause of people affected by natural disasters. In the near future, estimated flood intensity and frequency are projected to increase. In this context, automatic and accurate satellite-derived flood maps are key for fast emergency response and damage assessment. However, current approaches for operational flood mapping present limitations due to cloud coverage on acquired satellite images, the accuracy of flood detection, and the generalization of methods across different geographies. In this work, a machine learning framework for operational flood mapping from optical satellite images addressing these problems is presented. It is based on a clouds-aware segmentation model trained in an extended version of the WorldFloods dataset. The model produces accurate and fast water segmentation masks even in areas covered by semitransparent clouds, increasing the coverage for emergency response scenarios. The proposed approach can be applied to both Sentinel-2 and Landsat 8/9 data, which enables a much higher revisit of the damaged region, also key for operational purposes. Detection accuracy and generalization of proposed model is carefully evaluated in a novel global dataset composed of manually labeled flood maps. We provide evidence of better performance than current operational methods based on thresholding spectral indices. Moreover, we demonstrate the applicability of our pipeline to map recent large flood events that occurred in Pakistan, between June and September 2022, and in Australia, between February and April 2022. Finally, the high-resolution (10-30m) flood extent maps are intersected with other high-resolution layers of cropland, building delineations, and population density. Using this workflow, we estimated that approximately 10 million people were affected and 700k buildings and 25,000 km^2 of cropland were flooded in 2022 Pakistan floods.

Gravimetry and hydrologic data to constrain the hydrodynamics of a karstic area: The Škocjan Caves study case

In this paper we employ a combination of gravity and hydrologic data to constrain a hydraulic model of the Škocjan Caves, an allogenic dominated karstic system in Slovenia. The gravity time-series recorded by a spring-based gravimeter, are carefully analyzed to remove tidal and non-tidal effects and unveil the local hydrologic contribution, which is influenced by the temporary accumulation of water in the cave system during the flood events of the Reka river.We make use of a combined analysis of three large flood events with peak river discharge of about 200, 230 and 300 m3/s, that caused significant water level and gravity variations sensed by the pressure transducer and by the gravimeter. By the integration of hydraulic modelling we study the different coupled gravimetric-hydrologic responses to these flood events: we show that, depending on the peak discharge and duration of the event, different flow conditions are present in the cave system. In addition to the information provided by the pressure transducer, the gravimeter is sensitive to the flow dynamics in a different sector of the cave due to the choice of its location; this configuration helps to better constrain the hydraulic model.Moreover, we find that the autogenic recharge by percolating water can significantly affect the gravity time-series and must be considered in related models. By inclusion of both the hydraulic model outcomes and of the modelling of the autogenic recharge, we are able to better explain the gravity transients during the two smaller magnitude events. In particular, during such events the autogenic contribution produces a transient gravity signal, which is about 4 times larger than the allogenic one, while during the largest flood the allogenic contribution drastically overcomes the autogenic effect by a factor 20.By discussing this case, we prove the potential of terrestrial gravity observation to depict the hydro-dynamics of these complex karstic systems as well as the potential of gravimetry to remotely monitor these storage units.

Nonstationary stochastic paired watershed approach: Investigating forest harvesting effects on floods in two large, nested, and snow-dominated watersheds in British Columbia, Canada

Drawing on advances in nonstationary frequency analysis and the science of causation and attribution, this study employs a newly developed nonstationary stochastic paired watershed approach to determine the effect of forest harvesting on snowmelt-generated floods. Moreover, this study furthers the application of stochastic physics to evaluate the environmental controls and drivers of flood response. Physically-based climate and time-varying harvesting data are used as covariates to drive the nonstationary flood frequency distribution parameters to detect, attribute, and quantify the effect of harvesting on floods in the snow-dominated Deadman River (878 km2) and nested Joe Ross Creek (99 km2) watersheds. Harvesting only 21% of the watershed caused a 38% and 84% increase in the mean but no increase in variability around the mean of the frequency distribution in the Deadman River and Joe Ross Creek, respectively. Consequently, the 7-year, 20-year, 50-year, and 100-year flood events became approximately two, four, six, and ten times more frequent in both watersheds. An increase in the mean is posited to occur from an increase in moisture availability following harvest from suppressed snow interception and increased net radiation reaching the snowpack. Variability was not increased because snowmelt synchronization was inhibited by the buffering capacity of abundant lakes, evenly distributed aspects, and widespread spatial distribution of cutblocks in the watersheds, preventing any potential for harvesting to increase the efficiency of runoff delivery to the outlet. Consistent with similar recent studies, the effect of logging on floods is controlled not only by the harvest rate but most importantly the physiographic characteristics of the watershed and the spatial distribution of the cutblocks. Imposed by the probabilistic framework to understanding and predicting the relation between extremes and their environmental controls, commonly used in the general sciences but not forest hydrology, it is the inherent nature of snowmelt-driven flood regimes which cause even modest increases in magnitude, especially in the upper tail of the distribution, to translate into surprisingly large changes in frequency. Contrary to conventional wisdom, harvesting influenced small, medium, and very large flood events, and the sensitivity to harvest increased with increasing flood event size and watershed area.

An Integrated Approach for Analyzing the Morphological Evolution of the Lower Reaches of the Minjiang River Based on Long-Term Remote Sensing Data

Understanding the evolution of river morphology is crucial for comprehending changes in water resources and implementing development projects along rivers. This study proposes an integrated approach utilizing remote sensing image data combined with deep learning and visual interpretation algorithms to analyze continuous-type changes in river morphology. This research focuses on the lower reaches of the Minjiang River in China and comprehensively analyzes the river’s morphological evolution from 1986 to 2021. The results show that the proposed method of river water identification in this study demonstrates high accuracy and effectiveness, with an F1 score and Kappa coefficient greater than 0.96 and 0.91, respectively. The morphology of the river channel remains stable in the upstream and estuarine sections of the study region while undergoing substantial alterations in the middle section. Additionally, this study also identifies several factors that significantly impact the evolution of river morphology, including reservoir construction, river sediment mining, river training measures, geological conditions, and large flood events. The findings of this study can provide some insights into the management and conservation of water resources.

Environmental fluid mechanics of minimum energy loss weirs: hydrodynamics and self-aeration at Chinchilla MEL weir during the November–December 2021 flood event

During the recent decades, a number of overflow embankment protection systems were implemented. One design is the Minimum Energy Loss (MEL) weir, developed to pass large flood events with minimum energy loss and low erosion. Several MEL weirs have successfully operated for decades in Australian catchments affected by heavy tropical and sub-tropical rainfalls with very flat gradients. Their historical performances are discussed and confirmed the design capability to pass large floods with small afflux and very small energy loss and little environmental impact including no significant erosion at the abutments. During a major flood, visual and quantitative observations were undertaken at the Chinchilla MEL weir. On the smooth converging chute, the observations showed that the inception of self‐aeration was a three‐dimensional process with a gradual change in free‐surface roughness, from a smooth glassy free-surface to a very-rough choppy surface. An optical technique (OF) was implemented to derive the contour maps of surface velocities based upon video movies taken from a sturdy tripod. The self-aerated flow velocity data were close to the backwater equation in the self‐aerated region, while highlighting regions of high- and low-velocities across the chute. The OF data showed large streamwise surface velocity fluctuations in the aerated flow region, consistent with the broad literature on self-aerated flow measurements. The ratio of transverse to streamwise surface turbulence intensity indicated a strong anisotropy of the free-surface turbulence.

Towards developing comparable optical and SAR remote sensing inundation mapping with hydrodynamic modelling

ABSTRACT Inundation mapping is an essential part of environmental monitoring, flood disaster management and risk mitigation. There are many earth observation sensors that can provide spatial information about inundation extent at different times. However, these extents need to be comparable to provide an accurate and consistent estimate of a flood’s progression. Monitoring inundation extent around the peak of a flood event is important because it captures the hazardous period during a large flood event, and it identifies connections between off-stream waterholes and flood waters for environmental monitoring. This paper presents results from a study comparing near-coincident flood inundation maps derived from optical (Landsat, MODIS, Sentinel-2 and VIIRS) and Synthetic Aperture Radar (SAR) (Sentinel-1 and NovaSAR-1) remote sensing imagery. We also compare all inundation maps derived from remote sensing data with near-coincident hydrodynamic (HD) modelling. The study was conducted for the Fitzroy floodplain in Western Australia, a large, complex and remotely located river basin. The results show that optical remote sensing data have average F1 scores ranging from 0.57 to 0.65 when compared to the HD model results, with Landsat and MODIS performing best. Sentinel-1 and NovaSAR-1 SAR have a poor agreement (average F1 score of 0.31 and 0.35 respectively) when compared to the HD model results, particularly within scattered vegetation adjacent to the river channel, with better results in open water on the floodplain and in the river. If comparisons are made only during the peak flood stage, the F1 scores improve for the optical data (0.61 up to 0.8). Comparisons of the remote sensing inundation maps show that the optical data are suitable for interchangeable mapping during large flood events.

Phytoplankton community response to a drought‐to‐wet climate transition in a subtropical estuary

AbstractArid subtropical climates often oscillate between drought and wet conditions, leading to a “flood or famine” paradigm for estuarine freshwater inflow, in which sporadic storm events drive dynamic changes in salinity and nutrient availability. Transitioning from prolonged drought to wet conditions can impact phytoplankton communities. The Mission‐Aransas Estuary, located on the south Texas coast, transitioned from a 5‐yr drought (2010–2015) to wet conditions (2015–2020), punctuated by several large flood events and the direct impact of category 4 Hurricane Harvey. Using an 8‐yr bimonthly sample set (2012–2019), we evaluated particulate organic carbon, chlorophyll a, nutrient concentrations, and accessory pigments to characterize the response of the phytoplankton community to these climate events. We found that phytoplankton biomass was diminished during severe drought and increased during prolonged wet conditions. The phytoplankton community switched from being diatom‐dominated during drought to cyanobacteria‐dominated following estuarine freshening, driven by lower salinity and increased nutrient availability. Seasonal fluctuations between taxa persisted regardless of climate condition. The drought‐to‐wet transition prompted a regime shift of the estuarine phytoplankton community to a new quasi‐steady state in the studied estuary. Globally, changing climate regimes may cause longer periods of extreme drought or wet conditions for estuarine systems. Detailed, long‐term ecosystem monitoring is necessary to fully evaluate ecological responses to extreme weather events, especially links between biogeochemical cycling and ecosystem function. These results suggest that oscillations between distinct wet and dry periods have lasting effects on primary productivity, phytoplankton community composition, and organic matter cycling in subtropical estuaries with long residence times.