Modelling of flood hazard extent in data sparse areas: a case study of the Oti River basin, West Africa

Study region: Terrain and hydrological data are scarce in many African countries. The coarse spatial resolution of freely available Shuttle Radar Topographic Mission elevation data and the absence of flow gauges on flood-prone reaches, such as the Oti River studied here, make flood inundation modelling challenging in West Africa. Study focus: A flood modelling approach is developed here to simulate flood extent in data scarce regions. The methodology is based on a calibrated, distributed hydrological model for the whole basin to simulate the input discharges for a hydraulic model which is used to predict the flood extent for a 140 km reach of the Oti River. New hydrological insight for the region: Good hydrological model calibration (Nash Sutcliffe coefficient: 0.87) and validation (Nash Sutcliffe coefficient: 0.94) results demonstrate that even with coarse scale (5 km) input data, it is possible to simulate the discharge along this region's rivers, and importantly with a distributed model, derive model flows at any ungauged location within basin. With a lack of surveyed channel bathymetry, modelling the flood was only possible with a parametrized sub-grid hydraulic model. Flood model fit results relative to the observed 2007 flood extent and extensive sensitivity testing shows that this fit (64%) is likely to be as good as is possible for this region, given the coarseness of the terrain digital elevation model

Microheated substrates for patterning cells and controlling development

Here, we seek to control cellular development by devising a means through which cells can be subjected to a microheated environment in standard culture conditions. Numerous techniques have been devised for controlling cellular function and development via manipulation of surface environmental cues at the micro- and nanoscale. It is well understood that temperature plays a significant role in the rate of cellular activities, migratory behavior (thermotaxis), and in some cases, protein expression. Yet, the effects and possible utilization of micrometer-scale temperature fields in cell cultures have not been explored. Toward this end, two types of thermally isolated microheated substrates were designed and fabricated, one with standard backside etching beneath a dielectric film and another with a combination of surface and bulk micromachining and backside etching. The substrates were characterized with infrared microscopy, finite element modeling, scanning electron microscopy, stylus profilometry, and electrothermal calibrations. Neuron culture studies were conducted on these substrates to 1) examine the feasibility of using a microheated environment to achieve patterned cell growth and 2) selectively accelerate neural development on regions less than 100$mu m$wide. Results show that attached neurons, grown on microheated regions set at 37$~^circ C$, extended processes substantially faster than those incubated at 25$~^circ C$on the same substrate. Further, unattached neurons were positioned precisely along the length of the heater filament (operating at 45$~^circ C$) using free convection currents. These preliminary findings indicate that microheated substrates may be used to direct cellular development spatially in a practical manner.$hfillhbox[1414]

An investigation into the impact of reservoir management Kerala floods 2018: A case study of the Kakki reservoir

The coastal state of Kerala, India experienced unprecedented levels of rainfall and flooding in August 2018, resulting in huge life and property loss. Since then the impact reservoir management may have had on the severity of the 2018 Kerala floods has been in question. This study presents a novel approach to developing a reservoir model using HECHMS and HEC-ResSim models, combined with satellite remote sensing data. In order to establish a link between flood severity and reservoir management, a model of the Kakki reservoir in southern Kerala was created. Simulations were carried out for six long term, two short term, and two immediate run cases. It was found that all cases except the immediate simulation run resulted in a reduced peak flow. The long simulation run, which altered the guide curve after the heavy rainfall occurring on 14th August 2018, while constraining the outflow, was found to produce the greatest reduction in peak outflow. The significant peak outflow reduction achieved suggests that improved reservoir management could have reduced the severity of the 2018 floods

Advancing global flood hazard simulations by improving comparability, benchmarking, and integration of global flood models

In recent years, a range of global flood models (GFMs) were developed, each utilizing different process descriptions as well as validation data sets and methods. To quantify the magnitude of these differences, studies assessed the performance of GFMs on the continental and catchment level. Since the default models set-ups resulted in locally marked deviations, there is a clear need for further and especially more standardized research to not only maintain credibility, but also support the application of GFM products by end-users. Consequently, we here outline the basic requirements and challenges of a Global Flood Model Validation Framework for more standardized model validation and benchmarking in the hope of encouraging the much needed debate, research developments in this direction, and involvement of science with end-users. By means of the framework, it is possible to streamline the data sets used for input and validation as well as the validation approach itself. By subjecting GFMs to more thorough and standardized methods, we think their quality as well as acceptance will increase as a result, especially amongst end-users of their outputs. Otherwise GFMs may only serve a purely scientific purpose of continued model improvement but without practical use. Furthermore, we want to invite GFM developers to make their models more integratable which would allow for representation of more physical processes and even more detailed comparison on a model component basis. We think this is pivotal to not only improve the accuracy of model input data sets, but to focus on the core of each model, the process descriptions. Only if we know more about why GFMs deviate, are we able to improve them accordingly and develop a next generation of models, not only providing first-order estimates of flood extent but supporting the global disaster risk reduction community with more accurate and actionable information

Establishing uncertainty ranges of hydrologic indices across climate and physiographic regions of the Congo River Basin

Study region The five drainage systems of the Congo River Basin in central Africa. Study focus This study aims to establish uncertainty ranges of hydrologic indices and to provide a basis for transferring hydrologic indices from gauged to ungauged sub-basins by identifying the most influential climate and physiographic attributes. New insights for this region Only limited information on individual sub-basins natural hydrology exists across the Congo River Basin, limiting the application of commonly used regionalization approaches for prediction in ungauged sub-basins. This study uses predictive equations for the hydrologic indices across all climate and physiographic regions based only on the aridity index. The degree of uncertainty in the derived uncertainty bounds is less than 41% for both Q10/MMQ and Q50/MMQ indices across the basin. A greater degree of uncertainty is associated with the runoff ratio and the Q90/MMQ indices. The uncertainty is assumed to be due to uncertainty in rainfall and evapotranspiration estimates, a lack of spatial representativeness of the available observed streamflow data and other factors (e.g., geology) that might control the hydrologic indices rather than the aridity index alone. The uncertainty ranges provide the first estimates of hydrologic indices that are intended to constrain the outputs from hydrologic models and appropriately quantify prediction uncertainty and risks associated with water resources decision making

An affordable, quality-assured community-based system for high-resolution entomological surveillance of vector mosquitoes that reflects human malaria infection risk patterns.

ABSTRACT: BACKGROUND: More sensitive and scalable entomological surveillance tools are required to monitor low levels of transmission that are increasingly common across the tropics, particularly where vector control has been successful. A large-scale larviciding programme in urban Dar es Salaam, Tanzania is supported by a community-based (CB) system for trapping adult mosquito densities to monitor programme performance. Methodology An intensive and extensive CB system for routine, longitudinal, programmatic surveillance of malaria vectors and other mosquitoes using the Ifakara Tent Trap (ITT-C) was developed in Urban Dar es Salaam, Tanzania, and validated by comparison with quality assurance (QA) surveys using either ITT-C or human landing catches (HLC), as well as a cross-sectional survey of malaria parasite prevalence in the same housing compounds. RESULTS: Community-based ITT-C had much lower sensitivity per person-night of sampling than HLC (Relative Rate (RR) [95% Confidence Interval (CI)] = 0.079 [0.051, 0.121], P < 0.001 for Anopheles gambiae s.l. and 0.153 [0.137, 0.171], P < 0.001 for Culicines) but only moderately differed from QA surveys with the same trap (0.536 [0.406,0.617], P = 0.001 and 0.747 [0.677,0.824], P < 0.001, for An. gambiae or Culex respectively). Despite the poor sensitivity of the ITT per night of sampling, when CB-ITT was compared with QA-HLC, it proved at least comparably sensitive in absolute terms (171 versus 169 primary vectors caught) and cost-effective (153US$versus 187US$ per An. gambiae caught) because it allowed more spatially extensive and temporally intensive sampling (4284 versus 335 trap nights distributed over 615 versus 240 locations with a mean number of samples per year of 143 versus 141). Despite the very low vectors densities (Annual estimate of about 170 An gambiae s.l bites per person per year), CB-ITT was the only entomological predictor of parasite infection risk (Odds Ratio [95% CI] = 4.43[3.027,7. 454] per An. gambiae or Anopheles funestus caught per night, P =0.0373). Discussion and conclusion CB trapping approaches could be improved with more sensitive traps, but already offer a practical, safe and affordable system for routine programmatic mosquito surveillance and clusters could be distributed across entire countries by adapting the sample submission and quality assurance procedures accordingly

Physical representation of hillslope leaky barriers in 2D hydraulic models: A case study from the Calder Valley

The resources of small-scale community-based flood risk action groups are often limited, hence studies to model and predict the effects of Natural Flood Management are often restrained by time and lack of empirical data to validate results. As a result, representations of hillslope leaky barriers are largely modelled as several equifinal approaches, often without survey data. The geometrical characteristics of hillslope leaky barriers were surveyed for the first time at Hardcastle Crags, Calder Valley. This data informed six 2D hydraulic model representation scenarios with varying combinations of topography modification and roughness increase, allowing the sensitivity of their results to be tested. Results from Scenario 3 (topography modification and roughness increase) estimated total hillslope runoff peak flow to reduce by 16.6% in a 1:1-year design return period; however, this reduction diminished as rainfall intensity increased. Return periods of over 1:30 year estimated peak flow reductions of <5%. Only 14.3%–21.7% (98–148 m3) of the total additional storage provided by the barriers is mobilised during simulated events. A multi-peaked rainfall event from December 2015 was also simulated. Although the initial peak flow was reduced by 22.7%, as storage became mobilised, effectiveness reduced significantly for subsequent peaks within the same event

Groundwater fluxes in a shallow seasonal wetland pond: The effect of bathymetric uncertainty on predicted water and solute balances

The successful management of groundwater dependent shallow seasonal wetlands requires a sound understanding of groundwater fluxes. However, such fluxes are hard to quantify. Water volume and solute mass balance models can be used in order to derive an estimate of groundwater fluxes within such systems. This approach is particularly attractive, as it can be undertaken using measurable environmental variables, such as; rainfall, evaporation, pond level and salinity. Groundwater fluxes estimated from such an approach are subject to uncertainty in the measured variables as well as in the process representation and in parameters within the model. However, the shallow nature of seasonal wetland ponds means water volume and surface area can change rapidly and non-linearly with depth, requiring an accurate representation of the wetland pond bathymetry. Unfortunately, detailed bathymetry is rarely available and simplifying assumptions regarding the bathymetry have to be made. However, the implications of these assumptions are typically not quantified. We systematically quantify the uncertainty implications for eight different representations of wetland bathymetry for a shallow seasonal wetland pond in South Australia. The predictive uncertainty estimation methods provided in the Model-Independent Parameter Estimation and Uncertainty Analysis software (PEST) are used to quantify the effect of bathymetric uncertainty on the modelled fluxes. We demonstrate that bathymetry can be successfully represented within the model in a simple parametric form using a cubic Bézier curve, allowing an assessment of bathymetric uncertainty due to measurement error and survey detail on the derived groundwater fluxes compared with the fixed bathymetry models. Findings show that different bathymetry conceptualisations can result in very different mass balance components and hence process conceptualisations, despite equally good fits to observed data, potentially leading to poor management decisions for the wetlands. Model predictive uncertainty increases with the crudity of the bathymetry representation, however, approximations that capture the general shape of the wetland pond such as a power law or Bézier curve show only a small increase in prediction uncertainty compared to the full dGPS surveyed bathymetry, implying these may be sufficient for most modelling purposes

Unknown risk: assessing refugee camp flood risk in Ethiopia

The number of global refugees has been rising annually for the last decade. Many of these refugees are housed within camps, in temporary structures, vulnerable to the impacts of flooding. The flood risk of refugees is not well understood. Flood risk guidance available for camp planners and managers is vague, and existing flood risk data is often lacking in the remote areas where camps are typically located. We show how global data should, and should not, be used to assess refugee flood risk in Ethiopia; a country hosting 725 000 refugees, primarily from four neighboring countries, in 24 camps. We find that global population (GP) datasets, typically used in national flood risk assessments, do not accurately capture camp populations (CPs). Even the most accurate GP datasets are missing three fifths of camp flood exposure. We propose, and test, alternative approaches for representing exposure that combine reported estimates of CP with data on camp area, building footprints, and population density. Applying these approaches in our national flood risk assessment, we find that 95.8% of camps in Ethiopia are exposed to flooding of some degree and between 143 208 (19.8%) and 182 125 refugees (25.2%) are exposed to a 1% annual exceedance probability flood (100 year return period). South Sudanese refugees are the nationality most exposed to flooding, but Eritrean refugees are the nationality most exposed to flooding with a high risk to life. Promisingly, we find that many camps may be set up in such a way that reduces the exposure of refugees to flooding. Our study demonstrates that global data, augmented with local data, can be useful for understanding the flood risk of refugee camps. The consistent scalable approach can be used as a first-order analysis of risk, identifying risk hotspots, and help to prioritize further detailed analyses to inform within-camp adaptation