Technical note: Introduction of a superconducting gravimeter as novel hydrological sensor for the Alpine research catchment Zugspitze

GFZ (German Research Centre for Geosciences) set up the Zugspitze Geodynamic Observatory Germany with a worldwide unique installation of a superconducting gravimeter at the summit of Mount Zugspitze on top of the Partnach spring catchment. This high alpine catchment is well instrumented, acts as natural lysimeter and has significant importance for water supply to its forelands, with a large mean annual precipitation of 2080ĝ€¯mm and a long seasonal snow cover period of 9 months, while showing a high sensitivity to climate change. However, regarding the majority of alpine regions worldwide, there is only limited knowledge on temporal water storage variations due to sparsely distributed hydrological and meteorological sensors and the large variability and complexity of signals in alpine terrain. This underlines the importance of well-equipped areas such as Mount Zugspitze serving as natural test laboratories for improved monitoring, understanding and prediction of alpine hydrological processes. The observatory superconducting gravimeter, OSG 052, supplements the existing sensor network as a novel hydrological sensor system for the direct observation of the integral gravity effect of total water storage variations in the alpine research catchment at Zugspitze. Besides the experimental set-up and the available data sets, the gravimetric methods and gravity residuals are presented based on the first 27 months of observations from 29 December 2018 to 31 March 2021. The snowpack is identified as being a primary contributor to seasonal water storage variations and, thus, to the gravity residuals with a signal range of up to 750ĝ€¯nms-2 corresponding to 1957ĝ€¯mm snow water equivalent measured with a snow scale at an altitude of 2420ĝ€¯m at the end of May 2019. Hydro-gravimetric sensitivity analysis reveal a snow-gravimetric footprint of up to 4ĝ€¯km distance around the gravimeter, with a dominant gravity contribution from the snowpack in the Partnach spring catchment. This shows that the hydro-gravimetric approach delivers representative integral insights into the water balance of this high alpine site. © Copyright

Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5)

Microwave remote sensing of snow and environment

Hemispheric snow extent and snow mass are two important parameters affecting the water cycle, carbon cycle and the radiation balance in particular at the high latitudes. In this dissertation these topics have been investigated focusing on the mapping of snow clearance day (melt-off day) and Snow Water Equivalent (SWE) by applying spaceborne microwave radiometer instruments. New algorithms have been developed and existing ones have been further advanced. Specific attention has been paid to estimate snow in boreal forests. This work has resulted in Climate Data Records (CDRs) of snow clearance day and daily values of SWE. Data are available for the entire Northern Hemisphere covering more than three decades. The developed CDRs are relevant for climate research, for example concerning the modeling of Earth System processes. CDR on snow clearance day can be used to map the CO2 balance between the biosphere and atmosphere in the case of boreal forests, which is demonstrated in the thesis. Further, methodologies to assess snow mass in terms of SWE for hemispherical and regional scales have been developed. The developed methodologies have also resulted in the establishment of new Near-Real-Time (NRT) satellite data services for hydrological end-use. In hydrology SWE data are used to enhance the performance of river discharge forecasts, which is highly important for hydropower industry and flood prevention activities

CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland

We present CAMELS-CH (Catchment Attributes and MEteorology for Large-sample Studies – Switzerland), a large-sample hydro-meteorological data set for hydrologic Switzerland in central Europe. This domain covers 331 basins within Switzerland and neighboring countries. About one-third of the catchments are located in Austria, France, Germany and Italy. As an Alpine country, Switzerland covers a vast diversity of landscapes, including mountainous environments, karstic regions, and several strongly cultivated regions, along with a wide range of hydrological regimes, i.e., catchments that are glacier-, snow- or rain dominated. Similar to existing data sets, CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes. CAMELS-CH (Höge et al., 2023; available at https://doi.org/10.5281/zenodo.7784632) encompasses 40 years of data between 1 January 1981 and 31 December 2020, including daily time series of stream flow and water levels, and of meteorological data such as precipitation and air temperature. It also includes daily snow water equivalent data for each catchment starting from 2 September 1998. Additionally, we provide annual time series of land cover change and glacier evolution per catchment. The static catchment attributes cover location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers. This Swiss data set complements comparable publicly accessible data sets, providing data from the “water tower of Europe”

Hydrological Extremes in a Warming Climate: Nonstationarity, Uncertainties and Impacts

This Special Issue comprises 11 papers that outline the advances in research on various aspects of climate change impacts on hydrologic extremes, including both drivers (temperature, precipitation, and snow) and effects (peak flow, low flow, and water temperature). These studies cover a broad range of topics on hydrological extremes, including hydro-climatic controls, trends, homogeneity, nonstationarity, compound events and associated uncertainties, for both historical and future climates

The Italian open data meteorological portal: MISTRAL

AbstractAt the national level, in Italy, observational and forecast data are collected by various public bodies and are often kept in various small, heterogeneous and non‐interoperable repositories, released under different licenses, thus limiting the usability for external users. In this context, MISTRAL (the Meteo Italian SupercompuTing PoRtAL) was launched as the first Italian meteorological open data portal, with the aim of promoting the reuse of meteorological data sets available at national level coverage. The MISTRAL portal provides (and archives) meteorological data from various observation networks, both public and private, and forecast data that are generated and post‐processed within the Consortium for Small‐scale Modeling‐Limited Area Model Italia (COSMO‐LAMI) agreement using high performance computing (HPC) facilities. Also incorporated is the Italy Flash Flood use case, implemented with the collaboration of European Centre for Medium‐Range Weather Forecasts (ECMWF), which exploits cutting edge advances in HPC‐based post‐processing of ensemble precipitation forecasts, for different model resolutions, and applies those to deliver novel blended‐resolution forecasts specifically for Italy. Finally, in addition to providing architectures for the acquisition and display of observational data, MISTRAL also delivers an interactive system for visualizing forecast data of different resolutions as superimposed multi‐layer maps

The Italian open data meteorological portal: MISTRAL

At the national level, in Italy, observational and forecast data are collected by various public bodies and are often kept in various small, heterogeneous and non-interoperable repositories, released under different licenses, thus limiting the usability for external users. In this context, MISTRAL (the Meteo Italian SupercompuTing PoRtAL) was launched as the first Italian meteorological open data portal, with the aim of promoting the reuse of meteorological data sets available at national level coverage. The MISTRAL portal provides (and archives) meteorological data from various observation networks, both public and private, and forecast data that are generated and post-processed within the Consortium for Small-scale Modeling-Limited Area Model Italia (COSMO-LAMI) agreement using high performance computing (HPC) facilities. Also incorporated is the Italy Flash Flood use case, implemented with the collaboration of European Centre for Medium-Range Weather Forecasts (ECMWF), which exploits cutting edge advances in HPC-based post-processing of ensemble precipitation forecasts, for different model resolutions, and applies those to deliver novel blended-resolution forecasts specifically for Italy. Finally, in addition to providing architectures for the acquisition and display of observational data, MISTRAL also delivers an interactive system for visualizing forecast data of different resolutions as superimposed multi-layer maps

Climate change impact on the cryosphere: from local to global scale

La criosfera, la porzione della superficie terrestre dove l'acqua è immagazzinata allo stato solido, svolge un ruolo di fondamentale importanza nella regolazione del bilancio energetico terrestre e del ciclo idrologico, fornendo risorse idriche a molte regioni del pianeta. La criosfera, regolando ed allo stesso tempo essendo influenzata dalle condizioni climatiche, è una importante sentinella dei cambiamenti climatici, subendone gli effetti e scaturendone ulteriori a scala globale e locale. In questo studio vengono analizzate diverse tematiche legate alla criosfera, dalle Alpi italiane alla Groenlandia. Viene studiata in primo luogo la climatologia di altezza ed equivalente in acqua del manto nevoso (SWE) tramite l'analisi statistica di altezza e densità della neve raccolte tra il 1967 ed il 2020 in un'ampia regione delle Alpi italiane. Dall'analisi statistica è emerso che l'altezza neve è diminuita di 12 cm e lo SWE di 37 mm per decade dal 1967. L'altezza media della neve si è ridotta del 33\% nel periodo 1994-2020 rispetto al periodo 1967-1993, mentre lo SWE del 37\%. Gli effetti del cambiamento climatico risultano essere più intensi a basse altitudini, con una riduzione dell'altezza del manto nevoso del 63\% al di sotto dei 1500 m. Questi risultati sono ulteriormente confermati dal change-point trovato a fine anni 1980. L'analisi del dataset HISTALP mostra la forte dipendenza dell'evoluzione del manto nevoso dalla temperatura, influenzando lo stato di precipitazione e regolando l'inizio della fusione. Gli effetti del manto nevoso a scala locale sono stati studiati analizzando il contributo della fusione nivale nel caso di eventi di precipitazione intensa con presenza di neve al suolo. L'analisi è stata limitata alle stazioni di Aprica e Pantano d'Avio, in Lombardia, dove sono stati raccolti i dati di temperatura, precipitazione ed altezza neve dal 1996. Con i dati osservati è stato calibrato un modello gradi-giorno tramite il quale è stato possibile ricostruire la serie temporale della somma di precipitazione e fusione nivale per le durate di 1, 3, 6, 12 e 24 ore. L'analisi degli annual maxima ha mostrato che la fusione nivale contribuisce ad un incremento medio dei quantili di circa il 2.2\%, aumentando con la durata fino a raggiungere, in un solo caso, il 10\%. Ad una più larga scala, lo studio della fusione superficiale della Groenlandia è di fondamentale importanza nella stima del contributo della calotta di ghiaccio all'innalzamento del livello medio degli oceani. Sono stati raccolti ed intercalibrati i dati satellitari a microonde passive raccolti da sensori montati su cinque diversi satelliti tra il 1979 ed il 2019. Il confronto con dati misurati da stazioni meteorologiche e con simulazioni del modello climatico regionale MAR hanno mostrato che un algoritmo basato sul modello di emissione elettromagnetica MEMLS riesce a cogliere l'evoluzione spaziale e temporale della fusione superficiale. L'analisi dei trend di lungo periodo ha mostrato che la superficie di fusione è aumentata tra il 3.6 ed il 6.9\% dell'intera area della Groenlandia per decennio durante il periodo di osservazione. Inoltre, la stagione di fusione è iniziata tra i 3 ed i 4 giorni prima e si è conclusa tra i 3 ed i 7 giorni dopo ogni decennio. Il numero totale medio di giorni di fusione è aumentato di circa 3-5 giorni per decennio. Per l'area della Groenlandia è stato poi implementato un algoritmo di downscaling statistico per il modello MAR. Il confronto con le misure di temperatura delle stazioni meteorologiche e con i dati di temperatura superficiale rilevati dal satellite Lansat-8 mostra come il dataset ad elevata risoluzione riesca meglio a cogliere la distribuzione spaziale della temperatura, senza perdere accuratezza a livello locale. Il confronto con le misure di bilancio di massa superficiale mostra invece un sostanziale miglioramento rispetto all'output originale a bassa risoluzioneThe cryosphere, the region of the Earth where water is stored in its solid form, plays a crucial role in regulating Earth’s energy balance and contributes to moisture fluxes and freshwater storage and release, providing water resources to many regions of the world. The cryosphere affects and is affected by climate conditions, being a driver and a sentinel of climate change, and playing a role of paramount importance from global to local scale processes. Here, different topics related to the cryosphere are investigated, spanning from the Greenland ice sheet to the Italian alps. A climatology of snow depth and snow water equivalent is carried out using a dataset of snow depth and snow density measurements collected at 299 sites between 1967 and 2020 over a wide portion of the Italian Alps. By performing different statistical analyses, a decrease of 12 cm every decade in snow depth and 37 mm every decade in SWE has been found since 1967. Average snow depth in the period 1994-2020 has been 33% lower than in the period 1967-1993, with stronger effects at low altitudes (reduction of 63% below 1500 m asl). The average SWE in 1994-2020 has been 36% lower than in 1967-1993. These results are confirmed by the increased elevation of the computed null snow depth elevation and the detected change-points at the end of the 1980s. The analysis of the HISTALP dataset confirmed the strong dependency of snow accumulation and melt on air temperature, impacting liquid/solid precipitation separation and timing of melt onset. The influence of snow on ground at local scale has been investigated evaluating the contribution of snowmelt to intense rain-on-snow events in Lombardy. By means of measured temperature, precipitation and snow depth data and the calibration of a snowmelt model, the timeseries of the combination of precipitation and melt has been obtained for the fixed durations 1, 3, 6, 12 and 24 h. The annual maxima analysis revealed that snowmelt increases the quantiles obtained from the selected extreme values distributions of about 2.2%, with stronger impacts for longer durations, up to 10%. At a larger scale, the analysis of surface melting over the Greenland ice sheet is of paramount importance to better estimate the ice sheet contribution to sea level rise. The cross-calibration of five different sensors collecting satellite data over the Greenland ice sheet between 1979 and 2019 has been performed. The comparison with in-situ observation and the output of the regional climate model MAR revealed that a threshold-based melt detection algorithm based on the electromagnetic emission model MEMLS shows the best performances in capturing surface melting evolution. The long-term trends analysis showed an increase of surface melting areal extension of about 3.6-6.9% of the Greenland ice sheet every decade. The melting season has started between 3 and 4 days earlier and between 3 and 7 days later every decade. The total number of melting days has increase by 3-5 days every decade. A statistical downscaling algorithm for the regional climate model MAR has been implemented. The comparison with in-situ observations and satellite measurements revealed that the downscaled dataset can well capture temperature temporal evolution and spatial distribution. It better captures at local scale the cumulated surface mass balance, exhibiting lower errors when compared with measured surface mass balance with respect to the original modelled outpu