dataesr/scientific-paragraphs-categorization huggingface dataset:
Combining both datasets results also in a multimodal distribution, but the set E-W is the dominant one (Fig. 8 ). Analysis of the size suggests that 50% of the interpreted structures in the satellite image are above 300 m, while 50% of outcrop fractures are below 2 m (Fig. 7 ). Combining both datasets suggest that the DFN would be characterized by 65% of structures below 50 m and 35% of structures between 50 to 1,437 m. This highlights the greater number of small fractures observed compared to larger structures, agreeing with the fact Page 11 of 27 85 Vol. : (0123456789) that a power-law distribution provides the best fit to the structures size. The RMSE for the power-law distribution was evaluated to be 0. 25, while for the exponential and lognormal distribution was 0. 29 and 0. 35, respectively.
The SSE for the power-law distribution was estimated to be 40, while for the exponential and lognormal distribution was 52 and 80, respectively. As the power-law distribution best describes the fracture sizes, it can be assumed that the network follows a fractal size distribution. Plotting the data collected in outcrops and observed in the satellite image in a log-log plot suggests a multifractal size distribution and application of the chord method suggests a lower cutoff fracture size of 200 m when both datasets are combined. The power-law exponent for fractures between 200 to 1437 m is -2. 0. The power-law exponent for the outcrop data is -1. 8 and the lower cutoff length is 2 m. The power-law exponent for the satellite image data is -2. 0 and the lower cutoff length is 200 m. The chord method is based on the log-log plot of fracture size against cumulative frequency.
A line is drawn through the data point with the shortest length and the data point with the longest length. Then, the data point with the longest perpendicular distance from this line is used as the lower cutoff  (Zeeb et al. 2013a ). The power-law functions to characterize our dataset are then: These power-law exponents closer to 2 suggest that the fracture size has a fractal character. Furthermore, the power-law exponent of the outcrop data suggests (1) Outcrop data ∶ f (l) = 1. 407l -1. 792 (2) Satellite image data ∶ f (l) = 39982l -1. 982 (3) Both databases ∶ f (l) = 14230l -1. 982 that based on that dataset the fracture network comprises short size elements  (Bossennec et al. 2021). Spacing data interpreted suggests that the best fit is given by an exponential distribution, which indicates a uniform spatial distribution for the structures (Fig.
1 mm and a maximum value of 165 mm (Fig. 7 ). These values indicate that the apertures range from tight to wide, but with different probability of occurrence (Table  2 ). The data collected also suggests that a power-law distribution has the best fit, with an RMSE of 0. 17 and an SSE of 9. A lognormal distribution reveals an RMSE of 0. 29 and a SSE of 27. The power-law function to characterize our dataset is then: It is important to highlight, however, that these apertures are greatly influenced by weathering of the fracture walls and may not correspond to the in situ aperture value. For this reason, these aperture values measured in the field were neglected for the numerical simulations undertaken.
(2019). A large structural lineament observed at the satellite image scale can indeed correspond to several smaller fractures at the outcrop scale. This is an important data resolution bias and introduces important uncertainty to conduct further simulations with subsurface numerical models. As the numerical simulations illustrate, a DFN built based on satellite image data, where a majority of fractures are longer than 200 m, could lead to an early thermal short-circuiting of the geothermal reservoir. This can have important economic impacts for a geothermal system. However, if the DFN is built based on outcrop data, where most of the fractures have a length of 5 m or less, this could have important technological issues in developing the geothermal reservoir by hydraulic stimulation. In fact, the effectiveness of the hydraulic stimulation treatment may be greatly diminished with such a dataset, according to the numerical simulations carried out.
Of course, field tests are needed to corroborate or refute the modeling exercise undertaken. One option to deal with this data resolution bias according to  Peacock et al. (2019)  is to define a resolution limit, and sampling only fractures of a certain length. According to these authors, defining this resolution limit is helpful to upscale the geometry of the fracture network to the scales of interest for reservoir models. In this paper, we decided not to sample only fractures of a certain length since our objective was to understand how fracture lengths measured at different scales would respond to hydroshearing. It is also important to discuss the fact that the satellite image analyzed corresponds to an outcrop made of gneiss of the Ungava Complex, while the field analysis was undertaken on outcrops of paragneiss of the False Suite unit, diorite of the Kaslac Complex and on outcrops on the contact between tonalite of the Aveneau Suite and paragneiss of the False Suite and between the tonalite and the gabbro rocks of the Ralleau Suite.
This problem can be commented on to remote regions where subsurface data is sparce and expensive to collect. Methods can also impose biases and uncertainty as explained in  Peacock et al. (2019). Despite different methods being used in this work, namely analysis of a satellite image, analysis of core data, and scanline Page 19 of 27 85 Vol. : (0123456789) sampling methods on outcrops surface, the dataset used in this work was useful to illustrate the objective of the paper. Fracture length and spacing can play a critical role in an effective hydraulic stimulation treatment, which will have important economic consequences for a potential EGS development in the study area. The authors are aware that the modeling exercise may be uncertain. Further field tests are of upmost importance to decrease the uncertainty and provide more accurate and realistic results.
It is also important to note that carrying out geothermal research in northern communities requires "out of the box" solutions for a first-order assessment of their geothermal potential. Field logistic is extremely challenging in communities not connected by road, where field material (e. g. , borehole tools) needs to be shipped by boat or airplane, and the fieldwork timing is reduced due to cost and short field seasons followed by the long winter. If the preliminary data suggests an interesting potential, then it opens the door to further geothermal research. Another source of bias and uncertainty in the fracture network characterization and modeling comes from the analysts themselves. If other people analyze our datasets and obtain a different interpretation, this may lead to different results, highlighting a reproducibility issue.
This can lead to another source of bias and uncertainty in the modeling results that the authors are aware. In fact, a next step can be the comparison of the results from FRACSIM3D with other numerical simulators to quantify the influence of the numerical simulator on forecasting EGS development and performance. Despite some bias, uncertainty and limitations related to the difficulty of collecting reliable datasets in remote regions, this research work qualitatively illustrates how the scale effect of fracture geometrical properties can have an important impact on hydroshearing treatment. The results, despite uncertain, suggest that further and more detailed geothermal research focused on the natural fracture network is worthwhile to assess accurately if EGS can be developed in the study area.
Conclusions This paper analyzed the natural fractures and structural lineaments in the community of Kuujjuaq (Nunavik, Canada) through different datasets and investigated how the observed variability could impact the network response to hydroshearing through numerical modeling. Despite the scale effect being known, our study illustrates challenges to consider when analyzing fracture data with the goal of generating stochastic DFNs and understand the deepseated structures. The results of this work highlight that orientation, size and connectivity are greatly dependent on the scale of observation, and less so for fracture spacing and linear frequency. Outcrop data alone suggests one main fracture set while the combination of satellite image and outcrop data indicates five main families. Furthermore, outcrop data indicates fractures smaller than 30 m while structural lineaments of up to ~ 1,500 m were identified in the satellite image.
Acknowledgements The Centre d'études nordiques (CEN), supported by the  Fonds de recherche du Québec -nature et technologies (FRQNT) , and the  Observatoire Homme Milieu Nunavik (OHMI)  are acknowledged for helping with field campaigns and logistics. The authors are also thankful to  Cynthia Brind'Amour-Cote  for the support during the field campaign and to  Jonathan Willis-Richards  who provided the code FRACSIM3D used to run the EGS numerical simulations. The authors would also like to acknowledge the two anonymous reviewers whose comments helped to greatly improve the manuscript.
, 2009). More coral community data, as well as high-resolution, long-term, multi-parameter abiotic datasets are therefore needed to improve our understanding of the environmental drivers of ecosystem structure and functioning in marginal and extreme coral communities and how they change over time. The lack of a consensus on how to define marginal coral communities, combined with the lack of any definition for extreme communities, has resulted in inconsistent usage of these terms in the literature. This highlights that they likely mean different things to different people, and, as an increasing number of scientists are now studying these coral communities, it is necessary to (re-)define coral reef "marginality" and "extremeness". In this review, we provide a new conceptual framework that will allow for more detailed and consistent characterization and categorisation of these coral communities and specifically address the following five questions: 1.
Acknowledgements The authors acknowledge the following funding: a  Vidi Fellowship of the Dutch Research Council  ( VI. Vidi. 203. 069 ) to VS, an  Australian Research Council   Discovery Early Career Research Award  ( DE190100142 ) to EFC, a  Rutherford Discovery Fellowship  from the  Royal Society of New Zealand Te Apārangi  to CEC, a  Victoria University of Wellington FSRG  to  CEC  and  VS , an  ANR  Grant n. ANR15CE02-0006-01  from the  French National Research Agency  and funding from the  International CO 2 Natural Analogues (ICONA) Network  to RRM, and a  Chancellor's Postdoctoral Research Fellowship  from the  University of Technology Sydney  to BS.
, 2014; Brocca et al. , 2016). The approach proved to be successful in estimating irrigation at the district level, using different SM datasets as inputs  (Dari et al. , 2020; 2023)  and coupling it with ET products  (Dari et al. , 2022). Nevertheless, the methodology showed increasing errors during rainier seasons and in discriminating between precipitation and irrigation amounts  (Brocca et al. , 2018; Dari et al. , 2023; Brombacher et al. (2022)  also proposed a simple approach based on the difference of actual ET between irrigated and natural hydrological similar pixels. The approach proved to be effective only for the case in which enough natural pixels were available in the study area, which is not always the case for intensively cultivated areas. Koch et al. (2020)  and  Kragh et al.
The approach proved useful in estimating total irrigation amounts over 5 irrigated fields in Marocco, but performances were not satisfactory at daily to weekly scale due to the low temporal resolution of the Landsat product. The authors concluded that satisfactory results could only be obtained with a temporal frequency of thermal data finer than 10 days, and also suggested that including an SM product would lead to improvements thanks to a better-defined topsoil condition. Similarly,  Ouaadi et al. (2021)  assimilated Sentinel-1 SM into an FAO-56 model. The study also showed how at least a 3-day revisit time is needed to correctly estimate irrigation amounts with this methodology. Modanesi et al. (2022)  proposed to assimilate Sentinel-1 SM into the Noah-MP LSM coupled with an irrigation scheme  (Niu et al.
This study proposes for the first time to adapt this methodology to estimate irrigation amounts instead of correcting precipitation amounts. Three main modifications to the algorithm are introduced in order to adapt the methodology to irrigation estimation and to overcome the limitations of previous methodologies: i) Fig. 1. On the top panel, a map representing the location of the two study areas, the Algerri-Balaguer irrigation district (green box) and the Foradada field (red box), which are located in the province of Lleida, in Catalunya, Spain. The main irrigation districts of the region are delineated. On the lower left panel, the Algerri-Balaguer irrigation district (in blue), on a map of irrigated and non-irrigated fields, from the SIGPAC-DUN administrative dataset. Meteo stations (red dots) and the water pumping station (green cross) are also shown.
2. For the filed-level study, the 100 m product is retrieved from a disaggregation of the SMAP SM using Landsat optical and thermal data, which has a 100 m resolution. Results at field scale produced using DISPATCH SM at 100 m are presented in 5. 3. Details about the DISPATCH methodology used can be found in  (Merlin et al. , 2013) , where disaggregation of SMOS data is proposed with both thermal products at 1 km and 100 m. The only variation applied for this study was the use of a vegetation extension version of DISPATCH, which overcomes the limitation of disaggregating SM over areas with high Normalized Difference Vegetation Index (NDVI), by using the Temperature Difference Vegetation Index (TDVI) instead of the Soil Evaporative Efficiency (SEE) as a disaggregation parameter, as described in  Ojha et al.
Meteorological data For the district-level and field-level studies, meteorological data were retrieved from the Catalan meteorological network, which is openly accessible (https://ruralcat. gencat. cat/web/guest/agrometeo. estacions). Hourly air temperature and precipitation were retrieved from these meteo stations for the years 2017-2021. For the district-scale analysis, even though the nearby stations showed very similar values, the data were spatially distributed into the same grid and projection as the observed DISPATCH SM data at 1 km using the nearest neighbor approach (using the kd-tree algorithm  (Maneewongvatana and Mount, 1999). Eleven of the closest stations were used to create this product, and they are identified in the lower left panel of Fig. 1. For the analysis at the field level, the data from the Baldomar meteo station was used, since it is the closest station to the Foradada fields, located approximately at a 6 km distance.
Methodology The PrISM methodology  (Pellarin et al. , 2020; Pellarin et al. , 2013)  was originally designed to correct precipitation amounts using remotely sensed SM. The method employs a simple soil moisture/precipitation model which is a slight modification of the Antecedent Precipitation Index (API) and a particle filter assimilation scheme to ingest these SM observations. The approach proposed in this study follows the same structure of the classical PrISM, even though some additional steps are added in the processing pipeline. The main change proposed is the use of two sets of inputs, extracted from two different adjacent areas, irrigated and rainfed respectively, with the assumption that they share similar meteo conditions and soil types. Data coming from the rainfed area is used for calibration, to estimate the main parameters of PrISM, which are then used to retrieve irrigation amounts coming from the irrigated pixel.
, 2020). In order to improve the quality of the first guess, a preprocessing step is applied before the ingestion of this first guess into the standard PrISM model. This preprocessing step involves the removal of all the negative estimation of precipitation and irrigation amounts, which are automatically set to 0, plus a temporal downscaling from the resolution of the observations (usually in the order of days, when the SM observations are retrieved from satellite products) to the resolution of the precipitation dataset (which is in the order of hours). Temporal downscaling is a particularly important step in this methodology. This step is needed to have the same temporal frequency between irrigation (which is retrieved from inverted SM with a temporal resolution usually in the order of days) and precipitation (which has a higher temporal resolution, in the order of hours).
Synthetic study This synthetic study is performed to assess the performance of PrISM in a controlled environment. Real data from one year of precipitation and irrigation are retrieved from the in situ data available in the Foradada field. From these data, a synthetic SM profile is created using the API formula, all the parameters needed were retrieved from air temperature values and average NDVI, using the formulas proposed in  Pellarin et al. (2020). A comprehensive number of tests are performed modifying this synthetic SM profile to adapt it to realistic scenarios. Three variations are introduced: the addition of white noise to simulate instrumental error from the observed SM, a variation of the temporal resolution of the data, a reduction in the frequency of available SM, and finally a variation of the spatial resolution of the data, which is simulated by changing the different percentage of irrigation present in the pixel detected by the observed SM.
District scale: Algerri-Balaguer The PrISM model adapted for irrigation estimation is consequently applied to observed SM data at 1 km resolution, derived from the disaggregation through DISPATCH of SMAP SM gridded at 9 km with NDVI and LST from MODIS at 1 km. Given that the SM and precipitation variables across the dryland area are quite homogeneous, calibration results do not vary across the pixels in the area, so a single representative pixel from the dry-land area is selected to calibrate the parameters p 1 , p 2 , sm res , sm sat , and τ, following the procedure illustrated in Fig. 2. Nevertheless, the methodology can be easily extended to a larger area and multiple pixels, as long as the assumption of similar meteo conditions and soil types is valid. Following calibration, results are then produced for each pixel of the irrigated area of Algerri-Balaguer.
This amount should be considered as an error of the model, irrigation is not practiced in this dryland area. This error is also visible in the time series of Fig. 6C , where the irrigation time series of the dryland area (red lines) shows some isolated irrigation events occurring both in winter and in summer. The fact that these events seem very homogeneous for all the drylands pixels (both in timing and amount) suggests that they are not real irrigation events, but they are most probably precipitation events that were not included in the Precipitation datasets used as input, and for this reason, are misclassified as irrigation. Another interesting result from the spatial distribution of Fig. 6B  is the difference in irrigation amounts between the northern and southern parts of the irrigated area.
11 and 8. 14 mm∕5-days, depending on the different dataset used, since multiple experiments were performed using SM and ET datasets at 1 km. When converting the results of our study to the same 5-day temporal resolution, a correlation r of 0. 78 is found, and a rmse of 6. 45 mm∕5-days for the year 2017. These results show a slight improvement concerning these two previous studies, in terms of correlation r, while a similar rmse is found. Dari et al. (2023)  reported for the years 2016-2019 a correlation coefficient r equals to 0. 78 and a rmse of 14. 41 mm∕14-days for the Algerri Balaguer district, using the SM2RAIN algorithm with SM from Fig. 6. Results from PrISM applied at the pixel level in the Algerri-Balaguer district and the adjacent dryland area. (A) shows the spatial distribution of the irrigation amounts in Algerri-Balaguer for the winter period (January to April and October to December of the year 2017).
The borders of the irrigated and dryland areas are delineated in blue and red, and the total amount of irrigation is shown. (B) shows the equivalent maps for the summer season (April to October of 2017). (C) shows the irrigation time series retrieved for all the pixels inside the irrigated area (blue lines) and the pixels in the dryland (red lines). The orange background shows the summer period. Sentinel-1. If converted to a 14-day period, the PrISM methodology shows higher performances, with a correlation r of 0. 84 and a rmse of 12. 16 mm∕14-days. Nevertheless, it is important to notice that the results from  Dari et al. (2023)  are retrieved from Sentinel-1 SM at 1 km, which is a different dataset from the one used in this study. Additionally, the results from  Dari et al. (2023)  are retrieved after a calibration of the model using in situ data from the year 2019, which is not the case for the other studies that employed SM2RAIN, which fully rely on remote sensing data.
Field scale: Foradada The PrISM model is also applied at the field level for the Foradada field, using the SM product from DISPATCH at 100 m. The model is calibrated with the same procedure described in Section 5. 2, using the time series from a non-irrigated field very close to the Foradada field, as shown in the lower right panel of Fig. 1. The results are shown in Fig. 8. Fig. 8A  shows in purple the in situ SM and in green the PrISM SM between the maximum and minimum scenarios, which follows closely the observations from DISPATCH 100 mSM, depicted as orange diamonds. It is important to notice that the purple line that indicates insitu SM is not scaled with the green line representing SM built from PrISM, since in-situ data are not calibrated against the Dispatch remotely sensed SM assimilated by PrISM.
As expected from the synthetic study, irrigation retrieved with PrISM at the field level shows an underestimation of the total irrigation amounts, mainly caused by the low temporal frequency of the observed SM, which is derived from Landsat LST, available at best every 8 days in cloud-free conditions. Nevertheless, the model shows a good correlation with the in situ data and seems to correctly reproduce the SM and irrigation profiles. Compared with other studies performed at the field level using remotely sensed SM, PrISM shows better performances in terms of correlation and rmse, even though experiments were performed in different areas and with different datasets. Ouaadi et al. (2021)  uses SM from Sentinel-1 data at field level to retrieve 15-day cumulated irrigated amounts, with a final correlation r of 0.
64, an rmse of 28. 7 mm∕15-days and a bias of 1. 9 mm∕15-days. Zappa et al. (2021)  also used Sentinel-1 data to detect irrigation events at field level, with a mean Pearson correlation r = 0. 64 and a large variability of the bias, varying from 8 mm∕season to -98 mm∕season for the different fields. Both these studies stress the importance of a higher temporal frequency of the SM observations, which is the main driver for improvements in irrigation amount retrieval. Brombacher et al. (2022)  proposed a methodology to estimate irrigation amounts at field level from the difference in actual ET of an irrigated pixel against a "hydrological similar pixel", found in the surrounding natural areas. Results in the Ebro Valley (same study region of this work) were not statistically significant, since only a few areas were identified as hydrological similar pixels and they were quite distant from the irrigated fields.