dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , recharge of aquifers by their impluvium. An initial implementation of this two-step methodology, each one being fully automated, was proposed by  Labarthe (2016) , and has been used to reconstruct the hydrological trajectory of the Seine basin since the beginning of the XX th century  (Flipo et al. , 2021a). This method, relying on the fact that, for a 17-year stationarity period, river baseflows can be equated to the aquifer recharge across the river basin, depends on additional information to the classic datasets of river discharges and groundwater hydraulic heads, namely, an estimate of the river baseflow. However, these estimates are still marred by an error that we acknowledge, but that we are unable to quantify, mainly due to the great difficulty (or even impossibility) of the measurement at large scale.
A highly anthropogenic river basin The Seine River basin ( 76,000 km 2 ) is a highly human-impacted basin with a population of 17 M inhabitants. It receives the highest anthropogenic pressure in France, due to the industry and agriculture linked to the development of the urban area of Paris, which coexists today with highly productive agricultural areas (66 % of the basin area, Fig  1 ). For more information on the Seine basin, please refer to  Billen et al. (2007)  and  Flipo et al. (2021b). The river network is composed of 28,000 km of perennial rivers (Fig. 2 ). Overall, 97 % of the river network lies within the sedimentary Paris basin  (Guillocheau et al. , 2000) , the largest GW reservoir in Europe. The inter-annual mean values (2003-2020 period) of the climate forcing  (SAFRAN database -Quintana-Seguí et al.
(2008) ) are 766 mm a -1 and 890 mm a -1 for precipitation and potential evapotranspiration (PET), respectively. The inter-annual module of the Seine river at Vernon (Fig. 2 ) is 477 m 3 s -1 (2010-2019, HYDRO database daily dataset). Water withdrawals in surface water and GW amount to 3 km 3 a -1. The pressure on water resources is high especially during low-flow periods, when river discharges are sustained by GW and by human-built reservoirs (Fig. 2 ), totalling 841 10 6 m 3.
The fitting of the surface balance model is detailed in the next section 2. 4. The power spectral density (PSD) of the effective rainfall sub-basin average is taken as input for the HYMIT parameter estimation. The PSD of the naturalized river discharges at the outlet is calculated as the output. The ratio of the latter over the former gives an experimental transfer function (ETF)  (Pedretti et al. , 2016; Schuite et al. , 2019 ). An MCMC inversion procedure is implemented to adjust HYMIT to each ETF in order to estimate all five parameters for all sub-basins for which discharge data are available. The MATLAB package of  Haario et al. (2006)  for MCMC inversion is used with the built-in Metropolis-Hastings sampler. A total of 384 gauging stations are (or were) operational across the different rivers of the Seine basin.
Daily discharge data were checked for completeness and overall quality. In the presence of any gap longer than 10% of the total series length, the station was discarded from the analysis. So were stations with overly short records (less than 10 years), to maximize the statistical power of the analysis. Small gaps are filled by linear interpolation. Minor data errors were corrected, such as inconsistent timelines, double records, or negative values. After data curation and selection, discharge series from 221 stations remained. Prior to Fourier transformation, all time series are detrended and windowed using a Hanning tapering function.
A trial-and-error approach is used to adjust simulated water tables to mean level values measured at control wells. To achieve this step, an aquifer compartment-only simulation is implemented. A mean aquifer recharge field, calculated over two 17-year cycles (i. e. , 1986-2020 period), derived from the HYMIT-calibrated CaWaQS surface module, is used to constrain the simulation (Q i on Fig. 5 , step 5); a fitting of the Nash-cascade parameters, namely, k and N (see sections 2. 3 and 2. 2). Usually associated with lithology, HYMIT-k values are spatially distributed along a functional sectorization combining two information types: dominant soil textures (Fig. 1 ) and upmost free aquifer formation lithology (Fig. 3 ). For each association identified, attribution of k values is made using an analogy method, as previously described in the case of HYMIT-λ coefficients distribution (see section 2.
Simulation of hydraulic heads in aquifers 355 Figure  7. RMSE criteria for GW simulation over the 2003-2020 period (see also Table  1 ). Water table contour lines (50-m span) and background map depict spatial evolution of simulated mean GW levels for all uppermost aquifers (subsurface) over the same period. asl : Above Sea Level. Within the modeled aquifer system limits, 340 head observation time series are compiled from the national ADES database as most control points are constrained by proximal river drainage levels. RM SE scores map (Fig. 7 ) shows a homogeneous distribution of lower values over the entire basin, apart from the Evreux-Dreux area (left bank of the downstream Seine River, see Fig. 2 ) gathering the highest misfits, where local disturbances of aquifer flows are known to be heavily karst-induced  (El Janyani et al.
Simulation of river discharges Similarly to raw piezometry data, a pre-processing work is carried out on river discharge observations using data compiled from the HYDRO database (http://hydro. eaufrance. fr/), totalling 384 river stations (see section 2. 3). As previously stated, since calculations of stream-aquifer exchanges are constrained to the main river network (see section 2. 2 and Fig. 4 ), 167 gauging stations only are considered as valid discharge calibration control points. Usual Nash-Sutcliffe  (Nash and Sutcliffe, 1970)  and Kling-Gupta efficiency coefficients (labeled N SE and KGE, respectively, in Tab. 2) are selected to evaluate simulation performances on river discharges. At the basin scale, 50 and 53 % of control stations, respectively, show N SE and KGE scores above or equal to 0.
By taking into account only the longest and most complete hydroclimatic datasets, which is necessary in order to benefit from the statistical power of HYMIT analysis, we also maximize variance stability over time, owing to the large ratio of short-term fluctuations to long-term oscillations. https://doi. org/10. 5194/gmd-2022-24 Preprint. Discussion started: 14 April 2022 c Author(s) 2022. CC BY 4. 0 License.
, 2014) , seems important and needs to be tested. It would also be challenging to evaluate in a heuristic way until which fine scale the hypothesis of the overlapping of surface and subsurface catchments holds. The results of this evaluation could provide a breakthrough in hydrological modeling of the critical zone, since most of the data are acquired in fine-scale catchments, for instance, at the scale of critical zone observatories  (Gaillardet et al. , 2018). The Orgeval catchment located in the Seine basin corresponds to a hydrosystem on which (i) many long-term high-frequency hydrological datasets exist  (Mouhri et al. , 2013; Floury et al. , 2017)  and (ii) such an evaluation could be carried out especially to elucidate water pathways that constitute fundamental information for the understanding of the biogeochemical behavior of hydrosystems  (Floury et al.
To the authors' knowledge, it is the first time that such a characterization of hydrological system behavior of a regional-scale river basin is proposed, and which reproduces the observations fairly well in both time and frequency domains. This development paves the way for significant breakthroughs in hydrological modeling of systems over a large range of scales, from small catchments to regional/continental river basins. Code and data availability. CaWaQS3. 02 is available under an Eclipse Public Licence 2. 0 in the following zenodo deposit  (Flipo et al. , 2022b). MATLAB-based HYMIT scripts are available under an Eclipse Public Licence 2. 0 in the following zenodo deposit  (Schuite, 2022). Hydrological datasets, especially the one associated with Figs. 7, 8c&d, 9, 10 and 11, are available in the following zenodo deposit  (Flipo et al.
and performed the first proof of concept of the step-wise methodology. NF managed the development of the CaWaQS software since https://doi. org/10. 5194/gmd-2022-24 Preprint. Discussion started: 14 April 2022 c Author(s) 2022. CC BY 4. 0 License. 2000. NF and NG developed with other collaborators the CaWaQS3. 02 software (Flipo et al. , 2022b). NG set up the coupled model and performed the calibration, all the calculations presented in this paper as well as pre-processing of the large datasets and post-processing of model outputs. NG, NF and JS analysed the results. NF, NG and JS co-authored the paper and NG designed the figures. NF secured the pluri-annual funding of the work and the management of the project.
Acknowledgements. This work is a contribution to the  PIREN-Seine research program  (www. piren-seine. fr),which is part of the  French e-LTER Zone Atelier Seine. The study was also partly funded by the  Seine Normandy Water Agency (AESN)  through the AQUIVAR and AQUIVAR+ projects. The authors kindly thank  Baptiste Labarthe ,  Agnès Rivière ,  Fulvia Baratelli ,  Mathias Maillot  and  Deniz Kilic  for their contribution to the development of the CaWaQS3. 02software. Daily volume records, for all four reservoirs since their respective construction dates, were kindly provided by the  Etablissement Public Territorial de Bassin Seine Grands-Lacs , basin institution for river water flow regulation. GW withdrawals were compiled from long-term data provided by the  Seine-Normandy Water Agency , over the 1994-2012 period.
NASA disasters program The NASA Disasters Program supports a range of research activities to further our understanding of hazards and utilizes diverse satellite and ground-based products to analyze the interconnected hazards, exposure and risk for a variety of disaster types. Alongside research activities, the program also supports response activities during disasters, enabling stakeholders to use NASA data and analysis to improve decision-making before, during and after disasters. The program engages with over 300 different stakeholders around the world and provided monitoring and support for over 40 different disasters in 2021. In the same period, over 2000 different data consisting of imagery and derived products were uploaded to the NASA Disasters Portal to support stakeholders in an open-science framework.
During earthquakes and tropical storms, the program utilizes satellites run by government agencies to conduct optical-and Synthetic Aperture Radar (SAR)-based analysis of ground surface deformation, flooding, and landslides, among other hazards. In addition, very-high (< 3 m) and high (3-5 m) resolution commercial imagery is also used as part of the ongoing Commercial Satellite Data Acquisition (CSDA) Program agreement between NASA and several private sector entities. These datasets provide overlapping and complementary hazard analysis. A combined team consisting of the NASA Goddard Space Flight Center landslides team, NASA Jet Propulsion Laboratory, University of California Los Angeles, and University of Maryland (herein NASA Team) responded on behalf of the NASA Disasters program. The primary landslide product produced and shared by the NASA Disaster program was the Sentinel-1 SAR backscatter change-based landslide heatmap  (Handwerger et al.
International charter space and major disasters The International Charter Space and Major Disasters (herein the Charter) is a worldwide collaboration of space agencies and disaster monitoring organizations, through which satellite datasets are made available for the benefit of disaster management. By combining Earth observation (EO) assets from 17 space agencies, the Charter allows resources and expertise to be coordinated for rapid response to major disaster situations, thereby helping civil protection authorities and the international humanitarian community. The Charter allows mobilizing agencies around the world and benefits from their know-how and their satellites through a single access point that operates 24 h a day, 7 days a week and at no cost to the user. Since 1999, 770 activations over 131 countries allowed the documentation of many geohazard and hydro-meteorological hazard disasters.
Optical imagery Optical imagery from different satellites with varying degrees of coverage and cloud cover were used by the different groups. Freely accessible medium (5-10 m) resolution imagery, and commercial very-high resolution (< 3 m) and high-resolution (3-5 m) imagery provided for the Charter was accessed using the governmental data access webtool known as the USGS Hazard Data Distribution System (HDDS): • Copernicus Sentinel-2 images (10 m) covering the full area of interest. • Airbus Pléiades imagery (multispectral image at 2 m resolution, and a panchromatic image at 0. 5 m resolution) acquired through the Charter. A cloud-free image over the central zone of the Pic Macaya National Park covering a 385 km 2 area was acquired on August 23, 2021. • Maxar WorldView images (0. 3-0. 5 m) covering only the surrounding area of the main affected zone.
SAR imagery SAR data from the C-band (~ 5. 6 cm wavelength) Copernicus Sentinel-1 satellite constellation archived on GEE was used to detect landslides. GEE provides Level-1 Sentinel-1 Ground Range Detected (GRD) backscatter intensity coefficient (σ∘) images with a 10 m pixel spacing that have been corrected for thermal noise, calibrated in terms of radiometry but are not terrain flattened. Data collected in vertical transmit/horizontal receive (VH) polarization were used. To reduce noise from poor-quality data, all pixels with values ≤ -30 dB were removed (based on a recommendation from the GEE S1 Data Catalog).
ALADIM ALADIM  (Déprez et al. 2022 ) combines object-based image analysis (OBIA) and machine learning to detect and map landslides triggered by large forcing events such as earthquakes and heavy rainfall. Instead of working with individual pixels, OBIA groups similar pixels to form homogenous objects using image segmentation  (Blaschke 2010). Landslide diagnostic features of these objects (e. g. , NDVI, brightness, slope etc. ) are used to detect landslides using supervised machine learning. ALADIM can process optical imagery of medium (typically Landsat 8/9 or Sentinel-2), high (PlanetScope) and very-high (typically Pléiades, WorldView) resolution and requires a small dataset of manually mapped landslides to train the machine learning model. ALADIM is an online web service accessible on the Geohazards Exploitation Platform (GEP, https:// geoha zards-tep.
Replicability for disaster response efforts for other landslide events SAR heatmap, HazMapper, and ALADIM were all designed with an eye toward reproducibility. At their core, each method can be applied at any time to any place on Earth with appropriate satellite imagery. Because SAR heatmap and HazMapper are built within GEE, these algorithms require updating of a few parameters to be applied to different events. ALADIM has a more complex workflow, introducing OBIA and machine learning model, but all components are optimized in the GEP platform, are based on High-Performance Computing (HPC) and are fully automated except for the setup of the training sample. SAR heatmap and HazMapper methods are reproducible by accessing and executing the scripts located in data availability section. A GEE account is required for access, however, though no special permissions are required.
ALADIM is freely accessible on the GEP and requires only the setup of an account; the will further be available in 2023 on the 1 3 Charter Mapper application (https:// eo4so ciety. esa. int/ 2022/ 05/ 24/ advan ced-cloud-basedenvir onment-to-suppo rt-immed iate-disas ter-respo nse/) of the International Charter Space and Major Disasters. The Charter Mapper is a processing environment which integrates a suite of online EO-based services. It can access and display any open access or useruploaded optical and SAR EO data, perform visual analysis, apply GIS functionalities, and perform EO-based processing to extract information from imagery. We see great utility in the further development of open access methods that rely on operational workflows that do not require significant user input or the use of complicated computing systems or environments.
2022; Zhao et al. 2022). A critical lesson that has emerged from this response and the collaborative effort in writing this manuscript is the importance of coordination between different agencies before and during the immediate aftermath of the disaster. This is important to provide mutual support (e. g. , data sharing, situational awareness learnings) and to ensure that efforts are aligned. For example, in the immediate aftermath of the event, authors of this manuscript were independently producing and submitting or releasing inventory products. Our collective contribution could have been more concise and potentially more accurate had we all been collaborating and sharing what we were learning about the landslide event in the early days after the event. For example, manual inventories generated by the USGS may have been able to assist in training the ALADIM automated mapping methods.
Similarly, early estimations of the most concentrated landslide activity identified from SAR heatmap, ALADIM, or HazMapper could have helped USGS analysts pinpoint and conduct detailed manual mapping and confirmation. To this end of increased coordination, the Committee on Earth Observing Satellites (CEOS) Working Group on Disasters (https:// ceos. org/ ourwo rk/ worki nggro ups/ disas ters/) has a Landslide Demonstrator Group whose mission is to ensure sustained coordination of disaster-related activities undertaken by CEOS agencies. It was this group that brought the authors of this manuscript together. The goal of this working group, and other efforts, such as NASA's Disasters Program and the implementation of the ESA Charter Mapper application, is to demonstrate the feasibility, utility, and approaches to leveraging remotely sensed data to improve situational awareness of landslide hazards.
Acknowledgements We thank the  European Space Agency (ESA) Copernicus   program , and the  Google Earth Engine  for providing freely available data and processing. The ALADIM-HR processing has been carried out on the Geohazards Exploitation Platform (GEP) through the  ESA Science Support Activity (SAT). The Pléiades images have been acquired with the support of  CNES (French Space Agency)  through the  CIEST2 (Cellule d'Intervention d'Expertise Scientifique et Technique) initiative  (https:// www. polet erres olide. fr/ ciest-2-nouve lle-gener ation-2/). Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). We thank  BGC   Squared , the philanthropic arm of  BGC Engineering , for supporting the initial rapid response data analysis and deliverable creation.
We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for most living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs, supplemented with community science data. Data from throughout the North Pacific were aggregated into 13 regions, including six breeding regions, six feeding regions, and one migratory corridor. All images were compared with minimal pre-processing using a recently developed image recognition algorithm based on machine learning through artificial intelligence; this system is capable of rapidly detecting matches between individuals with an estimated 97-99% accuracy. For the 2001-2021 study period, a total of 27,956 unique individuals were documented in 157,350 encounters.
Ironically, removal of the Hawaiʻi DPS's endangered status by the US coincided with the 2013-2016 marine heatwave that negatively affected population health  [13] [14] [15] [16] 28. Individual photo-ID data have advanced the understanding of humpback whale behavior, ecology and conservation issues based on many regional study efforts  13, 14, 25, [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41]. However, after SPLASH ended in 2006, local and regional photo-ID datasets were seldom integrated with one another. This was in part due to prohibitively time-intensive visual matching of individual ID fluke photos in ever-growing catalogs. The current study established the North Pacific Humpback Whale Photo-ID (NPPID) collaboration. The goal of this collaboration was to integrate and advance knowledge of humpback whale population structure and migratory movement in the North Pacific through creation of a shared repository of resighting data for individual whales across the full study region.
Therefore, the NPPID was developed as a shared effort utilizing the user-friendly and interactive web-based platform, www. Happy whale. com (Happywhale). Here we describe the process of building this ocean-basin-wide ongoing photo-ID collaboration involving 43 research groups and thousands of public contributors (also known as "community scientists" or "citizen scientists"). This approach has enabled rapid feedback for population and longitudinal studies of humpback whales across the North Pacific. The process and framework described here have broader practical relevance for navigating the use of complex multi-contributor datasets.
Materials and methods The North Pacific humpback whale Photo-ID (NPPID) collaboration. This effort began in 2018 as a data-sharing initiative to revive the collaboration established with the 2004-2006 SPLASH study  8  , supplemented by photo-ID images from community scientists. We built upon the SPLASH dataset, study methodology, and collaboration, but did not have a budget for data acquisition or fieldwork. All SPLASH collaborators known to be active in North Pacific humpback whale studies were invited to join, along with all known newer regional researchers and organizations. Data collection relied on existing archives and ongoing field efforts by the individual collaborators. All dedicated data collection by study collaborators was carried out in accordance with permitting requirements of respective authorities (permit details are listed in acknowledgements).
g. 21, 43. The broader aim was to create an ongoing, living dataset for continued population monitoring. Under the MOA terms, each data contributor chose whether their data were publicly visible via Happywhale or visible only to collaborators who had signed the MOA. The MOA limited data use to a defined set of publications about population status and migratory patterns; any additional use required agreement from all collaborators. The infrastructure, compiled data, and collaborator connections will remain after the period of the current MOA. Therefore, its use needs to be addressed with further agreement among collaborators if the dataset is going to be an ongoing, living entity. Data integration and quality control. Humpback whale encounter data were delivered to Happywhale data managers from collaborators in a wide range of states of reconciliation, from unmatched original scans and digital photos to fully edited sets of images (i.