1. SummaryThe Congo basin’s Groundwater Storage Anomaly (GWSA) datasets are generated by Benjamin M. Kitambo, Sly Wongchuig, Raphael M. Tshimanga, Adrien Paris, Alejandro Blazquez, Daniel Moreira, Frederic Frappart, Ayan Santos Fleischmann, Jean-Marie O. Kileshye , Mohammad J. Tourian, Fabrice Papa in the article entitled "Hydrogeological control of groundwater variations in the Congo River Basin revealed by GRACE water storage change decomposition", Water Resources Research (submitted).The dataset was generated using the decomposition method of Terrestrial Water Storage Anomaly (TWSA) which is the sum of the contributions of different hydrological storage compartments including changes in surface water storage anomaly (ΔSWSA), root zone soil moisture (ΔSMroot), snow storage (ΔSnow), and GWSA (ΔGWSA) (Frappart and Ramillien, 2018). The equation is as follows:                                                            ΔTWSA = ΔSWSA + ΔSMroot + ΔSnow + ΔGWSA  (1)ΔGWSA changes are obtained by subtracting ΔSWSA, ΔSMroot, ΔSnow from ΔTWSA on the same period of availability for all datasets, here 2002-2015. Since there is no contribution of snow storage in the Congo tropical region, this term is neglected in our study. Only GWSA datasets are provided from different Gravity Recovery and Climate Experiment (GRACE) solutions including Jet Propulsion Laboratory spherical harmonics (JPL-SH), JPL mass concentration blocks mascons (JPL-M), GeoforschungsZentrum Potsdam SH (GFZ-SH), CSR mascons (CSR-M), CNES-L3. To get ΔTWSA ,ΔSWSA, ΔSMroot datasets, please kindly refer to the aforementioned paper where all links are provided.2. Name Description●  Month_data_XX: Months of availability of GWSA data due to the missing values in GRACE’s TWSA XX (where XX stands for CNES-L3, Others which include JPL-SH, JPL-M, GFZ-SH, CSR-M).●  GWSA_XX: monthly groundwater storage anomaly variations from XX (where XX stands for JPL-SH, JPL-M, GFZ-SH, CSR-M, and CNES-L3 GRACE solutions).3. File Description The GWSA estimates from the decomposition method of TWSA provides the monthly variation of groundwater storage changes over 2002-2015, gridded on 0.25-degree of spatial resolution, each pixel covering almost 773 km². The files are organized in matrix:●  First column represents the latitude in degree.●  Second column represents the longitude in degree.●  From the third column: GWSA data in km³, there are 139 (respectively 147) columns representing each month of data availability of the period from 2002 to 2015 from CNES-L3 GRACE solution (respectively JPL-SH, JPL-M, GFZ-SH, CSR-M GRACE solutions).

1. SummaryThe Congo basin’s Groundwater Storage Anomaly (GWSA) datasets are generated by Benjamin M. Kitambo, Sly Wongchuig, Raphael M. Tshimanga, Adrien Paris, Alejandro Blazquez, Daniel Moreira, Frederic Frappart, Ayan Santos Fleischmann, Jean-Marie O. Kileshye , Mohammad J. Tourian, Fabrice Papa in the article entitled "Hydrogeological control of groundwater variations in the Congo River Basin revealed by GRACE water storage change decomposition", Water Resources Research (submitted).The dataset was generated using the decomposition method of Terrestrial Water Storage Anomaly (TWSA) which is the sum of the contributions of different hydrological storage compartments including changes in surface water storage anomaly (ΔSWSA), root zone soil moisture (ΔSMroot), snow storage (ΔSnow), and GWSA (ΔGWSA) (Frappart and Ramillien, 2018). The equation is as follows:                                                            ΔTWSA = ΔSWSA + ΔSMroot + ΔSnow + ΔGWSA  (1)ΔGWSA changes are obtained by subtracting ΔSWSA, ΔSMroot, ΔSnow from ΔTWSA on the same period of availability for all datasets, here 2002-2015. Since there is no contribution of snow storage in the Congo tropical region, this term is neglected in our study. Only GWSA datasets are provided from different Gravity Recovery and Climate Experiment (GRACE) solutions including Jet Propulsion Laboratory spherical harmonics (JPL-SH), JPL mass concentration blocks mascons (JPL-M), GeoforschungsZentrum Potsdam SH (GFZ-SH), CSR mascons (CSR-M), CNES-L3. To get ΔTWSA ,ΔSWSA, ΔSMroot datasets, please kindly refer to the aforementioned paper where all links are provided.2. Name Description●  Month_data_XX: Months of availability of GWSA data due to the missing values in GRACE’s TWSA XX (where XX stands for CNES-L3, Others which include JPL-SH, JPL-M, GFZ-SH, CSR-M).●  GWSA_XX: monthly groundwater storage anomaly variations from XX (where XX stands for JPL-SH, JPL-M, GFZ-SH, CSR-M, and CNES-L3 GRACE solutions).3. File Description The GWSA estimates from the decomposition method of TWSA provides the monthly variation of groundwater storage changes over 2002-2015, gridded on 0.25-degree of spatial resolution, each pixel covering almost 773 km². The files are organized in matrix:●  First column represents the latitude in degree.●  Second column represents the longitude in degree.●  From the third column: GWSA data in km³, there are 139 (respectively 147) columns representing each month of data availability of the period from 2002 to 2015 from CNES-L3 GRACE solution (respectively JPL-SH, JPL-M, GFZ-SH, CSR-M GRACE solutions).

As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2023-001_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.

This dataset was obtained using altimetry data, from CTOH (Center for Topographic studies of the Ocean and Hydrosphere, http://ctoh.legos.obs-mip.fr/), of the ENVISAT (2002-2010) and SARAL (2013-2016) satellite missions acquired over the Curuai floodplain along the Amazon and the Jurua watershed. The dataset contains classification results of radar altimetry echoes performed with the unsupervised K-means method (netCDF4 files) with each satellite missions (ENV for ENVISAT and SRL for SARAL) based on echo backscatter and normalized water height (sh) parameters. For each area, four classes were created to best represent the different hydrological regimes in the Amazon without over-interpreting radar echoes. The four classes are rated from 0 to 3. Class 0 is very often located on open water (lakes or rivers); on the contrary, class 3 is mainly located in non-flooded areas (not used here). Classes 1 and 2 correspond to intermediate hydrological environment such as permanently or seasonally flooded and seasonally or infrequently flooded, respectively. For each cluster of echoes classified in the same class along an altimetric track, a station is created containing the time series of local water level variation (.txt files). This is an important densification of water level information in the Amazon floodplains.

1. Summary The Congo basin’s Surface Water Storage (SWS) datasets are generated by Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, Sly Wongchuig in the article entitled "A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015", Earth System Science Data (submitted). The dataset was generated using two methods, one based on a multi-satellite approach and one on a hypsometric curve approach. The multi-satellite approach consists of the combination of surface water extent (SWE) from the Global Inundation Extent from Multi-satellite (GIEMS-2) and satellite-derived surface water height (SWH) from radar altimetry (long-term series ERS-2_ENV_SRL) on the same period of availability for the two datasets, here 1995-2015. The hypsometric curve approach consists of the combination of SWE from GIEMS-2 dataset and hypsometric curves obtained from various digital elevation models (DEMs) (i.e., ASTER, ALOS, MERIT, and FABDEM). Both methods estimate monthly spatio-temporal variations of SWS changes across the entire Congo River basin. 2. Name Description HYPSO_XX: hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). HYPSO_CORR_XX: corrected hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). AREA_STOR_XX: hypsometric curve providing the surface water extent area-storage relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). SWS_XX: monthly surface water storage variations from XX (where XX stands for ASTER, ALOS, MERIT, FABDEM DEMs, and Multi-satellite approach). 3. File Description The SWS estimates from the multi-satellite approach (1995-2015), as well as the hypsometric curves providing the surface water extent area-elevation relationship from the four DEMs (before and after the corrections), the surface water extent area-storage relationship, along with the four SWS estimates (1992-2005). The dataset is gridded on equal-area of 0.25° spatial resolution at the equator, each pixel covers almost 773 km². The reference point for calculating the volume variation is the minimum of surface water extent for each pixel. The files are organized in matrix: First column represents the latitude in degree. Second column represents the longitude in degree. From the third column: data. In case of hypsometric curve, the data represents elevation in meter on 101 columns representing the increment of 1% flooding in each 773 km² pixel from GIEMS-2. In case of SWS data (in km³), there are 288 (respectively 252) columns representing each month of the period over 1992-2015 (respectively 1995-2015) from hypsometric curve approach (respectively multi-satellite approach).

1. Summary The Congo basin’s Surface Water Storage (SWS) datasets are generated by Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, Sly Wongchuig in the article entitled "A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015", Earth System Science Data (submitted). The dataset was generated using two methods, one based on a multi-satellite approach and one on a hypsometric curve approach. The multi-satellite approach consists of the combination of surface water extent (SWE) from the Global Inundation Extent from Multi-satellite (GIEMS-2) and satellite-derived surface water height (SWH) from radar altimetry (long-term series ERS-2_ENV_SRL) on the same period of availability for the two datasets, here 1995-2015. The hypsometric curve approach consists of the combination of SWE from GIEMS-2 dataset and hypsometric curves obtained from various digital elevation models (DEMs) (i.e., ASTER, ALOS, MERIT, and FABDEM). Both methods estimate monthly spatio-temporal variations of SWS changes across the entire Congo River basin. 2. Name Description HYPSO_XX: hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). HYPSO_CORR_XX: corrected hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). AREA_STOR_XX: hypsometric curve providing the surface water extent area-storage relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). SWS_XX: monthly surface water storage variations from XX (where XX stands for ASTER, ALOS, MERIT, FABDEM DEMs, and Multi-satellite approach). 3. File Description The SWS estimates from the multi-satellite approach (1995-2015), as well as the hypsometric curves providing the surface water extent area-elevation relationship from the four DEMs (before and after the corrections), the surface water extent area-storage relationship, along with the four SWS estimates (1992-2005). The dataset is gridded on equal-area of 0.25° spatial resolution at the equator, each pixel covers almost 773 km². The reference point for calculating the volume variation is the minimum of surface water extent for each pixel. The files are organized in matrix: First column represents the latitude in degree. Second column represents the longitude in degree. From the third column: data. In case of hypsometric curve, the data represents elevation in meter on 101 columns representing the increment of 1% flooding in each 773 km² pixel from GIEMS-2. In case of SWS data (in km³), there are 288 (respectively 252) columns representing each month of the period over 1992-2015 (respectively 1995-2015) from hypsometric curve approach (respectively multi-satellite approach).

As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2022-002_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.

Gironde estuary environmental parameters and SPM maps generated from 41 Landsat-8/OLI and Sentinel-2/MSI images acquired over the period 2013-2018. Except bathymetry and daily river discharge data, that are accessible on public platforms, the dataset includes all of the time seris used in the publication: Analysis of suspended sediment variability in a large highly-turbid estuary using a 5-year-long remotely-sensed data archive at high resolution, Journal of Geophysical Research: Oceans, DOI:10.1029/2019JC015417.

We re-evaluate the Greenland mass balance for the recent period using low-pass Independent Component Analysis (ICA) post-processing of the Level-2 GRACE data (2002-2010) from different official providers (UTCSR, JPL, GFZ) and confirm the present important ice mass loss in the range of -70 and -90 Gt/y of this ice sheet, due to negative contributions of the glaciers on the east coast. We highlight the high interannual variability of mass variations of the Greenland Ice Sheet (GrIS), especially the recent deceleration of ice loss in 2009-2010, once seasonal cycles are robustly removed by Seasonal Trend Loess (STL) decomposition. Interannual variability leads to varying trend estimates depending on the considered time span. Correction of post-glacial rebound effects on ice mass trend estimates represents no more than 8 Gt/y over the whole ice sheet. We also investigate possible climatic causes that can explain these ice mass interannual variations, as strong correlations between GRACE-based mass balance and atmosphere/ocean parallels are established: (1) changes in snow accumulation, and (2) the influence of inputs of warm ocean water that periodically accelerate the calving of glaciers in coastal regions and, feed-back effects of coastal water cooling by fresh currents from glaciers melting. These results suggest that the Greenland mass balance is driven by coastal sea surface temperature at time scales shorter than accumulation.