scholarly journals RECOG RL01: Correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes

2020 ◽  
Author(s):  
Simon Deggim ◽  
Annette Eicker ◽  
Lennart Schawohl ◽  
Helena Gerdener ◽  
Kerstin Schulze ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Regional Scale ◽  
Water Storage ◽  
Water Volume ◽  
Optical Remote Sensing ◽  
Hydrological Models ◽  
Total Water ◽  
Satellite Mission ◽  
The Impact

Abstract. Observations of changes in terrestrial water storage obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighboring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes & reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a re-location of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction RECOG-EQ includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above 9 Mw. We can show that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, even in neighboring river basins, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available as open access via the Pangea database (RECOG-LR: Deggim et al. (2020a) https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al. (2020b, under revision), https://doi.pangaea.de/10.1594/PANGAEA.921923).

scholarly journals RECOG RL01: correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes

2021 ◽  
Vol 13 (5) ◽  
pp. 2227-2244
Author(s):  
Simon Deggim ◽  
Annette Eicker ◽  
Lennart Schawohl ◽  
Helena Gerdener ◽  
Kerstin Schulze ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Regional Scale ◽  
Water Storage ◽  
Water Volume ◽  
Optical Remote Sensing ◽  
Hydrological Models ◽  
Total Water ◽  
Lakes And Reservoirs ◽  
The Impact

Abstract. Observations of changes in terrestrial water storage (TWS) obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models, spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighbouring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology-related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes and reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a relocation of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction, RECOG-EQ, includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above Mw9. We discuss that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available on an open-access basis via the Pangaea database (RECOG-LR: Deggim et al., 2020a, https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al., 2020b, https://doi.org/10.1594/PANGAEA.921923).

scholarly journals GRACE water storage estimates for the Middle East and other regions with significant reservoir and lake storage

2012 ◽  
Vol 9 (10) ◽  
pp. 11131-11159 ◽  
Author(s):  
L. Longuevergne ◽  
C. R. Wilson ◽  
B. R. Scanlon ◽  
J. F. Crétaux
Keyword(s):  
Spatial Distribution ◽  
Surface Water ◽  
Numerical Experiments ◽  
Water Storage ◽  
Accurate Estimation ◽  
Total Water ◽  
Satellite Mission ◽  
Reservoir Storage ◽  
Aquifer Systems ◽  
The Impact

Abstract. While GRACE (Gravity Recovery and Climate Experiment) satellites are increasingly being used to monitor water storage changes globally, the impact of spatial distribution of water storage within a basin is generally ignored but may be substantial. In many basins, water may be stored in reservoirs, lakes, flooded areas, small aquifer systems, and other localized regions with sizes typically below GRACE resolution. The objective of this study was to assess the impact of non-uniform water storage distribution on GRACE estimates as basin-wide averages, focusing on surface water reservoirs. Analysis included numerical experiments testing the effect of mass size and position within a basin, and application to the Lower Nile (Lake Nasser) and Tigri–Euphrates (TE) basins as examples. Numerical experiments show that by assuming uniform mass distribution, GRACE estimates may under- or over-estimate basin-average water storage by up to a factor of two, depending on reservoir location and extent. Although their spatial extent may be unresolved by GRACE, reservoir storage may dominate in some basins. For example, it accounts for 95% of seasonal variations in the Lower Nile and 10% in the TE basins. Because reservoirs are used to mitigate droughts and buffer against climate extremes, their influence on interannual time scales can be large, for example accounting for 50% of total water storage decline during the 2007–2009 drought in the TE basin. Effects on GRACE estimates are not easily accounted for via simple multiplicative scaling, but in many cases independent information may be available to improve estimates. Accurate estimation of the reservoir contribution is critical, especially when separating groundwater from GRACE total water storage changes. Because the influence of spatially concentrated water storage – and more generally water distribution – is significant, GRACE estimates will be improved when it is possible to combine independent spatial distribution information with GRACE observations, even when reservoir storage is not a major factor. In this regard, data from the upcoming Surface Water Ocean Topography (SWOT) satellite mission should be an especially important companion to GRACE-FO observations.

Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

2020 ◽  
Author(s):  
Alex Sun ◽  
Bridget Scanlon ◽  
Himanshu Save ◽  
Ashraf Rateb
Keyword(s):  
Machine Learning ◽  
Spatial Variability ◽  
Large Scale ◽  
Water Storage ◽  
Total Water ◽  
New Paradigm ◽  
Satellite Mission ◽  
Automated Machine Learning ◽  
The Mean ◽  
The Impact

<p>The GRACE satellite mission and its follow-on, GRACE-FO, have provided unprecedented opportunities to quantify the impact of climate extremes and human activities on total water storage at large scales. The approximately one-year data gap between the two GRACE missions needs to be filled to maintain data continuity and maximize mission benefits. There is strong interest in using machine learning (ML) algorithms to reconstruct GRACE-like data to fill this gap. So far, most studies attempted to train and select a single ML algorithm to work for global basins. However, hydrometeorological predictors may exhibit strong spatial variability which, in turn, may affect the performance of ML models. Existing studies have already shown that no single algorithm consistently outperformed others over all global basins. In this study, we applied an automated machine learning (AutoML) workflow to perform GRACE data reconstruction. AutoML represents a new paradigm for optimal model structure selection, hyperparameter tuning, and model ensemble stacking, addressing some of the most challenging issues related to ML applications. We demonstrated the AutoML workflow over the conterminous U.S. (CONUS) using six types of ML algorithms and multiple groups of meteorological and climatic variables as predictors. Results indicate that the AutoML-assisted gap filling achieved satisfactory performance over the CONUS. For the testing period (2014/06–2017/06), the mean gridwise Nash-Sutcliffe efficiency is around 0.85, the mean correlation coefficient is around 0.95, and the mean normalized root-mean square error is about 0.09. Trained models maintain good performance when extrapolating to the mission gap and to GRACE-FO periods (after 2017/06). Results further suggest that no single algorithm provides the best predictive performance over the entire CONUS, stressing the importance of using an end-to-end workflow to train, optimize, and combine multiple machine learning models to deliver robust performance, especially when building large-scale hydrological prediction systems and when predictor importance exhibits strong spatial variability.</p>

scholarly journals GRACE water storage estimates for the Middle East and other regions with significant reservoir and lake storage

2013 ◽  
Vol 17 (12) ◽  
pp. 4817-4830 ◽  
Author(s):  
L. Longuevergne ◽  
C. R. Wilson ◽  
B. R. Scanlon ◽  
J. F. Crétaux
Keyword(s):  
Spatial Distribution ◽  
Surface Water ◽  
Numerical Experiments ◽  
Water Storage ◽  
Accurate Estimation ◽  
Areal Extent ◽  
Total Water ◽  
Reservoir Storage ◽  
Average Water ◽  
The Impact

Abstract. While GRACE (Gravity Recovery and Climate Experiment) satellites are increasingly being used to monitor total water storage (TWS) changes globally, the impact of spatial distribution of water storage within a basin is generally ignored but may be substantial. In many basins, water is often stored in reservoirs or lakes, flooded areas, small aquifer systems, and other localized regions with areas typically below GRACE resolution (~200 000 km2). The objective of this study was to assess the impact of nonuniform water storage distribution on GRACE estimates of TWS changes as basin-wide averages, focusing on surface water reservoirs and using a priori information on reservoir storage from radar altimetry. Analysis included numerical experiments testing effects of location and areal extent of the localized mass (reservoirs) within a basin on basin-wide average water storage changes, and application to the lower Nile (Lake Nasser) and Tigris–Euphrates basins as examples. Numerical experiments show that by assuming uniform mass distribution, GRACE estimates may under- or overestimate basin-wide average water storage by up to a factor of ~2, depending on reservoir location and areal extent. Although reservoirs generally cover less than 1% of the basin area, and their spatial extent may be unresolved by GRACE, reservoir storage may dominate water storage changes in some basins. For example, reservoir storage accounts for ~95% of seasonal water storage changes in the lower Nile and 10% in the Tigris–Euphrates. Because reservoirs are used to mitigate droughts and buffer against climate extremes, their influence on interannual timescales can be large. For example, TWS decline during the 2007–2009 drought in the Tigris–Euphrates basin measured by GRACE was ~93 km3. Actual reservoir storage from satellite altimetry was limited to 27 km3, but their apparent impact on GRACE reached 45 km3, i.e., 50% of GRACE trend. Therefore, the actual impact of reservoirs would have been greatly underestimated (27 km3) if reservoir storage changes were assumed uniform in the basin. Consequently, estimated groundwater contribution from GRACE would have been largely overestimated in this region if the actual distribution of water was not explicitly taken into account. Effects of point masses on GRACE estimates are not easily accounted for via simple multiplicative scaling, but in many cases independent information may be available to improve estimates. Accurate estimation of the reservoir contribution is critical, especially when separating estimating groundwater storage changes from GRACE total water storage (TWS) changes. Because the influence of spatially concentrated water storage – and more generally water distribution – is significant, GRACE estimates will be improved by combining independent water mass spatial distribution information with GRACE observations, even when reservoir storage is not the dominant mechanism. In this regard, data from the upcoming Surface Water Ocean Topography (SWOT) satellite mission should be an especially important companion to GRACE-FO (Follow-On) observations.

scholarly journals Interannual variations of the terrestrial water storage in the Lower Ob' Basin from a multisatellite approach

2010 ◽  
Vol 14 (12) ◽  
pp. 2443-2453 ◽  
Author(s):  
F. Frappart ◽  
F. Papa ◽  
A. Güntner ◽  
S. Werth ◽  
G. Ramillien ◽  
...  
Keyword(s):  
Surface Water ◽  
Root Zone ◽  
Water Storage ◽  
Temporal Variations ◽  
Water Levels ◽  
The Arctic ◽  
Water Volume ◽  
Hydrological Models ◽  
Radar Altimetry ◽  
Terrestrial Water

Abstract. Temporal variations of surface water volume over inundated areas of the Lower Ob' Basin in Siberia, one of the largest contributor of freshwater to the Arctic Ocean, are estimated using combined observations from a multisatellite inundation dataset and water levels over rivers and floodplains derived from the TOPEX/POSEIDON (T/P) radar altimetry. We computed time-series of monthly maps of surface water volume over the common period of available T/P and multisatellite data (1993–2004). The results exhibit interannual variabilities similar to precipitation estimates and river discharge observations. This study also presents monthly estimates of groundwater and permafrost mass anomalies during 2003–2004 based on a synergistic analysis of multisatellite observations and hydrological models. Water stored in the soil is isolated from the total water storage measured by GRACE when removing the contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the snow estimated by inversion of GRACE measurements. The time variations of groundwater and permafrost are then obtained when removing the water content of the root zone reservoir simulated by hydrological models.

scholarly journals Water Storage Variations in Tibet from GRACE, ICESat, and Hydrological Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1103 ◽  
Author(s):  
Fang Zou ◽  
Robert Tenzer ◽  
Shuanggen Jin
Keyword(s):  
Tibetan Plateau ◽  
Water Budget ◽  
Hydrological Cycle ◽  
Water Storage ◽  
The Tibetan Plateau ◽  
Total Water ◽  
Satellite Mission ◽  
Mountain Glaciers ◽  
Grace Data ◽  
The Impact

The monitoring of water storage variations is essential not only for the management of water resources, but also for a better understanding of the impact of climate change on hydrological cycle, particularly in Tibet. In this study, we estimated and analyzed changes of the total water budget on the Tibetan Plateau from the Gravity Recovery And Climate Experiment (GRACE) satellite mission over 15 years prior to 2017. To suppress overall leakage effect of GRACE monthly solutions in Tibet, we applied a forward modeling technique to reconstruct hydrological signals from GRACE data. The results reveal a considerable decrease in the total water budget at an average annual rate of −6.22 ± 1.74 Gt during the period from August 2002 to December 2016. In addition to the secular trend, seasonal variations controlled mainly by annual changes in precipitation were detected, with maxima in September and minima in December. A rising temperature on the plateau is likely a principal factor causing a continuous decline of the total water budget attributed to increase melting of mountain glaciers, permafrost, and snow cover. We also demonstrate that a substantial decrease in the total water budget due to melting of mountain glaciers was partially moderated by the increasing water storage of lakes. This is evident from results of ICESat data for selected major lakes and glaciers. The ICESat results confirm a substantial retreat of mountain glaciers and an increasing trend of major lakes. An increasing volume of lakes is mainly due to an inflow of the meltwater from glaciers and precipitation. Our estimates of the total water budget on the Tibetan Plateau are affected by a hydrological signal from neighboring regions. Probably the most significant are aliasing signals due to ground water depletion in Northwest India and decreasing precipitation in the Eastern Himalayas. Nevertheless, an integral downtrend in the total water budget on the Tibetan Plateau caused by melting of glaciers prevails over the investigated period.

scholarly journals Quantifying the impacts of human water use and climate variations on recent drying of Lake Urmia basin: the value of different sets of spaceborne and in-situ data for calibrating a hydrological model

2018 ◽  
Author(s):  
Seyed-Mohammad Hosseini-Moghari ◽  
Shahab Araghinejad ◽  
Mohammad J. Tourian ◽  
Kumars Ebrahimi ◽  
Petra Döll
Keyword(s):  
Time Series ◽  
Water Use ◽  
Lake Water ◽  
Water Storage ◽  
Water Volume ◽  
Total Water ◽  
Lake Urmia ◽  
Groundwater Levels ◽  
Lake Volume

Abstract. During the last decades, the endorheic Lake Urmia basin in northwestern Iran has suffered from decreased precipitation, groundwater levels and a very strong reduction in the volume and more recently also in the extent of Lake Urmia. Human water use has exacerbated the desiccating impact of climatic variations. This study quantifies the contribution of human water use to the reduction of inflow into Lake Urmia, to the loss of lake water volume and to the loss of groundwater and total water storage in the entire Lake Urmia basin during 2003–2013. To this end, the WaterGAP Global Hydrology Model (WGHM) was manually calibrated specifically for the basin against multiple in-situ and spaceborne data, and the best-performing calibration variant was run with or without taking into account water use. Observation data encompass remote-sensing based time series of annual irrigated area in the basin from MODIS, monthly total water storage anomaly (TWSA) from GRACE satellites and monthly lake volume. In-situ observations include time series of annual inflow into the lake and basin averages of groundwater level variations based on 284 wells. In addition, local estimates of sectoral water withdrawals in 2009 and return flow fractions were utilized. Four calibration variants were set up in which the number of considered observation types was increased in a stepwise fashion. The best fit to each and all observations is achieved if the maximum amount of observations is used for calibration. Calibration against GRACE TWSA improves simulated inflow into Lake Urmia but still overestimates it by 90 %; it results in an overestimation of lake volume loss, underestimation of groundwater loss and a shifted seasonality of groundwater storage. Lake and groundwater dynamics can only be simulated well if calibration against groundwater levels leads to adjusting the fractions of human water use from groundwater and surface water. According to our study, human water use was the reason for 50 % of the total basin water loss of about 10 km3 during 2003–2013, for 40 % of the Lake Urmia water loss of about 8 km3 and for up to 90 % of the groundwater loss. Lake inflow was 40 % less than it would have been without human water use. We found that even without human water use, Lake Urmia would not have recovered from the significant loss of lake water volume caused by the drought year 2008. These findings may serve to support water management in the basin and more specifically Lake Urmia restoration plans.

scholarly journals Interannual variations of the terrestrial water storage in the Lower Ob' basin from a multisatellite approach

2010 ◽  
Vol 7 (5) ◽  
pp. 6647-6676
Author(s):  
F. Frappart ◽  
F. Papa ◽  
A. Güntner ◽  
S. Werth ◽  
G. Ramillien ◽  
...  
Keyword(s):  
Surface Water ◽  
Root Zone ◽  
Water Storage ◽  
Temporal Variations ◽  
Water Levels ◽  
The Arctic ◽  
Water Volume ◽  
Hydrological Models ◽  
Precipitation Estimates ◽  
Terrestrial Water

Abstract. Temporal variations of surface water volume over inundated areas of the Lower Ob' basin in Siberia, one of the largest contributor of freshwater to the Arctic Ocean, are estimated using combined observations from a multisatellite inundation dataset and water levels over rivers and floodplains derivec from the TOPEX/POSEIDON (T/P) altimetry satellite. We computed time-series of monthly maps of surface water volume over the period of common availability of T/P and the multisatellite data (1993–2004). The results exhibit similar interannual variabilities with precipitation estimates and river discharge observations. This study also presents monthly estimates of groundwater and permafrost mass anomalies during 2003–2004 based on a synergistic analysis using multisatellite observations and hydrological models. Water stored in aquifer is isolated from the total water storage measured by GRACE by removing the contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the root zone reservoir simulated by hydrological models.

Stepwise improvement of hydrological models using satellite-based evaporation and total water storage estimations

2021 ◽  
Author(s):  
Markus Hrachowitz ◽  
Petra Hulsman ◽  
Hubert Savenije
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Model Development ◽  
Water Storage ◽  
Spatiotemporal Variability ◽  
Model Structure ◽  
Hydrological Models ◽  
Total Water ◽  
Spatiotemporal Model ◽  
Multiple Variables

<p>Hydrological models are often calibrated with respect to flow observations at the basin outlet. As a result, flow predictions may seem reliable but this is not necessarily the case for the spatiotemporal variability of system-internal processes, especially in large river basins. Satellite observations contain valuable information not only for poorly gauged basins with limited ground observations and spatiotemporal model calibration, but also for stepwise model development. This study explored the value of satellite observations to improve our understanding of hydrological processes through stepwise model structure adaption and to calibrate models both temporally and spatially. More specifically, satellite-based evaporation and total water storage anomaly observations were used to diagnose model deficiencies and to subsequently improve the hydrological model structure and the selection of feasible parameter sets. A distributed, process based hydrological model was developed for the Luangwa river basin in Zambia and calibrated with respect to discharge as benchmark. This model was modified stepwise by testing five alternative hypotheses related to the process of upwelling groundwater in wetlands, which was assumed to be negligible in the benchmark model, and the spatial discretization of the groundwater reservoir. Each model hypothesis was calibrated with respect to 1) discharge and 2) multiple variables simultaneously including discharge and the spatiotemporal variability in the evaporation and total water storage anomalies. The benchmark model calibrated with respect to discharge reproduced this variable well, as also the basin-averaged evaporation and total water storage anomalies. However, the evaporation in wetland dominated areas and the spatial variability in the evaporation and total water storage anomalies were poorly modelled. The model improved the most when introducing upwelling groundwater flow from a distributed groundwater reservoir and calibrating it with respect to multiple variables simultaneously. This study showed satellite-based evaporation and total water storage anomaly observations provide valuable information for improved understanding of hydrological processes through stepwise model development and spatiotemporal model calibration.</p>

scholarly journals Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data

2012 ◽  
Vol 16 (9) ◽  
pp. 3083-3099 ◽  
Author(s):  
H. Xie ◽  
L. Longuevergne ◽  
C. Ringler ◽  
B. R. Scanlon
Keyword(s):  
Regional Scale ◽  
Water Storage ◽  
Hydrologic Model ◽  
Sub Saharan Africa ◽  
Model Parameters ◽  
Total Water ◽  
Discharge Data ◽  
Model Based ◽  
Grace Data ◽  
Sub Saharan

Abstract. Irrigation development is rapidly expanding in mostly rainfed Sub-Saharan Africa. This expansion underscores the need for a more comprehensive understanding of water resources beyond surface water. Gravity Recovery and Climate Experiment (GRACE) satellites provide valuable information on spatio-temporal variability in water storage. The objective of this study was to calibrate and evaluate a semi-distributed regional-scale hydrologic model based on the Soil and Water Assessment Tool (SWAT) code for basins in Sub-Saharan Africa using seven-year (July 2002–April 2009) 10-day GRACE data and multi-site river discharge data. The analysis was conducted in a multi-criteria framework. In spite of the uncertainty arising from the tradeoff in optimising model parameters with respect to two non-commensurable criteria defined for two fluxes, SWAT was found to perform well in simulating total water storage variability in most areas of Sub-Saharan Africa, which have semi-arid and sub-humid climates, and that among various water storages represented in SWAT, water storage variations in soil, vadose zone and groundwater are dominant. The study also showed that the simulated total water storage variations tend to have less agreement with GRACE data in arid and equatorial humid regions, and model-based partitioning of total water storage variations into different water storage compartments may be highly uncertain. Thus, future work will be needed for model enhancement in these areas with inferior model fit and for uncertainty reduction in component-wise estimation of water storage variations.

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