Analyzing the propagation of drought through water storages using global scale GRACE-based data assimilation

2021 ◽  
Author(s):  
Helena Gerdener ◽  
Kerstin Schulze ◽  
Olga Engels ◽  
Jürgen Kusche ◽  
Hannes Müller Schmied ◽  
...  
Keyword(s):  
Surface Water ◽  
Spatial Resolution ◽  
Land Surface ◽  
Hydrological Model ◽  
Crop Yields ◽  
Global Scale ◽  
Hydrological Drought ◽  
Time Lags ◽  
Satellite Mission

<p>The frequency and severity of drought increase in many regions of the world, which emphasizes the need for sufficient research to better monitor and trigger management plans. An important role hereby plays hydrological drought, because it affects water supply and crop yields that are necessary to ensure food security. Typically, hydrological drought detection is based on in-situ observations of fluxes or storages at the surface. However, this neglects the fact that drought might occur in multiple storages with different timing and severity.  The use of subsurface storage, e.g. groundwater, is rare because the available in-situ well level monitoring is irregularly distributed in space and time and access might be restricted, for example due to national security reasons or problems in converting them to storage estimates.</p><p>The satellite mission Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO offer a great possibility to observe the total water storage, i.e. the sum of surface and subsurface storages, on a global scale from space. However, GRACE is restricted to monthly data on a spatial resolution of about 300 km and the vertical sum of the storages. Hydrological models present another possibility to derive global storage information with a finer spatial (~50km), temporal and vertical resolution than GRACE but they do not perfectly represent the reality because they are underlying assumptions and are affected by uncertainty of forcing data. Therefore, to enable downscaling of GRACE while improving the models realism, the GRACE measurements are assimilated into a hydrological model.</p><p>In previous works we used a framework that assimilates GRACE into the WaterGAP Global Hydrological Model (WGHM) regionally or basin-wise. In this work we present a new framework that globally assimilates GRACE on a 4 degree grid with full uncertainty information from 2003 to 2018. The framework enables to assimilate about 95% of the global WGHM land surface except Greenland. With regard to vertical and spatial resolution the performance of model, observation and assimilation is compared. Global GRACE based drought indicators are applied and its development in the different compartments of surface water, soil and groundwater is analyzed to identify new insights into the propagation of drought. We expect that by including GRACE we derive new information especially for groundwater droughts, which might reveal time lags and a different severity as compared to surface water droughts for some regions.</p>

scholarly journals Challenges in developing a global gradient-based groundwater model (G<sup>3</sup>M v1.0) for the integration into a global hydrological model

2019 ◽  
Vol 12 (6) ◽  
pp. 2401-2418 ◽  
Author(s):  
Robert Reinecke ◽  
Laura Foglia ◽  
Steffen Mehl ◽  
Tim Trautmann ◽  
Denise Cáceres ◽  
...  
Keyword(s):  
Surface Water ◽  
Spatial Resolution ◽  
Land Surface ◽  
Groundwater Modeling ◽  
Global Scale ◽  
Sensitivity Analyses ◽  
Scale Model ◽  
Hydrological Models ◽  
Model Framework ◽  
Gradient Based

Abstract. In global hydrological models, groundwater (GW) is typically represented by a bucket-like linear groundwater reservoir. Reservoir models, however, (1) can only simulate GW discharge to surface water (SW) bodies but not recharge from SW to GW, (2) provide no information on the location of the GW table, and (3) assume that there is no GW flow among grid cells. This may lead, for example, to an underestimation of groundwater resources in semiarid areas where GW is often replenished by SW or to an underestimation of evapotranspiration where the GW table is close to the land surface. To overcome these limitations, it is necessary to replace the reservoir model in global hydrological models with a hydraulic head gradient-based GW flow model. We present G3M, a new global gradient-based GW model with a spatial resolution of 5′ (arcminutes), which is to be integrated into the 0.5∘ WaterGAP Global Hydrology Model (WGHM). The newly developed model framework enables in-memory coupling to WGHM while keeping overall runtime relatively low, which allows sensitivity analyses, calibration, and data assimilation. This paper presents the G3M concept and model design decisions that are specific to the large grid size required for a global-scale model. Model results under steady-state naturalized conditions, i.e., neglecting GW abstractions, are shown. Simulated hydraulic heads show better agreement to observations around the world compared to the model output of de Graaf et al. (2015). Locations of simulated SW recharge to GW are found, as is expected, in dry and mountainous regions but areal extent of SW recharge may be underestimated. Globally, GW discharge to rivers is by far the dominant flow component such that lateral GW flows only become a large fraction of total diffuse and focused recharge in the case of losing rivers, some mountainous areas, and some areas with very low GW recharge. A strong sensitivity of simulated hydraulic heads to the spatial resolution of the model and the related choice of the water table elevation of surface water bodies was found. We suggest to investigate how global-scale groundwater modeling at 5′ spatial resolution can benefit from more highly resolved land surface elevation data.

Analyzing different ways of assimilating volume change estimates for surface water bodies into a hydrological model

2020 ◽  
Author(s):  
Olga Engels ◽  
Kerstin Schulze ◽  
Jürgen Kusche ◽  
Simon Deggim ◽  
Annette Eicker ◽  
...  
Keyword(s):  
Surface Water ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Horizontal Resolution ◽  
Water Bodies ◽  
Satellite Mission ◽  
Grace Data ◽  
Spatial Noise ◽  
Lakes And Reservoirs ◽  
Surface Water Bodies

&lt;p&gt;To better understand global freshwater resources, we combine the state-of-the-art global hydrological model WGHM with Total Water Storage Anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission in an ensemble-based calibration and data assimilation (CDA) framework. However, when dealing with GRACE data, their limited horizontal resolution represents a major challenge. Filtering and/or &amp;#8217;destriping&amp;#8217; is the usual approach for suppressing GRACE-specific spatial noise, which causes spatial leakage and in turn attenuation of signal and reduction of spatial resolution. In GlobalCDA project, we derive altimetry-based storage variations along with corresponding uncertainties of surface water bodies, such as lakes and reservoirs, that feature significantly higher spatial resolution compared to GRACE-based TWSA. These can, additionally, be incorporated into the CDA framework.&lt;/p&gt;&lt;p&gt;In this study, we investigate several possibilities on how to use the additional remote sensing observations within the CDA over the Mississippi basin for the time span 2003 - 2016. For this, we run the CDA (i) using GRACE-based TWSA only, (ii) removing altimetry-based storage variations of surface water bodies from GRACE-TWSA, (iii) removing and restoring altimetry-based storage variations for GRACE-TWSA, and (iv) directly using altimetry-based storage variations. New observation operators are constructed for (ii) and (iv). The results are validated against independent discharge observations.&lt;/p&gt;

scholarly journals Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

2019 ◽  
Vol 11 (3) ◽  
pp. 327 ◽  
Author(s):  
Xia Wang ◽  
Feng Ling ◽  
Huaiying Yao ◽  
Yaolin Liu ◽  
Shuna Xu
Keyword(s):  
Surface Water ◽  
Real Time ◽  
Spatial Resolution ◽  
Water Body ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Body Mapping ◽  
Water Index ◽  
Land Surface Water ◽  
Body Map

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

scholarly journals Global downscaling of remotely sensed soil moisture using neural networks

2018 ◽  
Vol 22 (10) ◽  
pp. 5341-5356 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Jana Kolassa ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Pierre Gentine
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Ancillary Data ◽  
Repeat Time ◽  
Spatiotemporal Scales ◽  
Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

Airborne mapping and in situ validation of European land surface temperature using the NASA-JPL HyTES sensor. Results from the 2019 European NET-Sense Campaign in support of the Copernicus High Priority Candidate satellite mission development.

2020 ◽  
Author(s):  
Martin Wooster ◽  
James Johnson ◽  
Tom Dowling ◽  
Mark de Jong ◽  
Mark Grosvenor ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Emission ◽  
Spectral Emissivity ◽  
Development Effort ◽  
Satellite Mission ◽  
Evapotranspiration Estimation ◽  
The Uk

&lt;p&gt;The NASA ESA Temperature Sensing Experiment (NET-Sense) is a NASA and ESA funded campaign in support of the Copernicus Land Surface Temperature Monitoring (LSTM) satellite mission.&lt;/p&gt;&lt;p&gt;The LSTM mission would carry a calibrated, high spatial-temporal resolution thermal infrared imager whose data would be used to provide the land-surface temperature information required for such applications as evapotranspiration estimation at the European field-scale. The LSTM mission responds to priority requirements of the agricultural user community for improving sustainable agricultural productivity in a world of increasing water scarcity and variability.&lt;/p&gt;&lt;p&gt;As part of the effort to LSTM mission development effort, the first non-US flights of NASA JPL&amp;#8217;s state-of-the-art Hyperspectral Thermal Emission Spectrometer (HyTES) were conducted on a UK research aircraft in both the UK and Italy in June and July 2019. HyTES is an airborne thermal hyperspectral imager providing extremely high quality and radiometrically precise infrared radiances within 256 spectral channels across the spectral range 7.5 to 12 &amp;#181;m, with the primary aim to map LST and surface spectral emissivity. Flights in Italy were accompanied by the HyPLANT and TASI instruments, operated by FZ-Juelich, Germany installed aboard a second aircraft from CzechGlobe (CZ).&lt;/p&gt;&lt;p&gt;We provide an overview of the NET-Sense campaign, example results from HyTES and comparisons to in situ LST and surface spectral emissivity data collected co-incident with the aircraft overflights using tower-mounted radiometers and portable FTIR spectrometers adapted for the purpose. We explain the integration of NET-Sense into the broader science strategy for the LSTM mission, and highlight planned activities for the coming years, including NET-Sense 2020 European campaign plans.&lt;/p&gt;

Global Land Surface Temperature from historical NOAA AVHRR datasets (1981-2000)

2020 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shaofei Wang
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Community Land Model ◽  
Noaa Avhrr

&lt;p&gt;As one of the most important indicators in the energy exchange between land and atmosphere, Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. In contrast to &lt;em&gt;in-situ&lt;/em&gt; measurements, satellite remote sensing provides a practical approach to measure global and local land surface parameters. Although passive microwave remote sensing offers all-weather observation capability, retrieving LST from thermal infra-red data is still the most common approach. To date, a variety of global LST products have been published by the scientific community, e.g. MODIS and (A)ASTR /SLSTR LST products, and used in a broad range of research fields. Several global and regional satellite retrieved LSTs are available since 1995. However, the temporal-spatial resolution before 2000 is generally considerably lower than that after 2000. According to the latest IPCC report, 1983 &amp;#8211; 2012 are the warmest 30 years for nearly 1400 years. Therefore, for global climate change research, it is meaningful to extend the time series of global LST products with a relatively higher temporal-spatial resolution to before 2000, e.g. that of NOAA AVHRR. In this study, global daily NOAA AVHRR LST products with 5-km spatial resolution were generated for 1981-2000. The LST was retrieved using an ensemble of RF-SWAs (Random Forest and Split-Window Algorithm). For a maximum uncertainty in emissivity and water vapor content of 0.04 and 1.0 g/cm&lt;sup&gt;2&lt;/sup&gt;, respectively, the training and testing with simulated datasets showed a retrieval accuracy with MBE of less than 0.1 K and STD of 1.1 K. The generated RF-SWA LST product was also evaluated against &lt;em&gt;in-situ&lt;/em&gt; measurements: for water sites of the National Data Buoy Center (NDBC) between 1981 and 2000, it showed an accuracy similar to that for the simulated data, with a small MBE of less than 0.1 K and a STD between 0.79 K and 1.02 K. For SURFRAD data collected between 1995 and 2000, the MBE is -0.03 K with a range of -1.20 K &amp;#8211; 0.54 K and a STD with a mean of 2.55 K and a range of 2.08 K &amp;#8211; 3.0 K (site dependent). As a new global historical dataset, the RF-SWA LST product can help to close the gap in long-term LST data available to climate research. Furthermore, the data can be used as input to land surface process models, e.g. the Community Land Model (CLM). In support of the scientific research community, the RF-SWA LST product will be freely available at the National Earth System Science Data Center of China (http://www.geodata.cn/).&lt;/p&gt;

scholarly journals Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

2019 ◽  
Author(s):  
Renaud Hostache ◽  
Dominik Rains ◽  
Kaniska Mallick ◽  
Marco Chini ◽  
Ramona Pelich ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Model ◽  
Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

scholarly journals How well are we able to close the water budget at the global scale?

2022 ◽  
Vol 26 (1) ◽  
pp. 35-54
Author(s):  
Fanny Lehmann ◽  
Bramha Dutt Vishwakarma ◽  
Jonathan Bamber
Keyword(s):  
Water Balance ◽  
Balance Equation ◽  
Land Surface ◽  
Water Budget ◽  
Global Scale ◽  
Surface Model ◽  
Catchment Scale ◽  
Satellite Mission ◽  
Water Balance Equation ◽  
Data Products

Abstract. The water budget equation describes the exchange of water between the land, ocean, and atmosphere. Being able to adequately close the water budget gives confidence in our ability to model and/or observe the spatio-temporal variations in the water cycle and its components. Due to advances in observation techniques, satellite sensors, and modelling, a number of data products are available that represent the components of water budget in both space and time. Despite these advances, closure of the water budget at the global scale has been elusive. In this study, we attempt to close the global water budget using precipitation, evapotranspiration, and runoff data at the catchment scale. The large number of recent state-of-the-art datasets provides a new evaluation of well-used datasets. These estimates are compared to terrestrial water storage (TWS) changes as measured by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. We investigated 189 river basins covering more than 90 % of the continental land area. TWS changes derived from the water balance equation were compared against GRACE data using two metrics: the Nash–Sutcliffe efficiency (NSE) and the cyclostationary NSE. These metrics were used to assess the performance of more than 1600 combinations of the various datasets considered. We found a positive NSE and cyclostationary NSE in 99 % and 62 % of the basins examined respectively. This means that TWS changes reconstructed from the water balance equation were more accurate than the long-term (NSE) and monthly (cyclostationary NSE) mean of GRACE time series in the corresponding basins. By analysing different combinations of the datasets that make up the water balance, we identified data products that performed well in certain regions based on, for example, climatic zone. We identified that some of the good results were obtained due to the cancellation of errors in poor estimates of water budget components. Therefore, we used coefficients of variation to determine the relative quality of a data product, which helped us to identify bad combinations giving us good results. In general, water budget components from ERA5-Land and the Catchment Land Surface Model (CLSM) performed better than other products for most climatic zones. Conversely, the latest version of CLSM, v2.2, performed poorly for evapotranspiration in snow-dominated catchments compared, for example, with its predecessor and other datasets available. Thus, the nature of the catchment dynamics and balance between components affects the optimum combination of datasets. For regional studies, the combination of datasets that provides the most realistic TWS for a basin will depend on its climatic conditions and factors that cannot be determined a priori. We believe that the results of this study provide a road map for studying the water budget at catchment scale.

scholarly journals A generic method for hydrological drought identification across different climate regions

2012 ◽  
Vol 16 (8) ◽  
pp. 2437-2451 ◽  
Author(s):  
M. H. J. van Huijgevoort ◽  
P. Hazenberg ◽  
H. A. J. van Lanen ◽  
R. Uijlenhoet
Keyword(s):  
Land Surface ◽  
Threshold Level ◽  
Global Scale ◽  
Hydrological Drought ◽  
Dry Period ◽  
Drought Duration ◽  
Variable Threshold ◽  
The World ◽  
Level Method ◽  
Zero Runoff

Abstract. The identification of hydrological drought at global scale has received considerable attention during the last decade. However, climate-induced variation in runoff across the world makes such analyses rather complicated. This especially holds for the drier regions of the world (both cold and warm), where, for a considerable period of time, zero runoff can be observed. In the current paper, we present a method that enables to identify drought at global scale across climate regimes in a consistent manner. The method combines the characteristics of the classical variable threshold level method that is best applicable in regions with non-zero runoff most of the time, and the consecutive dry days (period) method that is better suited for areas where zero runoff occurs. The newly presented method allows a drought in periods with runoff to continue in the following period without runoff. The method is demonstrated by identifying droughts from discharge observations of four rivers situated within different climate regimes, as well as from simulated runoff data at global scale obtained from an ensemble of five different land surface models. The identified drought events obtained by the new approach are compared to those resulting from application of the variable threshold level method or the consecutive dry period method separately. Results show that, in general, for drier regions, the threshold level method overestimates drought duration, because zero runoff periods are included in a drought, according to the definition used within this method. The consecutive dry period method underestimates drought occurrence, since it cannot identify droughts for periods with runoff. The developed method especially shows its relevance in transitional areas, because, in wetter regions, results are identical to the classical threshold level method. By combining both methods, the new method is able to identify single drought events that occur during positive and zero runoff periods, leading to a more realistic global drought characterization, especially within drier environments.

scholarly journals Urban Land Surface Temperature Monitoring and Surface Thermal Runoff Pollution Evaluation Using UAV Thermal Remote Sensing Technology

2021 ◽  
Vol 13 (20) ◽  
pp. 11203
Author(s):  
Shanshan Xu ◽  
Kun Yang ◽  
Yuanting Xu ◽  
Yanhui Zhu ◽  
Yi Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Temperature ◽  
Water Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Surface ◽  
Surface Water Temperature ◽  
Before And After

With the continuous advancement of urbanization, the impervious surface expands. Urbanization has changed the structure of the natural land surface and led to the intensification of the urban heat island (UHI) effect. This will affect the surface runoff temperature, which, in turn, will affect the surface water temperature of urban lakes. This study will use UAS TIR (un-manned aerial system thermal infrared radiance) remote sensing and in situ observation technology to monitor the urban space surface temperature and thermal runoff in Kunming, Yunnan, in summer; explore the feasibility of UAS TIR remote sensing to continuously observe urban surface temperature during day and night; and analyze thermal runoff pollution. The results of the study show that the difference between UAS TIR LSTs and in situ LSTs (in situ air temperature 10 cm above the ground.) varies with the type of land covers. Urban surface thermal runoff has varying degrees of impact on water bodies. Based on the influence of physical factors such as vegetation and buildings and meteorological factors such as solar radiation, the RMSE between UAS LSTs and in situ LSTs varies from 1 to 5 °C. Land cover types such as pervious bricks, asphalt, and cement usually show higher RMSE values. Before and after rainfall, the in situ data of the lake surface water temperature (LSWT) showed a phenomenon of first falling and then rising. The linear regression analysis results show that the R2 of the daytime model is 0.92, which has high consistency; the average R2 at night is 0.38; the averages R2 before and after rainfall are 0.50 and 0.83, respectively; and the average RMSE is 1.94 °C. Observational data shows that thermal runoff quickly reaches thermal equilibrium with the land surface temperature about 30 min after rainfall. The thermal runoff around the lake has a certain warming effect on LSWT.

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