Flood models predict inundation extents, and can be an important source of information for flood risk studies. Accurate flood models require high resolution and high accuracy digital elevation models (DEM); current global DEMs do not capture the topographic details in floodplains, and this often leads to inaccurate prediction of flood extents by flood models. Flood extents obtained from remotely sensed data provide indirect information about topography. Here, we attempt to use this information along with model predictions to produce better floodplain topography. The algorithm we describe is a two-step process: first, we reduce the noise along the observed flood boundaries for all particles. Then, the model predictions from these modified DEMs are assimilated with observations using a particle batch smoother. We implemented the algorithm for a synthetic test case. For the nominal case, we observed a significant improvement in accuracy in terms of RMSE (35% reduction), bias (20%), and standard deviation (40%). We conducted sensitivity analysis by using priors of varying bias (0.5, 1, and 2 m) and standard deviation (1, 2, and 4 m). The bias reduced to ∼0.5 m or below in all the cases: the reduction in bias varied from 11 to 76%. The standard deviation of errors in the final estimate was almost half of the prior: the reduction varied from 40 to 49%. The reduction in RMSE ranged between 35 and 67%. For the case with 2 m bias and 4 m standard deviation (SRTM-like error levels), bias went down to 0.48 m (76% reduction), and standard deviation reduced to 2.24 m (44% reduction). Flood inundation maps produced from the final estimate DEMs also improved on its prior. For the 2 m bias cases, true positive rate (TPR) for peak inundation went from ∼30% to more than 57% in all three cases. The algorithm produces promising results, and this type of analysis can be performed in data-poor floodplains where high resolution DEMs do not exist.

Recent innovations in hydraulic modeling have enabled global simulation of rivers, includingsimulation of their coupled wetlands and floodplains. Accurate simulations of floodplains using these approaches may imply tremendous advances in global hydrologic studies and in biogeochemical cycling. One such innovation is to explicitly treat sub-grid channels within twodimensional models, given only remotely sensed data in areas with limited data availability.However, predicting inundated area in floodplains using a sub-grid model has not been rigorously validated. In this study, we apply the LISFLOOD-FP hydraulic model using a subgrid channel parameterization to simulate inundation dynamics on the Logone River floodplain, in northern Cameroon, from 2001 to 2007. Our goal was to determine whether floodplain dynamics could be simulated with sufficient accuracy to understand human and natural contributions to current and future inundation patterns. Model inputs in this data-sparse region include in situ river discharge, satellite-derived rainfall, and the shuttle radar topography mission (SRTM) floodplain elevation. We found that the model accurately simulated total floodplain inundation, with a Pearson correlation coefficient greater than 0.9, and RMSE less than 700 km 2 , compared to peak inundation greater than 6,000 km 2 . Predicted discharge downstream of the floodplain matched measurements (Nash-Sutcliffe efficiency of 0.81), and indicated that net flow from the channel to the floodplain was modeled accurately. However, the spatial pattern of inundation was not well simulated, apparently due to uncertainties in SRTM elevations. We evaluated model results at 250, 500 and 1000-m spatial resolutions, and found that results are insensitive to spatial resolution. We also compared the model output against results from run of LISFLOOD-FP in which the sub-grid channel parameterization was disabled, finding that the 6 sub-grid parameterization simulated more realistic dynamics. These results suggest that analysis of global inundation is feasible using a sub-grid model, but that spatial patterns at sub-kilometer resolutions still needs to be adequately predicted. Highlights*Sub-grid channel modeling in hydraulic simulations has not been fully validated in floodplains *We apply the LISFLOOD-FP model to simulate inundation dynamics on the Logone floodplain *Total inundation and flow into the floodplain are accurately simulated, but no spatial patterns *Mismatch in spatial patterns is likely due to uncertainties in the digital elevation model *Results suggest that analysis of global inundation is feasible using a sub-grid model

Assessing the impact of climate change on floodplain productivity poses unique challenges for hydrodynamic models. For example, the dynamics of floodplain fisheries are governed both by inundation dynamics across thousands of km 2 , and water storage timing within small depressions (which serve as fish habitat) connected to the river network by meter-scale manmade canals, controlled by flow across fishing weirs. Here, we propose to represent these features as a system of effective, interconnected sub-grid elements within a coarse-scale model.We test this strategy over the Logone floodplain in Cameroon, and its floodplain fishery. We first validate this strategy for a local study area (30 km 2 ); we find that hydraulic models at resolutions from 30 m to 500 m are able to reproduce hydraulic dynamics as documented by in situ water level observations. When applied to the entire floodplain (16,000 km 2 ), we find that the proposed modeling strategy allows accurate prediction of observed pattern of recession in the depressions. Artificially removing floodplain canals in the model causes residence time of water in depressions to be overpredicted by approximately 30 days. This study supports the strategy of modeling fine-scale interconnected features as a system of sub-grid elements in a coarse resolution model for applications such as assessing the sensitivity of floodplain fisheries to future climate change.

National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts for 2.7 million reaches in the National Hydrography Dataset for continental United States (U.S.). NWM uses Muskingum–Cunge channel routing, which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain more accurate estimates of streamflow and stage in rivers, especially for applications such as flood‐inundation mapping. Here, we used a steady‐state backwater version of Simulation Program for River NeTworks (SPRNT) model. We evaluated SPRNT’s and NWM’s abilities to predict inundated area for the record flood of Hurricane Matthew in October 2016. The Neuse River experienced record‐breaking floods and was well‐documented by U.S. Geological Survey. Streamflow simulations from NWM retrospective analysis were used as input for the SPRNT simulation. Retrospective NWM discharge predictions were converted to stage. The stages (from both SPRNT and NWM) were utilized to produce flood‐inundation maps using the Height Above Nearest Drainage method which uses the local relative heights to find out the local draining potentials and provide spatial representation of inundated area. The inundated‐area accuracies for NWM and SPRNT (based on comparison to a remotely sensed dataset) were 65.1% and 67.6%, respectively. These results show using steady‐state SPRNT results in a modest improvement of inundation‐forecast accuracy compared to NWM.

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Sub-Saharan floodplains are sensitive to climatic changes in their upstream drainage basin, a major concern is given the dependency of millions of people for their daily subsistence.Understanding hydroclimatic trends and variability is critical for developing integrated coupled human and natural system models to evaluate future scenarios of vulnerability. Here we describe the historical climatic changes in the Logone River basin using grid-based climatic data during a time of concomitant human hydrological modification of the floodplain. Temporal trends were analysed by comparing two periods i.e.

Fieldwork rarely goes to plan. In geography, anthropology, earth sciences and other research activities that rely on field data, trade‐offs are required between planning and execution. This article addresses the adaptation of research projects to changing fieldwork conditions. It is based on a case study of interdisciplinary and international “Coupled Human and Natural Systems” research situated in the Far North of Cameroon. The research project underwent drastic changes because of escalating insecurity in the field site, caused by Boko Haram, a terrorist group active in north‐east Nigeria and neighbouring regions. We use network analysis to show that our research team became larger, more connected and less clustered to accommodate for the changes in fieldwork accessibility. This process of adaptation led our research team towards tighter interdisciplinary collaboration, the co‐production of research between academic scientists with little or no local field experience and non‐academic practitioners with field knowledge, and a better dissemination of research outcomes through stronger partnerships with local organisations – overall, an increasingly transdisciplinary research pathway. Based on this case study, we discuss features of adaptive research projects facing high‐uncertainty field conditions, which is an increasingly relevant issue for many researchers, for example those working in parts of the Sahel or Middle East.

The modified topographic index (TI m ) based on digital elevation models (DEMs) was employed to delineate floodprone areas in Mahanadi basin, India. TI m and flood inundation maps were compared to obtain the threshold (t) beyond which the area is assumed to be inundated by flood and the exponent of the TI m . Scale dependence was also investigated to evaluate the sensitiveness of spatial resolution of the DEMs. DEMs of five resolutions, namely, ASTER global, SRTM, GMTED2010 (30 arc-seconds), GMTED 2010 (15 arc-seconds), and GMTED 2010 (7.5 arc-seconds), were used and ASTER global was preferred due to its low error compared to the remainder. Flood frequency analysis was conducted to obtain the relationship between flood-prone areas and flood magnitude. It was observed that (i) the exponent in the TI m showed little variation, (ii) t is reduced with reducing spatial resolution of the DEM, and (iii) error is also reduced as the DEMs' resolution is reduced. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited