@article{MohrMangaWald2018,
  author    = {Mohr, Christian Heinrich and Manga, Michael and Wald, David},
  title     = {Stronger peak ground motion, beyond the threshold to initiate a response, does not lead to larger stream discharge responses to earthquakes},
  series = {Geophysical research letters},
  volume    = {45},
  journal   = {Geophysical research letters},
  number    = {13},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0094-8276},
  doi       = {10.1029/2018GL078621},
  pages     = {6523 -- 6531},
  year      = {2018},
  abstract  = {The impressive number of stream gauges in Chile, combined with a suite of past and recent large earthquakes, makes Chile a unique natural laboratory to study several streams that recorded responses to multiple seismic events. We document changes in discharge in eight streams in Chile following two or more large earthquakes. In all cases, discharge increases. Changes in discharge occur for peak ground velocities greater than about 7-11cm/s. Above that threshold, the magnitude of both the increase in discharge and the total excess water do not increase with increasing peak ground velocities. While these observations are consistent with previous work in California, they conflict with lab experiments that show that the magnitude of permeability changes increases with increasing amplitude of ground motion. Instead, our study suggests that streamflow responses are binary. Plain Language Summary Earthquakes deform and shake the surface and the ground below. These changes may affect groundwater flows by increasing the permeability along newly formed cracks and/or clearing clogged pores. As a result, groundwater flow may substantially increase after earthquakes and remain elevated for several months. Here we document streamflow anomalies following multiple high magnitude earthquakes in multiple streams in one of the most earthquake prone regions worldwide, Chile. We take advantage of the dense monitoring network in Chile that recorded streamflow since the 1940s. We show that once a critical ground motion is exceeded, streamflow responses to earthquakes can be expected.},
  language  = {en}
}
@misc{AgarwalMarwanMaheswaranetal.2020,
  author    = {Agarwal, Ankit and Marwan, Norbert and Maheswaran, Rathinasamy and {\"O}zt{\"u}rk, Ugur and Kurths, J{\"u}rgen and Merz, Bruno},
  title     = {Optimal design of hydrometric station networks based on complex network analysis},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {951},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47100},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-471006},
  pages     = {19},
  year      = {2020},
  abstract  = {Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure - the weighted degree-betweenness (WDB) measure - to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.},
  language  = {en}
}
@article{AgarwalMarwanMaheswaranetal.2020,
  author    = {Agarwal, Ankit and Marwan, Norbert and Maheswaran, Rathinasamy and {\"O}zt{\"u}rk, Ugur and Kurths, J{\"u}rgen and Merz, Bruno},
  title     = {Optimal design of hydrometric station networks based on complex network analysis},
  series = {Hydrology and Earth System Sciences},
  volume    = {24},
  journal   = {Hydrology and Earth System Sciences},
  number    = {5},
  publisher = {Copernicus Publ.},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-24-2235-2020},
  pages     = {2235 -- 2251},
  year      = {2020},
  abstract  = {Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure - the weighted degree-betweenness (WDB) measure - to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.},
  language  = {en}
}
@misc{KormannBronstertFranckeetal.2017,
  author    = {Kormann, Christoph and Bronstert, Axel and Francke, Till and Recknagel, Thomas and Gr{\"a}ff, Thomas},
  title     = {Model-Based attribution of high-resolution streamflow trends in two alpine basins of Western Austria},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-400641},
  pages     = {21},
  year      = {2017},
  abstract  = {Several trend studies have shown that hydrological conditions are changing considerably in the Alpine region. However, the reasons for these changes are only partially understood and trend analyses alone are not able to shed much light. Hydrological modelling is one possible way to identify the trend drivers, i.e., to attribute the detected streamflow trends, given that the model captures all important processes causing the trends. We modelled the hydrological conditions for two alpine catchments in western Austria (a large, mostly lower-altitude catchment with wide valley plains and a nested high-altitude, glaciated headwater catchment) with the distributed, physically-oriented WaSiM-ETH model, which includes a dynamical glacier module. The model was calibrated in a transient mode, i.e., not only on several standard goodness measures and glacier extents, but also in such a way that the simulated streamflow trends fit with the observed ones during the investigation period 1980 to 2007. With this approach, it was possible to separate streamflow components, identify the trends of flow components, and study their relation to trends in atmospheric variables. In addition to trends in annual averages, highly resolved trends for each Julian day were derived, since they proved powerful in an earlier, data-based attribution study. We were able to show that annual and highly resolved trends can be modelled sufficiently well. The results provide a holistic, year-round picture of the drivers of alpine streamflow changes: Higher-altitude catchments are strongly affected by earlier firn melt and snowmelt in spring and increased ice melt throughout the ablation season. Changes in lower-altitude areas are mostly caused by earlier and lower snowmelt volumes. All highly resolved trends in streamflow and its components show an explicit similarity to the local temperature trends. Finally, results indicate that evapotranspiration has been increasing in the lower altitudes during the study period.},
  language  = {en}
}
@article{KormannFranckeBronstert2015,
  author    = {Kormann, Christoph and Francke, Till and Bronstert, Axel},
  title     = {Detection of regional climate change effects on alpine hydrology by daily resolution trend analysis in Tyrol, Austria},
  series = {Journal of water and climate change},
  volume    = {6},
  journal   = {Journal of water and climate change},
  number    = {1},
  publisher = {IWA Publ.},
  address   = {London},
  issn      = {2040-2244},
  doi       = {10.2166/wcc.2014.099},
  pages     = {124 -- 143},
  year      = {2015},
  abstract  = {Owing to average temperature increases of at least twice the global mean, climate change is expected to have strong impacts on local hydrology and climatology in the Alps. Nevertheless, trend analyses of hydro-climatic station data rarely reveal clear patterns concerning climate change signals except in temperature observations. However, trend research has thus far mostly been based on analysing trends of averaged data such as yearly, seasonal or monthly averages and has therefore often not been able to detect the finer temporal dynamics. For this reason, we derived 30-day moving average trends, providing a daily resolution of the timing and magnitude of trends within the seasons. Results are validated by including different time periods. We studied daily observations of mean temperature, liquid and solid precipitation, snow height and runoff in the relatively dry central Alpine region in Tyrol, Austria. Our results indicate that the vast majority of changes are observed throughout spring to early summer, most likely triggered by the strong temperature increase during this season. Temperature, streamflow and snow trends have clearly amplified during recent decades. The overall results are consistent over the entire investigation area and different time periods.},
  language  = {en}
}