A new version of the Global Multisensor Automated Snow and Ice Mapping System (GMASI) has been implemented into operations at NESDIS is summer 2023. The new system is an upgrade of the previous version of the GMASI which was operated since 2006. The system provides information on the snow and ice distribution for NOAA numerical weather prediction and climate models as well as for a number of other atmosphere and land remote sensing products. The retrieval algorithm uses satellite observations in the visible/infrared and in the microwave spectral bands and delivers daily spatially continuous (gap-free) maps of the snow and ice cover.  

Compared to previous version, the new system incorporates data from a larger set of microwave sensors and features an enhanced retrieval algorithm. The spatial resolution of the output maps has been improved from es improved from 4km to 2km.  In the presentation we provide details of the data processing algorithm and of the output product focusing on the improvements and upgrades. We demonstrate that the output of the new GMASI system closely matches the accuracy of snow maps produced within NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) and agrees well to in situ station snow depth report. Improvements to the retrieval algorithm mostly affected reproduction of small-scale features in the snow and ice cover distribution, particularly in alpine areas.  In the same time, large-scale climatologically-important cryosphere features as the continental and hemisphere snow and ice extent estimated with the new snow and ice maps remain consistent with the previous version of the product. 

How to cite: Romanov, P.: Enhancing NESDIS Global Automated Snow and Ice Cover Mapping System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14158, https://doi.org/10.5194/egusphere-egu24-14158, 2024.

    Many reservoirs have been constructed in the Yangtze River basin, however, spring floods in its source region pose increasingly severe challenges to reservoirs operation and water resources management due to increased climatic variability under global warming. Understanding spring flood variability and their major influential factors under changing climates is crucial to improving water management, agricultural irrigation, reservoir operation, and flood prevention. In this study, we have examined the spring flood characteristics and their influential factors in the source region of the Yangtze River based on station data and multisource remote sensing products during 2001–2020. Late Mays have seen most of the highest spring flood discharge, while some springs have experienced multiple peaks. Extreme spring floods were identified in the years 2012, 2013, 2019, and 2020, with the highest peak discharge (1365.83 m³/s) and longest flood duration (47 days) in 2019. Spring snowmelt played a key role in 2019 spring flood and others were also driven by snowmelt in the UJSB. We defined Snow Water Volume (SWV) as an indicator of the precondition for high spring flood. In 2019, large winter SWV along with spring snowfall melted into meltwater under the rising temperature, resulting in extreme spring flood event in late April. Whereas, in 2012 and 2020, snowmelt and rainfall combined to contribute to the extreme spring flood events in late Mays. In 2013, although snowmelt made a contribution to the first spring flood peak, the flood event at the end of May was primarily contributed by rainfall. Based on spatiotemporal variations in spring SWV and the isotherm of critical temperature for snow melting, the key regions dominating spring floods were identified as the regions with large amount of SWV. Weather pattern analysis showed that the enhanced Westerly jets in winters brought about large snowfall and extended snow cover in the region which can be released as floods triggered by rapid increase in air temperature in the coming spring.

How to cite: Yi, Y., Liu, S., Zhu, Y., Wu, K., and Xie, F.: Spring floods and their major influential factors in the source region of the Yangtze River during 2001–2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2310, https://doi.org/10.5194/egusphere-egu24-2310, 2024.

Snow Water Equivalent (SWE) is a critical parameter for understanding water availability in regions with seasonal snow cover. Ensuring an accurate representation of SWE across regular spatial and temporal intervals is essential and plays a pivotal role in hydrological and climatological studies. This work critically examines the ablation modeling strategy employed by the University of Arizona daily 4km SWE dataset (UA SWE), a widely adopted SWE gridded product in the United States, highlighting limitations inherent in methodologies that rely solely on temperature data.

Recognizing the utility of a more nuanced perspective to capture the complexities of snowmelt dynamics, we propose a novel method that incorporates a diverse set of meteorological and terrain characteristics as input variables in the predictive modeling of snow ablation. Our approach is further extended to directly model SWE, eliminating the need for intermediate ablation estimation and providing a more intuitive solution for empirical SWE prediction.

Our versatile methodology can be easily applied to produce high-resolution gridded SWE data. By addressing deficiencies in a leading empirical approach, our technique aims to enhance the accuracy of SWE representation at both the point and grid levels.

How to cite: Yu, M., Paciorek, C., Rhoades, A., Risser, M., and Perez, F.: Enhancing Snow Ablation Modeling in the Generation of Gridded Snow Water Equivalent Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5531, https://doi.org/10.5194/egusphere-egu24-5531, 2024.

Debris, ranging from thin surface dust to medial moraines and thick, continuous layers in ablation zones, partially covers glaciers all around the world. By modifying energy transfer from the atmosphere to the ice, the supraglacial debris layer fundamentally controls sub-debris melt rates. Debris physical properties such as surface roughness (z0) and thermal conductivity (k) have only been derived from local measurements at a few sites, and modelling studies of debris-covered glaciers have often relied on literature values. The correct representation of these properties in energy-balance models is crucial for understanding the climate-glacier dynamics and how debris-covered glaciers will behave in the future. There are several established methods to derive these properties from field measurements, yet relatively few studies undertake to measure properties for their sites, or to evaluate the resulting property values.

We undertook an observational campaign to investigate supraglacial debris properties at Pirámide Glacier, in the central Chilean Andes. First, we used established approaches, as well as some variations on those approaches, to derive z0 from wind-temperature tower data and k from thermistor strings in the debris at three glacier locations. Second, we determined locally-optimal k and z0 values to reproduce observed ice melt: we optimised k by simulating energy conduction through the debris with the surface temperature as an input, then optimised z0 by running a complete energy-balance model using the observed surface meteorology. We then conducted point-scale energy-balance modelling using the z0 and k values obtained i) with the derivations from field measurements; ii) through optimisation, or; iii) from the typical values found in literature. This allowed us to evaluate how the different methods perform by comparing the modelled and measured ice melt. 

Our results show that deriving local debris properties from measurements is challenging and that measured values can differ significantly from common literature values. The values derived from measured data can vary significantly depending on the method employed. It is important to note that these values can also differ significantly from the values required by an energy-balance model to accurately represent sub-debris ice melt. Furthermore, energy-balance models typically assume a representation of heat transfer within the supraglacial debris layer based solely on conduction and require a bulk thermal conductivity value. This highlights the necessity of efforts to reevaluate measurements in the field and reconsider our definition of debris properties in melt modelling.

How to cite: Melo Velasco, V., Miles, E., McCarthy, M., Shaw, T. E., Fyffe, C., and Pellicciotti, F.: Inferring Debris Properties on Debris-Covered Glaciers: Implications for Glacier Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9702, https://doi.org/10.5194/egusphere-egu24-9702, 2024.

High Mountain Asia is characterized by a substantial glacier coverage with glaciers of varying sizes. These glaciers are crucial in the area's hydrological cycle since they feed large rivers such as the Indus, Ganges, and Brahmaputra rivers. However, ongoing climate change is having a significant negative impact on glacier mass and projections show strong further declines of glacier mass in the future. This is raising concerns about future water security. How big the impact of the evolution of small glaciers (< 2 km²) is towards changing water availability remains to be investigated.

Most studies focus on the regional evolution of glaciers as a whole, which means that small-scale glaciers are often overlooked due to larger glaciers dominating the signal in area and volume changes, despite the fact that small glaciers make up about 30% of the glacierized area in High Mountain Asia. To address this issue, we applied the Global Glacier Evolution Model (GloGEM) to simulate all ca. 100’000 glaciers of High Mountain Asia (Regions 13-15 of the Randolph Glacier Inventory v6.0) under various climate scenarios in the period of 1980-2100. We compared the spatio-temporal variability of the timing of peak water, as well as glacier volume change, between small and large glaciers for a set of approximately 30 catchments in the headwater of Indus, Ganges and Brahmaputra rivers.

We find that there is a larger difference between future scenarios for the timing of peak water for smaller glacier, with it ranging from 2030-2060 and then runoff declining rapidly. Meanwhile, peak water for larger glaciers is likely to occur between 2070-2080 according to an intermediate emission scenario, with glacier runoff decreasing gradually thereafter. As for the ice volume change, smaller glaciers are expected to reach volumes close to zero near the year 2080, while larger glaciers are expected to reach this point only after 2100. The quicker response of small glaciers compared to large glaciers emphasize the need for a particular focus on small glaciers to better understand their responses to climate change and make accurate projections about local and regional scale near future water availability.

How to cite: von der Esch, A., Huss, M., Van Tiel, M., Berg, J., Patadiya, T., Horton, P., Vijay, S., and Farinotti, D.: The importance of small glaciers for accurate projection of future runoff in High Mountain Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15373, https://doi.org/10.5194/egusphere-egu24-15373, 2024.

Snow cover area, which can be obtained from satellite, is a valuable information to simulate streamflow in snow-dominated mountain basins where snowmelt is a major runoff factor. However, usually satellite do not provide long completed snow cover area spatiotemporal series, which are required to calibrate and validate hydrological models. It is due to difference limitations, as presence of clouds, sensor failure, low revisit time or spatial resolution, or recent launch.

Cellular automata models, which use precipitation and temperature as driving variables and some transition rules between cells through calibrated parameters, are capable of capturing the dynamics of snow cover area. Therefore, they can be used to complete and extend the information provided by satellite.

In this work, we simulate long series of daily streamflow in the Canales basin (Sierra Nevada, south Spain) by combining a cellular automata model and the Snowmelt Runoff Model. The Snowmelt Runoff Model is a degree-day model that requires data of temperature, precipitation, and snow cover area and has been widely used in simulation of streamflow in snow-dominated mountainous basins around the world.

The water resources in the Canales basin are regulated by a reservoir, which contributes to supply the Granada city water demand. The main resources stored in reservoir come from Sierra Nevada Mountains during the melting season. Therefore, the dynamics of snow is essential to simulate streamflow in the Canales basin.

It has been also used to simulate future local climate scenarios generated for specific level of warming in peninsular Spain.

Aknowledments: This research has been partially supported by the projects: STAGES-IPCC (TED2021-130744B-C21), SIGLO-PRO project (PID2021-128021OB-I00), SIERRA-CC project (PID2022-137623OA-I00) from the Spanish Ministry of Science, Innovation and Universities, and SER-PM (2908/22) from the National Park Research Program.

How to cite: Hidalgo Hidalgo, J. D., Collados Lara, A. J., Pulido Velazquez, D., Pardo Iguzquiza, E., Gomez Gomez, J. D. D., and Rueda Valdivia, F. J.: Combined use of evolutionary algorithms and hydrological models to simulate snow cover and flow in alpine basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15646, https://doi.org/10.5194/egusphere-egu24-15646, 2024.

Central Asia’s mountain rivers are to a large degree fed by snow and ice melt and are a crucial contributor of fresh water downstream for millions of people. The attribution of how these meltwater sources will change their contribution to stream flow in a warmer future are, however, very uncertain. A major reason for this is an extremely sparse hydrometeorological monitoring network. This affects the calibration and validation of large-scale cryo-hydrological models that could be used for the task, or the validation and bias correction of reanalysis and remote sensing data needed to run such models. In combination with uncertainties about the glacier mass balances in the region, hydrological models are facing a pronounced equifinality problem. In order to improve this, and to understand better the glacier response to the current meteorological forcing in different climate zones of Central Asia, we instrumented 8 pro-glacial streams in Kyrgyzstan and Tajikistan with automated runoff gauges running at hourly resolution to also capture diurnal variability. These measurements complement the already (re-)established glaciological monitoring network at most of these sites and allow tackling the equifinality problem by constraining many variables. Here, we present first results from this database that shall serve to improve hydrological model calibration and parameterization, and understand relationships between meteorological forcing, annual glacier mass balance and meltwater generation. We also discuss instrumenting strategies and problems, and uncertainties related to gauge calibration.

How to cite: Pohl, E., uulu Esenaman, M., Halimov, A., Amschwand, D., Saks, T., and Huang, J.: A Central Asian pro-glacial discharge database for improving hydrological models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16055, https://doi.org/10.5194/egusphere-egu24-16055, 2024.

Central Asia is a climate change hot spot, facing an unprecedented juxtaposition of regional climate- and water-related issues. Meltwater from the Pamir Mountains plays a crucial role in Central Asia's hydrological cycle, and its response to climate change has been widely investigated using glacial-hydrological models. However, the hydrological simulation in Pamirs is highly uncertain, primarily driven by data scarcity and the complex interplay between climatic factors and glacier dynamics. Ongoing efforts concentrate on including more calibration data and constraining the uncertainty about the exact internal process representation of hydrological models. However, the quality of the groundwater simulation is often neglected. Groundwater reservoirs, buffering meltwaters and providing river flow when little to no surface runoff occurs, are extremely important in the Pamir region. Although physically based groundwater models provide a more detailed picture of the possible evolution of the system, empirical groundwater models are often used in hydrological modeling due to their minimal input data requirements and low computational cost compared to physically based models. However, the traditional empirical groundwater model with single linear storage is not suitable for the Pamir region. The region is characterized by a variety of sedimentary deposits in different landscape morphologies, resulting in varying delays in water recharge, release, and storage capacities. We improved the baseflow representation by coupling two linear groundwater reservoirs (one fast and one slow) into a widely-used hydrological model in the region. A representative catchment in the central Pamir, the Gunt River basin, is used as a case study to demonstrate the importance of groundwater in constraining the hydrological calibration process. Groundwater is the only contribution to winter river discharge in the Gunt basin and can thus be used as an indicator of groundwater parameter constraint. Here we show that the hydrological model can achieve good performance (in terms of daily discharge, seasonal snow cover fraction, and annual glacier mass balance) even when calibrated with only total daily discharge and winter baseflow. Especially the baseflow calibration helps constraining snowmelt onset in spring and improving adjustments of precipitation and temperature, which are the most uncertain sources in hydrological modeling in the region. Despite improvements, degree day factors still show a large variability. The resulting model equifinality problem still leads to predictive uncertainty, indicating that more glacier observations are needed for a sound process understanding. Based on the simulated results, the hydrological cycle in Gunt was analyzed and compared with previous studies.

How to cite: Huang, J., Pohl, E., and Richard-Cerda, J. C.: On the Importance of groundwater constraining in hydrological modeling of the Pamir region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18059, https://doi.org/10.5194/egusphere-egu24-18059, 2024.

In cold regions, snow is a crucial component of the cryosphere, experiencing changes such as decreasing snowpack and snow cover. These changes impact the seasonal amount of snow and cause a shift in the timing of spring floods, particularly in mountainous areas. The complex and diverse snow processes and interactions in mountainous environments challenge making accurate predictions on snow and runoff. Moreover, snow is not uniformly distributed in space and time, which emphasizes the importance of monitoring mountain snowpack to enhance the understanding of hydrological processes and improve forecasting in the face of changing conditions. In the past few years, ground penetrating radar and drone acquisitions have emerged as a state-of-the-art methodology for obtaining snow data at high spatial resolution with a significant area coverage compared to traditional point observations. This study used data from ground penetrating radar and drone acquisitions to develop and evaluate a new snowfall distribution function based on wind speed, direction and topography to model wind redistribution in the semi-distributed hydrological model HYPE. We assessed the effect of the new snowfall distribution function compared to the one based on wind direction and topography on the snow distribution close to the accumulation peak in the Överuman catchment, Northern Sweden. We further assessed the impact of the two snowfall distribution functions on the catchment runoff predictions. Results show that the snowfall distribution function based on wind speed and direction better simulated the snow spatial distribution in the catchment than the snowfall function based on wind direction. Ground penetrating radar and drone acquisitions provided complementary model development and evaluation information.

How to cite: Clemenzi, I., Gustafsson, D., Fagerström, V., Wennerberg, D., Norell, B., Zhang, J., Pettersson, R., and Pohjola, V.: Assessing the use of GPR and drone snow data for model development and runoff predictions in Northern Sweden, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18338, https://doi.org/10.5194/egusphere-egu24-18338, 2024.

The Himalayan region is severely exposed to the flood risk due to the heavy rainfall during the summer monsoon. The dynamics of the hydrological response during extreme events is relatively less understood, because of several complex and interactive processes. In this scenario, the use of rainfall-runoff models capable of adequately taking these processes into account could be crucial for reliable flood forecasting. However, in areas with such complex topography, accurately characterizing meteorological forcing and streamflow dynamics remains a challenging task due to the lack of ground measurements. Furthermore, in highly glacierized Himalayan basins, the significant contribution to streamflow by snow and ice melting has been shown to be progressively increasing due to its sensitivity to climate change, in parallel with the loss in glacier mass.

In this study, a conceptual and parsimonious hydrological model was implemented in semi-distributed mode and calibrated against streamflow and glacier loss volume data simultaneously. The MISDc-2L model was modified to simulate not only the snow accumulation and melt, but also the glacier melting in the ice-covered fraction of sub-basin area, assumed to occur once the seasonal snowpack is locally depleted. The Alaknanda River (one of the two headstreams of the Ganges) was chosen as a case study because it experiences several disastrous flood events in recent years. The basin upstream the Rudraprayag gauge was considered (≈8600 km²), for the period 2000-2020. The Randolph Glacier Inventory v7.0 was employed to locate glacierized areas, while glacier storage change data were extracted from available literature studies. Elevation data from NASADEM and hourly variables from ERA5-Land reanalysis dataset were used. A joint objective function was considered for calibration, including the Kling-Gupta efficiency, a high-flows hydrological signature and the error in glacier stored water loss. The model, constrained with glacier storage change data, was found to be able to provide good hydrological performances, both in calibration and validation, also with specific reference to annual flood peaks.

Despite the simplicity and the flood-oriented approach, the proposed modelling procedure simulated the dominant hydrological processes in a physically plausible way, in a basin with high-altitude glacierized areas in a context of climate change. The goal of adequately characterizing the contribution of glacier melt to total streamflow was pursued by aiming for consistency with additional data sources.

This work was funded by:

-          the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

-          the FLOSET Project 'Probabilistic floods and sediment transport forecasting in the Himalayas during the extreme events’, funded in the context of the 'ITALY-INDIA JOINT SCIENCE AND TECHNOLOGY COOPERATION CALL FOR JOINT PROJECT PROPOSALS FOR THE YEARS 2021 2023'.

How to cite: De Santis, D., Massari, C., Barbetta, S., Bahmanpouri, F., Maggioni, V., Gupta, S., Sharma, A., Agarwal, A., and Sen, S.: Flood modelling of a partly glacierized catchment in the Himalayas in a context of climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19899, https://doi.org/10.5194/egusphere-egu24-19899, 2024.