Uncrewed aerial vehicles (UAVs), affordable precise global navigation satellite system hardware, multi-beam echo sounders, open-source 3D hydrodynamic modelling software, and freely available satellite data have opened up opportunities for a robust, affordable, physics-based approach to monitoring river flows. Traditional methods of river discharge estimation are based on point measurements, and heterogeneity of the river geometry is not contemplated. In contrast, a UAV-based system which makes use of geotagged images captured and merged through photogrammetry in order to generate a high-resolution digital elevation model (DEM) provides an alternative. This UAV system can capture the spatial variability in the channel shape for the purposes of input to a hydraulic model and hence probably a more accurate flow discharge. In short, the system can be used to produce the river geometry at greater resolution so as to improve the accuracy in discharge estimations. Three-dimensional hydrodynamic modelling offers a framework to establish relationships between river flow and state variables such as width and depth, while satellite images with surface water detection methods or altimetry records can be used to operationally monitor flows through the established rating curve. Uncertainties in the data acquisition may propagate into uncertainties in the relationships found between discharge and state variables. Variations in acquired geometry emanate from the different ground control point (GCP) densities and distributions used during photogrammetry-based terrain reconstruction. In this study, we develop a rating curve using affordable data collection methods and basic principles of physics. The basic principal involves merging a photogrammetry-based dry bathymetry and wet bathymetry measured using an acoustic Doppler current profiler (ADCP). The output is a seamless bathymetry which is fed into the hydraulic model so as to estimate discharge. The impact of uncertainties in the geometry on discharge estimation is investigated. The impact of uncertainties in satellite observation of depth and width is also analysed. The study shows comparable results between the 3D and traditional river rating discharge estimations. The rating curve derived on the basis of 3D hydraulic modelling was within a 95 % confidence interval of the traditional gauging-based rating curve. The 3D-hydraulic-model-based estimation requires determination of the roughness coefficient within the stable bed and the floodplain using field observation at the end of both the dry and wet season. Furthermore, the study demonstrates that variations in the density of GCPs beyond an optimal number have no significant influence on the resultant rating relationships. Finally, the study observes that which state variable approximation (water level and river width) is more accurate depends on the magnitude of the flow. Combining stage-appropriate proxies (water level when the floodplain is entirely filled and width when the floodplain is filling) in data-limited environments yields more accurate discharge estimations. The study was able to successfully apply advanced UAV and real-time kinematic positioning (RTK) technologies for accurate river monitoring through hydraulic modelling. This system may not be cheaper than in situ monitoring; however, it is notably more affordable than other systems such as crewed aircraft with lidar. In this study the calibration of the hydraulic model is based on surface velocity and the water depth. The validation is based on visual inspection of an RTK-based waterline. In future studies, a larger number of in situ gauge readings may be considered so as to optimize the validation process.@en

Droughts and changing rainfall patterns due to natural climate variability and climate change, threaten the livelihoods of Malawi's smallholder farmers, who constitute 80% of the population. Provision of seasonal climate forecasts (SCFs) is one means to potentially increase the resilience of rainfed farming to drought by informing farmers in their agricultural decisions. Local knowledge can play an important role in improving the value of SCFs, by making the forecast better-suited to the local environment and decision-making. This study explores whether the contextual relevance of the information provided in SCFs can be improved through the integration of farmers’ local knowledge in three districts in central and southern Malawi. A forecast threshold model is established that uses meteorological indicators before the rainy season as predictors of dry conditions during that season. Local knowledge informs our selection of the meteorological indicators as potential predictors. Verification of forecasts made with this model shows that meteorological indicators based on local knowledge have a predictive value for forecasting dry conditions in the rainy season. The forecast skill differs per location, with increased skill in the Southern Highlands climate zone. In addition, the local knowledge indicators show increased predictive value in forecasting locally relevant dry conditions, in comparison to the currently-used El Niño-Southern Oscillation (ENSO) indicators. We argue that the inclusion of local knowledge in the current drought information system of Malawi may improve the SCFs for farmers. We show that it is possible to capture local knowledge using observed station and climate reanalysis data. Our approach could benefit National Meteorological and Hydrological Services in the development of relevant climate services and support drought-risk reduction by humanitarian actors.

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Due to rising sea levels and projected socio-economic change, global coastal flood risk is expected to increase in the future. To reduce this increase in risk, one option is to reduce the probability or magnitude of the hazard through the implementation of structural, Nature-based or hybrid adaptation measures. Nature-based Solutions in coastal areas have the potential to reduce impacts of climate change and can provide a more sustainable and cost-effective alternative to structural measures. In this paper, we present the first global scale assessment of the benefits of conserving foreshore vegetation as a means of adaptation to future projections of change in coastal flood risk. In doing so, we extend the current knowledge on the economic feasibility of implementing global scale Nature-based Solutions. We show that globally foreshore vegetation can contribute to a large decrease in both absolute and relative flood risk (13% of present-day and 8.5% of future conditions in 2080 of global flood risk). Although this study gives a first proxy of the flood risk reduction benefits of conserving foreshore vegetation at the global scale, it shows promising results for including Nature-based and hybrid adaptation measures in coastal adaptation schemes.

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State-of-the-art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and their mutual dependence. In this article, we include the impact of these two factors in a computationally efficient probabilistic framework for flood risk calculation, using Ho Chi Minh City (HCMC) as a case study. HCMC can be flooded subannually by high tide, rainfall, and storm surge events or a combination thereof during the monsoon or tropical cyclones. Using long gauge observations, we stochastically model 10,000 years of rainfall and sea level events based on their monthly distributions, dependence structure and cooccurrence rate. The impact from each stochastic event is then obtained from a damage function built from selected rainfall and sea level combinations, leading to an expected annual damage (EAD) of $1.02 B (95th annual damage percentile of $2.15 B). We find no dependence for most months and large differences in expected damage across months ($36–166 M) driven by the seasonality of rainfall and sea levels. Excluding monthly variability leads to a serious underestimation of the EAD by 72–83%. This is because high-probability flood events, which can happen multiple times during the year and are properly captured by our framework, contribute the most to the EAD. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments.

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Small and medium-sized reservoirs play an important role in water systems that need to cope with climate variability and various other man-made and natural challenges. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. Multiple studies map large dams and analyze the dynamics of water stored in the reservoirs behind these dams, but very few studies focus on small and medium-sized reservoirs on a global scale. In this research, we use multi-annual multi-sensor satellite data, combined with cloud analytics, to monitor the state of small (10–100 ha) to medium-sized (> 100 ha, excluding 479 large ones) artificial water reservoirs globally for the first time. These reservoirs are of crucial importance to the well-being of many societies, but regular monitoring records of their water dynamics are mostly missing. We combine the results of multiple studies to identify 71,208 small to medium-sized reservoirs, followed by reconstructing surface water area changes from satellite data using a novel method introduced in this study. The dataset is validated using 768 daily in-situ water level and storage measurements (r2 > 0.7 for 67% of the reservoirs used for the validation) demonstrating that the surface water area dynamics can be used as a proxy for water storage dynamics in many cases. Our analysis shows that for small reservoirs, the inter-annual and intra-annual variability is much higher than for medium-sized reservoirs worldwide. This implies that the communities reliant on small reservoirs are more vulnerable to climate extremes, both short-term (within seasons) and longer-term (across seasons). Our findings show that the long-term inter-annual and intra-annual changes in these reservoirs are not equally distributed geographically. Through several cases, we demonstrate that this technology can help monitor water scarcity conditions and emerging food insecurity, and facilitate transboundary cooperation. It has the potential to provide operational information on conditions in ungauged or upstream riparian countries that do not share such data with neighboring countries. This may help to create a more level playing field in water resource information globally.

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Rapid modern technological advancements have led to significant improvements in river monitoring using unmanned aerial vehicles (UAVs), photogrammetric reconstruction software, and low-cost real-time kinematic Global Navigation Satellite System (RTK GNSS) equipment. UAVs allow for the collection of dry bathymetric data in environments that are difficult to access. Low-cost RTK GNSS equipment facilitates accurate measurement of wet bathymetry when combined with subaqueous measuring tools such as acoustic Doppler current profilers (ADCPs). Hydraulic models may be constructed from these data, which in turn can be used for various applications such as water management, forecasting, early warning and disaster preparedness by responsible water authorities, and construction of river rating curves. We hypothesise that the reconstruction of dry terrain with UAV-based photogrammetry combined with RTK GNSS equipment leads to accurate geometries particularly fit for hydraulic understanding and simulation models. This study sought to (1) compare open-source and commercial photogrammetry packages to verify if water authorities with low resource availability have the option to utilise open-source packages without significant compromise on accuracy; (2) assess the impact of variations in the number of ground control points (GCPs) and the distribution of the GCP markers on the quality of digital elevation models (DEMs), with a particular emphasis on characteristics that impact hydraulics; and (3) investigate the impact of using reconstructions based on different GCP numbers on conveyance and hydraulic slope. A novel method which makes use of a simple RTK tie line along the water edge measured using a low-cost but highly accurate GNSS is presented so as to correct the unwanted effect of lens distortion (“doming effect”) and enable the concatenation of geometric data from different sources. Furthermore, we describe how merging of the dry and wet bathymetry can be achieved through gridding based on linear interpolation. We tested our approach over a section of the Luangwa River in Zambia. Results indicate that the open-source software photogrammetry package is capable of producing results that are comparable to commercially available options. We determined that GCPs are essential for vertical accuracy, but also that an increase in the number of GCPs above a limited number of five only moderately increases the accuracy of results, provided the GCPs are well spaced in both the horizontal and vertical dimension. Furthermore, insignificant differences in hydraulic geometries among the various cross sections are observed, corroborating the fact that a limited well-spaced set of GCPs is enough to establish a hydraulically sound reconstruction. However, it appeared necessary to make an additional observation of the hydraulic slope. A slope derived merely from the UAV survey was shown to be prone to considerable errors caused by lens distortion. Combination of the photogrammetry results with the RTK GNSS tie line was shown to be essential to correct the slope and made the reconstruction suitable for hydraulic model setup.@en

The original version of this Article contained an error in the Data Availability section where “All new data and code generated in this research are available under the terms of Creative Commons BY 4.0 (for the data) and Apache 2.0 (for the code) licenses. Datasets and supplementary materials generated during this study are accessible from the TODO: upload and add Zenodo link here. The source code used to produce datasets is accessible from: https://github.com/global-water-watch/research-reservoir-water-dynamics. For more information about this research and to access the demo app visit: https://globalwaterwatch.earth.” now reads: “All new data and code generated in this research are available under the terms of Creative Commons BY 4.0 (for the data) and Apache 2.0 (for the code) licenses. Datasets and supplementary materials generated during this study are accessible from the supplementary materials document below. The source code used to produce datasets is accessible from: https://github.com/global-water-watch/research-reservoir-water-dynamics. For more information about this research and to access the demo app visit: https://globalwaterwatch.earth.” The original Article has been corrected.

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The rapid development of free and open-access hydrological models and coupling framework tools continues to present more opportunities for coupled model development for improved assessment of floodplain hydrology. In this study, we set up an Upper Zambezi hydrological model and a fully spatially hydrological-hydrodynamic coupled model for the Barotse Floodplain using GLOFRIM (GLObally applicable computational FRamework for Integrated hydrological– hydrodynamic Modelling). The hydrological and hydrodynamic models used are WFLOW and LISFLOOD-FP, respectively. The simulated flows generated by the wflow model for the upstream gauge stations before the Barotse Floodplain were quite similar and closely matched the observed flow as indicated by the evaluation statistics; Chavuma, nse = 0.738; kge = 0.738; pbias = 2.561 and RSR = 0.511; Watopa, nse = 0.684; kge = 0.816; pbias = 10.577 and RSR = 0.557; and Lukulu, nse = 0.736; kge = 0.795; pbias = 10.437 and RSR = 0.509. However, even though the wflow hydrological model was able to simulate the upstream hydrology very well, the results at the floodplain outlet gauge stations did not quite match the observed monthly flows at Senanga gauge station as indicated by the evaluation statistics: nse = 0.132; kge = 0.509; pbias = 37.740 and RSR = 0.9233. This is mainly because the representation of both floodplain channel hydrodynamics and vertical hydrological processes is necessary to correctly capture floodplain dynamics. Thus, the need for an approach that saves as a basis for developing fully spatially distributed coupled hydrodynamic and hydraulic models’ assessments for groundwater dependent tropical floodplains such as the Barotse floodplain, in closing the gap between hydrology and hydrodynamics in floodplain assessments. A fully coupled model has the potential to be used in implementing adaptive wetland management strategies for water resources allocation, environmental flow (eflows), flood control, land use and climate change impact assessments.

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Floodplain wetlands are a fundamental part of the African continent’s ecosystem and serve as habitat for fish and wildlife species, biodiversity, and micro-organisms that support life. It is generally recognised that wetlands are and remain fragile ecosystems that should be subject to sustainable conservation and management through the use of sustainable tools. In this paper, we propose a synthesis of the state of art concerning coupled hydrologic and hydraulic models for floodplains assessments in Africa. Case studies reviewed in this paper have pointed out the potential of applying coupled hydrologic and hydraulic models and the opportunities present to be used in Africa especially for data scarce and large basin for floodplain assessments through the use of available open access models, coupling frameworks and remotely sensed datasets. To our knowledge this is the first case study review of this kind on this topic. A Hydrological model coupled with Hydraulic Model of the floodplain provides improvements in floodplain model simulations and hence better information for floodplain management. Consequently, this would lead to improved decision-making and planning of adaption and mitigation measures for sound floodplain wetland management plans and programmes especially with the advent of climate change and variability.

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Exposure to coastal flooding is increasing due to growing population and economic activity. These developments go hand-in-hand with a loss and deterioration of ecosystems. Ironically, these ecosystems can play a buffering role in reducing flood hazard. The ability of ecosystems to contribute to reducing coastal flooding has been emphasized in multiple studies. However, the role of ecosystems in hybrid coastal protection (i.e. a combination of ecosystems and levees) has been poorly quantified at a global scale. Here, we evaluate the use of coastal vegetation, mangroves, and marshes fronting levees to reduce global coastal protection costs, by accounting for wave-vegetation interaction.The research is carried out by combining earth observation data and hydrodynamic modelling. We show that incooperating vegetation in hybrid coastal protection results in more sustainable and financially attractive coastal protection strategies. If vegetated foreshore levee systems were established along populated coastlines susceptible to flooding, the required levee crest height could be considerably reduced. This would result in a reduction of 320 (range: 107-961) billion USD2005 Power Purchasing Parity (PPP) in investments, of which 67.5 (range: 22.5- 202) billion USD2005 PPP in urban areas for a 1 in 100-year flood protection level.@en

Distributed hydrological models rely on hydrography data such as flow direction, river length, slope and width. For large-scale applications, many of these models still rely on a few flow direction datasets, which are often manually derived. We propose the Iterative Hydrography Upscaling (IHU) method to upscale high-resolution flow direction data to the typically coarser resolutions of distributed hydrological models. The IHU aims to preserve the upstream-downstream relationship of river structure, including basin boundaries, river meanders and confluences, in the D8 format, which is commonly used to describe river networks in models. Additionally, it derives representative sub-grid river length and slope parameters, which are required for resolution-independent model results. We derived the multi-resolution MERIT Hydro IHU dataset at resolutions of 30 arcsec (∼1 km), 5 arcmin (∼10 km) and 15 arcmin (∼30 km) by applying IHU to the recently published 3 arcsec MERIT Hydro data. Results indicate improved accuracy of IHU at all resolutions studied compared to other often-applied upscaling methods. Furthermore, we show that MERIT Hydro IHU minimizes the errors made in the timing and magnitude of simulated peak discharge throughout the Rhine basin compared to simulations at the native data resolutions. As the method is open source and fully automated, it can be applied to other high-resolution hydrography datasets to increase the accuracy and enhance the uptake of new datasets in distributed hydrological models in the future.

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Detecting and forecasting riverine floods is of paramount importance for adequate disaster risk management and humanitarian response. However, this is challenging in data-scarce and ungauged river basins in developing countries. Satellite remote sensing data offers a cost-effective, low-maintenance alternative to the limited in-situ data when training, parametrizing and operating flood models. Utilizing the signal difference between a measurement (M) and a dry calibration (C) location in Passive Microwave Remote Sensing (PMRS), the resulting r_cm index simulates river discharge in the measurement pixel. Whilst this has been demonstrated for several river basins, it is as of yet unknown at what ratio of the spatial scales of the river width vs. the PMRS pixel resolution it remains effective in East-Africa. This study investigates whether PMRS imagery at 37 GHz can be effectively used for flood preparedness in two small-scale basins in Malawi, the Shire and North Rukuru river basins. Two indices were studied: The m index (r_cm expressed as a magnitude relative to the average flow) and a new index that uses an additional wet calibration cell: r_cmc. Furthermore, the results of both indices were benchmarked against discharge estimates from the Global Flood Awareness System (GloFAS). The results show that the indices have a similar seasonality as the observed discharge. For the Shire River, r_cmc had a stronger correlation with discharge (ρ = 0.548) than m (ρ = 0.476), and the former predicts discharge more accurately (R² = 0.369) than the latter (R² = 0.245). In Karonga, the indices performed similarly. The indices do not perform well in detecting individual flood events when comparing the signal to a flood impact database. However, these results are sensitive to the threshold used and the impact database quality. The method presented simulated Shire River discharge and detected floods more accurately than GloFAS. It therefore shows potential for river monitoring in data-scarce areas, especially for rivers of a similar or larger spatial scale than the Shire River. Upstream pixels could not directly be used to forecast floods occurring downstream in these specific basins, as the time lag between discharge peaks did not provide sufficient warning time.

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Flood simulations are important for flood (fatality) risk assessment. This article provides insight into the sensitivity of flood fatality risks to the model resolution of flood simulations and to several uncertain parameters in the loss of life model used. A case study is conducted for river flooding in a polder in the Netherlands (the Bommelerwaard) where the Dutch approach for loss of life estimation is applied. Flood models with resolutions of 100, 25, and 5 m are considered. Results show locally increased mortality rates in higher resolution simulations nearby structures including road embankments, dikes, and culverts. This causes a larger maximum individual risk value (annual probability of death for a person due to flooding) which has consequences for safety standards based on the individual risk criterion. Mortality rate in the breach zone is also affected by representations of buildings as solid objects versus as roughness elements. Furthermore, changes in the loss of life estimation approach via alternative ways of including people's behaviour, building characteristics, and age of the population, have a significant impact on flood fatality risk. Results from this study can be used to support future risk assessments and decision making with respect to safety standards.

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Since 1990, natural hazards have led to over 1.6 million fatalities globally, and economic losses are estimated at an average of around USD 260-310 billion per year. The scientific and policy communities recognise the need to reduce these risks. As a result, the last decade has seen a rapid development of global models for assessing risk from natural hazards at the global scale. In this paper, we review the scientific literature on natural hazard risk assessments at the global scale, and we specifically examine whether and how they have examined future projections of hazard, exposure, and/or vulnerability. In doing so, we examine similarities and differences between the approaches taken across the different hazards, and we identify potential ways in which different hazard communities can learn from each other. For example, there are a number of global risk studies focusing on hydrological, climatological, and meteorological hazards that have included future projections and disaster risk reduction measures (in the case of floods), whereas fewer exist in the peer-reviewed literature for global studies related to geological hazards. On the other hand, studies of earthquake and tsunami risk are now using stochastic modelling approaches to allow for a fully probabilistic assessment of risk, which could benefit the modelling of risk from other hazards. Finally, we discuss opportunities for learning from methods and approaches being developed and applied to assess natural hazard risks at more continental or regional scales. Through this paper, we hope to encourage further dialogue on knowledge sharing between disciplines and communities working on different hazards and risk and at different spatial scales..

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Coastal flood hazard and exposure are expected to increase over the course of the 21st century, leading to increased coastal flood risk. In order to limit the increase in future risk, or even reduce coastal flood risk, adaptation is necessary. Here, we present a framework to evaluate the future benefits and costs of structural protection measures at the global scale, which accounts for the influence of different flood risk drivers (namely sea-level rise, subsidence, and socioeconomic change). Globally, we find that the estimated expected annual damage (EAD) increases by a factor of 150 between 2010 and 2080 if we assume that no adaptation takes place. We find that 15 countries account for approximately 90 % of this increase. We then explore four different adaptation objectives and find that they all show high potential in cost-effectively reducing (future) coastal flood risk at the global scale. Attributing the total costs for optimal protection standards, we find that sea-level rise contributes the most to the total costs of adaptation. However, the other drivers also play an important role. The results of this study can be used to highlight potential savings through adaptation at the global scale.

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The spatiotemporal dynamics of water volumes stored in the unsaturated root zone are a key control on the response of terrestrial hydrological systems. Robust, catchment-scale root-zone soil moisture estimates are thus critical for reliable predictions of river flow, groundwater recharge, or evaporation. Satellites provide estimates of near-surface soil moisture that can be used to approximate the moisture content in the entire unsaturated root zone through the Soil Water Index (SWI). The characteristic time length (T, in days), as only parameter in the SWI approach, characterizes the temporal variability of soil moisture. The factors controlling T are typically assumed to be related to soil properties and climate; however, no clear link has so far been established. In this study, we hypothesize that optimal T values (T_opt) are linked to the interplay of precipitation and evaporation during dry periods, thus to catchment-scale vegetation accessible water storage capacities in the unsaturated root zone. We identify T_opt by matching modeled time series of root-zone soil moisture from a calibrated process-based hydrological model to SWI from several satellite-based near-surface soil moisture products in 16 contrasting catchments in the Meuse river basin. T_opt values are strongly and positively correlated with vegetation accessible water volumes that can be stored in the root zone, here estimated for each study catchment both as model calibration parameter and from a water-balance approach. Differences in T_opt across catchments are also explained by land cover (% agriculture), soil texture (% silt), and runoff signatures (flashiness index).

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Limited availability of ground measurements in the vast majority of river basins world-wide increases the value of alternative data sources such as satellite observations in hydrological modelling. This study investigates the potential of using remotely sensed river water levels, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River basin in Zambia. A distributed process-based rainfall-runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. Next, three alternative ways of further restricting feasible model parameter sets using satellite altimetry time series from 18 different locations along the river were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash-Sutcliffe efficiency of ENS,Q = 0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95 = 0.61-0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q =-1.4 (ENS,Q,5/95 =-2.3-0.38) with respect to discharge was obtained. The direct use of altimetry-based river levels frequently led to overestimated flows and poorly identified feasible parameter sets (ENS,Q,5/95 =-2.9-0.10). Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an overestimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95 =-2.6-0.25). However, accounting for river geometry proved to be highly effective. This included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth and applying the Strickler-Manning equation to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well, with an optimum of ENS,Q = 0.60 (ENS,Q,5/95 =-0.31-0.50). It was further shown that more accurate river cross-section data improved the water-level simulations, modelled rating curve, and discharge simulations during intermediate and low flows at the basin outlet where detailed on-site cross-section information was available. Also, increasing the number of virtual stations used for parameter selection in the calibration period considerably improved the model performance in a spatial split-sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data-scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly gauged or ungauged basins.

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Understanding the canopy cover relationship with canopy water content and canopy temperature in the Miombo ecosystem is important for studying the consequences of climate change. To better understand these relationships, we studied the satellite data-based land surface temperature (LST) as proxy for canopy temperature, leaf area index (LAI), and the normalized difference vegetation index (NDVI) as proxies for canopy cover. Meanwhile, the normalized difference infrared index (NDII) was used as a proxy for canopy water content. We used several statistical approaches including the correlated component regression linear model (CCR.LM) to understand the relationships. Our results showed that the most determinant factor of variations in the canopy cover was the interaction between canopy water content (i.e., NDII) and canopy temperature (i.e., LST) with coefficients of determination (R²) ranging between 0.67 and 0.96. However, the coefficients of estimates showed the canopy water content (i.e., NDII) to have had the largest percentage of the interactive effect on the variations in canopy cover regardless of the proxy used i.e., LAI or NDVI. From 2009-2018, the NDII (proxy for canopy water content) showed no significant (at alpha level 0.05) trend. However, there was a-n significant upward trend in LST (proxy for canopy temperature) with a magnitude of 0.17 °C/year. Yet, the upward trend in LST did not result in significant (at alpha level 0.05) downward changes in canopy cover (i.e., proxied by LAI and NDVI). This result augments the observed least determinant factor characterization of temperature (i.e., LST) on the variations in canopy cover as compared to the vegetation water content (i.e., NDII).

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In this paper we demonstrate a framework for urban flood modeling with community mapped data, particularly suited for flood risk management in data-scarce environments. The framework comprises three principal stages: data acquisition with survey design and quality assurance, model development and model implementation for flood prediction. We demonstrate that data acquisition based on community mapping can be affordable, comprehensible, quality assured and open source, making it applicable in resource-strained contexts. The framework was demonstrated and validated on a case study in Dar es Salaam, Tanzania. The results obtained show that the community mapped data supports flood modeling on a level of detail that is currently inaccessible in many data-scarce environments. The results obtained also show that the community mapping approach is appropriate for datasets that do not require extensive training, such as flood extent surveys where it is possible to cross-validate the quality of reports given a suitable number and density of data points. More technically advanced features such as dimensions of urban drainage system elements still require trained mappers to create data of sufficient quality. This type of mapping can, however, now be performed in new contexts thanks to the development of smartphones. Future research is suggested to explore how community mapping can become an institutionalized practice to fill in important gaps in data-scarce environments.@en

The interaction between physical drivers from oceanographic, hydrological, and meteorological processes in coastal areas can result in compound flooding. Compound flood events, like Cyclone Idai and Hurricane Harvey, have revealed the devastating consequences of the co-occurrence of coastal and river floods. A number of studies have recently investigated the likelihood of compound flooding at the continental scale based on simulated variables of flood drivers, such as storm surge, precipitation, and river discharges. At the global scale, this has only been performed based on observations, thereby excluding a large extent of the global coastline. The purpose of this study is to fill this gap and identify regions with a high compound flooding potential from river discharge and storm surge extremes in river mouths globally. To do so, we use daily time series of river discharge and storm surge from state-of-the-art global models driven with consistent meteorological forcing from reanalysis datasets. We measure the compound flood potential by analysing both variables with respect to their timing, joint statistical dependence, and joint return period. Our analysis indicates many regions that deviate from statistical independence and could not be identified in previous global studies based on observations alone, such as Madagascar, northern Morocco, Vietnam, and Taiwan. We report possible causal mechanisms for the observed spatial patterns based on existing literature. Finally, we provide preliminary insights on the implications of the bivariate dependence behaviour on the flood hazard characterisation using Madagascar as a case study. Our global and local analyses show that the dependence structure between flood drivers can be complex and can significantly impact the joint probability of discharge and storm surge extremes. These emphasise the need to refine global flood risk assessments and emergency planning to account for these potential interactions.

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