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89 results for “river flooding”
Surface water and flooding dynamics data set based on seasonally continuous Landsat data (1986-2011) in a dryland river basin
<p>Animations of the data are available here: <a href="https://doi.org/10.5281/zenodo.2438110">https://doi.org/10.5281/zenodo.2438110</a></p> <p>If you are using this data set, please cite the following publication:</p> <p>Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466 </p> <p>The data represent statistically validated surface water and flooding extent dynamics derived from seasonally continous Landsat TM/ETM+ data and random forest models, and summarised to the maximum extent of surface water per season between 1986-2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157</p> <p>URL: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p> <p>Data are provided in GeoTIFF format per season per year. File naming convention is as follows:<br> yy_inund_freq_season_SamplingMethod. For example, "99_inund_freq_winter_max" will represent inundation frequency for winter 1999 resampled using a maximum resampling method. </p> <p>Inundation frequency represents the number of times a pixel has been flagged as flooded out of the times that pixel had valid observations * 100. Valid observation exclude no data values and clouds. The valid range of inundation frequency is 0-100 [%], with 255 indicating no data values. Data type is eight bit unsigned integer (uint8). </p> <p>The data were resampled to 120m resolution to reduce file size. The resampling methods used include max (e.g. selects the max value of all non-NODATA contributing 30m pixels) and mean (median and min can be provided upon request). If you are unsure which resampling to use, you may want to start with the mean. </p>
Stream Restoration and Flood Impacts in the Kickapoo River Watershed, Wisconsin, 2019
Data were collected from May to November 2019 at five sites on two stream reaches in the Kickapoo River Watershed. Sites include Billings Creek restoration site (BRES), Billings Creek reference site (BREF), Warner Creek upstream site (WUP), Warner Creek middle site (WMD), and Warner Creek downstream (WDN) site. Data were collected by Dr. Caroline Gottschalk Druschke as part of research into the impacts of stream restoration and flooding on Kickapoo River Watershed (WI, USA) streams. Data were collected on methane and carbon dioxide fluxes on all reaches, as well as cross sectional area and soft sediment depth on the restored reach on Billings Creek before and after restoration in July 2019.
Data and Code for Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances
<p>Data and code for analyses in Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances.</p> <p>See publication and ReadMe file for analysis description and further details. </p>
Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe
<p>Journal: Sustainability</p> <p>Abstract: Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological‒meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.</p> <p>URL: https://www.mdpi.com/2071-1050/14/15/9282</p> <p>The uploaded datasets are the Annual Maximum Series of Sava River runoff for the six analysed hydrological stations: Radovljica, Čatež, Zagreb, Jasenovac, Županja and S. Mitrovica.</p>
Surface water and flooding dynamics based on seasonally continuous Landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)
<p>The animations provided here are part of the following publication:<br> Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466</p> <p>Please refer to the above mentioned publication for a description of the data and interpretation of the patterns.</p> <p>The animations are based on statistically validated surface water and flooding extent dynamics data derived from seasonally continous Landsat TM/ETM+ and random forest models from 1986 to 2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157 and available here: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p>
Datasets for manuscript: Global River Discharge and Floods in the Warmer Climate of the Last Interglacial
<p>This datasets contains results of the global hydrological and hydrodynamic modeling presented in the paper referenced in the title (doi: 10.1029/2020GL089375). The dataset comprises results for one set of simulations, based on Global Climate Model CESM1.2, out of the eight sets of simulations for eight GCMs included in the paper. The corresponding results for the other seven sets of simulations (based on GCMs CESM2, EC‐EARTH3.2, HadGEM3‐GC3.1, IPSL‐CM6‐LR, MPI‐ESM 1.2.01p1‐LR, NorESM1‐F, and NUIST‐CSM) can be obtained by writing to the corresponding author at paolo.scussolini@vu.nl.</p> <p>Files description:</p> <p>fldare_yearmean_timmean_CEM1.2_LIG.nc : Annual average flood area for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldare_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average flood area for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldsto_yearmean_timmean_CESM1.2_LIG.nc 4 Mb : Annual average flood volume for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldsto_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average flood volume for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> outflw_yearmean_timmean_CESM1.2_LIG.nc 4 Mb : Annual average river discharge for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> outflw_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average river discharge for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> runoff_annuaTot_output_mergetime_timmean_CESM1.2_LIG.nc : Annual average runoff for the Last Interglacial simulation with GCM CESM1.2 and hydrological model PCR-GLOBWB.<br> <br> runoff_annuaTot_output_mergetime_timmean_CESM1.2_PI.nc : Annual average runoff for the Pre-Industrial simulation with GCM CESM1.2 and hydrological model PCR-GLOBWB.</p> <p> </p>
FloodSformer: River Flood datasets&checkpoints
<div> <div>Data used for the paper: Pianforini et al. (2025). FloodSformer: A transformer-based data-driven model for predicting the 2-D dynamics of fluvial floods. <em>Environmental Modelling & Software</em>. <a href="https://doi.org/10.1016/j.envsoft.2025.106599">https://doi.org/10.1016/j.envsoft.2025.106599</a></div> <div> </div> <div>The repository contains the training/testing datasets and the checkpoints for the Toce River case study.</div> <div>The data for the Po River test case are unavailable due to restrictions on data permissions.</div> <div> </div> <div> <div> <div>The python code of the FloodSformer model is available at the <a href="https://github.com/mpianforini/FloodSformer">GitHub repository</a>.</div> </div> </div> </div> <p>More detailed information on the repository content are provided in the README_dataset.md file.</p> <div> </div> <div><strong>Acknowledgements</strong></div> <div> <div>This research was granted by <span>University of Parma</span> through the action “Bando di Ateneo 2024 per la ricerca”. RV and SD acknowledge financial support from the <span>PNRR MUR project</span> <span><span>ECS_00000033_ECOSISTER</span></span>. This research also benefits from the HPC facility of the University of Parma. Finally, the Authors acknowledge the CINECA award under the ISCRA initiative, for the availability of high-performance computing resources and support (projects AMNERIS and MOZART).</div> <div> </div> </div>
Human and economic losses from river flooding with anthropogenic warming
<p>This repository contains the data set derived from a modelling framework developed to evaluate present and future impacts from river flooding and described in the scientific paper "Increased Human and economic losses from river flooding with anthropogenic warming", published in Nature Climate Change. The repository contains the following data, for more details please refer to the "readme.doc" file.</p> <ul> <li>Static and dynamic exposure maps (land use, economic variables, population distribution)</li> <li>global distributed maps of economic damage from river flooding for the present and future periods. Maps are provided for each model realization (i.e. combination of a global climate model and a global hydrological model)</li> <li>global maps of population affected by river flooding for the present and future periods, computed with current socioeconomic conditions and with SSP3 and SSP5 population projections. Maps are provided for each model realization</li> <li>tables of socio-economic impacts (fatalities, mortality rates, economic losses, population exposed) aggregated per continents, macro-regions and countries. Tables are provided for each model realization</li> <li>Tables of the variables used as input data in the impact assessment procedure</li> </ul>
River flooding impacts using CLIMRISK-RIVER
<p>Direct impacts of river flooding using the CLIMRISK-RIVER model. This includes direct impacts to built environment and infrastructure.</p> <p>The damage is expressed as a change in expected annual damage (EAD) w.r.t. 2010. </p> <p>Files include two adaptation assumptions: no additional adaptation and optimal adaptation (using CBA estimates).</p> <p>Units: millions EUR (2015) PPP.</p>
Joint series underlying the paper " Compound coastal-riverine flooding of St. Lawrence River coasts under sea level rise conditions"
<p>Compound coastal-riverine flooding, known as flooding events caused by the co-occurrence of high streamflow and coast water levels, can have substantial economic and social implications in low-lying coastal regions. Recent studies over Canada’s coasts have shown that neglecting the interdependency between flood drivers can underestimate the risk of flooding by up to 50%. However, to date, such interdependency and its effect on the frequency of compound riverine-coastal flooding has not been investigated for the coasts of the St. Lawrence River, Estuary, and Gulf system (StL), where Sea Level Rise (SLR), along with intensified river peaks, are already threatening communities. In this study, a copula-based bivariate frequency analysis (AND hazard scenario) was applied to quantify the differences between joint return periods computed under dependent and independent assumptions, for 26 sites along the StL. Furthermore, design pairs for 100-year joint events in the historical period (1986-2020) were compared with the 2100 horizon, where the SLR associated with the RCP8.5 emission scenario was incorporated into the water level time series. Results show that 1) the independence assumption can underestimate the frequency of compound flooding in the Fluvial Section of the StL by up to 30 times and 2) the SLR can increase the frequency of compound flooding by up to 50 times in the Estuary and the Gulf and by up to 5 times in the Fluvial Section of the StL. This study highlights the need for explicit consideration of the dependence between flood drivers and of SLR in the delineation of flood maps along all of the coasts of the St. Lawrence.</p>
The long term series of characteristics of floods that happened to 12 rivers in Finland and northern Russia.
<p>The dataset of flood’s characteristics (annual and spring): the volume of spring flood (in mm of the depth of runoff), the dates of spring flood begin and end, the length of spring flooding period, the yearly maximum daily discharge and its date were estimated for each year from the daily series of water discharges observed at the hydrometric sites. To define the dates of spring flood begin and end we applied the semi-empirical method given in Shevnina (2013). The yearly maximum water discharges have been obtained in Gudmundsson et al. (2018) for the period until 2017; this dataset gives a good agreement in the estimations for the overlapping periods. The series of volume of spring flood (in mm of the depth of runoff), the dates of spring flood begin and end, the length of spring flooding period, the yearly maximum daily discharge and its date are given in the dataset supplementing the study submitted to the Water resource research journal (<a href="https://agupubs.onlinelibrary.wiley.com/journal/19447973">https://agupubs.onlinelibrary.wiley.com/journal/19447973</a> ). </p> <p>The daily series of water river discharges at the sites located in Finland were extracted from (a) the Global runoff database <a href="https://portal.grdc.bafg.de/">https://portal.grdc.bafg.de/</a> (for the period from beginning of the observations to 2017); (b) the archive of the Finnish Environmental Institute <a href="https://www.syke.fi/">https://www.syke.fi</a> (for the period 2018–2020) and these series can be obtained after its representatives’ permission from the author. The daily series of water discharges at the sites located in the Russian Federation were extracted from (a) the yearly hydrological books published by the State Hydrological Institute <a href="http://www.hydrology.ru/en">http://www.hydrology.ru/en</a> (for the period from the beginning of observation to 2007); (b) the automated information system for state monitoring of water bodies <a href="https://gmvo.skniivh.ru/">https://gmvo.skniivh.ru/</a> (for the period 2008–2020) and these series are available from its web-site after a registration. </p> <p>The dataset consists of the CSV/TXT files, each file contains the long term series of the characteristics listed in the header: "year", "DFB" (date when a spring flooding period begins, day of year, DOY),"DFE" (date when the spring flooding period ends, DOY),"Length" (length of the spring flooding period, days), "DFMax" (date when the yearly maximum water discharge is recorded, DOY), "Qmax" (the yearly maximum water discharge, cubic m per second), "FRD" (the volume of spring flood expressed in mm per flooding period), "YRD" (volume of annual flow, expressed in mm per year),"Ftype" (the source of annual flood equaling to 1 of the yearly maximum water discharge is recorded in the spring flooding period or 0 if it is not). </p> <p>The dataset was obtained in the study funded by the Academy of Finland under the contract number 317999. It will become freely available once the manuscript is published. </p> <p>References</p> <p>Gudmundsson, L., Do, H. X., Leonard, M., & Westra, S. (2018), The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018.</p> <p>Shevnina E. (2013), Method to calculate characteristics of spring flood from daily water discharges, Problems of the Arctic and Antarctic, 1(95), pp. 12-21. In Russian</p>
Compound flood potential from river discharge and storm surge extremes at the global scale
<p>This dataset presents the results presented in <a href="https://doi.org/10.5194/nhess-20-489-2020">Couasnon et al. (2019) - Measuring compound flood potential from river discharge and storm surge extremes at the global scale</a>. For more information about the methods, please refer to the paper. This dataset was created using as input <a href="https://zenodo.org/record/3552820#.XmIdoVxKhaQ">time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014</a>.</p> <p>If using this data, please cite: </p> <p>Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.</p>
Dataset for Extreme Flooding and Nitrogen Dynamics of a Blackwater River
<p>Data used for analysis in the manuscript titled: Extreme Flooding and Nitrogen Dynamics of a Blackwater River.</p> <p> </p>
FIGURE 4 in Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River
FIGURE 4 | Trophic niche breadth of Pimelodus blochii collected in different hydrological periods in the middle Xingu River region (Eastern Amazon, Brazil). Based on centroid distances between groups from the Permutational Multivariate Dispersion Analysis (PERMDISP).
FIGURE 3 in Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River
FIGURE 3 | Non-metric Multidimensional Scaling (nMDS) graphical representation of the diet of Pimelodus blochii collected in different hydrological periods in the middle Xingu River region, Eastern Amazon, Brazil.
FIGURE 2 in Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River
FIGURE 2 | Alimentary index (Ai) of the diet of Pimelodus blochii collected in different hydrological periods in the middle Xingu River region, Eastern Amazon, Brazil. *Less than 5% of contribution.
FIGURE 1 in Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River
FIGURE 1 | Map depicting the Volta Grande do Xingu (Xingu River, Brazil), with emphasis on the reduced flow section created by the construction of the Belo Monte Dam (including the Pimental Dam). The orange circles represent the sampling sites where Pimelodus blochii specimens were collected, and the arrows indicate the direction of water flow. The orange star and triangle represent the Pimental Dam and the Belo Monte Dam, respectively.
Fig. 4 in Effects of the interannual variations in the flood pulse mediated by hypoxia tolerance: the case of the fish assemblages in the upper Paraná River floodplain
Fig. 4. Ordination of the samples of the upper Paraná River floodplain, through the detrended correspondence analysis (DCA), in years of short (diamond: 2000 white, 2001 gray) and moderate floods (square: 2002 gray, 2003 black). Numbers 1-6 are codes of the sampling stations (see Fig. 1).
Fig. 5 in Effects of the interannual variations in the flood pulse mediated by hypoxia tolerance: the case of the fish assemblages in the upper Paraná River floodplain
Fig. 5. Fish assemblage attributes in the main habitats of the upper Paraná River floodplain in years of short (2000 and 2001) and moderate (2002 and 2003) floods. The black area of the bars represents the proportion of STH. Numbers 1-6 on the abscissa are codes of the sampling stations (see Fig. 1).
Fig. 2 in Effects of the interannual variations in the flood pulse mediated by hypoxia tolerance: the case of the fish assemblages in the upper Paraná River floodplain
Fig. 2. Monthly (a) and daily level (b, between January and March) of the upper Paraná River recorded in Porto São José municipality. In b (right axis), the number of days between January and March, when the upper Paraná River surpassed the threshold of 350 cm (horizontal braked line). The years 2000 and 2001 were considered as years of short floods and 2002 and 2003 as years of moderate floods. Source: National Department of Waters and Electric Energy.
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