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1
Rahman, Shang, Shahid, and Wen. "Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan." Remote Sensing 11, no. 17 (August 29, 2019): 2040. http://dx.doi.org/10.3390/rs11172040.
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Accurate estimation of precipitation from satellite precipitation products (PPs) over the complex topography and diverse climate of Pakistan with limited rain gauges (RGs) is an arduous task. In the current study, we assessed the performance of two PPs estimated from soil moisture (SM) using the SM2RAIN algorithm, SM2RAIN-CCI and SM2RAIN-ASCAT, on the daily scale across Pakistan during the periods 2000–2015 and 2007–2015, respectively. Several statistical metrics, i.e., Bias, unbiased root mean square error (ubRMSE), Theil’s U, and the modified Kling–Gupta efficiency (KGE) score, and four categorical metrics, i.e., probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Bias score, were used to evaluate these two PPs against 102 RGs observations across four distinct climate regions, i.e., glacial, humid, arid and hyper-arid regions. Total mean square error (MSE) is decomposed into systematic (MSEs) and random (MSEr) error components. Moreover, the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TRMM TMPA 3B42v7) was used to assess the performance of SM2RAIN-based products at 0.25° scale during 2007–2015. Results shows that SM2RAIN-based product highly underestimated precipitation in north-east and hydraulically developed areas of the humid region. Maximum underestimation for SM2RAIN-CCI and SM2RIAN-ASCAT were 58.04% and 42.36%, respectively. Precipitation was also underestimated in mountainous areas of glacial and humid regions with maximum underestimations of 43.16% and 34.60% for SM2RAIN-CCI. Precipitation was overestimated along the coast of Arabian Sea in the hyper-arid region with maximum overestimations for SM2RAIN-CCI (SM2RAIN-ASCAT) of 59.59% (52.35%). Higher ubRMSE was observed in the vicinity of hydraulically developed areas. Theil’s U depicted higher accuracy in the arid region with values of 0.23 (SM2RAIN-CCI) and 0.15 (SM2RAIN-ASCAT). Systematic error components have larger contribution than random error components. Overall, SM2RAIN-ASCAT dominates SM2RAIN-CCI across all climate regions, with average percentage improvements in bias (27.01% in humid, 5.94% in arid, and 6.05% in hyper-arid), ubRMSE (19.61% in humid, 20.16% in arid, and 25.56% in hyper-arid), Theil’s U (9.80% in humid, 28.80% in arid, and 26.83% in hyper-arid), MSEs (24.55% in humid, 13.83% in arid, and 8.22% in hyper-arid), MSEr (19.41% in humid, 29.20% in arid, and 24.14% in hyper-arid) and KGE score (5.26% in humid, 28.12% in arid, and 24.72% in hyper-arid). Higher uncertainties were depicted in heavy and intense precipitation seasons, i.e., monsoon and pre-monsoon. Average values of statistical metrics during monsoon season for SM2RAIN-CCI (SM2RAIN-ASCAT) were 20.90% (17.82%), 10.52 mm/day (8.61 mm/day), 0.47 (0.43), and 0.47 (0.55) for bias, ubRMSE, Theil’s U, and KGE score, respectively. TMPA outperformed SM2RAIN-based products across all climate regions. SM2RAIN-based datasets are recommended for agricultural water management, irrigation scheduling, flood simulation and early flood warning system (EFWS), drought monitoring, groundwater modeling, and rainwater harvesting, and vegetation and crop monitoring in plain areas of the arid region.
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Asif, Muhammad, Muhammad Umer Nadeem, Muhammad Naveed Anjum, Bashir Ahmad, Gulakhmadov Manuchekhr, Muhammad Umer, Muhammad Hamza, Muhammad Mashood Javaid, and Tie Liu. "Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region." Atmosphere 14, no. 1 (December 20, 2022): 8. http://dx.doi.org/10.3390/atmos14010008.
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The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan).
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Paredes-Trejo, Franklin, Humberto Barbosa, and Carlos A. C. dos Santos. "Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil." Remote Sensing 11, no. 9 (May 9, 2019): 1113. http://dx.doi.org/10.3390/rs11091113.
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Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted.
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Zbiri, Asmae, Azeddine Hachmi, Dominique Haesen, and Fatima Ezzahrae El Alaoui-Faris. "New Investigation and Challenge for Spatiotemporal Drought Monitoring Using Bottom-Up Precipitation Dataset (SM2RAIN-ASCAT) and NDVI in Moroccan Arid and Semi-Arid Rangelands." Ekológia (Bratislava) 41, no. 1 (March 1, 2022): 90–100. http://dx.doi.org/10.2478/eko-2022-0010.
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Abstract Remotely sensed soil moisture products showed sensitivity to vegetation cover density and soil typology at regional dryland level. In these regions, drought monitoring is significantly performed using soil moisture index and rainfall data. Recently, rainfall and soil moisture observations have increasingly become available. This has hampered scientific progress as regards characterization of land surface processes not just in meteorology. The purpose of this study was to investigate the relationship between a newly developed precipitation dataset, SM2RAIN (Advanced SCATterometer (SM2RAIN-ASCAT), and NDVI (eMODIS-TERRA) in monitoring drought events over diverse rangeland regions of Morocco. Results indicated that the highest polynomial correlation coefficient and the lowest root mean square error (RMSE) between SM2RAIN-ASCAT and NDVI were found in a 10-year period from 2007 to 2017 in all rangelands (R = 0.81; RMSE = 0.05). This relationship was strong for degraded rangeland, where there were strong positive correlation coefficients for NDVI and SM2RAIN (R = 0.99). High correlations were found for sparse and moderate correlations for shrub rangeland (R = 0.82 and 0.61, respectively). The anomalies maps showed a very good similarity between SM2RAIN and Normalized Difference Vegetation Index (NDVI) data. The results revealed that the SM2RAIN-ASCAT and NDVI product could accurately predict drought events in arid and semi-arid rangelands.
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Brocca, Luca, Paolo Filippucci, Sebastian Hahn, Luca Ciabatta, Christian Massari, Stefania Camici, Lothar Schüller, Bojan Bojkov, and Wolfgang Wagner. "SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations." Earth System Science Data 11, no. 4 (October 22, 2019): 1583–601. http://dx.doi.org/10.5194/essd-11-1583-2019.
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Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).
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Ciabatta, Luca, Luca Brocca, Christian Massari, Tommaso Moramarco, Silvia Puca, Angelo Rinollo, Simone Gabellani, and Wolfgang Wagner. "Integration of Satellite Soil Moisture and Rainfall Observations over the Italian Territory." Journal of Hydrometeorology 16, no. 3 (May 27, 2015): 1341–55. http://dx.doi.org/10.1175/jhm-d-14-0108.1.
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Abstract State-of-the-art rainfall products obtained by satellites are often the only way of measuring rainfall in remote areas of the world. However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications. To address this issue, an integration between satellite rainfall and soil moisture SM products is proposed here by using an algorithm, SM2RAIN, which estimates rainfall from SM observations. A nudging scheme is used for integrating SM-derived and state-of-the-art rainfall products. Two satellite rainfall products are considered: H05 provided by EUMESAT and the real-time (3B42-RT) TMPA product provided by NASA. The rainfall dataset obtained through SM2RAIN, SM2RASC, considers SM retrievals from the Advanced Scatterometer (ASCAT). The rainfall datasets are compared with quality-checked daily rainfall observations throughout the Italian territory in the period 2010–13. In the validation period 2012–13, the integrated products show improved performances in terms of correlation with an increase in median values, for 5-day rainfall accumulations, of 26% (18%) when SM2RASC is integrated with the H05 (3B42-RT) product. Also, the median root-mean-square error of the integrated products is reduced by 18% and 17% with respect to H05 and 3B42-RT, respectively. The integration of the products is found to improve the threat score for medium–high rainfall accumulations. Since SM2RASC, H05, and 3B42-RT datasets are provided in near–real time, their integration might provide more reliable rainfall products for operational applications, for example, for flood and landslide early warning systems.
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Anjum, Muhammad Naveed, Muhammad Irfan, Muhammad Waseem, Megersa Kebede Leta, Usama Muhammad Niazi, Saif ur Rahman, Abdulnoor Ghanim, Muhammad Ahsan Mukhtar, and Muhammad Umer Nadeem. "Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region." Water 14, no. 2 (January 7, 2022): 147. http://dx.doi.org/10.3390/w14020147.
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This study compares the performance of four satellite-based rainfall products (SRPs) (PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0) in a semi-arid subtropical region. As a case study, Punjab Province of Pakistan was considered for this assessment. Using observations from in-situ meteorological stations, the uncertainty in daily, monthly, seasonal, and annual rainfall estimates of SRPs at pixel and regional scales during 2010–2018 were examined. Several evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias), as well as categorical indices (Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ration (FAR)) were used to assess the performance of the SRPs. The following findings were found: (1) CHIRPS-2.0 and SM2RAIN-ASCAT products were capable of tracking the spatiotemporal variability of observed rainfall, (2) all SRPs had higher overall performances in the northwestern parts of the province than the other parts, (3) all SRP estimates were in better agreement with ground-based monthly observations than daily records, and (4) on the seasonal scale, CHIRPS-2.0 and SM2RAIN-ASCAT were better than PERSIANN-CCS and PERSIANN. In all seasons, CHIRPS-2.0 and SM2RAIN-ASCAT outperformed PERSIANN-CCS and PERSIANN-CDR. Based on our findings, we recommend that hydrometeorological investigations in Pakistan’s Punjab Province employ monthly estimates of CHIRPS-2.0 and SM2RAIN-ASCAT products.
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Ciabatta, Luca, Christian Massari, Luca Brocca, Alexander Gruber, Christoph Reimer, Sebastian Hahn, Christoph Paulik, Wouter Dorigo, Richard Kidd, and Wolfgang Wagner. "SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture." Earth System Science Data 10, no. 1 (February 8, 2018): 267–80. http://dx.doi.org/10.5194/essd-10-267-2018.
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Abstract. Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998–2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value < 10.34 mm over 5 days) and bias (median value < −14.44 %) during the evaluation period. The validation has been carried out at original resolution (0.25°) over Europe, Australia and five other areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.
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Filippucci, Paolo, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli. "High-resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River basin as a case study." Hydrology and Earth System Sciences 26, no. 9 (May 12, 2022): 2481–97. http://dx.doi.org/10.5194/hess-26-2481-2022.
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Abstract. The use of satellite sensors to infer rainfall measurements has become a widely used practice in recent years, but their spatial resolution usually exceeds 10 km, due to technological limitations. This poses an important constraint on its use for applications such as water resource management, index insurance evaluation or hydrological models, which require more and more detailed information. In this work, the algorithm SM2RAIN (Soil Moisture to Rain) for rainfall estimation is applied to two soil moisture products over the Po River basin: a high-resolution soil moisture product derived from Sentinel-1, named S1-RT1, characterized by 1 km spatial resolution (500 m spacing), and a 25 (12.5 km spacing) product derived from ASCAT, resampled to the same grid as S1-RT1. In order to overcome the need for calibration and to allow for its global application, a parameterized version of SM2RAIN algorithm was adopted along with the standard one. The capabilities in estimating rainfall of each obtained product were then compared, to assess both the parameterized SM2RAIN performances and the added value of Sentinel-1 high spatial resolution. The results show that good estimates of rainfall are obtainable from Sentinel-1 when considering aggregation time steps greater than 1 d, since the low temporal resolution of this sensor (from 1.5 to 4 d over Europe) prevents its application for infer daily rainfall. On average, the ASCAT-derived rainfall product performs better than S1-RT1, even if the performances are equally good when 30 d accumulated rainfall is considered (resulting in a mean Pearson correlation for the parameterized SM2RAIN product of 0.74 and 0.73, respectively). Notwithstanding this, the products obtained from Sentinel-1 outperform those from ASCAT in specific areas, like in valleys inside mountain regions and most of the plains, confirming the added value of the high-spatial-resolution information in obtaining spatially detailed rainfall. Finally, the performances of the parameterized products are similar to those obtained with the calibrated SM2RAIN algorithm, confirming the reliability of the parameterized algorithm for rainfall estimation in this area and fostering the possibility to apply SM2RAIN worldwide, even without the availability of a rainfall benchmark product.
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Souto, Jefferson, Norma Beltrão, and Ana Teodoro. "Performance of Remotely Sensed Soil Moisture for Temporal and Spatial Analysis of Rainfall over São Francisco River Basin, Brazil." Geosciences 9, no. 3 (March 26, 2019): 144. http://dx.doi.org/10.3390/geosciences9030144.
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Variability in precipitation patterns in the northeast and southeast regions of Brazil are complex, and the combined effects of the Tropical Atlantic, Pacific Niños, and local characteristics influence the precipitation rates. This study assesses the performance of multi-satellite precipitation product SM2RAIN-Climate Change Initiative (SM2RAIN-CCI) for the period of 1998–2015 at monthly scale. To accomplish this aim, various statistical analyses and comparison of multi-satellite precipitation analysis products with rain gauge stations are carried out. In addition, we used three values corresponding to extreme events: The total daily precipitation (PRCPTOT) and the number of consecutive dry/wet days (CDD/CWD). Results reveal that monthly rainfall data from SM2RAIN-CCI are compatible with surface observations, showing a seasonal pattern typical of the region. Data correlate well with observations for the selected stations (r ≥ 0.85) but tend to overestimate high rainfall values (>80 mm/month) in the rainy area. There is a significant decrease in rainfall to the indices, especially in PRCPTOT during the occurrence of tropical ocean–atmosphere interactions, reflecting CWD and CDD values. Moreover, our findings also indicate a relationship, at interannual timescales, between the state of El Niño Southern-Oscillation (ENSO) and Tropical Atlantic (TA) annual precipitation variability from 1998 to 2015. The SM2RAIN-CCI could be a useful alternative for rain-gauge precipitation data in the São Francisco River basin.
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Abebe, Sintayehu A., Tianling Qin, Denghua Yan, Endalkachew B. Gelaw, Habtamu T. Workneh, Wang Kun, Shanshan Liu, and Biqiong Dong. "Spatial and Temporal Evaluation of the Latest High-Resolution Precipitation Products over the Upper Blue Nile River Basin, Ethiopia." Water 12, no. 11 (November 2, 2020): 3072. http://dx.doi.org/10.3390/w12113072.
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Quality and representative precipitation data play an essential role in hydro-meteorological analyses. However, the required reliability and coverage is often unavailable from conventional gauge observations. As a result, globally available precipitation datasets are being used as an alternative or supplementary to gauge observations. In this study, the accuracy of three recently released, high-resolution precipitation datasets with a spatial resolution of 0.1° and a daily temporal resolution is evaluated over the Upper Blue Nile River Basin (UBNRB) for the period of 2007 to 2016. The datasets are Integrated Multi-satellitE Retrievals for GPM version 6 (IMERG6), Multi-Source Weighted-Ensemble Precipitation version 2.2 (MSWEP2.2) and soil moisture to rain using Advanced SCATterometer version 1.1 (SM2RAIN-ASCAT1.1). The comparison was made between rain gauge observations and two other high-resolution precipitation datasets named Enhancing National Climate Services (ENACTS) and Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPS2). The modified Kling-Gupta efficiency (KGE’) and four categorical indices named probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias (fBIAS) was used to measure the skills of each dataset. Results revealed that, except SM2RAIN-ASCAT1.1, all other datasets show a better ability on a monthly time scale for areas with an elevation below 1500 m above sea level (m.a.s.l). The overall performance was better in the wetter months of March to August than the drier months of September to February. Besides, all products including SM2RAIN-ASCAT1.1 could detect no rain events (rain < 1 mm) correctly, but their skill deteriorates on identifying higher intensity events. By comparison, ENACTS (calibrated with most quality gauges of Ethiopia) and CHIRPS2 exhibited the best performance due to their high-resolution nature and inclusion of physiographic information in their data generation procedures. IMERG6 and MSWEP2.2 showed the next best performance according to both the continuous and categorical indices used. SM2RAIN-ASCAT1.1 demonstrates the least skill everywhere due to problems that could be associated with misinterpretations of soil moisture signals by the SM2RAIN algorithm. Considering the scarcity of gauged datasets over UBNRB, IMERG6 and MSWEP2.2 could be regarded as valuable datasets for hydro-climatic analysis, mainly where gauge density is low. SM2RAIN-ASCAT1.1, on the other hand, needs significant bias correction to treat its apparent wet biases before any application.
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Brocca, Luca, Christian Massari, Luca Ciabatta, Tommaso Moramarco, Daniele Penna, Giulia Zuecco, Luisa Pianezzola, Marco Borga, Patrick Matgen, and José Martínez-Fernández. "Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm." Journal of Hydrology and Hydromechanics 63, no. 3 (September 1, 2015): 201–9. http://dx.doi.org/10.1515/johh-2015-0016.
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Abstract Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range. SM2RAIN shows very high accuracy in the synthetic experiments with a correlation coefficient, R, between synthetically generated and simulated data, at daily time step, higher than 0.940 and an average Bias lower than 4%. When real datasets are used, the agreement between observed and simulated daily rainfall is slightly lower with average R-values equal to 0.87 and 0.85 in the calibration and validation periods, respectively. Overall, the performance is found to be better in humid temperate climates and for sensors installed vertically. Interestingly, algorithms of different complexity in the reproduction of the underlying hydrological processes provide similar results. The average contribution of surface runoff and evapotranspiration components amounts to less than 4% of the total rainfall, while the soil moisture variations (63%) and subsurface drainage (30%) terms provide a much higher contribution. Overall, the SM2RAIN algorithm is found to perform well both in the synthetic and real data experiments, thus offering a new and independent source of data for improving rainfall estimation, and consequently enhancing hydrological, meteorological and climatic studies.
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Tran, Thanh-Nhan-Duc, Binh Quang Nguyen, Runze Zhang, Aashutosh Aryal, Maria Grodzka-Łukaszewska, Grzegorz Sinicyn, and Venkataraman Lakshmi. "Quantification of Gridded Precipitation Products for the Streamflow Simulation on the Mekong River Basin Using Rainfall Assessment Framework: A Case Study for the Srepok River Subbasin, Central Highland Vietnam." Remote Sensing 15, no. 4 (February 13, 2023): 1030. http://dx.doi.org/10.3390/rs15041030.
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Many fields have identified an increasing need to use global satellite precipitation products for hydrological applications, especially in ungauged basins. In this study, we conduct a comprehensive evaluation of three Satellite-based Precipitation Products (SPPs): Integrated Multi–satellitE Retrievals for GPM (IMERG) Final run V6, Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT) V1.5, and Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2.2 for a subbasin of the Mekong River Basin (MRB). The study area of the Srepok River basin (SRB) represents the Central Highland sub-climatic zone in Vietnam under the impacts of newly built reservoirs during 2001–2018. In this study, our evaluation was performed using the Rainfall Assessment Framework (RAF) with two separated parts: (1) an intercomparison of rainfall characteristics between rain gauges and SPPs; and (2) a hydrological comparison of simulated streamflow driven by SPPs and rain gauges. Several key findings are: (1) IMERGF-V6 shows the highest performance compared to other SPP products, followed by SM2RAIN-ASCAT V1.5 and MSWEP V2.2 over assessments in the RAF framework; (2) MSWEP V2.2 shows discrepancies during the dry and wet seasons, exhibiting very low correlation compared to rain gauges when the precipitation intensity is greater than 15 mm/day; (3) SM2RAIN–ASCAT V1.5 is ranked as the second best SPP, after IMERGF-V6, and shows good streamflow simulation, but overestimates the wet seasonal rainfall and underestimates the dry seasonal rainfall, especially when the precipitation intensity is greater than 20 mm/day, suggesting the need for a recalibration and validation of its algorithm; (4) SM2RAIN-ASCAT had the lowest bias score during the dry season, indicating the product’s usefulness for trend analysis and drought detection; and (5) RAF shows good performance to evaluate the performance of SPPs under the impacts of reservoirs, indicating a good framework for use in other similar studies. The results of this study are the first to reveal the performance of MSWEP V2.2 and SM2RAIN-ASCAT V1.5. Additionally, this study proposes a new rainfall assessment framework for a Vietnam basin which could support future studies when selecting suitable products for input into hydrological model simulations in similar regions.
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Fereidoon, Majid, and Manfred Koch. "Rainfall Prediction with AMSR–E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran." Water 10, no. 7 (July 23, 2018): 964. http://dx.doi.org/10.3390/w10070964.
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Accurate estimates of daily rainfall are essential for understanding and modeling the physical processes involved in the interaction between the land surface and the atmosphere. In this study, daily satellite soil moisture observations from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR–E) generated by implementing the standard National Aeronautics and Space Administration (NASA) algorithm are employed for estimating rainfall, firstly, through the use of recently developed approach, SM2RAIN and, secondly, the nonlinear autoregressive network with exogenous inputs (NARX) neural modelling at five climate stations in the Karkheh river basin (KRB), located in south-west Iran. In the SM2RAIN method, the period 1 January 2003 to 31 December 2005 is used for the calibration of algorithm and the remaining 9 months from 1 January 2006 to 30 September 2006 is used for the validation of the rainfall estimates. In the NARX model, the full study period is split into training (1 January 2003 to 31 September 2005) and testing (1 September 2005 to 30 September 2006) stages. For the prediction of the rainfall as the desired target (output), relative soil moisture changes from AMSR–E and measured air temperature time series are chosen as exogenous (external) inputs in NARX. The quality of the estimated rainfall data is evaluated by comparing it with observed rainfall data at the five rain gauges in terms of the coefficient of determination R2, the RMSE and the statistical bias. For the SM2RAIN method, R2 ranges between 0.32 and 0.79 for all stations, whereas for the NARX- model the values are generally slightly lower. Moreover, the values of the bias for each station indicate that although SM2RAIN is likely to underestimate large rainfall intensities, due to the known effect of soil moisture saturation, its biases are somewhat lower than those of NARX. Moreover, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN–CDR) is employed to evaluate its potential for predicting the ground-based observed station rainfall, but it is found to work poorly. In conclusion, the results of the present study show that with the use of AMSR–E soil moisture products in the physically based SM2RAIN algorithm as well as in the NARX neural network, rainfall for poorly gauged regions can be predicted satisfactorily.
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Massari, Christian, Wade Crow, and Luca Brocca. "An assessment of the performance of global rainfall estimates without ground-based observations." Hydrology and Earth System Sciences 21, no. 9 (September 5, 2017): 4347–61. http://dx.doi.org/10.5194/hess-21-4347-2017.
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Abstract. Satellite-based rainfall estimates over land have great potential for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of triple collocation (TC) to characterize uncertainties associated with rainfall estimates by using three collocated rainfall products. However, TC requires the simultaneous availability of three products with mutually uncorrelated errors, a requirement which is difficult to satisfy with current global precipitation data sets. In this study, a recently developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the-art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high-quality ground rainfall product over the contiguous United States (CONUS), showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (vs. an unknown truth) of the aforementioned products without using a ground benchmark data set. The analysis is carried out during the period 2007–2012 using daily rainfall accumulation products obtained at 1° × 1° spatial resolution. Results convey the relatively high performance of the satellite rainfall estimates in eastern North and South America, southern Africa, southern and eastern Asia, eastern Australia, and southern Europe, as well as complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the Northern Hemisphere and the second providing very good performance in the Southern Hemisphere. The methodology presented in this study can be used to identify the best rainfall product for hydrologic models with sparsely gauged areas and provide the basis for an optimal integration among different rainfall products.
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Hamza, Ali, Muhammad Naveed Anjum, Muhammad Jehanzeb Masud Cheema, Xi Chen, Arslan Afzal, Muhammad Azam, Muhammad Kamran Shafi, and Aminjon Gulakhmadov. "Assessment of IMERG-V06, TRMM-3B42V7, SM2RAIN-ASCAT, and PERSIANN-CDR Precipitation Products over the Hindu Kush Mountains of Pakistan, South Asia." Remote Sensing 12, no. 23 (November 26, 2020): 3871. http://dx.doi.org/10.3390/rs12233871.
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In this study, the performances of four satellite-based precipitation products (IMERG-V06 Final-Run, TRMM-3B42V7, SM2Rain-ASCAT, and PERSIANN-CDR) were assessed with reference to the measurements of in-situ gauges at daily, monthly, seasonal, and annual scales from 2010 to 2017, over the Hindu Kush Mountains of Pakistan. The products were evaluated over the entire domain and at point-to-pixel scales. Different evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias)) and categorical indices (False Alarm Ration (FAR), Critical Success Index (CSI), Success Ratio (SR), and Probability of Detection (POD)) were used to assess the performances of the products considered in this study. Our results indicated the following. (1) IMERG-V06 and PERSIANN capably tracked the spatio-temporal variation of precipitation over the studied region. (2) All satellite-based products were in better agreement with the reference data on the monthly scales than on daily time scales. (3) On seasonal scale, the precipitation detection skills of IMERG-V06 and PERSIANN-CDR were better than those of SM2Rain-ASCAT and TRMM-3B42V7. In all seasons, overall performance of IMERG-V06 and PERSIANN-CDR was better than TRMM-3B42V7 and SM2Rain-ASCAT. (4) However, all products were uncertain in detecting light and moderate precipitation events. Consequently, we recommend the use of IMERG-V06 and PERSIANN-CDR products for subsequent hydro-meteorological studies in the Hindu Kush range.
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Rahman, Khalil Ur, and Songhao Shang. "A Regional Blended Precipitation Dataset over Pakistan Based on Regional Selection of Blending Satellite Precipitation Datasets and the Dynamic Weighted Average Least Squares Algorithm." Remote Sensing 12, no. 24 (December 8, 2020): 4009. http://dx.doi.org/10.3390/rs12244009.
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Substantial uncertainties are associated with satellite precipitation datasets (SPDs), which are further amplified over complex terrain and diverse climate regions. The current study develops a regional blended precipitation dataset (RBPD) over Pakistan from selected SPDs in different regions using a dynamic weighted average least squares (WALS) algorithm from 2007 to 2018 with 0.25° spatial resolution and one-day temporal resolution. Several SPDs, including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42-v7, Precipitation Estimates from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), ERA-Interim (reanalysis dataset), SM2RAIN-CCI, and SM2RAIN-ASCAT are evaluated to select appropriate blending SPDs in different climate regions. Six statistical indices, including mean bias (MB), mean absolute error (MAE), unbiased root mean square error (ubRMSE), correlation coefficient (R), Kling–Gupta efficiency (KGE), and Theil’s U coefficient, are used to assess the WALS-RBPD performance over 102 rain gauges (RGs) in Pakistan. The results showed that WALS-RBPD had assigned higher weights to IMERG in the glacial, humid, and arid regions, while SM2RAIN-ASCAT had higher weights across the hyper-arid region. The average weights of IMERG (SM2RAIN-ASCAT) are 29.03% (23.90%), 30.12% (24.19%), 31.30% (27.84%), and 27.65% (32.02%) across glacial, humid, arid, and hyper-arid regions, respectively. IMERG dominated monsoon and pre-monsoon seasons with average weights of 34.87% and 31.70%, while SM2RAIN-ASCAT depicted high performance during post-monsoon and winter seasons with average weights of 37.03% and 38.69%, respectively. Spatial scale evaluation of WALS-RPBD resulted in relatively poorer performance at high altitudes (glacial and humid regions), whereas better performance in plain areas (arid and hyper-arid regions). Moreover, temporal scale performance assessment depicted poorer performance during intense precipitation seasons (monsoon and pre-monsoon) as compared with post-monsoon and winter seasons. Skill scores are used to quantify the improvements of WALS-RBPD against previously developed blended precipitation datasets (BPDs) based on WALS (WALS-BPD), dynamic clustered Bayesian model averaging (DCBA-BPD), and dynamic Bayesian model averaging (DBMA-BPD). On the one hand, skill scores show relatively low improvements of WALS-RBPD against WALS-BPD, where maximum improvements are observed in glacial (humid) regions with skill scores of 29.89% (28.69%) in MAE, 27.25% (23.89%) in ubRMSE, and 24.37% (28.95%) in MB. On the other hand, the highest improvements are observed against DBMA-BPD with average improvements across glacial (humid) regions of 39.74% (36.93%), 38.27% (33.06%), and 39.16% (30.47%) in MB, MAE, and ubRMSE, respectively. It is recommended that the development of RBPDs can be a potential alternative for data-scarce regions and areas with complex topography.
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Filippucci, Paolo, Luca Brocca, Christian Massari, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli. "Toward a self-calibrated and independent SM2RAIN rainfall product." Journal of Hydrology 603 (December 2021): 126837. http://dx.doi.org/10.1016/j.jhydrol.2021.126837.
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Zhang, Ying, Jinliang Hou, and Chinlin Huang. "Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China." Remote Sensing 14, no. 21 (October 26, 2022): 5355. http://dx.doi.org/10.3390/rs14215355.
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Precipitation monitoring is important for earth system modeling and environmental management. Low spatial representativeness limits gauge measurements of rainfall and low spatial resolution limits satellite-derived rainfall. SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture (SM) observations, can be an alternative. However, the quality of SM data limits the accuracy of rainfall. The goal of this work was to improve the accuracy of rainfall estimation through merging multiple soil moisture (SM) datasets. This study proposed an integration framework, which consists of multiple machine learning methods, to use satellite and ground-based soil moisture observations to derive a precipitation product. First, three machine learning (ML) methods (random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN)) were used, respectively to generate three SM datasets (RF-SM, LSTM-SM, and CNN-SM) by merging satellite (SMOS, SMAP, and ASCAT) and ground-based SM observations. Then, these SM datasets were merged using the Bayesian model averaging method and validated by wireless sensor network (WSN) observations. Finally, the merged SM data were used to produce a rainfall dataset (SM2R) using SM2RAIN. The SM2R dataset was validated using automatic meteorological station (AMS) rainfall observations recorded throughout the Upper Heihe River Basin (China) during 2014–2015 and compared with other rainfall datasets. Our results revealed that the quality of the SM2R data outperforms that of GPM-SM2RAIN, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ERA5-Land (ERA5) and multi-source weighted-ensemble Precipitation (MSWEP). Triple-collocation analysis revealed that SM2R outperformed China Meteorological Data and the China Meteorological Forcing Dataset. Ultimately, the SM2R rainfall product was considered successful with acceptably low spatiotemporal errors (RMSE = 3.5 mm, R = 0.59, and bias = −1.6 mm).
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Goudarzi, Fatemeh Moazami, Amirpouya Sarraf, and Hassan Ahmadi. "Assessment of SM2RAIN-ASCAT and CMORPH satellite precipitation products over Maharlu Lake basin in Iran." Water Supply 20, no. 5 (May 13, 2020): 1799–806. http://dx.doi.org/10.2166/ws.2020.088.
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Abstract In recent years, the use of climatic databases and satellite products by researchers has become increasingly common in the field of climate modeling and research. These datasets play an important role in developing countries. This study evaluated two reanalyses, CMORPH and SM2RAIN-ASCAT over Maharlu Lake, a semi-arid region in Iran. The results showed that these two near-time datasets do not have accurate data over this basin. However, the probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR) statistics showed acceptable accuracy in the detection of precipitation. The coefficient of determination and root mean square error statistics have unacceptable accuracy over this area. The monthly changes in each of the indices showed that the CMORPH database had more errors in the spring months, but in other months the error rate was improved. SM2RAIN-ASCAT had better accuracy over this area relative to CMORPH. The estimation of the total accuracy of the data showed that these two satellite databases were not capable of estimating precipitation in the area.
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Fereidoon, Majid, Manfred Koch, and Luca Brocca. "Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran." Water 11, no. 3 (March 21, 2019): 594. http://dx.doi.org/10.3390/w11030594.
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Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.
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Pradhan, Ankita, and J. Indu. "Assessment of SM2RAIN derived and IMERG based precipitation products for hydrological simulation." Journal of Hydrology 603 (December 2021): 127191. http://dx.doi.org/10.1016/j.jhydrol.2021.127191.
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Lai, Yao, Jie Tian, Weiming Kang, Chao Gao, Weijie Hong, and Chansheng He. "Rainfall estimation from surface soil moisture using SM2RAIN in cold mountainous areas." Journal of Hydrology 606 (March 2022): 127430. http://dx.doi.org/10.1016/j.jhydrol.2022.127430.
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Brunetti, Maria Teresa, Massimo Melillo, Stefano Luigi Gariano, Luca Ciabatta, Luca Brocca, Giriraj Amarnath, and Silvia Peruccacci. "Satellite rainfall products outperform ground observations for landslide prediction in India." Hydrology and Earth System Sciences 25, no. 6 (June 16, 2021): 3267–79. http://dx.doi.org/10.5194/hess-25-3267-2021.
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Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1∘ vs. 0.25∘) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.
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Dari, Jacopo, Luca Brocca, Pere Quintana-Seguí, María José Escorihuela, Vivien Stefan, and Renato Morbidelli. "Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region." Remote Sensing 12, no. 16 (August 12, 2020): 2593. http://dx.doi.org/10.3390/rs12162593.
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Despite irrigation being one of the main sources of anthropogenic water consumption, detailed information about water amounts destined for this purpose are often lacking worldwide. In this study, a methodology which can be used to estimate irrigation amounts over a pilot area in Spain by exploiting remotely sensed soil moisture is proposed. Two high-resolution DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled soil moisture products have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) at 1 km. The irrigation estimates have been obtained through the SM2RAIN algorithm, in which the evapotranspiration term has been improved to adequately reproduce the crop evapotranspiration over irrigated areas according to the FAO (Food and Agriculture Organization) model. The experiment exploiting the SMAP data at 1 km represents the main work analyzed in this study and covered the period from January 2016 to September 2017. The experiment with the SMOS data at 1 km, for which a longer time series is available, allowed the irrigation estimates to be extended back to 2011. For both of the experiments carried out, the proposed method performed well in reproducing the magnitudes of the irrigation amounts that actually occurred in four of the five pilot irrigation districts. The SMAP experiment, for which a more detailed analysis was performed, also provided satisfactory results in representing the spatial distribution and the timing of the irrigation events. In addition, the investigation into which term of the SM2RAIN algorithm plays the leading role in determining the amount of water entering into the soil highlights the importance of correct representation of the evapotranspiration process.
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Wang, Xiangzhen, Baofu Li, Yaning Chen, Hao Guo, Yunqian Wang, and Lishu Lian. "Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas." Remote Sensing 12, no. 18 (September 6, 2020): 2886. http://dx.doi.org/10.3390/rs12182886.
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Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R2). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R2 > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas.
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Krishnan, Sooraj, Ankita Pradhan, and J. Indu. "Estimation of high-resolution precipitation using downscaled satellite soil moisture and SM2RAIN approach." Journal of Hydrology 610 (July 2022): 127926. http://dx.doi.org/10.1016/j.jhydrol.2022.127926.
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Prakash, Satya. "Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India." Journal of Hydrology 571 (April 2019): 50–59. http://dx.doi.org/10.1016/j.jhydrol.2019.01.036.
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Guo, Binbin, Tingbao Xu, Qin Yang, Jing Zhang, Zhong Dai, Yunyuan Deng, and Jun Zou. "Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China." Remote Sensing 15, no. 5 (February 28, 2023): 1373. http://dx.doi.org/10.3390/rs15051373.
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Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of major concern. There have been numerous evaluation/validation studies worldwide. However, a convincing systematic evaluation/validation of satellite precipitation remains unrealized. In particular, there are still only a limited number of hydrologic evaluations/validations with a long temporal period. Here we present a systematic evaluation of eight popular SPPs (CHIRPS, CMORPH, GPCP, GPM, GSMaP, MSWEP, PERSIANN, and SM2RAIN). The evaluation area used, using daily data from 2007 to 2020, is the Xiangjiang River basin, a mountainous catchment with a humid sub-tropical monsoon climate situated in south China. The evaluation was conducted at various spatial scales (both grid-gauge scale and watershed scale) and temporal scales (annual and seasonal scales). The evaluation paid particular attention to precipitation intensity and especially its impact on hydrologic modelling. In the evaluation of the results, the overall statistical metrics show that GSMaP and MSWEP rank as the two best-performing SPPs, with KGEGrid ≥ 0.48 and KGEWatershed ≥ 0.67, while CHIRPS and SM2RAIN were the two worst-performing SPPs with KGEGrid ≤ 0.25 and KGEWatershed ≤ 0.42. GSMaP gave the closest agreement with the observations. The GSMaP-driven model also was superior in depicting the rainfall-runoff relationship compared to the hydrologic models driven by other SPPs. This study further demonstrated that satellite remote sensing still has difficulty accurately estimating precipitation over a mountainous region. This study provides helpful information to optimize the generation of algorithms for satellite precipitation products, and valuable guidance for local communities to select suitable alternative precipitation datasets.
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Thaler, Sabina, Luca Brocca, Luca Ciabatta, Josef Eitzinger, Sebastian Hahn, and Wolfgang Wagner. "Effects of Different Spatial Precipitation Input Data on Crop Model Outputs under a Central European Climate." Atmosphere 9, no. 8 (July 26, 2018): 290. http://dx.doi.org/10.3390/atmos9080290.
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Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems (GIS) integrates information from soil, climate, and topography data into a larger area, providing a basis for spatial and temporal analysis. In the current study, the crop growth model Decision Support System for Agrotechnology Transfer (DSSAT) was used to evaluate five gridded precipitation input data at three locations in Austria. The precipitation data sets consist of the INtegrated Calibration and Application Tool (INCA) from the Meteorological Service Austria, two satellite precipitation data sources—Multisatellite Precipitation Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)—and two rainfall estimates based on satellite soil moisture data. The latter were obtained through the application of the SM2RAIN algorithm (SM2RASC) and a regression analysis (RAASC) applied to the Metop-A/B Advanced SCATtermonter (ASCAT) soil moisture product during a 9-year period from 2007–2015. For the evaluation, the effect on winter wheat and spring barley yield, caused by different precipitation inputs, at a spatial resolution of around 25 km was used. The highest variance was obtained for the driest area with light-textured soils; TMPA and two soil moisture-based products show very good results in the more humid areas. The poorest performances at all three locations and for both crops were found with the CMORPH input data.
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Chiaravalloti, Francesco, Luca Brocca, Antonio Procopio, Christian Massari, and Salvatore Gabriele. "Assessment of GPM and SM2RAIN-ASCAT rainfall products over complex terrain in southern Italy." Atmospheric Research 206 (July 2018): 64–74. http://dx.doi.org/10.1016/j.atmosres.2018.02.019.
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Sharifi, Ehsan, Josef Eitzinger, and Wouter Dorigo. "Performance of the State-Of-The-Art Gridded Precipitation Products over Mountainous Terrain: A Regional Study over Austria." Remote Sensing 11, no. 17 (August 28, 2019): 2018. http://dx.doi.org/10.3390/rs11172018.
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During the last decade, satellite-based precipitation products have been believed to be a potential source for forcing inputs in hydro-meteorological and agricultural models, which are essential especially over the mountains area or in basins where ground gauges are generally sparse or nonexistent. This study comprehensively evaluates several newly released precipitation products, i.e., MSWEP-V2.2, IMERG-V05B, IMERG-V06A, IMERG-V05-RT, ERA5, and SM2RAIN-ASCAT, at daily and monthly time-scales over Austria. We show that all the examined products are able to reproduce the spatial precipitation distribution over the country. MSWEP, followed by IMERG-V05B and -V06A, show the strongest agreement with in situ observations and perform better than other products with respect to spatial patterns and statistical metrics. Both IMERG and ERA5 products seem to have systematic precipitation overestimation at the monthly time-scale. IMERG-V06A performs slightly better than IMERG-V05B. With respect to heavy precipitation (P > 10 mm/day), MSWEP compare to other products demonstrate advantages in detecting precipitation events with a higher spatial average of probability of detection (POD) and lower false alarm ratio (FAR) scores skill (0.74 and 0.28), while SM2RAIN and ERA5 reveal lower POD (0.35) and higher FAR (0.56) in this precipitation range in comparison with other products. However, the ERA5 and MSWEP indicate robust average POD and FAR values with respect to light/moderate precipitation (10 mm > P ≥ 0.1 mm) with 0.94 and 0.11, respectively. Such robustness of MSWEP may be rooted in applying the daily rain gauges in calibration processes. Moreover, although all products accurately map the spatial precipitation distribution they still have difficulty capturing the effects of topography on precipitation. The overall performance of the precipitation products was lower in the peripheries of the study area where most stations are situated in the mountainous area and was higher over the low altitude regions. However, according to our analysis of the considered products, MSWEP-V2.2, followed by IMERG-V06S and -V05B, are the most suitable for driving hydro-meteorological, agricultural, and other models over mountainous terrain.
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Koohi, Sakine, Asghar Azizian, and Luca Brocca. "Spatiotemporal drought monitoring using bottom-up precipitation dataset (SM2RAIN-ASCAT) over different regions of Iran." Science of The Total Environment 779 (July 2021): 146535. http://dx.doi.org/10.1016/j.scitotenv.2021.146535.
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Paredes-Trejo, Franklin, Humberto Barbosa, and Luciana Rossato Spatafora. "Assessment of SM2RAIN-Derived and State-of-the-Art Satellite Rainfall Products over Northeastern Brazil." Remote Sensing 10, no. 7 (July 9, 2018): 1093. http://dx.doi.org/10.3390/rs10071093.
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Hoang, K. O., and M. J. Lu. "Impacts of temperature effect removal on rainfall estimation from soil water content by using SM2RAIN algorithm." IOP Conference Series: Earth and Environmental Science 344 (November 1, 2019): 012046. http://dx.doi.org/10.1088/1755-1315/344/1/012046.
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Saeedi, Mohammad, Sina Nabaei, Hyunglok Kim, Ameneh Tavakol, and Venkataraman Lakshmi. "Performance assessment of SM2RAIN-NWF using ASCAT soil moisture via supervised land cover-soil-climate classification." Remote Sensing of Environment 285 (February 2023): 113393. http://dx.doi.org/10.1016/j.rse.2022.113393.
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Camici, Stefania, Christian Massari, Luca Ciabatta, Ivan Marchesini, and Luca Brocca. "Which rainfall score is more informative about the performance in river discharge simulation? A comprehensive assessment on 1318 basins over Europe." Hydrology and Earth System Sciences 24, no. 10 (October 13, 2020): 4869–85. http://dx.doi.org/10.5194/hess-24-4869-2020.
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Abstract. The global availability of satellite rainfall products (SRPs) at an increasingly high temporal and spatial resolution has made their exploitation in hydrological applications possible, especially in data-scarce regions. In this context, understanding how uncertainties transfer from SRPs to river discharge simulations, through the hydrological model, is a main research question. SRPs' accuracy is normally characterized by comparing them with ground observations via the calculation of categorical (e.g. threat score, false alarm ratio and probability of detection) and/or continuous (e.g. bias, root mean square error, Nash–Sutcliffe index, Kling–Gupta efficiency index and correlation coefficient) performance scores. However, whether these scores are informative about the associated performance in river discharge simulations (when the SRP is used as input to a hydrological model) is an under-discussed research topic. This study aims to relate the accuracy of different SRPs both in terms of rainfall and in terms of river discharge simulation. That is, the following research questions are addressed: is there any performance score that can be used to select the best performing rainfall product for river discharge simulation? Are multiple scores needed? And, which are these scores? To answer these questions, three SRPs, namely the Tropical Rainfall Measurement Mission (TRRM) Multi-satellite Precipitation Analysis (TMPA), the Climate Prediction Center MORPHing (CMORPH) algorithm and the SM2RAIN algorithm applied to the Advanced SCATterometer (ASCAT) soil moisture product (SM2RAIN–ASCAT) have been used as input into a lumped hydrologic model, “Modello Idrologico Semi-Distribuito in continuo” (MISDc), for 1318 basins over Europe with different physiographic characteristics. Results suggest that, among the continuous scores, the correlation coefficient and Kling–Gupta efficiency index are not reliable indices to select the best performing rainfall product for hydrological modelling, whereas bias and root mean square error seem more appropriate. In particular, by constraining the relative bias to absolute values lower than 0.2 and the relative root mean square error to values lower than 2, good hydrological performances (Kling–Gupta efficiency index on river discharge greater than 0.5) are ensured for almost 75 % of the basins fulfilling these criteria. Conversely, the categorical scores have not provided suitable information for addressing the SRP selection for hydrological modelling.
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Ciabatta, Luca, Luca Brocca, Christian Massari, Tommaso Moramarco, Simone Gabellani, Silvia Puca, and Wolfgang Wagner. "Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy." International Journal of Applied Earth Observation and Geoinformation 48 (June 2016): 163–73. http://dx.doi.org/10.1016/j.jag.2015.10.004.
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Wild, Ashley, Zhi-Weng Chua, and Yuriy Kuleshov. "Evaluation of Satellite Precipitation Estimates over the South West Pacific Region." Remote Sensing 13, no. 19 (September 30, 2021): 3929. http://dx.doi.org/10.3390/rs13193929.
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Rainfall estimation over the Pacific region is difficult due to the large distances between rain gauges and the high convection nature of many rainfall events. This study evaluates space-based rainfall observations over the South West Pacific Region from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP), the USA National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH), the Climate Hazards group Infrared Precipitation with Stations (CHIRPS), and the National Aeronautics and Space Administration’s (NASA) Integrated Multi-Satellite Retrievals for GPM (IMERG). The technique of collocation analysis (CA) is used to compare the performance of monthly satellite precipitation estimates (SPEs). Multi-Source Weighted-Ensemble Precipitation (MSWEP) was used as a reference dataset to compare with each SPE. European Centre for Medium-range Weather Forecasts’ (ECMWF) ERA5 reanalysis was also combined with Soil Moisture-2-Rain–ASCAT (SM2RAIN–ASCAT) to perform triple CA for the six sub-regions of Fiji, New Caledonia, Papua New Guinea (PNG), the Solomon Islands, Timor, and Vanuatu. It was found that GSMaP performed best over low rain gauge density areas, including mountainous areas of PNG (the cross-correlation, CC = 0.64), and the Solomon Islands (CC = 0.74). CHIRPS had the most consistent performance (high correlations and low errors) across all six sub-regions in the study area. Based on the results, recommendations are made for the use of SPEs over the South West Pacific Region.
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Venkatesh, Kolluru, N. Y. Krakauer, E. Sharifi, and H. Ramesh. "Evaluating the Performance of Secondary Precipitation Products through Statistical and Hydrological Modeling in a Mountainous Tropical Basin of India." Advances in Meteorology 2020 (November 16, 2020): 1–23. http://dx.doi.org/10.1155/2020/8859185.
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This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.
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Beck, Hylke E., Noemi Vergopolan, Ming Pan, Vincenzo Levizzani, Albert I. J. M. van Dijk, Graham P. Weedon, Luca Brocca, Florian Pappenberger, George J. Huffman, and Eric F. Wood. "Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling." Hydrology and Earth System Sciences 21, no. 12 (December 8, 2017): 6201–17. http://dx.doi.org/10.5194/hess-21-6201-2017.
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Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( < 50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.
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Satgé, Frédéric, Denis Ruelland, Marie-Paule Bonnet, Jorge Molina, and Ramiro Pillco. "Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow- hydrological modelling in the Lake Titicaca region." Hydrology and Earth System Sciences 23, no. 1 (February 1, 2019): 595–619. http://dx.doi.org/10.5194/hess-23-595-2019.
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Abstract. This paper proposes a protocol to assess the space–time consistency of 12 satellite-based precipitation products (SPPs) according to various indicators, including (i) direct comparison of SPPs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPPs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPPs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000–2004, 2004–2008, 2008–2012 and 2000–2012) to account for the space–time variability of the SPPs and to a large dataset composed of 12 SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TMPA–RT v.7, TMPA–Adj v.7 and SM2Rain–CCI v.2), an unprecedented comparison. The aim of using different space scales and timescales and indicators was to evaluate whether the efficiency of SPPs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPPs. Compared to precipitation gauge observations, some SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, GSMaP v.6, PERSIANN, and TMPA–RT v.7) are unable to estimate regional precipitation, whereas the others (CHIRP v.2, CHIRPS v.2, CMORPH–BLD v.1, MSWEP v.2.1, PERSIANN–CDR, and TMPA–Adj v.7) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In 9 out of 10 of the cases studied, streamflow was more realistically simulated when SPPs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks, whereas the SPP's ability to reproduce the duration of MODIS-based snow cover resulted in poorer simulations than simulation using available precipitation gauges. Interestingly, the potential of the SPPs varied significantly when they were used to reproduce gauge precipitation estimates, streamflow observations or snow cover duration and depending on the time window considered. SPPs thus produce space–time errors that cannot be assessed when a single indicator and/or time window is used, underlining the importance of carefully considering their space–time consistency before using them for hydro-climatic studies. Among all the SPPs assessed, MSWEP v.2.1 showed the highest space–time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration.
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Chen, F., W. T. Crow, L. Ciabatta, P. Filippucci, G. Panegrossi, A. C. Marra, S. Puca, and C. Massari. "Enhanced large-scale validation of satellite-based land rainfall products." Journal of Hydrometeorology, November 20, 2020. http://dx.doi.org/10.1175/jhm-d-20-0056.1.
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AbstractSatellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we evaluate the potential for large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques (i.e., triple collocation (TC) and quadruple collocation (QC) analyses). Our collocation approach leverages the SM2RAIN (Soil Moisture to Rain) rainfall product, derived from the time series analysis of satellite-based soil moisture retrievals, in combination with independent rainfall datasets acquired from ground observations and climate reanalysis to validate four years of the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) H-SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC analyses. Results demonstrate that the SM2RAIN product is a uniquely valuable independent product for collocation analyses, as other available large-scale rainfall datasets are often based on overlapping data sources and algorithms. In particular, the availability of SM2RAIN facilitates the large-scale evaluation of SPE products like H23 – even in areas lacking adequate ground-based observations to apply traditional validation approaches.
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Hengl, Tomislav, Matthew A. E. Miller, Josip Križan, Keith D. Shepherd, Andrew Sila, Milan Kilibarda, Ognjen Antonijević, et al. "African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning." Scientific Reports 11, no. 1 (March 17, 2021). http://dx.doi.org/10.1038/s41598-021-85639-y.
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AbstractSoil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ($$N \approx 150,000$$ N ≈ 150 , 000 ) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the (Machine Learning in ) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.
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azizian, asghar. "Spatiotemporal drought monitoring using bottom-up precipitation dataset (SM2RAIN-ASCAT) over different regions of Iran." Qeios, April 5, 2022. http://dx.doi.org/10.32388/ctctnw.
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Satgé, Frédéric, Yawar Hussain, Jorge Molina‐Carpio, Ramiro Pillco, Coralie Laugner, Gulraiz Akhter, and Marie‐Paule Bonnet. "Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions." International Journal of Climatology, July 22, 2020. http://dx.doi.org/10.1002/joc.6704.
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Fan, Yixi, Ziqiang Ma, Yaoming Ma, Weiqiang Ma, Zhipeng Xie, Leiding Ding, Yizhe Han, Wei Hu, and Rongmingzhu Su. "Respective Advantages of “Top‐Down” Based GPM IMERG and “Bottom‐Up” Based SM2RAIN‐ASCAT Precipitation Products Over the Tibetan Plateau." Journal of Geophysical Research: Atmospheres 126, no. 7 (April 6, 2021). http://dx.doi.org/10.1029/2020jd033946.
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Saeedi, Mohammad, Hyunglok Kim, Sina Nabaei, Luca Brocca, Venkataraman Lakshmi, and Hamidreza Mosaffa. "A comprehensive assessment of SM2RAIN-NWF using ASCAT and a combination of ASCAT and SMAP soil moisture products for rainfall estimation." Science of The Total Environment, June 2022, 156416. http://dx.doi.org/10.1016/j.scitotenv.2022.156416.
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Saeedi, Mohammad, Ahmad Sharafati, Luca Brocca, and Ameneh Tavakol. "Estimating rainfall depth from satellite-based soil moisture data: A new algorithm by integrating SM2RAIN and the analytical net water flux models." Journal of Hydrology, April 2022, 127868. http://dx.doi.org/10.1016/j.jhydrol.2022.127868.
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Saeedi, Mohammad, Ahmad Sharafati, Luca Brocca, and Ameneh Tavakol. "Estimating rainfall depth from satellite-based soil moisture data: A new algorithm by integrating SM2RAIN and the analytical net water flux models." Journal of Hydrology, April 2022, 127868. http://dx.doi.org/10.1016/j.jhydrol.2022.127868.
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