Multi-physics ensemble snow modelling in the western Himalaya

Combining multiple data sources with multi-physics simulation frameworks offers new potential to extend snow model inter-comparison efforts to the Himalaya. As such, this study evaluates the sensitivity of simulated regional snow cover and runoff dynamics to different snowpack process representations. The evaluation is based on a spatially distributed version of the Factorial Snowpack Model (FSM) set up for the Astore catchment in the upper Indus basin. The FSM multi-physics model was driven by climate fields from the High Asia Refined Analysis (HAR) dynamical downscaling product. Ensemble performance was evaluated primarily using MODIS remote sensing of snow-covered area, albedo and land surface temperature. In line with previous snow model inter-comparisons, no single FSM configuration performs best in all of the years simulated. However, the results demonstrate that performance variation in this case is at least partly related to inaccuracies in the sequencing of inter-annual variation in HAR climate inputs, not just FSM model limitations. Ensemble spread is dominated by interactions between parameterisations of albedo, snowpack hydrology and atmospheric stability effects on turbulent heat fluxes. The resulting ensemble structure is similar in different years, which leads to systematic divergence in ablation and mass balance at high elevations. While ensemble spread and errors are notably lower when viewed as anomalies, FSM configurations show important differences in their absolute sensitivity to climate variation. Comparison with observations suggests that a subset of the ensemble should be retained for climate change projections, namely those members including prognostic albedo and liquid water retention, refreezing and drainage processes

Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus Basin

Assessing local climate change impacts requires downscaling from Global Climate Model simulations. Here, a stochastic rainfall model (RainSim) combined with a rainfall conditioned weather generator (CRU WG) have been successfully applied in a semi-arid mountain climate, for part of the Upper Indus Basin (UIB), for point stations at a daily time-step to explore climate change impacts. Validation of the simulated time-series against observations (1961–1990) demonstrated the models’ skill in reproducing climatological means of core variables with monthly RMSE of <2.0 mm for precipitation and ⩽0.4 °C for mean temperature and daily temperature range. This level of performance is impressive given complexity of climate processes operating in this mountainous context at the boundary between monsoonal and mid-latitude (westerly) weather systems. Of equal importance the model captures well the observed interannual variability as quantified by the first and last decile of 30-year climatic periods. Differences between a control (1961–1990) and future (2071–2100) regional climate model (RCM) time-slice experiment were then used to provide change factors which could be applied within the rainfall and weather models to produce perturbed ‘future’ weather time-series. These project year-round increases in precipitation (maximum seasonal mean change:+27%, annual mean change: +18%) with increased intensity in the wettest months (February, March, April) and year-round increases in mean temperature (annual mean +4.8 °C). Climatic constraints on the productivity of natural resource-dependent systems were also assessed using relevant indices from the European Climate Assessment (ECA) and indicate potential future risk to water resources and local agriculture. However, the uniformity of projected temperature increases is in stark contrast to recent seasonally asymmetrical trends in observations, so an alternative scenario of extrapolated trends was also explored. We conclude that interannual variability in climate will continue to have the dominant impact on water resources management whichever trajectory is followed. This demonstrates the need for sophisticated downscaling methods which can evaluate changes in variability and sequencing of events to explore climate change impacts in this region

Statistical and Dynamical Downscaling of Numerical Climate Simulations : Enhancement and Evaluation for East Asia

The overall aim of this thesis is to present methods, which improve evaluating dynamical downscaling approaches or enhance statistical downscaling schemes. These methods are illustrated along examples of both approaches for the East Asian region. The transfer of information from a large scale to a smaller scale is referred to as downscaling. Two different approaches are employed in climate science for downscaling purposes, i.e. textit{dynamical downscaling} and textit{statistical downscaling}. In order to give a better description of the downscaled data, this thesis presents methods, which help evaluating and interpreting the data and results of further studies in a better way, for both approaches. Dynamical Downscaling is based on a spatially limited atmospheric general circulation model, a so-called regional climate model (RCM). At the boundaries of the RCM lateral boundary conditions (LBC) are provided by a climate simulation performed with a global general circulation model (GCM). This thesis proposes methods for evaluating RCM simulations. First, a qualitative evaluation, that investigates whether single atmospheric dynamics are resolved by the RCM, is presented. Second, a newly developed evaluation method, that investigates by cross-spectral analysis on which temporal scales a RCM is able to generate variability independently from the GCM defining the LBC, is introduced. To this end, cross-spectra are estimated point-to-point between the RCM and a bi-linearly interpolated version of the GCM defining the LBC. Both methods are illustrated along RCM simulations performed for a domain covering East Asia. The RCM COSMO-CLM has been adapted for this purpose, and was driven by climate simulations performed with ECHAM5 and the re-analysis ERA-40 at its boundaries. The qualitative evaluation shows that both summer monsoon and winter monsoon dynamics are resolved by COSMO-CLM. The cross-spectral analysis suggests that the potential of COSMO-CLM to generate variability independently from the GCM depends on both dynamical features, i.e. monsoons and inter-tropical convergence zone, and on numerical parameters, i.e. horizontal resolution and domain extension. Statistical downscaling is based on statistical transfer functions between the output of large scale climate simulations and observations on the local scale. While an abundance of statistical methods for this kind of purpose are available, it is crucial from case to case to find physically meaningful predictors, which allow further interpretations of the results. Deriving and applying such predictors is demonstrated along a statistical downscaling study for precipitation properties in the Poyang catchment in Eastern China. The dichotomous variable, if 24~h accumulated rainfall exceeds a certain threshold, is taken from local rain gauges for summer. Empirical orthogonal functions (EOF) are calculated for relative vorticity at 850~hPa and vertical velocity at 500~hPa taken from ERA-40 re-analysis data. Both information are linked by logistic regression. The most dominant EOF-predictor can be associated with meso-$alpha$-scale disturbances, which are part of the summer monsoon dynamics in this region. Downscaled data is often requested for further studies in climate science, but also in other disciplines. Thus, developing evaluation methods for assessing the quality of RCM simulations, and deriving physically interpretable predictors for statistical downscaling schemes are crucial enhancements for the downscaling procedure

Evidence of elevation-dependent warming from the Chinese Tian Shan

The phenomenon in which the warming rate of air temperature is amplified with elevation is termed elevation-dependent warming (EDW). It has been clarified that EDW can accelerate the retreat of glaciers and melting of snow, which can have significant impacts on the regional ecological environment. Owing to the lack of high-density ground observations in high mountains, there is widespread controversy regarding the existence of EDW. Current evidence is mainly derived from typical high-mountain regions such as the Swiss Alps, the Colorado Rocky Mountains, the tropical Andes and the Tibetan Plateau–Himalayas. Rare evidence in other mountain ranges has been reported, especially in arid regions. In this study, EDW features (regional warming amplification and altitude warming amplification) in the Chinese Tian Shan (CTM) were detected using a unique high-resolution (1 km, 6-hourly) air temperature dataset (CTMD) from 1979 to 2016. The results showed that there were significant EDW signals at different altitudes on different timescales. The CTM showed significant regional warming amplification in spring, especially in March, and the warming trends were greater than those of continental China with respect to three temperatures (minimum temperature, mean temperature and maximum temperature). The significance values of EDW above different altitude thresholds are distinct for three temperatures in 12 months. The warming rate of the minimum temperature in winter showed a significant elevation dependence (p<0.01), especially above 3000 m. The greatest altitudinal gradient in the warming rate of the maximum temperature was found above 4000 m in April. For the mean temperature, the warming rates in June and August showed prominent altitude warming amplification but with different significance above 4500 m. Within the CTM, the Tolm Mountains, the eastern part of the Borokoonu Mountains, the Bogda Mountains and the Balikun Mountains are representative regions that showed significant altitude warming amplification on different timescales. This new evidence could partly explain the accelerated melting of snow in the CTM, although the mechanisms remain to be explored

Statistical downscaling to analyze the impacts of climate change on future water resources of the Indus river basin of Pakistan

The Indus River system derives the bulk of its runoff from complex processes operating simultaneously at various scales within the cryosphere-dominated Upper Indus Basin (UIB) to support the livelihood of a large population. However, the mountain climate of the UIB has shown conflicting signals to draw uncertain inferences about its future hydrology and cryosphere stability. This implicates rational adaptation planning across the basin. The UIB is a known climate hotspot, and observational and general circulation model (GCM) simulation challenges influence its meteo-hydrological uncertainties. The current PhD study used an advanced statistical downscaling framework to improve climate simulations over Pakistan’s Indus River basin by focusing on the UIB within available observational constraints. Besides precipitation, both maximum and minimum temperatures were modeled due to their distinct influence on regional water balance and glacial stability. Given their relatively skillful representation in the GCMs, large-scale atmospheric dynamics rather than precipitation were used in the downscaling process as modeled precipitation is affected by a lack of reliability associated with its complex generating mechanism and high spatial variability. Such model limitations manifest over high mountain regions like the UIB, where orography results in additional precipitation variation. Severe observational constraints further restrict the ability to correct such GCM biases adequately over the UIB. The downscaling focused on the spatiotemporal variability of the regional climate, reference and GCM level of uncertainties, and statistical skills of regression models using an observational profile that the recent high altitude (HA) observatories have significantly improved. K-means clustering initially helped identify homogeneous precipitation regions by using observed precipitation variability on seasonal scales. The clustering process established how relatively low altitude stations could be used to explain the precipitation dynamics over HA regions. Avoiding downscaling over the recently established HA stations is advantageous for circumventing temporal and quantitative biases associated with shorter data and erroneous precipitation measurements over these regions. Atmospheric drivers of the sub-regional precipitation (temperature) were then identified from the ERA-Interim reanalysis dataset by implementing a robust cross-validation framework. The identified ERA-predictors were compared separately with two additional reanalysis datasets (ERA5 and NCEP-NCAR-II) to estimate their suitability for regional analysis by quantifying reference uncertainties. Such predictor comparisons with GCMs’ historical simulations helped rank the models and quantify uncertainties (model uncertainty) according to the ability of models to simulate precipitation (temperature) predictors. Predictors under two Representative Concentration Pathway scenarios (i.e., RCP4.5 and RCP8.5) were used in the respective regression models to assess future precipitation and temperatures over the basin. Overall, significantly wetter and warmer conditions could dominate the basin hydrology throughout the 21st century compared to the base climate (1976-2005). These multi-model (median) signals intensified towards the end of the century (2071-2100) under the RCP8.5 scenario. Signal-to-noise ratio (SNR) and better-performing GCMs further validated these findings. Seasonally, the future winter (DJFM) would experience the highest precipitation and (Tmin dominated) warming. The Karakoram region would experience the highest precipitation increase. Meanwhile, significant decreases in precipitation as well as (Tmax dominated) increased warming was projected during the pre-monsoon (AMJ) period. The future monsoon (JAS) season showed increased precipitation and a striking feature of low-warming conditions over the UIB. The Lower Indus showed more warming compared to the UIB during all seasons. These typical climatic changes would exert tremendous influence on the hydrology of the basin. For instance, the liquid precipitation proportion may significantly increase due to rising nighttime temperatures in winter. Decreasing spring precipitation may be compensated to some extent by increased melting (glacial and previously accumulated snow) and favorable climatic changes over the eastern tributaries. Increased winter precipitation could increase base flows, which may cause heavy flooding under the projected monsoon strengthening. Increased minimum temperatures could also trigger avalanches and ice mass redistribution, affecting water resources, infrastructure, and communities. Increased warming over the Lower Indus may prompt an early start of the agricultural seasons and a tremendous increase in future water demand. Such favorable meteorological changes over the UIB may significantly increase future water availability to help meet water demand. Contrary to some downscaling studies, an increased water supply may remain possible without a rapid glacial retreat, particularly over the Karakoram region. Therefore, the continuation of the Karakoram anomaly until the end of the 21st century is possible even under the RCP8.5 forcing. Increased albedo, aerosol forcing, and cloud radiative feedbacks associated with stronger and further northward penetrating westerly (monsoon) circulations may induce glacial stability in the future. Adopting uniform lapse rates without considering spatial heterogeneity within the UIB and improper representation of the monsoon-topography internal feedbacks in GCMs may induce warm biases, which overestimate glacier retreats in such bias-corrected (downscaling) studies. Such predictor-predictand modeling to estimate future climate response and associated robustness at finer scales was never performed in the region, presenting an alternate simulation perspective. Such a statistical framework may influence future climate research in the region and other complex and data constraint settings to support regional adaptations.Das Indus-Flusssystem gewinnt den Großteil seines Abflusses auf Basis komplexer Prozesse, die zeitgleich auf verschiedenen Ebenen innerhalb des von der Kryosphäre dominierten Oberen Indus-Beckens (engl. Upper Indus Basin, UIB) ablaufen, um den Lebensunterhalt einer großen Bevölkerung zu sichern. Das Gebirgsklima des UIB, einem bekannten Klimahotspot, hat bisher jedoch widersprüchliche Signale gezeigt, die zuverlässige Rückschlüsse auf die künftige Hydrologie und die Stabilität der Kryosphäre erschweren und eine rationale, zukunftsgerichtete Anpassungsplanung im gesamten Einzugsgebiet erforderlich machen. Die Beschaffenheit des Untersuchungsgebietes erschwert sowohl eine zuverlässige Messung als auch die Simulation von klimatischen Parametern, welche weitestgehend für die bestehenden meteorologischen und hydrologischen Unsicherheiten verantwortlich sind. In der vorliegenden Arbeit wurde ein innovatives statistisches Downscaling-Verfahren angewandt, um die Klimasimulationen über dem pakistanischen Indus-Einzugsgebiet zu verbessern, wobei der Schwerpunkt auf dem UIB im Rahmen der verfügbaren Beobachtungen lag. Neben dem Niederschlag wurden auch die Maximal- und Minimallufttemperaturen modelliert, da diese einen großen Einfluss auf den regionalen Wasserhaushalt und die Gletscherstabilität aufweisen. Die großräumige atmosphärische Dynamik wurde anstelle des Niederschlags, den es aufgrund von komplexen Entstehungsmechanismen und einer hohen räumlichen Variabilität noch immer an Zuverlässigkeit in den neuesten globalen Klimamodellen (GCMs) mangelt, in den Downscaling-Prozess einbezogen, da diese vergleichsweise relativ gut dargestellt wird. Solche Modellbeschränkungen zeigen sich besonders in Hochgebirgsregionen wie der UIB, wo die Orographie einen zusätzlichen Einfluss auf Niederschlagsbildung, -mengen und -variabilität darstellt. Darüber hinaus führen die komplexen lokalen Bedingungen zu Ungenauigkeiten bei der Erhebung von Messdaten, wodurch die Möglichkeiten zur Korrektur solcher GCM-Bias über dem UIB entsprechend weiter eingeschränkt werden. Beim Downscaling lag ein besonderer Schwerpunkt auf der räumlich-zeitlichen Variabilität des regionalen Klimas, den Unsicherheiten auf Messdaten- und GCM-Ebene und den statistischen Fähigkeiten der Regressionsmodelle unter Verwendung von Beobachtungsdaten, die durch den zusätzlichen Einbezug von Observationen aus der Hochgebirgsregion (engl. high altitude, HA) deutlich für die Anwendung verbessert wurden. Basierend auf der beobachteten saisonalen Niederschlagsvariabilität wurde zunächst eine K-Means-Clusteranalyse zur Ermittlung von homogenen Niederschlagsregionen durchgeführt. Der Clustering-Prozess zeigte dabei auch das Potential auf, inwieweit anhand von relativ niedrig gelegenen Messstationen die HA-Niederschlagsdynamik erklärt werden kann. Die Vermeidung von den erst kürzlich installierten HA-Stationen beim Downscaling ist von Vorteil, um, mit kürzeren Datenzeitreihen und Niederschlagsmessungen verbundene, zeitliche und quantitative Bias zu umgehen. Durch die Anwendung eines robusten Kreuzvalidierungsverfahrens wurden die atmosphärischen Einflussfaktoren auf den subregionalen Niederschlag (Temperatur) anhand des ERA-Interim-Reanalysedatensatzes ermittelt. Die identifizierten ERA-Prädiktoren wurden separat mit zwei weiteren Reanalysedatensätzen (ERA5 und NCEP-NCAR-II) verglichen, um ihre Eignung für die regionale Analyse basierend auf der Quantifizierung der Referenzunsicherheiten zu bewerten. Solche Vergleiche der Prädiktoren mit den historischen Simulationen der GCMs halfen bei der Beurteilung der Modelle (Modellunsicherheit) entsprechend ihrer Fähigkeit, Niederschlags- (Temperatur-) Prädiktoren zu simulieren. GCM-Prädiktoren für zwei repräsentative Konzentrationspfadszenarien (RCP4.5 und RCP8.5) wurden in den jeweiligen Regressionsmodellen verwendet, um die künftigen Niederschläge und Temperaturen im Einzugsgebiet abzuschätzen. Die Ergebnisse zeigen, dass im Vergleich zur Referenz (1976-2005) deutlich nassere und wärmere Bedingungen die Hydrologie des Einzugsgebiets im 21. Jahrhundert dominieren werden. Diese Multimodell-Signale (Median) verstärken sich unter dem RCP8.5-Szenario und gegen Ende des Jahrhunderts (2071-2100) unter dem RCP8.5-Szenario. Das Signal-Rausch-Verhältnis und die besser abschneidenden GCMs bestätigten zusätzlich diese Ergebnisse. In der Karakorum-Region würde saisonal betrachtet der Winter (DJFM) in Zukunft die stärksten Niederschläge und die (von Tmin dominierte) stärkste Erwärmung erfahren. Für den Vormonsun (AMJ) wurden dagegen deutlich geringere Niederschläge und eine starke Erwärmung (dominiert von Tmax) projiziert. Die künftige Monsun-(JAS)Saison zeigte erhöhte Niederschläge und eine moderate Erwärmung der UIB-Region. Die untere Indus-Region erwärmte sich stärker im Vergleich zur UIB in allen Jahreszeiten. Diese projizierten klimatischen Veränderungen würden einen enormen Einfluss auf die Hydrologie des Einzugsgebiets haben. So könnte sich beispielsweise der Anteil des flüssigen Niederschlags aufgrund steigender nächtlicher Temperaturen im Winter deutlich erhöhen. Abnehmende Frühjahrsniederschläge könnten bis zu einem gewissen Grad durch einen zunehmenden Schmelzwasseranteil (Gletscherschmelze und zuvor akkumulierter Schnee) und günstige klimatische Veränderungen über den östlich gelegenen Nebenflüssen kompensiert werden. Erhöhte Winterniederschläge führen möglicherweise zu einem Anstieg des Basisabflusses, was bei der projizierten Verstärkung des Monsuns zu schweren Überschwemmungen führen könnte. Erhöhte Minimumtemperaturen könnten auch Lawinenabgänge und die Umverteilung von Eismassen begünstigen und somit auch Wasserressourcen, Infrastruktur und die ansässige Bevölkerung beeinflussen. Die starke Erwärmung über dem unteren Indus könnte zu einem früheren Beginn der landwirtschaftlichen Saison und einem enormen Anstieg des zukünftigen Wasserbedarfs führen. Solche meteorologischen Veränderungen über der UIB könnten die künftige Wasserverfügbarkeit erheblich steigern und dazu beitragen, den Wasserbedarf im Untersuchungsgebiet zu decken. Im Gegensatz zu einigen Downscaling-Studien zeigen die Ergebnisse der vorliegenden Arbeit, dass eine erhöhte Wasserversorgung auch ohne einen raschen Gletscherrückgang möglich sein kann, insbesondere in der Karakorum-Region. Daher wäre eine Fortsetzung der Karakorum-Anomalie bis zum Ende des 21. Jahrhunderts möglich, selbst unter dem RCP8.5-Szenario. Eine erhöhte Albedo, der Einfluss von Aerosolen und Wolkenstrahlungsrückkopplungen in Verbindung mit stärkeren und weiter nach Norden vordringenden westlichen (Monsun-)Zirkulationen führen gegebenenfalls zu einer zukünftigen Gletscherstabilität. Die Annahme einheitlicher Schmelzraten ohne Berücksichtigung der räumlichen Heterogenität innerhalb der UIB und die unsachgemäße Darstellung der internen Rückkopplungen zwischen Monsun und Topographie in den GCMs können für zu warme Bias und damit einhergehend für zu hoch geschätzte Gletscherrückgänge in bisher veröffentlichten Biaskorrektur-basierten (Downscaling) Studien verantwortlich sein. Eine in dieser Arbeit durchgeführte Prädiktor-Prädiktand-Modellierung zur Abschätzung der künftigen Klimareaktion und der damit verbundenen Robustheit auf feineren Skalen wurde in der Region noch nie durchgeführt, was eine alternative Perspektive für Simulationen darstellt. Die hier entwickelte statistische Methodik könnte die künftige Klimaforschung in der Region und in anderen komplexen und von eingeschränkter Datenverfügbarkeit betroffenen Umgebungen positiv beeinflussen, um regionale Anpassungen an den zu erwartenden Klimawandel zu unterstützen

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22 degrees resolution over the CORDEX Central Asia domain

To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22 degrees (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980-2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temper-ature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified

Master of Science

thesisDue to their high sensitivity to changes in climate, alpine glaciers are one of the best natural indicators of climate change. Despite this, some of the underlying processes that control glacier response to climate change are not well understood. One potentially important set of such processes are feedback mechanisms that amplify and dampen melt. Though these feedbacks are widely recognized as important processes affecting glacier mass balances, little has been done to quantify their effects in a systematic way. This study develops a fully distributed surface energy and mass balance model to quantify the contributions of three precipitation-induced melt feedbacks to glacier mass balance. Specifically, we focus on feedbacks associated with sensible heat of liquid rain, snowpack thickness, and frequency of snowfall events. The model follows well-known energy balance methods, but includes "switches" that allow individual feedbacks to be turned off. The model utilizes an idealized glacier and meteorological inputs from the High Asia Refined analysis for two different climate regions in High Mountain Asia (HMA). The results show that melt feedbacks can nearly triple melt due to a +1°C temperature forcing scenario. System gains are highest near the equilibrium line altitude (ELA). Furthermore, system gains due to melt feedbacks depend most on the frequency of snowfall events that occur concurrently with the melt season. These results highlight the potential significance of melt feedbacks on glacier mass balance, how this may vary across differing climatic regions, and the need to further explore feedbacks associated with other glacier surface processes

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

[EN] The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m-1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Ben Dor, E.; Szabó, B.... (2020). An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water. 12(5):1-36. https://doi.org/10.3390/w12051495S13612

Climatologies at high resolution for the earth's land surface areas

High resolution information of climatic conditions is essential to many application in environmental sciences. Here we present the CHELSA algorithm to downscale temperature and precipitation estimates from the European Centre for Medium-Range Weather Forecast (ECMWF) climatic reanalysis interim (ERA-Interim) to a high resolution of 30 arc sec. The algorithm for temperature is based on a statistical downscaling of atmospheric temperature from the ERA-Interim climatic reanalysis. The precipitation algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, and a bias correction using Global Precipitation Climatology Center (GPCC) gridded and Global Historical Climate Network (GHCN) station data. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We present a comparison of data derived from the CHELSA algorithm with two other high resolution gridded products with overlapping temporal resolution (Tropical Rain Measuring Mission (TRMM) for precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) for temperature) and station data from the Global Historical Climate Network (GHCN). We show that the climatological data from CHELSA has a similar accuracy to other products for temperature, but that the predictions of orographic precipitation patterns are both better and at a high spatial resolution

Evaluation of Spatial and Temporal Performances of ERA-Interim Precipitation and Temperature in Mainland China

ERA-Interim has been widely considered as a valid proxy for observations at global and regional scales. However, the verifications of ERA-Interim precipitation and temperature in mainland China have been rarely conducted, especially in the spatial and long-term performances. Therefore, in this study, we employed the interpolated ground station (STA) data to evaluate the spatial and temporal patterns and trends of ERA-Interim precipitation and temperature during 1980-2012. The results showed that relatively weaker performances were observed in ERA-Interim precipitation, with the skill score (S index) ranging from 0.41 to 0.50. Interannual ERA-Interim precipitation presented comparable trends with STA precipitation at the annual and seasonal scales. Spatial patterns of empirical orthogonal function (EOF) modes and corresponding principal components were evidently different between annual ERA-Interim and STA precipitation. For temperature, annual and seasonal patterns of ERA-Interim data were in good consistency with those of STA over China with the S index ranging from 0.59 to 0.70. Yet interannual STA temperature recorded stronger warming trends (from 0.37K decade(-1) of wintertime to 0.53 Kdecade(-1) of springtime) at the annual and seasonal scales compared to corresponding periods for ERA-Interim temperature (from 0.03Kdecade 21 of wintertime to 0.25Kdecade(-1) of summertime). Overall, ERA-Interim precipitation and temperature had good agreement with STA data in east China with lower elevation (&lt; 1000m above sea level), but good agreements were not observed in west China with higher elevation. The findings suggest that caution should be paid when using ERA-Interim precipitation and temperature in areas with complex orography