scholarly journals Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Guanghua Wei ◽  
Haishen Lü ◽  
Wade T. Crow ◽  
Yonghua Zhu ◽  
Jianqun Wang ◽  
...  
Keyword(s):  
Comprehensive Evaluation ◽  
Tropical Rainfall Measuring Mission ◽  
Probability Of Detection ◽  
The Tibetan Plateau ◽  
Data Set ◽  
Seasonal Scale ◽  
Categorical Verification ◽  
Annual Scale ◽  
Verification Techniques ◽  
Trmm 3B42

The comprehensive assessment of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) V05B is important for benchmarking the product’s continued improvement and future development. The performance of IMERG V05B precipitation products was systematically evaluated using 542 precipitation gauges at multiple spatiotemporal scales from March 2014 to February 2017 over China. Moreover, IMERG V05B was compared with IMERG V04A, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Climate Prediction Center Morphing technique (CMORPH)-CRT in this study. Categorical verification techniques and statistical methods are used to quantify their performance. Results illustrate the following. (1) Except for IMERG V04A’s severe underestimation over the Tibetan Plateau (TP) and Xinjiang (XJ) with high negative relative biases (RBs) and CMORPH-CRT’s overestimation over XJ with high positive RB, the four satellite-based precipitation products generally capture the same spatial patterns of precipitation over China. (2) At the annual scale over China, the IMERG products do not show an advantage over its predecessor (TRMM 3B42) in terms of RMSEs, RRMSEs, and Rs; meanwhile, the performance of IMERG products is worse than TRMM 3B42 in spring and summer according to the RMSE, RRMSE, and R metrics. Between the two IMERG products, IMERG V05B shows the anticipated improvement (over IMERG V04A) with a decrease in RMSE from 0.4496 to 0.4097 mm/day, a decrease of RRMSE from 16.95% to 15.44%, and an increase of R from 0.9689 to 0.9759 during the whole study period. Similar results are obtained at the seasonal scale. Among the four satellite products, CMORPH-CRT shows the worst seasonal performance with the highest RMSE (0.6247 mm/day), RRMSE (23.55%), and lowest R (0.9343) over China. (3) Over XJ and TP, IMERG V05B clearly improves the strong underestimation of precipitation in IMERG V04A with the RBs of 5.2% vs. −21.8% over XJ, and 2.78% vs. −46% over TP. Results at the annual scale are similar to those obtained at the seasonal scale, except for summer results over XJ. While, over the remaining subregions, the two IMERG products have a close performance; meanwhile, IMERG V04A slightly improves IMERG V05B’s overestimation according to RBs (except for HN) at the annual scale. However, all four products are unreliable over XJ at both an annual and seasonal scale. (4) Across all products, TRMM 3B42 best reproduces the probability density function (PDF) of daily precipitation intensity. (5) According to the categorical verification technique in this study, both IMERG products yield better results for the detection of precipitation events on the basis of probability of detection (POD) and critical success index (CSI) categorical evaluations compared to TRMM 3B42 and CMORPH-CRT over China and across most of the subregions. However, all four products have room for further improvement, especially in high-latitude and dry climate regions. These findings provide valuable feedback for both IMERG algorithm developers and data set users.

scholarly journals Suitability of TRMM Products with Different Temporal Resolution (3-Hourly, Daily, and Monthly) for Rainfall Erosivity Estimation

2020 ◽  
Vol 12 (23) ◽  
pp. 3924
Author(s):  
Xianghu Li ◽  
Zhen Li ◽  
Yaling Lin
Keyword(s):  
Temporal Resolution ◽  
Tropical Rainfall Measuring Mission ◽  
Relative Bias ◽  
Rainfall Erosivity ◽  
Lake Basin ◽  
Monthly Data ◽  
Poyang Lake Basin ◽  
Spatial Performance ◽  
Annual Scale ◽  
Trmm 3B42

Rainfall erosivity (RE) is a significant indicator of erosion capacity. The application of Tropical Rainfall Measuring Mission (TRMM) rainfall products to deal with RE estimation has not received much attention. It is not clear which temporal resolution of TRMM data is most suitable. This study quantified the RE in the Poyang Lake basin, China, based on TRMM 3B42 3-hourly, daily, and 3B43 monthly rainfall data, and investigated their suitability for estimating RE. The results showed that TRMM 3-hourly product had a significant systematic underestimation of monthly RE, especially during the period of April–June for the large values. The TRMM 3B42 daily product seems to have better performance with the relative bias of 3.0% in summer. At the annual scale, TRMM 3B42 daily and 3B43 monthly data had acceptable accuracy, with mean error of 1858 and −85 MJ∙mm/ha∙h and relative bias of 18.3% and −0.85%, respectively. A spatial performance analysis showed that all three TRMM products generally captured the overall spatial patterns of RE, while the TRMM 3B43 product was more suitable in depicting the spatial characteristics of annual RE. This study provides valuable information for the application of TRMM products in mapping RE and risk assessment of soil erosion.

scholarly journals Evaluation and Comparison of Daily GPM/TRMM Precipitation Products over the Tianshan Mountains in China

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3088
Author(s):  
Yin Zhang ◽  
Gulimire Hanati ◽  
Sulitan Danierhan ◽  
Qianqian Liu ◽  
Zhiyuan Xu
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Tianshan Mountains ◽  
Probability Of Detection ◽  
Rainfall Rate ◽  
Annual Precipitation ◽  
Seasonal Precipitation ◽  
Climate Conditions ◽  
The North ◽  
Trmm 3B42 ◽  
Better Than

Based on the complex topography and climate conditions over the Tianshan Mountains (TSM) in Xinjiang, China, the new precipitation product, the Global Precipitation Measurement (GPM) (IMERG), and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) 3B42 (TMPA), were evaluated and compared. The evaluation was based on daily-scale data from April 2014 to March 2015 and analyses at annual, seasonal and daily scales were performed. The results indicated that, overall, the annual precipitation in the Tianshan area tends to be greater in the north than in the south and greater in the west than in the east. Compared with the ground reference dataset, GPM and TRMM datasets represent the spatial variation of annual and seasonal precipitation over the TSM well; however, both measurements underestimate the annual precipitation. Seasonal analysis found that the spatial variability of seasonal precipitation has been underestimated. For the daily assessment, the coefficient of variation (CV), correlation coefficient (R) and relative bias (RB) were calculated. It was found that the GPM and TRMM data underestimated the larger CV. The TRMM data performed better on the daily variability of precipitation in the TSM. The R and RB data indicate that the performance of GPM is generally better than that of TRMM. The R value of GPM is generally greater than that of TRMM, and the RB value is closer to 0, indicating that it is closer to the measured value. As for the ability to detect precipitation events, the GPM products have significantly improved the probability of detection (POD) (POD values are all above 0.8, the highest is 0.979, increased by nearly 17%), and the critical success index (CSI) (increased by nearly 9% in the TSM) is also better than TRMM, although it is only slightly weaker than TRMM in terms of the false alarm ratio (FAR) and frequency bias index (FBI). Overall, GPM underestimates the low rainfall rate by 6.4% and high rainfall rate by 22.8% and overestimates middle rain rates by 29.1%. However, GPM is better than TRMM in capturing all types of rainfall events. Based on these results, GPM-IMERG presents significant improvement over its predecessor TRMM 3B42. Considering the performance of GPM in different subregions, a lot of work still needs to be done to improve the performance of the satellite before being used for research.

scholarly journals A novel algorithm for detection of precipitation in tropical regions using PMW radiometers

2015 ◽  
Vol 8 (3) ◽  
pp. 1217-1232 ◽  
Author(s):  
D. Casella ◽  
G. Panegrossi ◽  
P. Sanò ◽  
L. Milani ◽  
M. Petracca ◽  
...  
Keyword(s):  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Probability Of Detection ◽  
Arid Land ◽  
False Alarms ◽  
Data Set ◽  
Tropical Regions ◽  
Brightness Temperatures ◽  
Trmm Pr ◽  
Novel Algorithm

Abstract. A novel algorithm for the detection of precipitation is described and tested. The algorithm is applicable to any modern passive microwave radiometer on board polar orbiting satellites independent of the observation geometry and channel frequency assortment. The algorithm is based on the application of canonical correlation analysis and on the definition of a threshold to be applied to the resulting linear combination of the brightness temperatures in all available channels. The algorithm has been developed using a 2-year data set of co-located Special Sensor Microwave Imager/Sounder (SSMIS) and Tropical Rainfall Measuring Mission precipitation radar (TRMM-PR) measurements and Advanced Microwave Sounding Unit (AMSU) Microwave Humidity Sounder and TRMM-PR measurements. This data set was partitioned into four classes depending on the background surface emissivity (vegetated land, arid land, ocean, and coast) with the same procedure applied for each surface class. In this paper we describe the procedure and evaluate the results in comparison with many well-known algorithms for the detection of precipitation. The algorithm shows a small rate of false alarms and superior detection capability; it can efficiently detect (probability of detection between 0.55 and 0.71) minimum rain rate varying from 0.14 mm h-1 (AMSU over ocean) to 0.41 (SSMIS over coast) with the remarkable result of 0.25 mm h-1 over arid land surfaces.

scholarly journals Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Junzhi Liu ◽  
Zheng Duan ◽  
Jingchao Jiang ◽  
A-Xing Zhu
Keyword(s):  
Comprehensive Evaluation ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Lake Basin ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Daily Scale ◽  
Using Data ◽  
Hydrological Applications ◽  
Trmm 3B42

This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. All the three products were compared and evaluated during a 9-year period at different spatial (grid and watershed) and temporal (daily, monthly, and annual) scales. The results showed that at daily scale, CMORPH had the best performance with coefficients of determination (R2) of 0.61 at grid scale and 0.74 at watershed scale. For precipitation intensities larger than or equal to 25 mm, RMSE% of CMORPH and TRMM 3B42 were less than 50%, indicating CMORPH and TRMM 3B42 might be useful for hydrological applications at daily scale. At monthly and annual temporal scales, TRMM 3B42 had the best performances, with highR2ranging from 0.93 to 0.99, and thus was deemed to be reliable and had good potential for hydrological applications at monthly and annual scales. PERSIANN had the worst performance among the three products at all cases.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

2021 ◽  
Vol 13 (6) ◽  
pp. 1208
Author(s):  
Linfei Yu ◽  
Guoyong Leng ◽  
Andre Python ◽  
Jian Peng
Keyword(s):  
Comprehensive Evaluation ◽  
Probability Of Detection ◽  
Light Rain ◽  
Spatial And Temporal Scales ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Major River ◽  
Low Levels ◽  
Rain Events ◽  
Early Late

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

scholarly journals Using Fractal Downscaling of Satellite Precipitation Products for Hydrometeorological Applications

2010 ◽  
Vol 27 (3) ◽  
pp. 409-427 ◽  
Author(s):  
Kun Tao ◽  
Ana P. Barros
Keyword(s):  
Spatial Resolution ◽  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Skill Score ◽  
Probability Of Detection ◽  
Grid Spacing ◽  
Satellite Precipitation ◽  
Target Field ◽  
Skill Scores ◽  
Central United States

Abstract The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L ≫ l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (∼25-km grid spacing) to the same resolution as the NCEP stage IV products (∼4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied.

scholarly journals <sup>10</sup>Be systematics in the Tsangpo-Brahmaputra catchment: the cosmogenic nuclide legacy of the eastern Himalayan syntaxis

2017 ◽  
Author(s):  
Maarten Lupker ◽  
Jérôme Lavé ◽  
Christian France-Lanord ◽  
Marcus Christl ◽  
Didier Bourlès ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Sediment Flux ◽  
Coarse Grained ◽  
The Tibetan Plateau ◽  
Data Set ◽  
Denudation Rate ◽  
Cosmogenic Nuclide ◽  
Denudation Rates ◽  
The Impact ◽  
Eastern Himalayan Syntaxis

Abstract. The Tsangpo-Brahmaputra River drains the eastern part of the Himalayan range, flowing from the Tibetan Plateau through the eastern Himalayan syntaxis and downstream to the Indo-Gangetic floodplain. As such it is a unique natural laboratory to study how denudation and sediment production processes are transferred to river detrital signals. In this study, we present a new 10Be data set to constrain denudation rates across the catchment and to quantify the impact of rapid erosion within the syntaxis region on cosmogenic nuclide budgets and signals. 10Be denudation rates span around two orders of magnitude across the catchments (ranging from 0.03 mm/yr to > 4 mm/yr) and sharply increase as the Tsangpo-Brahmaputra flows across the eastern Himalaya. The increase in denudation rates however occurs ~ 150 km downstream of the Namche Barwa-Gyala Peri massif (NBGPm), an area which has been previously characterized by extremely high erosion and exhumation rates. We suggest that this downstream lag is mainly due to the physical abrasion of coarse grained, low 10Be concentration, landslide material produced within the syntaxis that dilutes the upstream high concentration 10Be flux from the Tibetan Plateau only after abrasion has transferred sediment to the studied sand fraction. A simple abrasion model produces typical lag distances of 50 to 150 km compatible with our observations. Abrasion effects reduce the spatial resolution over which denudation can be constrained in the eastern Himalayan syntaxis. In addition, we also highlight that denudation rate estimates are dependent on the sediment connectivity, storage and quartz content of the upstream Tibetan Plateau part of the catchment which tends to lead to an overestimation of downstream denudations rates. Taking these effects into account we estimate a denudation rates of ca. 2 to 5 mm/yr for the entire syntaxis and ca. 4 to 28 mm/yr for the NBGPm, which is significantly higher than other to other large catchments. Overall, 10Be concentrations measured at the outlet of the Tsangpo-Brahmaputra in Bangladesh suggest a sediment flux between 780 and 1430 Mt/yr equivalent to a denudation rate between 0.7 and 1.2 mm/yr for the entire catchment.

scholarly journals Precipitation Trends over the Indus Basin

Climate ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 116 ◽  
Author(s):  
Nir Y. Krakauer ◽  
Tarendra Lakhankar ◽  
Ghulam H. Dars
Keyword(s):  
Remote Sensing ◽  
Large Population ◽  
Tropical Rainfall Measuring Mission ◽  
Indus Basin ◽  
Data Sets ◽  
Precipitation Trend ◽  
Precipitation Data ◽  
Data Set ◽  
Station Data ◽  
Precipitation Trends

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

scholarly journals Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

2016 ◽  
Vol 13 (5) ◽  
pp. 1387-1408 ◽  
Author(s):  
Zhen Zhang ◽  
Niklaus E. Zimmermann ◽  
Jed O. Kaplan ◽  
Benjamin Poulter
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Spatiotemporal Dynamics ◽  
Fundamental Parameter ◽  
Data Set ◽  
Dynamic Global Vegetation Model ◽  
Wetland Dynamics ◽  
Cost Efficient ◽  
Elevation Model

Abstract. Simulations of the spatiotemporal dynamics of wetlands are key to understanding the role of wetland biogeochemistry under past and future climate. Hydrologic inundation models, such as the TOPography-based hydrological model (TOPMODEL), are based on a fundamental parameter known as the compound topographic index (CTI) and offer a computationally cost-efficient approach to simulate wetland dynamics at global scales. However, there remains a large discrepancy in the implementations of TOPMODEL in land-surface models (LSMs) and thus their performance against observations. This study describes new improvements to TOPMODEL implementation and estimates of global wetland dynamics using the LPJ-wsl (Lund–Potsdam–Jena Wald Schnee und Landschaft version) Dynamic Global Vegetation Model (DGVM) and quantifies uncertainties by comparing three digital elevation model (DEM) products (HYDRO1k, GMTED, and HydroSHEDS) at different spatial resolution and accuracy on simulated inundation dynamics. In addition, we found that calibrating TOPMODEL with a benchmark wetland data set can help to successfully delineate the seasonal and interannual variation of wetlands, as well as improve the spatial distribution of wetlands to be consistent with inventories. The HydroSHEDS DEM, using a river-basin scheme for aggregating the CTI, shows the best accuracy for capturing the spatiotemporal dynamics of wetlands among the three DEM products. The estimate of global wetland potential/maximum is  ∼ 10.3 Mkm2 (106 km2), with a mean annual maximum of  ∼ 5.17 Mkm2 for 1980–2010. When integrated with wetland methane emission submodule, the uncertainty of global annual CH4 emissions from topography inputs is estimated to be 29.0 Tg yr−1. This study demonstrates the feasibility of TOPMODEL to capture spatial heterogeneity of inundation at a large scale and highlights the significance of correcting maximum wetland extent to improve modeling of interannual variations in wetland area. It additionally highlights the importance of an adequate investigation of topographic indices for simulating global wetlands and shows the opportunity to converge wetland estimates across LSMs by identifying the uncertainty associated with existing wetland products.

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