{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T03:54:36Z","timestamp":1777780476689,"version":"3.51.4"},"reference-count":116,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Centre for Polar and Ocean Research, Goa, India","award":["NCAOR\/ 2018\/HiCOM\/ 03"],"award-info":[{"award-number":["NCAOR\/ 2018\/HiCOM\/ 03"]}]},{"name":"National Centre for Polar and Ocean Research, Goa, India","award":["NCAOR\/ 2018\/HiCOM\/ 03"],"award-info":[{"award-number":["NCAOR\/ 2018\/HiCOM\/ 03"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s12145-024-01322-6","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T18:01:55Z","timestamp":1715623315000},"page":"2973-2994","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Synergistic approach for streamflow forecasting in a glacierized catchment of western Himalaya using earth observation and machine learning techniques"],"prefix":"10.1007","volume":"17","author":[{"given":"Jaydeo K.","family":"Dharpure","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajanta","family":"Goswami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akansha","family":"Patel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dharmaveer","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay K.","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anil V.","family":"Kulkarni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"1322_CR1","doi-asserted-by":"publisher","first-page":"1957","DOI":"10.1080\/02626667.2018.1557335","volume":"64","author":"MI Abro","year":"2019","unstructured":"Abro MI, Zhu D, Wei M, Majidano AA, Khaskheli MA, Ul Abideen Z, Memon MS (2019) Hydrological appraisal of rainfall estimates from radar, satellite, raingauge and satellite\u2013gauge combination on the Qinhuai River Basin. China Hydrol Sci J 64:1957\u20131971. https:\/\/doi.org\/10.1080\/02626667.2018.1557335","journal-title":"China Hydrol Sci J"},{"key":"1322_CR2","doi-asserted-by":"publisher","unstructured":"Abro MI, Wei M, Zhu D, Elahi E, Ali G, Khaskheli MA, Shah AR, Nkunzimana A (2020) Hydrological evaluation of satellite and reanalysis precipitation products in the glacier-fed river basin (Gilgit). Arab J Geosci 13 (631):1\u201313. https:\/\/doi.org\/10.1007\/s12517-020-05621-2","DOI":"10.1007\/s12517-020-05621-2"},{"key":"1322_CR3","doi-asserted-by":"publisher","unstructured":"Adnan RM, Liang Z, Trajkovic S, Zounemat-Kermani M, Li B, Kisi O (2019) Daily streamflow prediction using optimally pruned extreme learning machine. J Hydrol 577:123981. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123981","DOI":"10.1016\/j.jhydrol.2019.123981"},{"key":"1322_CR4","doi-asserted-by":"publisher","unstructured":"Adnan, R.M., Zounemat-Kermani, M., Kuriqi, A., Kisi, O., 2021. Machine Learning Method in Prediction Streamflow Considering Periodicity Component. https:\/\/doi.org\/10.1007\/978-981-15-5772-9_18","DOI":"10.1007\/978-981-15-5772-9_18"},{"key":"1322_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-022-09545-x","volume":"15","author":"BA Akpovi","year":"2022","unstructured":"Akpovi BA, Zhu D, Abro MI, Lawin AE, Houngnibo M, Bessou J (2022) Hydrological appraisal using multi-source rainfall data in PDM model over the Qinhuai River basin in China. Arab J Geosci 15:1\u201314. https:\/\/doi.org\/10.1007\/s12517-022-09545-x","journal-title":"Arab J Geosci"},{"key":"1322_CR6","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1680\/jwama.14.00146","volume":"170","author":"MJ Alizadeh","year":"2017","unstructured":"Alizadeh MJ, Rajaee T, Motahari M (2017) Flow forecasting models using hydrologic and hydrometric data. Proc Inst Civ Eng Water Manag 170:150\u2013162. https:\/\/doi.org\/10.1680\/jwama.14.00146","journal-title":"Proc Inst Civ Eng Water Manag"},{"key":"1322_CR7","doi-asserted-by":"publisher","unstructured":"Althelaya KA, El-Alfy ESM, Mohammed S (2018) Evaluation of Bidirectional LSTM for Short and Long-Term Stock Market Prediction. In: 2018 9th International Conference on Information and Communication Systems (ICICS). IEEE, Irbid, pp 151\u2013156. https:\/\/doi.org\/10.1109\/IACS.2018.8355458","DOI":"10.1109\/IACS.2018.8355458"},{"key":"1322_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/ngeo1356","volume":"5","author":"C Andermann","year":"2012","unstructured":"Andermann C, Longuevergne L, Bonnet S, Crave A, Davy P, Gloaguen R (2012) Impact of transient groundwater storage on the discharge of Himalayan rivers. Nat Geosci 5:127\u2013132. https:\/\/doi.org\/10.1038\/ngeo1356","journal-title":"Nat Geosci"},{"key":"1322_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-53055-y","volume":"9","author":"D Bandyopadhyay","year":"2019","unstructured":"Bandyopadhyay D, Singh G, Kulkarni AV (2019) Spatial distribution of decadal ice-thickness change and glacier stored water loss in the Upper Ganga basin, India during 2000\u20132014. Sci Rep 9:1\u20139. https:\/\/doi.org\/10.1038\/s41598-019-53055-y","journal-title":"Sci Rep"},{"key":"1322_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2021.102490","volume":"103","author":"A Banerjee","year":"2021","unstructured":"Banerjee A, Chen R, Meadows ME, Sengupta D, Pathak S, Xia Z, Mal S (2021) Tracking 21st century climate dynamics of the Third Pole: An analysis of topo-climate impacts on snow cover in the central Himalaya using Google Earth Engine. Int J Appl Earth Obs Geoinf 103:102490. https:\/\/doi.org\/10.1016\/j.jag.2021.102490","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"1322_CR11","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1002\/joc.1920","volume":"30","author":"MR Bhutiyani","year":"2010","unstructured":"Bhutiyani MR, Kale VS, Pawar NJ (2010) Climate change and the precipitation variations in the northwestern Himalaya: 1866\u20132006. Int J Climatol 30:535\u2013548. https:\/\/doi.org\/10.1002\/joc.1920","journal-title":"Int J Climatol"},{"key":"1322_CR12","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","volume":"25","author":"G Biau","year":"2016","unstructured":"Biau G, Scornet E (2016) A Random Forest Guided Tour TEST 25:197\u2013227. https:\/\/doi.org\/10.1007\/s11749-016-0481-7","journal-title":"A Random Forest Guided Tour TEST"},{"key":"1322_CR13","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1038\/s41893-019-0305-3","volume":"2","author":"H Biemans","year":"2019","unstructured":"Biemans H, Siderius C, Lutz AF, Nepal S, Ahmad B, Hassan T, von Bloh W, Wijngaard RR, Wester P, Shrestha AB, Immerzeel WW (2019) Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nat Sustain 2:594\u2013601. https:\/\/doi.org\/10.1038\/s41893-019-0305-3","journal-title":"Nat Sustain"},{"key":"1322_CR14","doi-asserted-by":"publisher","unstructured":"Bouktif S, Fiaz A, Ouni A, Serhani MA (2018) Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies 11:1636. https:\/\/doi.org\/10.3390\/en11071636","DOI":"10.3390\/en11071636"},{"key":"1322_CR15","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/ICCECE51280.2021.9342376","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random Forests. Mach Learn 45:5\u201332. https:\/\/doi.org\/10.1109\/ICCECE51280.2021.9342376","journal-title":"Mach Learn"},{"key":"1322_CR16","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.3390\/w7052494","volume":"7","author":"M Callegari","year":"2015","unstructured":"Callegari M, Mazzoli P, de Gregorio L, Notarnicola C, Pasolli L, Petitta M, Pistocchi A (2015) Seasonal river discharge forecasting using support vector regression: A case study in the Italian Alps. Water (switzerland) 7:2494\u20132515. https:\/\/doi.org\/10.3390\/w7052494","journal-title":"Water (switzerland)"},{"key":"1322_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-41583-6","volume":"9","author":"Y Chai","year":"2019","unstructured":"Chai Y, Li Y, Yang Y, Zhu B, Li S, Xu C, Liu C (2019) Influence of Climate Variability and Reservoir Operation on Streamflow in the Yangtze River. Sci Rep 9:1\u201310. https:\/\/doi.org\/10.1038\/s41598-019-41583-6","journal-title":"Sci Rep"},{"key":"1322_CR18","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.jhydrol.2010.02.019","volume":"385","author":"CS Chen","year":"2010","unstructured":"Chen CS, Chen BPT, Chou FNF, Yang CC (2010) Development and application of a decision group Back-Propagation Neural Network for flood forecasting. J Hydrol 385:173\u2013182. https:\/\/doi.org\/10.1016\/j.jhydrol.2010.02.019","journal-title":"J Hydrol"},{"key":"1322_CR19","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1088\/1742-6596\/628\/1\/012073","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20:273\u2013297. https:\/\/doi.org\/10.1088\/1742-6596\/628\/1\/012073","journal-title":"Mach Learn"},{"key":"1322_CR20","doi-asserted-by":"publisher","unstructured":"Cutler A, Cutler DR, Stevens JR (2012) Chapter 5: Random Forests. In: Zhang C, Ma Y (eds) Ensemble Machine Learning: Methods and Applications. Springer Science, pp 157\u2013175. https:\/\/doi.org\/10.1007\/978-1-4419-9326-7","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"1322_CR21","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1002\/2017EF000539","volume":"5","author":"A Dale","year":"2017","unstructured":"Dale A, Fant C, Strzepek K, Lickley M, Solomon S (2017) Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa. Earth\u2019s Futur 5:337\u2013353. https:\/\/doi.org\/10.1002\/2017EF000539","journal-title":"Earth\u2019s Futur"},{"key":"1322_CR22","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.2166\/ws.2020.062","volume":"20","author":"HY Dalkili\u00e7","year":"2020","unstructured":"Dalkili\u00e7 HY, Hashimi SA (2020) Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models. Water Sci Technol Water Supply 20:1396\u20131408. https:\/\/doi.org\/10.2166\/ws.2020.062","journal-title":"Water Sci Technol Water Supply"},{"key":"1322_CR23","doi-asserted-by":"publisher","first-page":"294","DOI":"10.18178\/ijesd.2019.10.10.1190","volume":"10","author":"HG Damavandi","year":"2019","unstructured":"Damavandi HG, Shah R, Stampoulis D, Wei Y, Boscovic D, Sabo J (2019) Accurate prediction of streamflow using long short-term memory network: A case study in the Brazos river basin in Texas. Int J Environ Sci Dev 10:294\u2013300. https:\/\/doi.org\/10.18178\/ijesd.2019.10.10.1190","journal-title":"Int J Environ Sci Dev"},{"key":"1322_CR24","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1109\/ICIEA.2019.8834205","volume":"2019","author":"Y Deng","year":"2019","unstructured":"Deng Y, Jia H, Li P, Tong X, Qiu X, Li F (2019) A deep learning methodology based on bidirectional gated recurrent unit for wind power prediction. Proc. 14th IEEE Conf. Ind Electron Appl ICIEA 2019:591\u2013595. https:\/\/doi.org\/10.1109\/ICIEA.2019.8834205","journal-title":"Ind Electron Appl ICIEA"},{"key":"1322_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15481603.2020.1821150","volume":"00","author":"JK Dharpure","year":"2020","unstructured":"Dharpure JK, Patel A, Goswami A, Kulkarni AV (2020) Spatiotemporal snow cover characterization and its linkage with climate change over the Chenab river basin, western Himalayas. Giscience Remote Sens 00:1\u201325. https:\/\/doi.org\/10.1080\/15481603.2020.1821150","journal-title":"Giscience Remote Sens"},{"key":"1322_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/02626667.2021.1985125","volume":"00","author":"JK Dharpure","year":"2021","unstructured":"Dharpure JK, Goswami A, Patel A, Kulkarni AV, Snehmani, (2021) Assessment of snow cover variability and its sensitivity to hydro-meteorological factors in the Karakoram and Himalayan region. Hydrol Sci J 00:1\u201318. https:\/\/doi.org\/10.1080\/02626667.2021.1985125","journal-title":"Hydrol Sci J"},{"key":"1322_CR27","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1002\/met","volume":"14","author":"AP Dimri","year":"2007","unstructured":"Dimri AP, Mohanty UC (2007) Location-specific prediction of maximum and minimum temperature over the western Himalayas. Meteorol Appl 14:79\u201393. https:\/\/doi.org\/10.1002\/met","journal-title":"Meteorol Appl"},{"key":"1322_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/w12113032","volume":"12","author":"L Dong","year":"2020","unstructured":"Dong L, Fang D, Wang X, Wei W, Dama\u0161evi\u010dius R, Scherer R, Wo\u017aniak M (2020) Prediction of streamflow based on dynamic sliding window LSTM. Water (switzerland) 12:1\u201311. https:\/\/doi.org\/10.3390\/w12113032","journal-title":"Water (switzerland)"},{"key":"1322_CR29","doi-asserted-by":"publisher","first-page":"4727","DOI":"10.1029\/EO064i046p00929-04","volume":"51","author":"D Duethmann","year":"2015","unstructured":"Duethmann D, Bolch T, Farinotti D, Kriegel D, Vorogushyn S, Merz B, Pieczonka T, Jiang T, Su B, Guntner A (2015) Atribution of streamflow trends in snow and glacier melt-dominated catchments of the Tarim River. Central Asia Water Resour Res Res 51:4727\u20134750. https:\/\/doi.org\/10.1029\/EO064i046p00929-04","journal-title":"Central Asia Water Resour Res Res"},{"key":"1322_CR30","doi-asserted-by":"publisher","first-page":"2819","DOI":"10.1007\/s00704-023-04797-3","volume":"155","author":"E Elahi","year":"2024","unstructured":"Elahi E, Abro MI, Khaskheli MA, Kandhro GA, Zehra T, Ali S, Shaikh MN, Laghari BA, Hassan M, Memon MA (2024) Long-term evaluation of rainfall in the arid region of Pakistan using multi-source data. Theor Appl Climatol 155:2819\u20132840. https:\/\/doi.org\/10.1007\/s00704-023-04797-3","journal-title":"Theor Appl Climatol"},{"key":"1322_CR31","first-page":"71","volume":"6","author":"R Esmaeelzadeh","year":"2014","unstructured":"Esmaeelzadeh R, Dariane A (2014) Long-term Streamflow Forecasting by Adaptive Neuro-Fuzzy Inference System Using K-fold Cross-validation: (Case Study: Taleghan Basin, Iran). J Water Sci Res 6:71\u201383","journal-title":"J Water Sci Res"},{"key":"1322_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/w12010175","volume":"12","author":"H Fan","year":"2020","unstructured":"Fan H, Jiang M, Xu L, Zhu H, Cheng J, Jiang J (2020) Comparison of long short term memory networks and the hydrological model in runoff simulation. Water (switzerland) 12:1\u201315. https:\/\/doi.org\/10.3390\/w12010175","journal-title":"Water (switzerland)"},{"key":"1322_CR33","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.5194\/hess-13-1361-2009","volume":"13","author":"A Gafurov","year":"2009","unstructured":"Gafurov A, B\u00e1rdossy A (2009) Cloud removal methodology from MODIS snow cover product. Hydrol Earth Syst Sci 13:1361\u20131373. https:\/\/doi.org\/10.5194\/hess-13-1361-2009","journal-title":"Hydrol Earth Syst Sci"},{"key":"1322_CR34","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: Continual prediction with LSTM. Neural Comput 12:2451\u20132471. https:\/\/doi.org\/10.1162\/089976600300015015","journal-title":"Neural Comput"},{"key":"1322_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-96751-4","volume":"11","author":"S Ghimire","year":"2021","unstructured":"Ghimire S, Yaseen ZM, Farooque AA, Deo RC, Zhang J, Tao X (2021) Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks. Sci Rep 11:1\u201326. https:\/\/doi.org\/10.1038\/s41598-021-96751-4","journal-title":"Sci Rep"},{"key":"1322_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-45213-z","volume":"9","author":"PP Gogoi","year":"2019","unstructured":"Gogoi PP, Vinoj V, Swain D, Roberts G, Dash J, Tripathy S (2019) Land use and land cover change effect on surface temperature over Eastern India. Sci Rep 9:1\u201310. https:\/\/doi.org\/10.1038\/s41598-019-45213-z","journal-title":"Sci Rep"},{"key":"1322_CR37","doi-asserted-by":"publisher","first-page":"11738","DOI":"10.1038\/s41598-021-90964-3","volume":"11","author":"S Ha","year":"2021","unstructured":"Ha S, Liu D, Mu L (2021) Prediction of Yangtze River streamflow based on deep learning neural network with El Ni\u00f1o-Southern Oscillation. Sci Rep 11:11738. https:\/\/doi.org\/10.1038\/s41598-021-90964-3","journal-title":"Sci Rep"},{"key":"1322_CR38","doi-asserted-by":"publisher","first-page":"67","DOI":"10.5194\/esd-5-67-2014","volume":"5","author":"S Hasson","year":"2014","unstructured":"Hasson S, Lucarini V, Pascale S, B\u00f6hner J (2014) Seasonality of the hydrological cycle in major South and Southeast Asian river basins as simulated by PCMDI\/CMIP3 experiments. Earth Syst Dyn 5:67\u201387. https:\/\/doi.org\/10.5194\/esd-5-67-2014","journal-title":"Earth Syst Dyn"},{"key":"1322_CR39","doi-asserted-by":"publisher","first-page":"4373","DOI":"10.5194\/hess-25-4373-2021","volume":"25","author":"HMVV Herath","year":"2021","unstructured":"Herath HMVV, Chadalawada J, Babovic V (2021) Hydrologically informed machine learning for rainfall-runoff modelling: Towards distributed modelling. Hydrol Earth Syst Sci 25:4373\u20134401. https:\/\/doi.org\/10.5194\/hess-25-4373-2021","journal-title":"Hydrol Earth Syst Sci"},{"key":"1322_CR40","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Comput 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"1322_CR41","doi-asserted-by":"publisher","unstructured":"Hong J, Lee S, Lee G, Yang D, Bae J, Kim J, Kim K, Lim K (2021) Comparison of machine learning algorithms for discharge prediction of multipurpose dam. Water (Switzerland) 13:124296. https:\/\/doi.org\/10.3390\/w13233369","DOI":"10.3390\/w13233369"},{"key":"1322_CR42","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1038\/s41586-021-03436-z","volume":"592","author":"R Hugonnet","year":"2021","unstructured":"Hugonnet R, McNabb R, Berthier E, Menounos B, Nuth C, Girod L, Farinotti D, Huss M, Dussaillant I, Brun F, K\u00e4\u00e4b A (2021) Accelerated global glacier mass loss in the early twenty-first century. Nature 592:726\u2013731. https:\/\/doi.org\/10.1038\/s41586-021-03436-z","journal-title":"Nature"},{"key":"1322_CR43","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.5194\/hess-13-1413-2009","volume":"13","author":"NQ Hung","year":"2009","unstructured":"Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009) An artificial neural network model for rainfall forecasting in Bangkok. Thailand Hydrol Earth Syst Sci 13:1413\u20131425. https:\/\/doi.org\/10.5194\/hess-13-1413-2009","journal-title":"Thailand Hydrol Earth Syst Sci"},{"key":"1322_CR44","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1007\/s12145-020-00450-z","volume":"13","author":"D Hussain","year":"2020","unstructured":"Hussain D, Khan AA (2020) Machine learning techniques for monthly river flow forecasting of Hunza River. Pakistan Earth Sci Informatics 13:939\u2013949. https:\/\/doi.org\/10.1007\/s12145-020-00450-z","journal-title":"Pakistan Earth Sci Informatics"},{"key":"1322_CR45","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1080\/02626667.2021.1935964","volume":"66","author":"AM Ilyas","year":"2021","unstructured":"Ilyas AM, Pham QB, Zhu D, Elahi E, Linh NTT, Anh DT, Khedher KM, Ahmadlou M (2021) Multi sources hydrological assessment over Vu Gia Thu Bon Basin. Vietnam Hydrol Sci J 66:1383\u20131392. https:\/\/doi.org\/10.1080\/02626667.2021.1935964","journal-title":"Vietnam Hydrol Sci J"},{"key":"1322_CR46","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1038\/ngeo1643","volume":"5","author":"WW Immerzeel","year":"2012","unstructured":"Immerzeel WW, Bierkens MFP (2012) Asia\u2019s water balance. Nat Geosci 5:841\u2013842. https:\/\/doi.org\/10.1038\/ngeo1643","journal-title":"Nat Geosci"},{"key":"1322_CR47","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1038\/s41586-019-1822-y","volume":"577","author":"WW Immerzeel","year":"2020","unstructured":"Immerzeel WW, Lutz AF, Andrade M, Bahl A, Biemans H, Bolch T, Hyde S, Brumby S, Davies BJ, Elmore AC, Emmer A, Feng M, Fern\u00e1ndez A, Haritashya U, Kargel JS, Koppes M, Kraaijenbrink PDA, Kulkarni AV, Mayewski PA, Nepal S, Pacheco P, Painter TH, Pellicciotti F, Rajaram H, Rupper S, Sinisalo A, Shrestha AB, Viviroli D, Wada Y, Xiao C, Yao T, Baillie JEM (2020) Importance and vulnerability of the world\u2019s water towers. Nature 577:364\u2013369. https:\/\/doi.org\/10.1038\/s41586-019-1822-y","journal-title":"Nature"},{"key":"1322_CR48","doi-asserted-by":"publisher","unstructured":"Irving K, Kuemmerlen M, Kiesel J, Kakouei K, Domisch S, J\u00e4hnig SC (2018) Data Descriptor: A high-resolution stream flow and hydrological metrics dataset for ecological modeling using a regression model. Sci Data 5:180224. https:\/\/doi.org\/10.1038\/sdata.2018.224","DOI":"10.1038\/sdata.2018.224"},{"key":"1322_CR49","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1680\/jwama.19.00001","volume":"173","author":"S Kabir","year":"2020","unstructured":"Kabir S, Patidar S, Pender G (2020) Investigating capabilities of machine learning techniques in forecasting stream flow. Proc Inst Civ Eng Water Manag 173:69\u201386. https:\/\/doi.org\/10.1680\/jwama.19.00001","journal-title":"Proc Inst Civ Eng Water Manag"},{"key":"1322_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrh.2020.100750","volume":"32","author":"M Kadir","year":"2020","unstructured":"Kadir M, Fehri R, Souag D, Vanclooster M (2020) Exploring causes of streamflow alteration in the Medjerda river. Algeria J Hydrol Reg Stud 32:100750. https:\/\/doi.org\/10.1016\/j.ejrh.2020.100750","journal-title":"Algeria J Hydrol Reg Stud"},{"key":"1322_CR51","doi-asserted-by":"crossref","unstructured":"Kratzert F, Herrnegger M, Klotz D, Hochreiter S, Klambauer G (2019a) NeuralHydrology - Interpreting LSTMs in hydrology. In: Samek W, Montavon G, Vedaldi A et al (eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer, Cham","DOI":"10.1007\/978-3-030-28954-6_19"},{"key":"1322_CR52","doi-asserted-by":"publisher","first-page":"5089","DOI":"10.5194\/hess-23-5089-2019","volume":"23","author":"F Kratzert","year":"2019","unstructured":"Kratzert F, Klotz D, Shalev G, Klambauer G, Hochreiter S, Nearing G (2019b) Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrol Earth Syst Sci 23:5089\u20135110. https:\/\/doi.org\/10.5194\/hess-23-5089-2019","journal-title":"Hydrol Earth Syst Sci"},{"key":"1322_CR53","doi-asserted-by":"publisher","first-page":"6005","DOI":"10.5194\/hess-22-6005-2018","volume":"22","author":"F Kratzert","year":"2018","unstructured":"Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M (2018) Rainfall\u2013runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrol Earth Syst Sci 22:6005\u20136022. https:\/\/doi.org\/10.5194\/hess-22-6005-2018","journal-title":"Hydrol Earth Syst Sci"},{"key":"1322_CR54","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.advwatres.2013.03.006","volume":"56","author":"M Kumar","year":"2013","unstructured":"Kumar M, Marks D, Dozier J, Reba M, Winstral A (2013) Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models. Adv Water Resour 56:77\u201389. https:\/\/doi.org\/10.1016\/j.advwatres.2013.03.006","journal-title":"Adv Water Resour"},{"key":"1322_CR55","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/978-3-030-67187-7_17","volume":"175","author":"A Kumar","year":"2021","unstructured":"Kumar A, Purohit K, Kumar K (2021) Stock Price Prediction Using Recurrent Neural Network and Long Short-Term Memory. Lect Notes Networks Syst 175:153\u2013160. https:\/\/doi.org\/10.1007\/978-3-030-67187-7_17","journal-title":"Lect Notes Networks Syst"},{"key":"1322_CR56","unstructured":"Larsen KG (2017) Influence of summer snowfall on discharge emanating from the Gangotri glacier. Dissertation. Environmental Science, Geography. University of Salford"},{"key":"1322_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/w11071387","volume":"1387","author":"XH Le","year":"2019","unstructured":"Le XH, Ho HV, Lee G, Jung S (2019) Application of Long Short-Term Memory (LSTM) neural network for flood forecasting. Water (Switzerland) 1387:1\u201319. https:\/\/doi.org\/10.3390\/w11071387","journal-title":"Water (Switzerland)"},{"key":"1322_CR58","doi-asserted-by":"publisher","unstructured":"Lee G, Kim D, Kwon HH, Choi E (2019) Estimation of maximum daily fresh snow accumulation using an artificial neural network model. Adv Meteorol 2019:2709351. https:\/\/doi.org\/10.1155\/2019\/2709351","DOI":"10.1155\/2019\/2709351"},{"key":"1322_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs8090750","volume":"8","author":"H Li","year":"2016","unstructured":"Li H, Li X, Xiao P (2016) Impact of sensor zenith angle on MOD10A1 data reliability and modification of snow cover data for the Tarim River Basin. Remote Sens 8:1\u201318. https:\/\/doi.org\/10.3390\/rs8090750","journal-title":"Remote Sens"},{"key":"1322_CR60","doi-asserted-by":"publisher","first-page":"56425","DOI":"10.1007\/s11356-023-26271-3","volume":"30","author":"M Li","year":"2023","unstructured":"Li M, Gu H, Wang H, Wang Y, Chi B (2023) Quantifying the impact of climate variability and human activities on streamflow variation in Taoer River Basin, China. Environ Sci Pollut Res 30:56425\u201356439. https:\/\/doi.org\/10.1007\/s11356-023-26271-3","journal-title":"Environ Sci Pollut Res"},{"key":"1322_CR61","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1623\/hysj.51.4.599","volume":"51","author":"JY Lin","year":"2006","unstructured":"Lin JY, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51:599\u2013612. https:\/\/doi.org\/10.1623\/hysj.51.4.599","journal-title":"Hydrol Sci J"},{"key":"1322_CR62","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.2166\/hydro.2019.073","volume":"21","author":"K Lin","year":"2021","unstructured":"Lin K, Lu P, Xu CY, Yu X, Lan T, Chen X (2021) Modeling saltwater intrusion using an integrated Bayesian model averaging method in the Pearl River Delta. J Hydroinformatics 21:1147\u20131162. https:\/\/doi.org\/10.2166\/hydro.2019.073","journal-title":"J Hydroinformatics"},{"key":"1322_CR63","doi-asserted-by":"publisher","first-page":"90069","DOI":"10.1109\/ACCESS.2020.2993874","volume":"8","author":"D Liu","year":"2020","unstructured":"Liu D, Jiang W, Mu L, Wang S (2020) Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River. IEEE Access 8:90069\u201390086. https:\/\/doi.org\/10.1109\/ACCESS.2020.2993874","journal-title":"IEEE Access"},{"key":"1322_CR64","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1023\/A:1010784727448","volume":"49","author":"H Middelkoop","year":"2001","unstructured":"Middelkoop H, Daamen K, Gellens D, Grabs W, Kwadijk JCJ, Lang H, Parmet BWAH, Schulla J, Wilke K (2001) Impact of climate change on hydrological regime and water resources management in the Rhine basin. Clim Change 49:105\u2013128","journal-title":"Clim Change"},{"key":"1322_CR65","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s12040-015-0539-z","volume":"124","author":"RA Mir","year":"2015","unstructured":"Mir RA, Jain SK, Saraf AK, Goswami A (2015) Decline in snowfall in response to temperature in Satluj basin, western Himalaya. J Earth Syst Sci 124:365\u2013382. https:\/\/doi.org\/10.1007\/s12040-015-0539-z","journal-title":"J Earth Syst Sci"},{"key":"1322_CR66","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1002\/2013WR013810","volume":"50","author":"R Modarres","year":"2014","unstructured":"Modarres R, Ouarda TBMJ (2014) Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. Water Resour Res 50:601\u2013618. https:\/\/doi.org\/10.1002\/2013WR013810","journal-title":"Water Resour Res"},{"key":"1322_CR67","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1016\/j.jhydrol.2018.02.005","volume":"558","author":"A Mukherjee","year":"2018","unstructured":"Mukherjee A, Ramachandran P (2018) Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India\u202f: Analysis of comparative performances of SVR, ANN and LRM. J Hydrol 558:647\u2013658. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.02.005","journal-title":"J Hydrol"},{"key":"1322_CR68","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1080\/15715124.2019.1613413","volume":"18","author":"P Munoth","year":"2020","unstructured":"Munoth P, Goyal R (2020) Impacts of land use land cover change on runoff and sediment yield of Upper Tapi River Sub-Basin. India Int J River Basin Manag 18:177\u2013189. https:\/\/doi.org\/10.1080\/15715124.2019.1613413","journal-title":"India Int J River Basin Manag"},{"key":"1322_CR69","doi-asserted-by":"publisher","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","volume":"13","author":"J Mu\u00f1oz-Sabater","year":"2021","unstructured":"Mu\u00f1oz-Sabater J, Dutra E, Agust\u00ed-Panareda A, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S, Hersbach H, Martens B, Miralles DG, Piles M, Rodr\u00edguez-Fern\u00e1ndez NJ, Zsoter E, Buontempo C, Th\u00e9paut JN (2021) ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth Syst Sci Data 13:4349\u20134383. https:\/\/doi.org\/10.5194\/essd-13-4349-2021","journal-title":"Earth Syst Sci Data"},{"key":"1322_CR70","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1080\/00750770109555783","volume":"10","author":"JE Nash","year":"1970","unstructured":"Nash JE, Sutcliffe IV (1970) River forecasting through conceptual models. J Hydrol 10:282\u2013290. https:\/\/doi.org\/10.1080\/00750770109555783","journal-title":"J Hydrol"},{"key":"1322_CR71","doi-asserted-by":"publisher","unstructured":"Ni L, Wang D, Singh VP, Wu J, Wang Y, Tao Y, Zhang J (2020) Streamflow and rainfall forecasting by two long short-term memory-based models. J Hydrol 583:124296. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124296","DOI":"10.1016\/j.jhydrol.2019.124296"},{"key":"1322_CR72","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1038\/s43017-020-00124-w","volume":"2","author":"Y Nie","year":"2021","unstructured":"Nie Y, Pritchard HD, Liu Q, Hennig T, Wang W, Wang X, Liu S, Nepal S, Samyn D, Hewitt K, Chen X (2021) Glacial change and hydrological implications in the Himalaya and Karakoram. Nat Rev Earth Environ 2:91\u2013106. https:\/\/doi.org\/10.1038\/s43017-020-00124-w","journal-title":"Nat Rev Earth Environ"},{"key":"1322_CR73","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.jhydrol.2015.11.050","volume":"533","author":"N Noori","year":"2016","unstructured":"Noori N, Kalin L (2016) Coupling SWAT and ANN models for enhanced daily streamflow prediction. J Hydrol 533:141\u2013151. https:\/\/doi.org\/10.1016\/j.jhydrol.2015.11.050","journal-title":"J Hydrol"},{"key":"1322_CR74","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.jhydrol.2011.03.002","volume":"402","author":"V Nourani","year":"2011","unstructured":"Nourani V, Kisi \u00d6, Komasi M (2011) Two hybrid Artificial Intelligence approaches for modeling rainfall-runoff process. J Hydrol 402:41\u201359. https:\/\/doi.org\/10.1016\/j.jhydrol.2011.03.002","journal-title":"J Hydrol"},{"key":"1322_CR75","unstructured":"Olah C (2015) Understanding LSTM Networks. In: Blog. http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/"},{"key":"1322_CR76","doi-asserted-by":"publisher","first-page":"679","DOI":"10.5194\/hess-10-679-2006","volume":"10","author":"J Parajka","year":"2006","unstructured":"Parajka J, Bl\u00f6schl G (2006) Validation of MODIS snow cover images over Austria. Hydrol Earth Syst Sci 10:679\u2013689. https:\/\/doi.org\/10.5194\/hess-10-679-2006","journal-title":"Hydrol Earth Syst Sci"},{"key":"1322_CR77","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.jhydrol.2009.11.042","volume":"381","author":"J Parajka","year":"2010","unstructured":"Parajka J, Pepe M, Rampini A, Rossi S, Bl\u00f6schl G (2010) A regional snow-line method for estimating snow cover from MODIS during cloud cover. J Hydrol 381:203\u2013212","journal-title":"J Hydrol"},{"key":"1322_CR78","doi-asserted-by":"publisher","first-page":"4113","DOI":"10.1007\/s11269-020-02659-5","volume":"34","author":"P Parisouj","year":"2020","unstructured":"Parisouj P, Mohebzadeh H, Lee T (2020) Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States. Water Resour Manag 34:4113\u20134131. https:\/\/doi.org\/10.1007\/s11269-020-02659-5","journal-title":"Water Resour Manag"},{"key":"1322_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15481603.2021.1930730","volume":"00","author":"A Patel","year":"2021","unstructured":"Patel A, Goswami A, Dharpure JK, Thamban M, Kulkarni V, Sharma P (2021) Regional mass variations and its sensitivity to climate drivers over glaciers of Karakoram and Himalayas. Giscience Remote Sens 00:1\u201323. https:\/\/doi.org\/10.1080\/15481603.2021.1930730","journal-title":"Giscience Remote Sens"},{"key":"1322_CR80","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1016\/j.rse.2011.01.006","volume":"115","author":"KP Paudel","year":"2011","unstructured":"Paudel KP, Andersen P (2011) Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology. Remote Sens Environ 115:1234\u20131246","journal-title":"Remote Sens Environ"},{"key":"1322_CR81","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/w9060406","volume":"9","author":"T Peng","year":"2017","unstructured":"Peng T, Zhou J, Zhang C, Fu W (2017) Streamflow forecasting using empirical wavelet transform and artificial neural networks. Water (switzerland) 9:1\u201320. https:\/\/doi.org\/10.3390\/w9060406","journal-title":"Water (switzerland)"},{"key":"1322_CR82","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1038\/s41586-019-1240-1","volume":"569","author":"HD Pritchard","year":"2019","unstructured":"Pritchard HD (2019) Asia\u2019s shrinking glaciers protect large populations from drought stress. Nature 569:649\u2013654. https:\/\/doi.org\/10.1038\/s41586-019-1240-1","journal-title":"Nature"},{"key":"1322_CR83","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2011\/686258","volume":"2011","author":"MM Raju","year":"2011","unstructured":"Raju MM, Srivastava RK, Bisht DCS, Sharma HC, Kumar A (2011) Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge. Hindawi Publ Corp Adv Artif Intell 2011:1\u201311. https:\/\/doi.org\/10.1155\/2011\/686258","journal-title":"Hindawi Publ Corp Adv Artif Intell"},{"key":"1322_CR84","doi-asserted-by":"publisher","unstructured":"Rao MP, Cook ER, Cook BI, Arrigo RDD, Palmer JG, Lall U, Woodhouse CA, Buckley BM, Uriarte M, Bishop DA, Jian J, Webster PJ (2020) Seven centuries of reconstructed Brahmaputra river discharge demonstrate underestimated high discharge and flood hazard frequency. Nat Commun 11:6017. https:\/\/doi.org\/10.1038\/s41467-020-19795-6","DOI":"10.1038\/s41467-020-19795-6"},{"key":"1322_CR85","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1023\/A:1026530522612","volume":"150","author":"JF Reynolds","year":"2000","unstructured":"Reynolds JF, Kemp PR, Tenhunen JD (2000) Effects of long-term rainfall variability on evapotranspiration and soil water distribution in the Chihuahuan Desert\u202f: A modeling analysis. Plant Ecol 150:145\u2013159","journal-title":"Plant Ecol"},{"key":"1322_CR86","doi-asserted-by":"publisher","unstructured":"RGI Consortium (2017) Randolph glacier inventory \u2013 a dataset of global glacier outlines: version 6.0. Technical Report, Global Land Ice Measurements from Space, Colorado, USA Digital Media 1\u201314. https:\/\/doi.org\/10.7265\/N5-RGI-60","DOI":"10.7265\/N5-RGI-60"},{"key":"1322_CR87","first-page":"1","volume":"6","author":"GA Riggs","year":"2016","unstructured":"Riggs GA, Hall DK, Roman MO (2016) MODIS Snow Products Collection 6 User Guide. Natl. Snow Ice Data Cent. Boulder, CO, USA 6:1\u201380","journal-title":"Boulder, CO, USA"},{"key":"1322_CR88","doi-asserted-by":"publisher","first-page":"765","DOI":"10.5194\/essd-9-765-2017","volume":"9","author":"GA Riggs","year":"2017","unstructured":"Riggs GA, Hall DK, Rom\u00e1n MO (2017) Overview of NASA\u2019s MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records. Earth Syst Sci Data 9:765\u2013777. https:\/\/doi.org\/10.5194\/essd-9-765-2017","journal-title":"Earth Syst Sci Data"},{"key":"1322_CR89","unstructured":"Riggs GA, Hall DK, Rom\u00e1n MO (2019) Modis snow products collection 6.1 user guide. National Snow and Ice Data Center, Boulder"},{"key":"1322_CR90","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45:2673\u20132681. https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans Signal Process"},{"key":"1322_CR91","doi-asserted-by":"publisher","first-page":"4065","DOI":"10.1007\/s13369-016-2095-5","volume":"41","author":"F Sedighi","year":"2016","unstructured":"Sedighi F, Vafakhah M, Javadi MR (2016) Rainfall-Runoff Modeling Using Support Vector Machine in Snow-Affected Watershed. Arab J Sci Eng 41:4065\u20134076. https:\/\/doi.org\/10.1007\/s13369-016-2095-5","journal-title":"Arab J Sci Eng"},{"key":"1322_CR92","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10106049.2018.1469675","volume":"6049","author":"MU Shafiq","year":"2018","unstructured":"Shafiq MU, Ahmed P, Islam ZU, Joshi PK, Bhat WA (2018) Snow cover area change and its relations with climatic variability in Kashmir Himalayas. India Geocarto Int 6049:1\u201315. https:\/\/doi.org\/10.1080\/10106049.2018.1469675","journal-title":"India Geocarto Int"},{"key":"1322_CR93","doi-asserted-by":"publisher","first-page":"3036","DOI":"10.1080\/01431161.2014.894665","volume":"35","author":"V Sharma","year":"2014","unstructured":"Sharma V, Mishra VD, Joshi PK (2014) Topographic controls on spatio-temporal snow cover distribution in Northwest Himalaya. Int J Remote Sens 35:3036\u20133056. https:\/\/doi.org\/10.1080\/01431161.2014.894665","journal-title":"Int J Remote Sens"},{"key":"1322_CR94","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1080\/10106049.2016.1206975","volume":"32","author":"S Shukla","year":"2016","unstructured":"Shukla S, Kansal ML, Jain SK (2016) Snow cover area variability assessment in the upper part of the Satluj river basin in India. Geocarto Int 32:1285\u20131306. https:\/\/doi.org\/10.1080\/10106049.2016.1206975","journal-title":"Geocarto Int"},{"key":"1322_CR95","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1002\/hyp.5509","volume":"18","author":"A Simic","year":"2004","unstructured":"Simic A, Fernandes R, Brown R, Romanov P, Park W (2004) Validation of VEGETATION, MODIS, and GOES+ SSM\/I snow-cover products over Canada based on surface snow depth observations. Hydrol Process 18:1089\u20131104","journal-title":"Hydrol Process"},{"key":"1322_CR96","doi-asserted-by":"publisher","first-page":"2363","DOI":"10.1002\/hyp.1468","volume":"18","author":"P Singh","year":"2004","unstructured":"Singh P, Bengtsson L (2004) Hydrological sensitivity of a large Himalayan basin to climate change. Hydrol Process 18:2363\u20132385. https:\/\/doi.org\/10.1002\/hyp.1468","journal-title":"Hydrol Process"},{"key":"1322_CR97","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.5194\/hess-27-1047-2023","volume":"27","author":"D Singh","year":"2023","unstructured":"Singh D, Vardhan M, Sahu R, Chatterjee D, Chauhan P, Liu S (2023) Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data. Hydrol Earth Syst Sci 27:1047\u20131075. https:\/\/doi.org\/10.5194\/hess-27-1047-2023","journal-title":"Hydrol Earth Syst Sci"},{"key":"1322_CR98","doi-asserted-by":"publisher","first-page":"87","DOI":"10.24057\/2071-9388-2014-7-3-87-96","volume":"7","author":"D Singh","year":"2014","unstructured":"Singh D, Gupta RD, Jain SK (2014) Study of Long-Term Trend in River Discharge of Sutlej River (N-W Himalayan Region). Geogr Environ Sustain 7:87\u201396. https:\/\/doi.org\/10.24057\/2071-9388-2014-7-3-87-96","journal-title":"Geogr Environ Sustain"},{"key":"1322_CR99","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1080\/10106049.2015.1120350","volume":"31","author":"Dharpure JK Snehmani","year":"2016","unstructured":"Snehmani Dharpure JK, Kochhar I, Hari Ram RP, Ganju A (2016) Analysis of snow cover and climatic variability in Bhaga basin located in western Himalaya. Geocarto Int 31:1094\u20131107. https:\/\/doi.org\/10.1080\/10106049.2015.1120350","journal-title":"Geocarto Int"},{"key":"1322_CR100","doi-asserted-by":"publisher","unstructured":"Sprenger M, Carroll RWH, Dennedy-frank J, Siirila-woodburn ER, Newcomer ME, Brown W, Newman A, Beutler C, Bill M, Hubbard SS, Williams KH (2022) Variability of snow and rainfall partitioning into evapotranspiration and summer runoff across nine mountainous catchments. Geophys Res Lett 49:e2022GL099324. https:\/\/doi.org\/10.1029\/2022GL099324","DOI":"10.1029\/2022GL099324"},{"key":"1322_CR101","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1007\/s00521-013-1341-y","volume":"24","author":"C Sudheer","year":"2014","unstructured":"Sudheer C, Maheswaran R, Panigrahi BK, Mathur S (2014) A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput Appl 24:1381\u20131389. https:\/\/doi.org\/10.1007\/s00521-013-1341-y","journal-title":"Neural Comput Appl"},{"key":"1322_CR102","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.3390\/w12061734","volume":"12","author":"S Thapa","year":"2020","unstructured":"Thapa S, Zhao Z, Li B, Lu L, Fu D, Shi X, Tang B, Qi H (2020) Snowmelt-driven streamflow prediction using machine learning techniques (LSTM, NARX, GPR, and SVR). Water (Switzerland) 12:1734. https:\/\/doi.org\/10.3390\/w12061734","journal-title":"Water (Switzerland)"},{"key":"1322_CR103","doi-asserted-by":"publisher","unstructured":"Thapa S, Li H, Li B, Fu D, Shi X, Yabo S, Lu L, Qi H, Zhang W (2021) Impact of climate change on snowmelt runoff in a Himalayan basin, Nepal. Environ Monit Assess 193:393. https:\/\/doi.org\/10.1007\/s10661-021-09197-6","DOI":"10.1007\/s10661-021-09197-6"},{"key":"1322_CR104","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.300","volume":"6","author":"H Tran","year":"2019","unstructured":"Tran H, Nguyen P, Ombadi M, Hsu KL, Sorooshian S, Qing X (2019) A cloud-free modis snow cover dataset for the contiguous United States from 2000 to 2017. Sci Data 6:1\u201313. https:\/\/doi.org\/10.1038\/sdata.2018.300","journal-title":"Sci Data"},{"key":"1322_CR105","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1016\/j.jhydrol.2016.10.037","volume":"543","author":"G Uysal","year":"2016","unstructured":"Uysal G, Aynur S, Arda AS (2016) Improving daily streamflow forecasts in mountainous Upper Euphrates basin by multi-layer perceptron model with satellite snow products. J Hydrol 543:630\u2013650. https:\/\/doi.org\/10.1016\/j.jhydrol.2016.10.037","journal-title":"J Hydrol"},{"key":"1322_CR106","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1002\/met.1491","volume":"22","author":"M Valipour","year":"2015","unstructured":"Valipour M (2015) Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorol Appl 22:592\u2013598. https:\/\/doi.org\/10.1002\/met.1491","journal-title":"Meteorol Appl"},{"key":"1322_CR107","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1038\/s41893-020-0559-9","volume":"3","author":"D Viviroli","year":"2020","unstructured":"Viviroli D, Kummu M, Meybeck M, Kallio M, Wada Y (2020) Increasing dependence of lowland populations on mountain water resources. Nat Sustain 3:917\u2013928. https:\/\/doi.org\/10.1038\/s41893-020-0559-9","journal-title":"Nat Sustain"},{"key":"1322_CR108","doi-asserted-by":"publisher","unstructured":"Wester P, Chaudhary S, Chettri N, Jackson M, Maharjan A, Nepal S, Steiner JF (2023) Water, ice, society, and ecosystems in the Hindu Kush Himalaya. https:\/\/doi.org\/10.53055\/icimod.1028","DOI":"10.53055\/icimod.1028"},{"key":"1322_CR109","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1038\/nature11616","volume":"492","author":"R Winkelmann","year":"2012","unstructured":"Winkelmann R, Levermann A, Martin MA, Frieler K (2012) Increased future ice discharge from Antarctica owing to higher snowfall. Nat Lett 492:239\u2013242. https:\/\/doi.org\/10.1038\/nature11616","journal-title":"Nat Lett"},{"key":"1322_CR110","doi-asserted-by":"publisher","unstructured":"Xu F, Zhao L, Niu C, Qiu Y (2022) Effect of climate change and anthropogenic activities on streamflow indicators in a Tropical River Basin in Southern China. Water (Switzerland) 14:304. https:\/\/doi.org\/10.3390\/w14030304","DOI":"10.3390\/w14030304"},{"key":"1322_CR111","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.envsoft.2018.11.007","volume":"112","author":"Q Yang","year":"2019","unstructured":"Yang Q, Zhang H, Wang G, Luo S, Chen D, Peng W, Shao J (2019) Dynamic runoff simulation in a changing environment: A data stream approach. Environ Model Softw 112:157\u2013165. https:\/\/doi.org\/10.1016\/j.envsoft.2018.11.007","journal-title":"Environ Model Softw"},{"key":"1322_CR112","unstructured":"Yao X (2021) Daily Streamflow prediction using deep learning\u202f: a case study on Russian River, CA. Stanford University"},{"key":"1322_CR113","doi-asserted-by":"publisher","first-page":"4125","DOI":"10.1007\/s11269-016-1408-5","volume":"30","author":"ZM Yaseen","year":"2016","unstructured":"Yaseen ZM, Kisi O, Demir V (2016) Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence. Water Resour Manag 30:4125\u20134151. https:\/\/doi.org\/10.1007\/s11269-016-1408-5","journal-title":"Water Resour Manag"},{"key":"1322_CR114","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.5194\/tc-14-2267-2020","volume":"14","author":"S Yi","year":"2020","unstructured":"Yi S, Song C, Heki K, Kang S, Wang Q, Chang L (2020) Satellite-observed monthly glacier and snow mass changes in southeast Tibet: Implication for substantial meltwater contribution to the Brahmaputra. Cryosphere 14:2267\u20132281. https:\/\/doi.org\/10.5194\/tc-14-2267-2020","journal-title":"Cryosphere"},{"key":"1322_CR115","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.still.2015.04.006","volume":"153","author":"Z Yuan","year":"2015","unstructured":"Yuan Z, Chu Y, Shen Y (2015) Simulation of surface runoff and sediment yield under different land-use in a Taihang Mountains watershed. North China Soil Tillage Res 153:7\u201319. https:\/\/doi.org\/10.1016\/j.still.2015.04.006","journal-title":"North China Soil Tillage Res"},{"key":"1322_CR116","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/met.1991","volume":"28","author":"D Zhu","year":"2021","unstructured":"Zhu D, Ilyas AM, Wang G, Zeng B (2021) Long-term hydrological assessment of remote sensing precipitation from multiple sources over the lower Yangtze River basin. China Meteorol Appl 28:1\u201313. https:\/\/doi.org\/10.1002\/met.1991","journal-title":"China Meteorol Appl"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01322-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01322-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01322-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T17:10:03Z","timestamp":1726765803000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01322-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":116,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1322"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01322-6","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"6 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}