about
sameAs
Distributed optical fibre sensing for early detection of shallow landslides triggering.Water balance complexities in ephemeral catchments with different land uses: Insights from monitoring and distributed hydrologic modelingHigh density distributed strain sensing of landslide in large scale physical modelIdentification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filterDesign and performance of a nozzle-type rainfall simulator for landslide triggering experimentsAn overview of current applications, challenges, and future trends in distributed process-based models in hydrologyRainfall-triggered shallow landslides: infiltration dynamics in a physical hillslope modelAssessment of hydraulic conductivity distributions through assimilation of travel time data from ERT-monitored tracer testsCalibration of Water Content Reflectometer Sensors with a Large Soil SampleCatchment-scale Richards equation-based modeling of evapotranspiration via boundary condition switching and root water uptake schemesCoupled and uncoupled hydrogeophysical inversions using ensemble Kalman filter assimilation of ERT-monitored tracer test dataA field and modeling study of nonlinear storage-discharge dynamics for an Alpine headwater catchmentClosure to “Optimal Design of Horizontally Framed Miter Gates” by Matteo CamporeseSimplified modeling of catchment-scale evapotranspiration via boundary condition switchingEnsemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilationOptimal Design of Horizontally Framed Miter GatesEnsemble Kalman filter versus particle filter for a physically-based coupled surface–subsurface modelAssessment of local hydraulic properties from electrical resistivity tomography monitoring of a three-dimensional synthetic tracer test experimentSurface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation dataComparison of Data Assimilation Techniques for a Coupled Model of Surface and Subsurface FlowEnsemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow
P50
Q44986956-ECEF71BC-4525-4ECD-B0D8-9E59E7856937Q53959189-98814122-56EE-44C8-A9A8-BBAE07A53397Q53959985-D26B36C3-0D4A-4458-A2DD-1EDD9878BE1FQ53959991-7E34459E-73EB-48E7-835A-73B6D9E8BE6EQ53959995-0FB13EC7-AC24-440B-992D-50400BD58075Q53959999-6711F40C-79C5-4093-B905-0CC044279EE2Q53960011-C4926A77-DEB9-49D5-9728-58E736E5C633Q53960016-41204A65-7A3C-401B-84E5-6B0F27FF156BQ53960021-319AC22A-1DEE-4F1B-9931-DFD02CE30889Q53960028-8864FE9E-5881-448F-9037-69BFE81964F9Q53960034-249A1206-4B73-45C5-8DCF-0D8DB6F8CD49Q53960040-CF4BF792-DC4A-4F25-A1F8-502731444B8AQ53960049-100BC213-66CF-4431-8719-4823C769D256Q53960053-8049070A-458B-46E8-B7F3-09BE1451D1FBQ53960058-E6C6B524-DA4E-41D9-B315-62FDA8C43C51Q53960064-15F6704C-A9EF-48B0-AE1F-35223D67A145Q53960068-0BB4A75A-8279-4541-A601-A114965D1813Q53960074-596EFEF0-C301-4E43-AE7C-0756248B025CQ58330188-6D960FEA-D05E-4CAC-832E-A58A3634E4CFQ58330191-B2B8D3F9-A20C-4C39-BAEC-DA0878F7CF6DQ58330193-0C46A949-0485-48A4-A003-50AEDD891FA4
P50
description
scientist
@en
wetenschapper
@nl
name
Matteo Camporese
@ast
Matteo Camporese
@en
Matteo Camporese
@es
Matteo Camporese
@nl
Matteo Camporese
@sl
type
label
Matteo Camporese
@ast
Matteo Camporese
@en
Matteo Camporese
@es
Matteo Camporese
@nl
Matteo Camporese
@sl
altLabel
M. Camporese
@en
prefLabel
Matteo Camporese
@ast
Matteo Camporese
@en
Matteo Camporese
@es
Matteo Camporese
@nl
Matteo Camporese
@sl
P214
P106
P1153
14048038900
P214
P31
P396
IT\ICCU\CFIV\274239
P496
0000-0002-7505-798X
P569
1977-01-01T00:00:00Z
P734
P735
P7859
viaf-307344793