{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T20:47:22Z","timestamp":1776113242937,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T00:00:00Z","timestamp":1567036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51779119 and 51839006"],"award-info":[{"award-number":["51779119 and 51839006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u20132015 and 2007\u20132015, respectively. Several statistical metrics, i.e., Bias, unbiased root mean square error (ubRMSE), Theil\u2019s U, and the modified Kling\u2013Gupta 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\u00b0 scale during 2007\u20132015. 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\u2019s 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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs11172040","type":"journal-article","created":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T11:26:22Z","timestamp":1567077982000},"page":"2040","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8927-3467","authenticated-orcid":false,"given":"Khalil Ur","family":"Rahman","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2971-2621","authenticated-orcid":false,"given":"Songhao","family":"Shang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0771-4498","authenticated-orcid":false,"given":"Muhammad","family":"Shahid","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"},{"name":"NICE, SCEE, National University of Sciences &amp; Technology Islamabad, Islamabad 44000, Pakistan"}]},{"given":"Yeqiang","family":"Wen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.jhydrol.2018.01.039","article-title":"On the performance of satellite precipitation products in riverine flood modeling: A review","volume":"558","author":"Maggioni","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1175\/BAMS-D-15-00306.1","article-title":"The global precipitation measurement (GPM) mission for science and society","volume":"98","author":"Petersen","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2018.03.016","article-title":"How far are we from the use of satellite rainfall products in landslide forecasting?","volume":"210","author":"Brunetti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1016\/j.scitotenv.2018.09.231","article-title":"Understanding the global hydrological droughts of 2003\u20132016 and their relationships with teleconnections","volume":"650","author":"Forootan","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11,980","DOI":"10.1029\/2018GL080298","article-title":"The uneven nature of daily precipitation and its change","volume":"45","author":"Pendergrass","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.jhydrol.2019.01.036","article-title":"Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India","volume":"571","author":"Prakash","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.atmosres.2016.11.006","article-title":"Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile","volume":"186","author":"Zambrano","year":"2017","journal-title":"Atmos. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6602","DOI":"10.1073\/pnas.1203333109","article-title":"Reassessment of the 2010\u20132011 Haiti cholera outbreak and rainfall-driven multiseason projections","volume":"109","author":"Rinaldo","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5128","DOI":"10.1002\/2014JD021489","article-title":"Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data","volume":"119","author":"Brocca","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1175\/JHM-D-15-0190.1","article-title":"A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era","volume":"17","author":"Maggioni","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Villarini, G., Mandapaka, P.V., Krajewski, W.F., and Moore, R.J. (2008). Rainfall and sampling uncertainties: A rain gauge perspective. J. Geophys. Res. Atmos., 113.","DOI":"10.1029\/2007JD009214"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-14-00283.1","article-title":"So, how much of the Earth\u2019s surface is covered by rain gauges?","volume":"98","author":"Kidd","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1175\/BAMS-88-1-47","article-title":"Comparison of near-real-time precipitation estimates from satellite observations and numerical models","volume":"88","author":"Ebert","year":"2007","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4347","DOI":"10.5194\/hess-21-4347-2017","article-title":"An assessment of the performance of global rainfall estimates without ground-based observations","volume":"21","author":"Massari","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1175\/BAMS-D-13-00164.1","article-title":"The global precipitation measurement mission","volume":"95","author":"Hou","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.5194\/hess-15-1109-2011","article-title":"Status of satellite precipitation retrievals","volume":"15","author":"Kidd","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.atmosres.2016.04.017","article-title":"Comparative evaluation of different satellite rainfall estimation products and bias correction in the Upper Blue Nile (UBN) basin","volume":"178","author":"Abera","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kimani, M., Hoedjes, J., and Su, Z. (2017). An assessment of satellite-derived rainfall products relative to ground observations over East Africa. Remote Sens., 9.","DOI":"10.3390\/rs9050430"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1175\/JHM560.1","article-title":"The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales","volume":"8","author":"Huffman","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"150066","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations\u2014a new environmental record for monitoring extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci. Data"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1175\/JHM-D-11-022.1","article-title":"Kalman filter\u2013based CMORPH","volume":"12","author":"Joyce","year":"2011","journal-title":"J. Hydrometeorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1175\/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2","article-title":"Evaluation of PERSIANN system satellite-based estimates of tropical rainfall","volume":"81","author":"Sorooshian","year":"2000","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5801","DOI":"10.5194\/hess-22-5801-2018","article-title":"The PERSIANN family of global satellite precipitation data: A review and evaluation of products","volume":"22","author":"Nguyen","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.2151\/jmsj.87A.137","article-title":"A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data","volume":"87","author":"Ushio","year":"2009","journal-title":"J. Meteorol. Soc. Jpn. Ser. II"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Crow, W., van Den Berg, M., Huffman, G., and Pellarin, T. (2011). Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART). Water Resour. Res., 47.","DOI":"10.1029\/2011WR010576"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2013.04.011","article-title":"A simple and effective method for correcting soil moisture and precipitation estimates using AMSR-E measurements","volume":"136","author":"Pellarin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1002\/grl.50173","article-title":"A new method for rainfall estimation through soil moisture observations","volume":"40","author":"Brocca","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7213","DOI":"10.1002\/2016WR019024","article-title":"Precipitation estimation using L-band and C-band soil moisture retrievals","volume":"52","author":"Koster","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.advwatres.2017.08.010","article-title":"Exploiting a constellation of satellite soil moisture sensors for accurate rainfall estimation","volume":"108","author":"Tarpanelli","year":"2017","journal-title":"Adv. Water Resour."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1175\/JHM-D-14-0108.1","article-title":"Integration of satellite soil moisture and rainfall observations over the Italian territory","volume":"16","author":"Ciabatta","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"ref_31","first-page":"e2131v4","article-title":"Using Python\u00ae Language for the Validation of the CCI Soil Moisture Products Via SM2RAIN","volume":"4","author":"Ciabatta","year":"2016","journal-title":"PeerJ Preprints"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1515\/johh-2015-0016","article-title":"Rainfall estimation from in situ soil moisture observations at several sites in Europe: An evaluation of the SM2RAIN algorithm","volume":"63","author":"Brocca","year":"2015","journal-title":"J. Hydrol. Hydromech."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.atmosres.2018.02.019","article-title":"Assessment of GPM and SM2RAIN-ASCAT rainfall products over complex terrain in southern Italy","volume":"206","author":"Chiaravalloti","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Massari, C., Camici, S., Ciabatta, L., and Brocca, L. (2018). Exploiting satellite-based surface soil moisture for flood forecasting in the Mediterranean area: State update versus rainfall correction. Remote Sens., 10.","DOI":"10.3390\/rs10020292"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12,062","DOI":"10.1002\/2016JD025382","article-title":"Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia","volume":"121","author":"Brocca","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.jhydrol.2016.12.057","article-title":"Daily precipitation estimation through different microwave sensors: Verification study over Italy","volume":"545","author":"Ciabatta","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"267","DOI":"10.5194\/essd-10-267-2018","article-title":"SM2RAIN-CCI: A new global long-term rainfall data set derived from ESA CCI soil moisture","volume":"10","author":"Ciabatta","year":"2018","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2015.01.016","article-title":"Correction of real-time satellite precipitation with multi-sensor satellite observations of land surface variables","volume":"160","author":"Wanders","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.advwatres.2014.08.004","article-title":"Potential of soil moisture observations in flood modelling: Estimating initial conditions and correcting rainfall","volume":"74","author":"Massari","year":"2014","journal-title":"Adv. Water Resour."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2017.08.022","article-title":"Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX","volume":"200","author":"Pellarin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Paredes-Trejo, F., Barbosa, H., and dos Santos, C.A. (2019). Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil. Remote Sens., 11.","DOI":"10.3390\/rs11091113"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Paredes-Trejo, F., Barbosa, H., and Rossato Spatafora, L. (2018). Assessment of SM2RAIN-Derived and State-of-the-Art Satellite Rainfall Products over Northeastern Brazil. Remote Sens., 10.","DOI":"10.3390\/rs10071093"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1109\/JSTARS.2017.2651140","article-title":"A review of the applications of ASCAT soil moisture products","volume":"10","author":"Brocca","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1175\/2008JHM986.1","article-title":"Improving satellite-based rainfall accumulation estimates using spaceborne surface soil moisture retrievals","volume":"10","author":"Crow","year":"2009","journal-title":"J. Hydrometeorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1016\/j.jhydrol.2018.06.067","article-title":"How reliable are satellite precipitation estimates for driving hydrological models: A verification study over the Mediterranean area","volume":"563","author":"Camici","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Sch\u00fcller, L., Bojkov, B., and Wolfgang, W. (2019). SM2RAIN-ASCAT (2007\u20132018): Global daily satellite rainfall from ASCAT soil moisture. Earth Syst. Sci. Data, Under Review.","DOI":"10.5194\/essd-2019-48"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rahman, K.U., Shang, S., Shahid, M., and Li, J. (2018). Developing an Ensemble Precipitation Algorithm from Satellite Products and Its Topographical and Seasonal Evaluations over Pakistan. Remote Sens., 10.","DOI":"10.3390\/rs10111835"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.apenergy.2016.12.012","article-title":"Sustainable and economical small-scale and low-head hydropower generation: A promising alternative potential solution for energy generation at local and regional scale","volume":"188","author":"Balkhair","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_49","first-page":"37","article-title":"Rainfall trends in different climate zones of Pakistan","volume":"9","author":"Salma","year":"2012","journal-title":"Pak. J. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1002\/2014RG000460","article-title":"Western disturbances: A review","volume":"53","author":"Dimri","year":"2015","journal-title":"Rev. Geophys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s00704-015-1616-6","article-title":"Run-based multi-model interannual variability assessment of precipitation and temperature over Pakistan using two IPCC AR4-based AOGCMs","volume":"127","author":"Asmat","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"425","DOI":"10.5194\/hess-15-425-2011","article-title":"Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals","volume":"15","author":"Liu","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.rse.2014.07.023","article-title":"Evaluation of the ESA CCI soil moisture product using ground-based observations","volume":"162","author":"Dorigo","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Cui, C., Xu, J., Zeng, J., Chen, K.-S., Bai, X., Lu, H., Chen, Q., and Zhao, T. (2018). Soil moisture mapping from satellites: An intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales. Remote Sens., 10.","DOI":"10.3390\/rs10010033"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1127\/0941-2948\/2013\/0399","article-title":"The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications","volume":"22","author":"Wagner","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2017.07.001","article-title":"ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions","volume":"203","author":"Dorigo","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"9426","DOI":"10.1002\/2015JD023797","article-title":"Performance evaluation of rainfall estimates by TRMM Multi-satellite Precipitation Analysis 3B42V6 and V7 over Brazil","volume":"120","author":"Melo","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Caparoci Nogueira, S., Moreira, M., and Lordelo Volpato, M. (2018). Evaluating precipitation estimates from Eta, TRMM and CHRIPS Data in the south-southeast region of Minas Gerais State\u2014Brazil. Remote Sens., 10.","DOI":"10.3390\/rs10020313"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.atmosres.2017.10.026","article-title":"Validation of satellite based precipitation over diverse topography of Pakistan","volume":"201","author":"Iqbal","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Bliemel, F. (1973). Theil\u2019s Forecast Accuracy Coefficient: A Clarification, SAGE Publications Sage CA.","DOI":"10.2307\/3149394"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"AghaKouchak, A., Mehran, A., Norouzi, H., and Behrangi, A. (2012). Systematic and random error components in satellite precipitation data sets. Geophys. Res. Lett., 39.","DOI":"10.1029\/2012GL051592"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"10654","DOI":"10.1002\/2016JD025456","article-title":"Evaluation of multisatellite precipitation products by use of ground-based data over China","volume":"121","author":"Huang","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.jhydrol.2012.01.011","article-title":"Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios","volume":"424","author":"Kling","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Chen, F., and Li, X. (2016). Evaluation of IMERG and TRMM 3B43 monthly precipitation products over mainland China. Remote Sens., 8.","DOI":"10.3390\/rs8060472"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.3390\/rs70201758","article-title":"Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique","volume":"7","author":"Patricio","year":"2015","journal-title":"Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1007\/s11802-017-3350-4","article-title":"Assessment of sea water inundation along Daboo Creek area in Indus Delta Region, Pakistan","volume":"16","author":"Zia","year":"2017","journal-title":"J. Ocean. Univ. China"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.asr.2014.04.017","article-title":"Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan","volume":"54","author":"Khan","year":"2014","journal-title":"Adv. Space Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1007\/s00704-016-2027-z","article-title":"Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan","volume":"131","author":"Hussain","year":"2018","journal-title":"Theor. Appl. Climatol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5201","DOI":"10.5194\/hess-21-5201-2017","article-title":"SMOS near-real-time soil moisture product: Processor overview and first validation results","volume":"21","author":"Richaume","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Bhatti, H., Rientjes, T., Haile, A., Habib, E., and Verhoef, W. (2016). Evaluation of bias correction method for satellite-based rainfall data. 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