@@ -4762,3 +4762,228 @@ @incollection{ecwmf_physical_2016
47624762 https://www.ecmwf.int/sites/default/files/elibrary/2016/17117-part-iv-physical-processes.pdf
47634763 }
47644764}
4765+ @article {alonso_gonzalez_2022 ,
4766+ title = {
4767+ Combined influence of maximum accumulation and melt rates on the duration
4768+ of the seasonal snowpack over temperate mountains
4769+ } ,
4770+ journal = { Journal of Hydrology}
4771+ volume = { 608}
4772+ pages = { 127574}
4773+ year = { 2022}
4774+ issn = { 0022-1694}
4775+ doi = { https://doi.org/10.1016/j.jhydrol.2022.127574}
4776+ url = { https://www.sciencedirect.com/science/article/pii/S0022169422001494}
4777+ author = {
4778+ Esteban Alonso-Gonz\'{a}lez and Jes\'{u}s Revuelto and Steven R. Fassnacht
4779+ and Juan {Ignacio L\'{o}pez-Moreno}
4780+ } ,
4781+ abstract = {
4782+ The duration of the seasonal snowpack determines numerous aspects of the
4783+ water cycle, ecology and the economy in cold and mountainous regions, and
4784+ is a balance between the magnitude of accumulated snow and the rate of
4785+ melt. The contribution of each component has not been well quantified under
4786+ contrasting topography and climatological conditions although this may
4787+ provide useful insights into how snow cover duration could respond to
4788+ climate change. Here, we examined the contribution of the annual peak snow
4789+ water equivalent (SWE) and the seasonal melt rate to define the duration of
4790+ the snowpack over temperate mountains, using snow data for mountain areas
4791+ with different climatological characteristics across the Iberian Peninsula.
4792+ We used a daily snowpack database for the period 1980--2014 over Iberia to
4793+ derive the seasonal peak SWE, melt rate and season snow cover duration. The
4794+ influence of peak SWE and melt rates on seasonal snow cover duration was
4795+ estimated using a stepwise linear model approach. The stepwise linear
4796+ models showed high R-adjusted values (average R-adjusted = 0.7), without
4797+ any clear dependence on the elevation or geographical location. In general,
4798+ the peak SWE influenced the snow cover duration over all of the mountain
4799+ areas analysed to a greater extent than the melt rates (89.1\%, 89.2\%,
4800+ 81.6\%, 93.2\% and 95.5\% in the areas for the Cantabrian, Central,
4801+ Iberian, Pyrenees and Sierra Nevada mountain ranges, respectively). At
4802+ these colder sites, the melt season occurs mostly in the spring and tends
4803+ to occur very fast. In contrast, the areas where the melt rates dominated
4804+ snow cover duration were located systematically at lower elevations, due to
4805+ the high interannual variability in the occurrence of annual peak SWE (in
4806+ winter or early spring), yielding highly variable melt rates. However, in
4807+ colder sites the melt season occurs mostly in spring and it is very fast in
4808+ most of the years. The results highlight the control that the seasonal
4809+ precipitation patterns, in combination with temperature, exert on the
4810+ seasonal snow cover duration by influencing the peak SWE and suggest a
4811+ future increased importance of melt rates as temperatures increase. Despite
4812+ the high climatological variability of the Iberian mountain ranges, the
4813+ results showed a consistent behaviour along the different mountain ranges,
4814+ indicating that the methods and results may be transferrable to other
4815+ temperate mountain areas of the world.
4816+ }
4817+ }
4818+ @article {sauquet_2025 ,
4819+ author = {
4820+ Sauquet, E. and Evin, G. and Siauve, S. and Aissat, R. and Arnaud, P. and
4821+ B\'erel, M. and Bonneau, J. and Branger, F. and Caballero, Y. and
4822+ Coll\'eoni, F. and Ducharne, A. and Gailhard, J. and Habets, F. and
4823+ Hendrickx, F. and H\'eraut, L. and Hingray, B. and Huang, P. and Jaouen, T.
4824+ and Jeantet, A. and Lanini, S. and Le Lay, M. and Magand, C. and Mimeau, L.
4825+ and Monteil, C. and Munier, S. and Perrin, C. and Robelin, O. and Rousset,
4826+ F. and Soubeyroux, J.-M. and Strohmenger, L. and Thirel, G. and Tocquer, F.
4827+ and Tramblay, Y. and Vergnes, J.-P. and Vidal, J.-P.
4828+ } ,
4829+ title = {
4830+ A large transient multi-scenario multi-model ensemble of future streamflow
4831+ and groundwater projections in France
4832+ } ,
4833+ journal = { EGUsphere}
4834+ volume = { 2025}
4835+ year = { 2025}
4836+ pages = { 1--41}
4837+ url = { https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1788/}
4838+ doi = { 10.5194/egusphere-2025-1788}
4839+ }
4840+ @article {burn_2010 ,
4841+ author = { Burn, Donald H. and Sharif, Mohammed and Zhang, Kan}
4842+ title = { Detection of trends in hydrological extremes for Canadian watersheds}
4843+ journal = { Hydrological Processes}
4844+ volume = { 24}
4845+ number = { 13}
4846+ pages = { 1781--1790}
4847+ keywords = { flood analysis, low flow events, climate change, trend analysis, Canada}
4848+ doi = { https://doi.org/10.1002/hyp.7625}
4849+ url = { https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.7625}
4850+ eprint = { https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.7625}
4851+ abstract = {
4852+ Abstract The potential impacts of climate change can alter the risk to
4853+ critical infrastructure resulting from changes to the frequency and
4854+ magnitude of extreme events. As well, the natural environment is affected
4855+ by the hydrologic regime, and changes in high flows or low flows can have
4856+ negative impacts on ecosystems. This article examines the detection of
4857+ trends in extreme hydrological events, both high and low flow events, for
4858+ streamflow gauging stations in Canada. The trend analysis involves the
4859+ application of the Mann–Kendall non-parametric test. A bootstrap resampling
4860+ process has been used to determine the field significance of the trend
4861+ results. A total of 68 gauging stations having a nominal record length of
4862+ at least 50 years are analysed for two analysis periods of 50 and 40 years.
4863+ The database of Canadian rivers investigated represents a diversity of
4864+ hydrological conditions encompassing different extreme flow generating
4865+ processes and reflects a national scale analysis of trends. The results
4866+ reveal more trends than would be expected to occur by chance for most of
4867+ the measures of extreme flow characteristics. Annual and spring maximum
4868+ flows show decreasing trends in flow magnitude and decreasing trends in
4869+ event timing (earlier events). Low flow magnitudes exhibit both decreasing
4870+ and increasing trends. Copyright \textcopyright{} 2010 John Wiley \& Sons,
4871+ Ltd.
4872+ } ,
4873+ year = { 2010}
4874+ }
4875+ @article {zomer_2022 ,
4876+ title = {
4877+ Version 3 of the global aridity index and potential evapotranspiration
4878+ database
4879+ } ,
4880+ author = { Zomer, Robert J and Xu, Jianchu and Trabucco, Antonio}
4881+ journal = { Scientific Data}
4882+ volume = { 9}
4883+ number = { 1}
4884+ pages = { 409}
4885+ year = { 2022}
4886+ publisher = { Nature Publishing Group UK London}
4887+ }
4888+ @article {knoben_2024 ,
4889+ title = {
4890+ Setting expectations for hydrologic model performance with an ensemble of
4891+ simple benchmarks
4892+ } ,
4893+ author = { Knoben, Wouter JM}
4894+ journal = { Hydrological Processes}
4895+ volume = { 38}
4896+ number = { 10}
4897+ pages = { e15288}
4898+ year = { 2024}
4899+ publisher = { Wiley Online Library}
4900+ }
4901+ @article {singh_2019 ,
4902+ title = { Towards baseflow index characterisation at national scale in New Zealand}
4903+ journal = { Journal of Hydrology}
4904+ volume = { 568}
4905+ pages = { 646--657}
4906+ year = { 2019}
4907+ issn = { 0022-1694}
4908+ doi = { https://doi.org/10.1016/j.jhydrol.2018.11.025}
4909+ url = { https://www.sciencedirect.com/science/article/pii/S0022169418308801}
4910+ author = {
4911+ Shailesh Kumar Singh and Markus Pahlow and Doug J. Booker and Ude Shankar
4912+ and Alejandro Chamorro
4913+ } ,
4914+ keywords = { Baseflow, Quickflow, Prediction, BFI, Random forests technique}
4915+ abstract = {
4916+ Streamflow is typically divided into two components for hydrograph
4917+ separation, quickflow and baseflow. Baseflow is the portion of streamflow
4918+ that contains groundwater flow and flow from other delayed sources and is
4919+ of key importance for river basin ecology and water resources planning and
4920+ management. The BaseFlow Index (BFI) is defined as the ratio of long-term
4921+ mean baseflow to total streamflow. Knowledge of the BFI is not directly
4922+ available for ungauged catchments and hence for most of the terrestrial
4923+ land surface. In this study, the BFI was determined for all river reaches
4924+ in New Zealand. First a recursive digital filtering technique was applied
4925+ to separate baseflow from total streamflow for 482 gauged sites across New
4926+ Zealand, whereby an individual filter parameter was determined for each
4927+ catchment. Based on the baseflow and total streamflow data the long-term
4928+ BFI for each gauged site was determined, as well as seasonal values of BFI.
4929+ BFI varies between 0.20 and 0.96 with an average of 0.53, which indicates
4930+ that 53\% of long-term streamflow in New Zealand is likely to originate
4931+ from groundwater discharge and other delayed sources. Long-term BFI values
4932+ for all river reaches that comprise the New Zealand river network were
4933+ predicted using the random forest technique. Furthermore, the winter to
4934+ summer BFI for all river reaches in New Zealand were also determined.
4935+ Distinct spatial patterns of the BFI were identified. While the spatial
4936+ distribution and the magnitude of the BFI was determined by a combination
4937+ of factors, certain patterns can be attributed to geological formations in
4938+ New Zealand, namely the volcanic plateau region and the Southern Alps.
4939+ While the dataset determined in this work can support work specifically
4940+ pertaining to water resources planning and management in New Zealand, in
4941+ particular water supply, stream ecology and pollution risk, the methodology
4942+ devised to calculate the BFI for gauged sites and to predict the BFI for
4943+ ungauged sites is applicable to any region around the world.
4944+ }
4945+ }
4946+ @article {jaffres_2021 ,
4947+ title = {
4948+ Hydrological characteristics of Australia: relationship between surface
4949+ flow, climate and intrinsic catchment properties
4950+ } ,
4951+ journal = { Journal of Hydrology}
4952+ volume = { 603}
4953+ pages = { 126911}
4954+ year = { 2021}
4955+ issn = { 0022-1694}
4956+ doi = { https://doi.org/10.1016/j.jhydrol.2021.126911}
4957+ url = { https://www.sciencedirect.com/science/article/pii/S0022169421009616}
4958+ author = {
4959+ Jasmine B.D. Jaffr\'{e}s and Ben Cuff and Chris Cuff and Iain Faichney and
4960+ Matthew Knott and Cecily Rasmussen
4961+ } ,
4962+ keywords = {
4963+ Climate variability, Non-perennial streams, Surface hydrology, Topography,
4964+ Soil field capacity, Water infiltration
4965+ } ,
4966+ abstract = {
4967+ Streamflow and baseflow dynamics are driven by complex, interconnected
4968+ catchment properties. A national study was conducted to assess the
4969+ relationship between surface flow, climate and intrinsic catchment
4970+ attributes in Australia. Subcatchments were delineated based on Horton's
4971+ 5th stream order and were characterised by identifying parameters that
4972+ influence streamflow and flood behaviour. Because observational datasets
4973+ like rainfall and streamflow commonly have a non-normal distribution, the
4974+ method of L-moments was applied to several time series. Surface hydrology
4975+ and baseflow patterns were represented by twenty indices, which were
4976+ statistically summarised via principal component (PC) analysis, yielding
4977+ six PCs. Forty catchment descriptors from the themes of climate,
4978+ topography, surface condition and hydrogeology were used to investigate
4979+ their link with runoff patterns. Among these is the land surface value, a
4980+ newly defined index incorporating soil properties and land use to estimate
4981+ the capacity for water infiltration. All metrics were explored via
4982+ correlation and regression analysis against the surface hydrology PCs and
4983+ their influence on runoff discussed. The predictive skill of the regression
4984+ models is improved when non-perennial waterways are excluded. Although
4985+ rainfall characteristics dominate streamflow behaviour, topographical and
4986+ surface conditions also greatly impact on runoff, especially during
4987+ low-flow periods.
4988+ }
4989+ }
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