[{"page":"457-469","keyword":["Water Science and Technology"],"date_published":"2016-08-01T00:00:00Z","scopus_import":"1","publication_identifier":{"issn":["0309-1708"]},"day":"01","oa_version":"Published Version","type":"journal_article","date_updated":"2023-02-24T10:33:41Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","doi":"10.1016/j.advwatres.2016.05.001","language":[{"iso":"eng"}],"publisher":"Elsevier","article_type":"original","volume":94,"status":"public","month":"08","date_created":"2023-02-20T08:15:11Z","intvolume":"        94","year":"2016","quality_controlled":"1","extern":"1","author":[{"last_name":"Carenzo","first_name":"M.","full_name":"Carenzo, M."},{"last_name":"Pellicciotti","first_name":"Francesca","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","full_name":"Pellicciotti, Francesca"},{"first_name":"J.","last_name":"Mabillard","full_name":"Mabillard, J."},{"first_name":"T.","last_name":"Reid","full_name":"Reid, T."},{"last_name":"Brock","first_name":"B.W.","full_name":"Brock, B.W."}],"citation":{"ama":"Carenzo M, Pellicciotti F, Mabillard J, Reid T, Brock BW. An enhanced temperature index model for debris-covered glaciers accounting for thickness effect. <i>Advances in Water Resources</i>. 2016;94:457-469. doi:<a href=\"https://doi.org/10.1016/j.advwatres.2016.05.001\">10.1016/j.advwatres.2016.05.001</a>","ieee":"M. Carenzo, F. Pellicciotti, J. Mabillard, T. Reid, and B. W. Brock, “An enhanced temperature index model for debris-covered glaciers accounting for thickness effect,” <i>Advances in Water Resources</i>, vol. 94. Elsevier, pp. 457–469, 2016.","ista":"Carenzo M, Pellicciotti F, Mabillard J, Reid T, Brock BW. 2016. An enhanced temperature index model for debris-covered glaciers accounting for thickness effect. Advances in Water Resources. 94, 457–469.","short":"M. Carenzo, F. Pellicciotti, J. Mabillard, T. Reid, B.W. Brock, Advances in Water Resources 94 (2016) 457–469.","chicago":"Carenzo, M., Francesca Pellicciotti, J. Mabillard, T. Reid, and B.W. Brock. “An Enhanced Temperature Index Model for Debris-Covered Glaciers Accounting for Thickness Effect.” <i>Advances in Water Resources</i>. Elsevier, 2016. <a href=\"https://doi.org/10.1016/j.advwatres.2016.05.001\">https://doi.org/10.1016/j.advwatres.2016.05.001</a>.","apa":"Carenzo, M., Pellicciotti, F., Mabillard, J., Reid, T., &#38; Brock, B. W. (2016). An enhanced temperature index model for debris-covered glaciers accounting for thickness effect. <i>Advances in Water Resources</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.advwatres.2016.05.001\">https://doi.org/10.1016/j.advwatres.2016.05.001</a>","mla":"Carenzo, M., et al. “An Enhanced Temperature Index Model for Debris-Covered Glaciers Accounting for Thickness Effect.” <i>Advances in Water Resources</i>, vol. 94, Elsevier, 2016, pp. 457–69, doi:<a href=\"https://doi.org/10.1016/j.advwatres.2016.05.001\">10.1016/j.advwatres.2016.05.001</a>."},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.advwatres.2016.05.001"}],"abstract":[{"lang":"eng","text":"Debris-covered glaciers are increasingly studied because it is assumed that debris cover extent and thickness could increase in a warming climate, with more regular rockfalls from the surrounding slopes and more englacial melt-out material. Debris energy-balance models have been developed to account for the melt rate enhancement/reduction due to a thin/thick debris layer, respectively. However, such models require a large amount of input data that are not often available, especially in remote mountain areas such as the Himalaya, and can be difficult to extrapolate. Due to their lower data requirements, empirical models have been used extensively in clean glacier melt modelling. For debris-covered glaciers, however, they generally simplify the debris effect by using a single melt-reduction factor which does not account for the influence of varying debris thickness on melt and prescribe a constant reduction for the entire melt across a glacier.\r\n\r\nIn this paper, we present a new temperature-index model that accounts for debris thickness in the computation of melt rates at the debris-ice interface. The model empirical parameters are optimized at the point scale for varying debris thicknesses against melt rates simulated by a physically-based debris energy balance model. The latter is validated against ablation stake readings and surface temperature measurements. Each parameter is then related to a plausible set of debris thickness values to provide a general and transferable parameterization. We develop the model on Miage Glacier, Italy, and then test its transferability on Haut Glacier d’Arolla, Switzerland.\r\n\r\nThe performance of the new debris temperature-index (DETI) model in simulating the glacier melt rate at the point scale is comparable to the one of the physically based approach, and the definition of model parameters as a function of debris thickness allows the simulation of the nonlinear relationship of melt rate to debris thickness, summarised by the Østrem curve. Its large number of parameters might be a limitation, but we show that the model is transferable in time and space to a second glacier with little loss of performance. We thus suggest that the new DETI model can be included in continuous mass balance models of debris-covered glaciers, because of its limited data requirements. As such, we expect its application to lead to an improvement in simulations of the debris-covered glacier response to climate in comparison with models that simply recalibrate empirical parameters to prescribe a constant across glacier reduction in melt."}],"publication_status":"published","_id":"12620","oa":1,"title":"An enhanced temperature index model for debris-covered glaciers accounting for thickness effect","publication":"Advances in Water Resources"},{"doi":"10.1016/j.advwatres.2015.01.013","language":[{"iso":"eng"}],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","date_updated":"2023-02-24T09:28:04Z","oa_version":"None","day":"01","publication_identifier":{"issn":["0309-1708"]},"scopus_import":"1","date_published":"2015-04-01T00:00:00Z","keyword":["Water Science and Technology"],"page":"94-111","publication":"Advances in Water Resources","title":"Unraveling the hydrology of a Himalayan catchment through integration of high resolution in situ data and remote sensing with an advanced simulation model","_id":"12630","publication_status":"published","abstract":[{"text":"The hydrology of high-elevation watersheds of the Hindu Kush-Himalaya region (HKH) is poorly known. The correct representation of internal states and process dynamics in glacio-hydrological models can often not be verified due to missing in situ measurements. We use a new set of detailed ground data from the upper Langtang valley in Nepal to systematically guide a state-of-the art glacio-hydrological model through a parameter assigning process with the aim to understand the hydrology of the catchment and contribution of snow and ice processes to runoff. 14 parameters are directly calculated on the basis of local data, and 13 parameters are calibrated against 5 different datasets of in situ or remote sensing data. Spatial fields of debris thickness are reconstructed through a novel approach that employs data from an Unmanned Aerial Vehicle (UAV), energy balance modeling and statistical techniques. The model is validated against measured catchment runoff (Nash–Sutcliffe efficiency 0.87) and modeled snow cover is compared to Landsat snow cover. The advanced representation of processes allowed assessing the role played by avalanching for runoff for the first time for a Himalayan catchment (5% of annual water inputs to the hydrological system are due to snow redistribution) and to quantify the hydrological significance of sub-debris ice melt (9% of annual water inputs). Snowmelt is the most important contributor to total runoff during the hydrological year 2012/2013 (representing 40% of all sources), followed by rainfall (34%) and ice melt (26%). A sensitivity analysis is used to assess the efficiency of the monitoring network and identify the timing and location of field measurements that constrain model uncertainty. The methodology to set up a glacio-hydrological model in high-elevation regions presented in this study can be regarded as a benchmark for modelers in the HKH seeking to evaluate their calibration approach, their experimental setup and thus to reduce the predictive model uncertainty.\r\n\r\n","lang":"eng"}],"citation":{"ista":"Ragettli S, Pellicciotti F, Immerzeel WW, Miles ES, Petersen L, Heynen M, Shea JM, Stumm D, Joshi S, Shrestha A. 2015. Unraveling the hydrology of a Himalayan catchment through integration of high resolution in situ data and remote sensing with an advanced simulation model. Advances in Water Resources. 78(4), 94–111.","chicago":"Ragettli, S., Francesca Pellicciotti, W.W. Immerzeel, E.S. Miles, L. Petersen, M. Heynen, J.M. Shea, D. Stumm, S. Joshi, and A. Shrestha. “Unraveling the Hydrology of a Himalayan Catchment through Integration of High Resolution in Situ Data and Remote Sensing with an Advanced Simulation Model.” <i>Advances in Water Resources</i>. Elsevier, 2015. <a href=\"https://doi.org/10.1016/j.advwatres.2015.01.013\">https://doi.org/10.1016/j.advwatres.2015.01.013</a>.","short":"S. Ragettli, F. Pellicciotti, W.W. Immerzeel, E.S. Miles, L. Petersen, M. Heynen, J.M. Shea, D. Stumm, S. Joshi, A. Shrestha, Advances in Water Resources 78 (2015) 94–111.","ieee":"S. Ragettli <i>et al.</i>, “Unraveling the hydrology of a Himalayan catchment through integration of high resolution in situ data and remote sensing with an advanced simulation model,” <i>Advances in Water Resources</i>, vol. 78, no. 4. Elsevier, pp. 94–111, 2015.","ama":"Ragettli S, Pellicciotti F, Immerzeel WW, et al. Unraveling the hydrology of a Himalayan catchment through integration of high resolution in situ data and remote sensing with an advanced simulation model. <i>Advances in Water Resources</i>. 2015;78(4):94-111. doi:<a href=\"https://doi.org/10.1016/j.advwatres.2015.01.013\">10.1016/j.advwatres.2015.01.013</a>","mla":"Ragettli, S., et al. “Unraveling the Hydrology of a Himalayan Catchment through Integration of High Resolution in Situ Data and Remote Sensing with an Advanced Simulation Model.” <i>Advances in Water Resources</i>, vol. 78, no. 4, Elsevier, 2015, pp. 94–111, doi:<a href=\"https://doi.org/10.1016/j.advwatres.2015.01.013\">10.1016/j.advwatres.2015.01.013</a>.","apa":"Ragettli, S., Pellicciotti, F., Immerzeel, W. W., Miles, E. S., Petersen, L., Heynen, M., … Shrestha, A. (2015). Unraveling the hydrology of a Himalayan catchment through integration of high resolution in situ data and remote sensing with an advanced simulation model. <i>Advances in Water Resources</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.advwatres.2015.01.013\">https://doi.org/10.1016/j.advwatres.2015.01.013</a>"},"quality_controlled":"1","extern":"1","author":[{"full_name":"Ragettli, S.","last_name":"Ragettli","first_name":"S."},{"full_name":"Pellicciotti, Francesca","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","last_name":"Pellicciotti","first_name":"Francesca"},{"full_name":"Immerzeel, W.W.","last_name":"Immerzeel","first_name":"W.W."},{"full_name":"Miles, E.S.","first_name":"E.S.","last_name":"Miles"},{"full_name":"Petersen, L.","last_name":"Petersen","first_name":"L."},{"last_name":"Heynen","first_name":"M.","full_name":"Heynen, M."},{"last_name":"Shea","first_name":"J.M.","full_name":"Shea, J.M."},{"last_name":"Stumm","first_name":"D.","full_name":"Stumm, D."},{"full_name":"Joshi, S.","first_name":"S.","last_name":"Joshi"},{"full_name":"Shrestha, A.","first_name":"A.","last_name":"Shrestha"}],"year":"2015","intvolume":"        78","date_created":"2023-02-20T08:16:21Z","month":"04","status":"public","volume":78,"issue":"4","article_type":"original","publisher":"Elsevier"}]