dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, two different flood forecasting approaches are compared: the first one relies on the coupling of meteorological forecasts with a hydrological model and the second one is a based on a linear relationship between event rainfall, antecedent soil moisture and runoff. Three different soil moisture products (in-situ measurements, European Space Agency's Climate Change Initiative ESA-CCI remote sensing data and ERA5 reanalysis) are compared to estimate the initial soil moisture conditions before flood events for both methods. Results showed that the WRF and AROME models better simulate precipitation amounts compared to ALADIN, indicating the added value of convection-permitting models. The regression-based flood forecasting method outperforms the hydrological model-based approach, and the maximum discharge is better reproduced when using the WRF forecasts in combination with ERA5.
is concentrated only in the highest elevation areas. The geology of the basins consists of impervious formations in the upper part of the basins mainly by igneous rocks such as granite, dolerite and andesite, while in lower part of the basins; the dominant formations are clays and massive sandstones  [31]. The slopes of the basins are devoid of vegetation because of the steep slopes that favor thin and rocky soils and erosion; these conditions also favor the genesis of high magnitude floods. Vegetation cover exists only along the riverbed. Both basins have a flood warning system based on pre-defined threshold exceedance of observed water levels and rainfall intensities. The rainfall data recorded by all the warning system stations are used in this study. Raw rainfall is recorded at 10 min time step which is then converted to 3 h time step accumulations in order to be comparable with meteorological models' outputs (Figure  1 ).
The slopes of the basins are devoid of vegetation because of the steep slopes that favor thin and rocky soils and erosion; these conditions also favor the genesis of high magnitude floods. Vegetation cover exists only along the riverbed. Both basins have a flood warning system based on pre-defined threshold exceedance of observed water levels and rainfall intensities. The rainfall data recorded by all the warning system stations are used in this study. Raw rainfall is recorded at 10 min time step which is then converted to 3 h time step accumulations in order to be comparable with meteorological models' outputs (Figure  1 ). In addition, the Rheraya watershed is also monitored by the rainfall network deployed by the Joint International Laboratory Télédétection et Ressources en Eau en Méditerranée semi-Aride (LMI TREMA,  [42, 43] ).
This rainfall network covered the 2003-2016 periods with recording period of 30 min. 30-min accumulations have also been converted to 3-h accumulations for the LMI TREMA network. In total, 9 rainfall stations are available in the Rheraya basin and 6 in the Ourika basin (Figure  1 ). As most of Moroccan basins are only monitored by daily rain-gauges, a 3 h rainfall accumulation has also been aggregated to daily accumulations, in order to mimic the actual case when implementing the regression method. Rainfall spatial distributions have been obtained after applying the Inverse Distance Weighting (IDW) interpolation method. The discharge data is provided by two ultrasonic water level gauges covering the period from 2013 to 2016 (Figure  1 ). There are 13 flood events available for the Rheraya and 7 for Ourika (Table  1 ).
Soil Moisture Datasets The European Space Agency's Climate Change Initiative (ESA-CCI; http://www. esa-soilmoisturecci. org/) produced the satellite moisture product ESA-CCI Sm V03. 2 in order to obtain long time series of soil moisture data  [44] [45] [46]. The procedure is based on the fusion between active and passive microwave sensors from 1978 and 2016 with a temporal resolution of 1 day and a spatial resolution of 25 km. Within, the Copernicus Climate Change service (https://climate. copernivus. eu/) satellite soil moisture products will be release with a short latency, between a few hours to a day. The product has been validated over the world by Dorigo et al. [47]. ESA-CCI has been used to estimate the antecedent wetness conditions prior to flood events in Europe  [46] [47] [48]  and in Morocco  [41].
• The ALADIN (ALADIN-France, Aladin International Team 1997) with a three-hour time step, 72 h term and a spatial resolution of 10 km. The model's runs are with a two-time-level semi-Lagrangian advection scheme are used with a complete package of physical parameterizations. The physics are the same as in ARPÈGE model  [57]. The operational version at Morocco is ALADIN-Morocco  [58]. The full set-up of the two models over Morocco are described in Hdidou et al. [59]. Since forecast data are not stored by the DMN, AROME and ALADIN models have been run to simulate the flood events considered in the present work (Table  1 ). These runs are fully comparable to real-time forecasts, since no data assimilation is performed during the events. The ALADIN and AROME models have been developed by Météo France and are used both for precipitation  [60, 61]  and flash flood forecasts  [62].
Regression Model In addition to the hydrological model, a simple statistical model based on a multiple regression adjustment is also tested. This is due to the fact that the lack of long time series of complete rainfall and runoff data can make difficult to develop a forecasting system based on a hydrological model. Note that over most basins of Morocco, and in other developing countries, the only hydro-meteorological data available are maximum discharge and rainfall at daily time step. Therefore, it also becomes an important objective to develop a flood forecasting system compatible with the existing limited observed databases. According to Penna et al. [30] , maximum discharge and soil moisture are well correlated for alpine, impervious and semi-arid basins. Therefore, a multiple regression model is fitted individually for the Rheraya and Ourika basins to the end of correlating maximum precipitation, peak discharge and the different soil moisture products available for each flash-flood event.
Regression Approach As aforementioned, the regression approach is composed by three equations per basin, each equation representing a different soil moisture dataset (i. e. , observed, ESA-CCI and ERA5) used to perform the regression. The Equations (  10 )-(  12 ) correspond to Thetaprobes, ESA-CCI and ERA5 soil moisture product respectively for the Rheraya basin, and the equation 13, 14 and 15 for the Ourika basin using the same three soil moisture data. Q obs-Rh = 0. 67 × P + 6. 31 × SM + 24. 24  ( With Q obs-Rh the predicted maximum discharge using in-situ measurements of soil moisture for Rheraya basin, Q obs-Ou predicted maximum discharge using in-situ measurements of soil moisture for Ourika basin, Q ESA-CCI-Rh predicted maximum discharge using ESA-CCI for Rheraya basin, Q ESA-CCI-Ou predicted maximum discharge using ESA-CCI product for Ourika basin, Q ERA5-Rh Predicted maximum discharge using ERA5 product for Rheraya basin, Q ERA5-Ou predicted maximum discharge using ERA5 for Ourika basin, P: Precipitation in 24 h of the meteorological models and SM: Soil Moisture of the used product before the flood event.
Those equations illustrate the good relationship between precipitation, event maximum discharge and soil moisture. The squared-correlation coefficient is larger than 0. 78, which allow validating the equations by the leave-one-out method. Validation shows a good performance in the Rheraya basin with a RMSE of 16 m3/s provided by ERA5 dataset. On the other hand, the multiple regression models based on the ESA-CCI and Thetaprobe datasets exhibit weaker results (26 m 3 /s and 31 m 3 /s, respectively). It is worth noting that the small number of events considered strongly reduces the robustness of the regression model for the Ourika basin. That is, the three multiple regression equations provide an overestimation of the maximum discharge, being the lowest RMSE of 216 m 3 /s for the ERA5 product.
Overall, the ERA5 soil moisture dataset gives the best performance s when compared against the observed soil moisture or ESA-CCI database (Table  5 ). On the other hand, the best performance of mixing soil moisture products and NWP models comes from the combination of the WRF and the ERA-5 database. This combination presents the lowest RMSE and bias. In the Ourika basin, the rainfall amount of some events is strongly underestimated by ALADIN resulting in maximum discharges close to zero. To alleviate this problem, the multi-model mean of the three NWP model simulations has been used. This approach reduces errors when using all the soil moisture products and renders an efficient result over the Ourika basin, with bias reductions up to 26%, 3. 4% and 20. 2% for the ESA-CCI, ERA5 and observed soil moistures, respectively.
Comparing the results between the hydrological and multiple regression approaches when forced by the QPFs, most of the cases results in EFF greater than 0 (Table  5 ). These results suggest that a better performance is obtained when using the regression method instead of the hydrological model. However, these results also depend on the basin and the model and dataset used. For instance, the ALADIN model in combination with the soil moisture obtained for the ESA-CCI and Thetaprobe products yields an EFF smaller than 0 over the Ourika basin. In this case, the hydrological model approach performs better than the regression method. Conversely, the EFF coefficients are positive when using the ERA5 dataset. Table  5. Statistical indices after the application of the linear regression equations approach for the Rheraya and Ourika basins.
The first method relies on a standard hydrological model based on the SCS-CN infiltration method. The second method is based on a linear regression approach linking event rainfall, antecedent soil moisture and event peak discharge. The comparison of these two distinct flood forecasting approaches has shown that the regression method outperforms the hydrological model to simulate maximum discharge with a 24 h lead time. The best results have been obtained by combining the WRF model with the ERA5 dataset as the estimate of the initial soil moisture conditions. This improvement is more marked in the case of the Rheraya basin, although the same conclusion can also be drawn for the Ourika catchment. However, the Ourika basin has a smaller number of events, thus caution is required since with small sample size the results cannot be tested with a great robustness.
This study provides the first evaluation of flash-flood forecasting methodologies in Morocco. Results indicated that a relatively simple forecasting model based on linear regression model could be efficient in the case of a relatively small database of events. Yet, this approach does not take into account the spatial variability of precipitation and there is not a physical definition of the model parameters. In the other hand, the hydrological modeling approach is more difficult to implement in the context of data scarcity, where the high-resolution spatial and temporal hydro-meteorological data required to setup such model are rare. However, prior to the operational implementation of such forecasting systems, there is a strong need to test and validate different methods on other basins with different sizes, physiographic characteristics and climate conditions.
Experimental setup Experiments were conducted under laboratory conditions (25 ± 1 °C, 70 ± 5% RH and 14L:10D). To determine the performance of each of the six egg parasitoids on differentaged JGS eggs, four different ages (0, 10, 20 and 30-d old) of JGS eggs were selected to cover a wide range of aged hosts that could be attacked by these parasitoids in the field. Because adult JGS females start oviposition in September in Kangxian, China, where monthly field temperatures vary from 8 to 28 °C (mean 15. 5 °C) in September and 2 to 21 °C (mean 11. 5 °C) in October based on weather data available at https ://www. tianq i. com. Field temperatures drop dramatically after October (≈ 30-d old of early laid JGS eggs) when these parasitoids are likely no longer active in the field  (Fang et al. 1995).
Acknowledgments We are grateful to  Dr. Gary Gibson  ( Honorary Research Associate, Agriculture and Agri-Food Canada, Canadian National Collection of insects ) for the identification of six Eupelmidae species. This research was funded by the  National Key R&D Program of China  (Nos:  2017YFE0104900  and  2017YFD0201000 ), and  Gansu Provincial Fiscal Science and Technology Award and Compensation Project  ( 2018-01 ).
The RCEMIP ensemble presents a large collection of simulations in an idealized framework spanning a wide range of model types. The results presented here are just the beginning of what we can learn from this ensemble, and we hope that they motivate further analyses of this now public data set. those with parameterized convection (F Net = 2. 19 W m -2 ). This relationship is also true in RCE_large300 (Table  A2 ). Note that F Net should be zero if energy is conserved and the simulation is equilibrated. The actual value of F Net is positive in some models and negative in others; here we consider the magnitude only. The implied ocean heat update is on average Q OCN = 98. 14 W m -2 in RCE_small300 (Table  A1 ) and is larger in models with explicit convection (Q OCN = 101. 75 W m -2 ) than those with parameterized convection (Q OCN = 87.
Acknowledgments We acknowledge high-performance computing support from Cheyenne at the  NCAR-Wyoming Supercomputing Center  (doi: 10. 5065/D6RX99HX )provided by  NCAR 's Computational by the  RIKEN R-CCS  through the  HPCI System Research project  (Project  ID :  hp170323 ) and Oakbridge-FX by  Information Technology Center, The University of Tokyo. Z. M. and S. W. acknowledge support from  NSF   AGS-1543932 , Z. M. additionally acknowledges support from  NASA Headquarters  under the  NASA Earth and Space Science Fellowship Program Grant   80NSSC18K1347. R. R. and T. B. acknowledge support from the  European Union 's  Horizon 2020 research and innovation programme CONSTRAIN  under Grant Agreement No 820829. M. S. acknowledges resources and services from the  National Computational Infrastructure (NCI) , which is supported by the  Australian Government , and is funded through  Australian Research Council   DECRA Award  ( DE190100866 ).
However, the quality-control procedures were not fully automatic, and a manual check of each dataset had to be performed. The goal of the present study is to evaluate the contribution of PWSs to the existing standard weather station (SWS) network in the observation of deep-convection processes at midlatitudes over France. A fully automatic PWS processing algorithm based on comparisons with SWSs was developed. The features near the ground of isolated storms, multicellular systems or supercell storms are observed, extending the work of  Clark et al. (2018) , which focused on a sole supercell storm. Observed features of processes responsible for their formation or generated by these systems such as cold pools, gust fronts and sea breeze effects are studied. In order to do so, mean sea level pressure (MSLP), temperature and humidity gridded analyses including PWSs are built.
Data processing To compare PWS and SWS time series, a linear interpolation of each PWS time series was done at the minutes 5, 15, 25, 35, 45 and 55 of each hour because most of the measurements are done at these times. The result is a missing value if the two closest measurements around the interpolation time are separated by a period of 700 s or more. These interpolated time series are referred to as raw PWS time series. The inspection of raw PWS time series for all parameters shows major departures compared to SWS time series, which confirms the necessity of a quality control, as already stated in previous studies  (Bell et al. , 2013; Muller et al. , 2015; Meier et al. , 2017; Napoly et al. , 2018). Measurements provided by PWSs have a lot of uncertainties due to heterogeneous and unknown environmental conditions.
They show the same diurnal cycle of T Netatmo-SWS as Because of the uncertainties affecting PWS measurements and the departures observed in comparison to reference measurements, an automatic PWS data processing algorithm was built. This includes a quality control in pressure, temperature and humidity which is designed to be simple and efficient whatever the meteorological situation is. The algorithm is mainly based on comparisons with a quality-controlled reference network as was done by  Meier et al. (2017)  and  Clark et al. (2018). The data processing is performed during the periods of time indicated in Table  1. Cases begin before convection initiation and end after convection dissipation of the storm systems studied over the area of interest. In order to accurately evaluate PWSs and be able to detect abnormal behaviour, calm conditions are necessary for most of the time.
For small values of x, p(x) approaches m, the number of SWSs, and RMSE LOOCV (p(x)) approaches quite large values because of the small number of SWSs and their large spacing. The resulting RMSE thresh picked up by the algorithm depends on the case, varying from 1. 10 to 1. 45 • C in temperature and from 5. 5 % to 7. 5 % in relative humidity. For MSLP and surface pressure, instead of a threshold, PWSs providing suspicious measurements are eliminated one by one by an algorithm. This consists of a LOOCV using SWSs and PWSs as validation stations (m = p) that eliminates one suspicious PWS at each step s. PWSs are used in the validation dataset this time because SWS coverage is quite sparse. A one-by-one elimination is possible because only few PWS errors remain after the first three filters in pressure.
Validation After PWS time series were processed, the remaining PWSs were combined to SWSs. This network is called hereafter the standard and personal weather stations (SPWS) network (Fig. 10 ), and the gridded fields produced with this network are called SPWS analyses. The additional value of SPWS analyses compared to SWS analyses is evaluated quantitatively in MSLP, temperature and relative humidity. Also, for temperature and relative humidity, in order to evaluate the role of processing in the results obtained, raw PWS time series and SWS time series were combined. The network associated to this dataset is called the SPWS_raw network. It is not done for pressure because the raw dataset blends MSLP and surface pressure as explained in Sect. 4. 2. LOOCVs are performed on SWS, SPWS and SPWS_raw observations (p observations) and validated on SWS observations (m observations) included in these datasets.
Conclusions Some PWS networks now sample the atmosphere at high spatial and temporal resolution: the Netatmo network, on which this study is focused, constitutes a network of weather stations of identical sensors with unprecedented density available in near-real time, with a minimum 5 min temporal resolution. Adding raw PWS data in observed surface analyses strongly deteriorates the RMSE calculated by LOOCV in comparison with using only SWS analyses. This increased the RMSE LOOCV from 41 % to 72 % in temperature and from 6 % to 31 % in relative humidity depending on the case, showing the negative contribution of PWSs if they are not properly preprocessed and quality-controlled. An automatic processing including a quality control was designed and based on comparison with SWS analyses over short temporal windows.
Median systematic errors are computed and corrected at first for all parameters. Simple quality checks with four steps in pressure, and only three in temperature and humidity, were designed. In terms of temperature and humidity, the main step eliminates PWS time series with departures that are too high when compared to SWS analyses in RMSE. The RMSE threshold is automatically chosen by minimizing a RMSE LOOCV , taking the SWS network as the validation dataset. With respect to pressure, an algorithm performing, at each step, a LOOCV taking all stations as the validation dataset was developed. The PWS providing the highest RMSE LOOCV,k is eliminated at each step. The algorithm stops when the first local minimum in RMSE LOOCV is obtained. Over the four case studies, the mean number of PWS observations kept after processing is 91 % ± 3 % in MSLP, 37 % ± 7 % in temperature and 39 % ± 12 % in relative humidity.
Acknowledgements. We wish to thank the  Météo-France Direction des Systèmes d'Observations  for the field tests of the PWSs they performed and for all datasets provided. We acknowledge the use of imagery from the  NASA  Worldview application (https://worldview. earthdata. nasa. gov,last access:  15 January 2020 ), part of the NASA Earth Observing System Data and Information System (EOSDIS). This work was supported by the  French National programme LEFE (Les Enveloppes Fluides  et l'Environnement; project  ASMA ). The authors would also like to thank the two anonymous referees for their comments that helped to significantly improve the quality of the article. Review statement. This paper was edited by  Vassiliki Kotroni  and reviewed by two anonymous referees.
Livestock mobility network Livestock mobility data are collected by field Veterinarian Services in Senegal, Gambia and Mauritania. In those countries, a certificate system based on sanitary movement permits (Sanitary "Laissez-Passer" or LPS) has been set up to keep track of animal mobility and map the main axes of movements in the area. Every time herders move their herds towards markets, or to other grazing area, a certificate is issued declaring, among other things, the date, the location of origin, the location of destination, the species and number of head, and the means of transportation. In this article, we consider only information relative to cattle movements, on foot, in 2014. We aggregated our data on a timescale of one month, providing a representation of the mobility dynamics over the year.
A map of the road network is available in Appendix (Figure  S1 ). • • The administrative border line between Senegal and Mauritania comes from the GADM web platform (data available online at https: //gadm. org/, last accessed 18/02/2020). The border crossing points (red points in Figure  S1  in Appendix) were given by an expert from a Senegalese's institute specialist in cattle mobility. The bottom level of information regarding livestock movements is called the walking layer W. On this layer, conductance is based on landscape features and changes according to the season. We relied on expert knowledge to assign a conductance weight to each type of land use (Table  1 ). To do so, we conducted four individual interviews with experts, asking them to rank and then estimate the conductance value of different types of land use according to their knowledge of breeder mobility strategies.
Type R i = 1 if a road cross i δ W W i otherwise (1) Finally, the last level of information is given by the administrative border line. To adjust the permeability of the border line to pixels that are not border crossing points, we introduced the parameter δ R ∈ [0, 1]. The value C i of a pixel i on conductance map C according to R and δ R is given by: C i = δ R R i if i is not a border crossing point R i otherwise (2) Livestock movement modeling. The last step consisted in assigning a potential route between every pair of nodes of our livestock mobility network using the conductance maps described in the previous section. To do so, we conducted a connectivity analysis based on concepts from electronic circuit theory  [23]  using Circuitscape software (v4) (https: //pypi. org/project/Circuitscape/, last accessed 18/02/2020).