https://research-explorer.ista.ac.at 2000-01-01T00:00+00:00 1 weekly https://research-explorer.ista.ac.at/record/22543 Peleg, Nadav Fatichi, Simone Paschalis, Athanasios Molnar, Peter Burlando, Paolo 2017 A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications. https://research-explorer.ista.ac.at/record/22543 eng American Geophysical Union info:eu-repo/semantics/altIdentifier/doi/10.1002/2016ms000854 info:eu-repo/semantics/altIdentifier/e-issn/1942-2466 info:eu-repo/semantics/openAccess Peleg N, Fatichi S, Paschalis A, Molnar P, Burlando P. An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables. <i>Journal of Advances in Modeling Earth Systems</i>. 2017;9(3):1595-1627. doi:<a href="https://doi.org/10.1002/2016ms000854">10.1002/2016ms000854</a> ddc:550 An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_2df8fbb1 https://research-explorer.ista.ac.at/record/22551 Peleg, Nadav Molnar, Peter Burlando, Paolo Fatichi, Simone 2019 Exploring the effects of climate change on the hydrological response at the local scale requires climate data at high spatial and temporal resolutions. This is best achieved by generating downscaled ensembles of future climate variables derived from climate models. For this purpose we present a methodology to re-parameterize the AWE-GEN-2d model (Advanced WEather GENerator for a two-dimensional grid). The model simulates key meteorological variables needed by hydrological models and is particularly suitable to explore the effects of stochastic (natural) climatic uncertainty, which is fundamental for hydrological applications, especially at sub-kilometer and hourly scales. Factors of change for different climate statistics are calculated from climate model simulations of present and future climates and subsequently applied to the statistics derived from observations to re-parameterize AWE-GEN-2d. The model abilities in generating an ensemble of future climate variables for the transient period 2020–2089 is presented with examples of precipitation and near-surface air temperature fields from hourly to multi-annual scales for a small mountainous region in the Swiss Alps. The stochastic uncertainty is examined for present and future periods and for spatial scales from the RCM scale (12-km, daily) to 2-km demonstrating the potential use of AWE-GEN-2d outputs. At the RCM scale, model results yield a small increase in annual precipitation (4%) which is within the stochastic uncertainty range for present and future periods (7%). At the fine scale of 2-km, the increase in annual precipitation can exceed the stochastic uncertainty, but for less than 10% of the domain area. On the contrary, changes in annual near-surface air temperature exceed stochastic uncertainty both at the RCM and finer scales. Stochastic climate uncertainty was concluded to be very similar when comparing present and future periods and 12-km and 2-km scales. The benefits of using AWE-GEN-2d in hydrological climate change impact assessments are finally discussed. https://research-explorer.ista.ac.at/record/22551 eng Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jhydrol.2019.02.010 info:eu-repo/semantics/altIdentifier/issn/0022-1694 info:eu-repo/semantics/altIdentifier/e-issn/1879-2707 info:eu-repo/semantics/closedAccess Peleg N, Molnar P, Burlando P, Fatichi S. Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator. <i>Journal of Hydrology</i>. 2019;571:627-641. doi:<a href="https://doi.org/10.1016/j.jhydrol.2019.02.010">10.1016/j.jhydrol.2019.02.010</a> Weather generator Stochastic downscaling Climate change Internal climate variability Climate uncertainty High-resolution rainfall model Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_2df8fbb1 https://research-explorer.ista.ac.at/record/9402 Schmid, Laura ; https://orcid.org/0000-0002-6978-7329 Chatterjee, Krishnendu ; https://orcid.org/0000-0002-4561-241X Hilbe, Christian ; https://orcid.org/0000-0001-5116-955X Nowak, Martin A. 2021 Direct and indirect reciprocity are key mechanisms for the evolution of cooperation. Direct reciprocity means that individuals use their own experience to decide whether to cooperate with another person. Indirect reciprocity means that they also consider the experiences of others. Although these two mechanisms are intertwined, they are typically studied in isolation. Here, we introduce a mathematical framework that allows us to explore both kinds of reciprocity simultaneously. We show that the well-known ‘generous tit-for-tat’ strategy of direct reciprocity has a natural analogue in indirect reciprocity, which we call ‘generous scoring’. Using an equilibrium analysis, we characterize under which conditions either of the two strategies can maintain cooperation. With simulations, we additionally explore which kind of reciprocity evolves when members of a population engage in social learning to adapt to their environment. Our results draw unexpected connections between direct and indirect reciprocity while highlighting important differences regarding their evolvability. https://research-explorer.ista.ac.at/record/9402 https://research-explorer.ista.ac.at/download/9402/14496 eng Springer Nature info:eu-repo/semantics/altIdentifier/doi/10.1038/s41562-021-01114-8 info:eu-repo/semantics/altIdentifier/e-issn/2397-3374 info:eu-repo/semantics/altIdentifier/wos/000650304000002 info:eu-repo/semantics/altIdentifier/pmid/33986519 info:eu-repo/semantics/openAccess Schmid L, Chatterjee K, Hilbe C, Nowak MA. A unified framework of direct and indirect reciprocity. <i>Nature Human Behaviour</i>. 2021;5(10):1292–1302. doi:<a href="https://doi.org/10.1038/s41562-021-01114-8">10.1038/s41562-021-01114-8</a> ddc:000 A unified framework of direct and indirect reciprocity info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_2df8fbb1 https://research-explorer.ista.ac.at/record/22438 Lian, Taiqi Fatichi, Simone Stähli, Manfred Bonetti, Sara 2025 The topography of a landscape regulates the spatial distribution of water and energy fluxes, which are main drivers of vegetation and soil carbon and nutrient dynamics. Despite the recognized role of topography in mediating such processes, quantifying and predicting the spatial distribution of carbon and nutrient fluxes and stocks in highly heterogeneous landscapes remains challenging. The main limitations stem from the prevalence of largely decoupled modeling approaches which fail to concurrently account for ecohydrological and biogeochemical processes as well as the lack of adequate frameworks describing the links among topography, water and energy balances, and soil biogeochemical dynamics. Here, we extend the capabilities of the mechanistic ecohydrological model Tethys-Chloris-Biogeochemistry (T&C-BG) by including a soil carbon and nutrient routing module in the distributed model version. The newly developed T&C-BG-2D model is validated against long-term hydrological and biogeochemical measurements from the Hafren catchment in Wales (UK) and the Erlenbach catchment in the Swiss pre-Alps. The model successfully captures carbon and nutrient concentrations and dynamics in these catchments, with relative differences between simulated and observed median values of between −4% and −0.3% for dissolved organic carbon, and between 1% and 20% for ammonia. A sensitivity analysis in the Erlenbach basin suggests that elevation explains over 80% of the observed spatial patterns, followed by topographic wetness index (12.6%), aspect (2.9%), and curvature (2.1%). These findings underscore topography's critical role in shaping water, carbon, and nutrient dynamics, which cannot be reflected in plot-scale simulations neglecting spatial interactions and topographic effects. https://research-explorer.ista.ac.at/record/22438 eng American Geophysical Union info:eu-repo/semantics/altIdentifier/doi/10.1029/2025wr040260 info:eu-repo/semantics/altIdentifier/issn/0043-1397 info:eu-repo/semantics/altIdentifier/e-issn/1944-7973 info:eu-repo/semantics/openAccess Lian T, Fatichi S, Stähli M, Bonetti S. Assessing spatial patterns of carbon and nutrient dynamics in catchments of complex topography. <i>Water Resources Research</i>. 2025;61(10). doi:<a href="https://doi.org/10.1029/2025wr040260">10.1029/2025wr040260</a> ddc:550 Assessing spatial patterns of carbon and nutrient dynamics in catchments of complex topography info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_2df8fbb1