| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | AA | AB | AC | AD | AE | AF | ||
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1 | Model | General | Technical Information | Further Information | |||||||||||||||||||||||||||||
2 | Sector(s) | Region | Model | Simulation round | Contact | Model version | Reference | Short model description | Resolution | Input data | Exceptions to protocol | Spin-up | Natural vegetation* | Management* | Anything else? | Extreme events | Additional comments | ||||||||||||||||
3 | scale | Name | Institution(s) | Contact person(s) | Email address(es) | Which single paper should be cited when referring to your model? | Additional papers | Spatial aggregation | Spatial resolution | Temporal resolution of input data | Climate variables | Socio-economic input variables | Climate data sets used | Soil dataset | Additional input data sets | Were any settings prescribed by the protocol overruled in order to run the model? | Did you spin-up your model, or did you start your simulations in 1971? | Describe the spin-up design | How are areas covered by different types of natural vegetation partitioned? Do your simulate your own (dynamic) natural vegetation? If you prescribe natural vegetation cover, which dataset do you use? | Natural vegetation dynamics | Natural vegetation dynamics | Which specific management and autonomous adaptation measures were applied? | Anything else necessary to reproduce and/or understand the simulation output | Which are the key challenges for this model in reproducing impacts of extreme events? | |||||||||
4 | global, regional (specify) | one per contact person | one per sector with name of sector in brackets | one per contact person | additional papers introducing new models, sector-specific updates etc. | e.g. regular grid, points, hydrotopes... | if regular grid | climate variables (daily data was provided) | CO2 (annual data was provided) | Land use/land cover (annual data was provided) | soil (time-constant data was provided) | include variables that you derived from those provided by ISI-MIP | HWSD or GSWP3 was provided | List here any data sets used to drive the model that were not provided by ISIMIP e.g. nitrogen deposition; please indicate source | If yes, please provide detail | Include the length of the spin up, the CO2 concentration used, and any deviations from the spin-up procedure defined in the protocol. | e.g. varying sowing dates in crop models, dbh-related harvesting in forest models | ||||||||||||||||
5 | Biomes, Permafrost, Water | global | LPJmL | PIK | Sebastian Ostberg [biomes], Dieter Gerten [water], Sibyll Schaphoff [permafrost] | ostberg@pik-potsdam.de [biomes], sibylls@pik-potsdam.de [permafrost], gerten@pik-potsdam.de [water] | Sitch et al. Global Change Biology 9 (2003) 161-185 | Bondeau et al. Global Change Biology 13 (2007) 679-706 [adds agriculture module], Schaphoff et al. Environ. Res. Lett. 8 (2013) 014026 [new hydrology (all sectors) and permafrost implementation] | regular grid | 0.5° | daily | annual | annual | constant | lwnet (derived from tas and rlds), pr, rsds, tas | USDA soil texture classification based on HWSD | spin-up for all simulations (biome, permafrost and water sector) | 5000 years of PNV spin-up, followed by 390 years of land-use spin-up, both recycling 120-year random climate sequence (taken from 1901-1930), spin-up leads into transient run 1901 - end of climate dataset; 278 ppm CO2 used before 1765, after 1765 CO2 from provided input file | dynamic vegetation distribution | crop sowing dates computed internally but fixed after 1960 in biome, permafrost and water sector runs | |||||||||||||
6 | Water | global | VIC | ISIMIP2a | PNNL | Tian Zhou | tizhou@uw.edu; tian.zhou@pnnl.gov | VIC.4.2.a.irrigation | Liang, Xu, et al. "A simple hydrologically based model of land surface water and energy fluxes for general circulation models." Journal of Geophysical Research: Atmospheres 99.D7 (1994): 14415-14428. | Haddeland, Ingjerd, Thomas Skaugen, and Dennis P. Lettenmaier. "Anthropogenic impacts on continental surface water fluxes." Geophysical Research Letters 33.8 (2006). Zhou, Tian, et al. "The Contribution of Reservoirs to Global Land Surface Water Storage Variations*." Journal of Hydrometeorology 17.1 (2016): 309-325. | regular grid | 0.5° | daily | constant (no effect on the simulation) | annual | constant | tasmax, tasmin, pr, wind | crop area and GranD dataset | FAO Digital Soil Map of the World | - | no | yes | 1901-1970 | Natural vegetation was fixed, provided by the Advanced Very High Resolution Radiometer–based, 1-km, global land classification. Irrigation fraction was dynamic, derived from the provided MIRCA dataset | Irrigation starts as additional precipitation during the growing season when soil moisture drops below the wilting point and stops until field capacity is reached. Dam opeartion is optimized based on reservoir functions such as flood control, irrigation, hydropower generation, and water supply | ||||||||
7 | Water | global | CLM | ISIMIP2a | PNNL | Maoyi Huang | maoyi.huang@pnnl.gov; guoyong.leng@pnnl.gov | CLM4 driven by satellite phenology with modifications documented in Leng et al.,2015 | Leng G, M Huang, Q Tang, and LR Leung. 2015. “A Modeling Study of Irrigation Effects on Global Surface Water and Groundwater Resources under a Changing Climate: Irrigation Effects on Water Resources.” Journal of Advances in Modeling Earth Systems. 7(3):1285-1304, DOI:10.1002/2015MS000437. | Oleson, K.W. et al. (2010), Technical description of version 4.0 of the Community Land Model [CLM], NCAR Technical Note NCAR/TN-478+STR, 257 pp. | regular grid, multiple soil columns co-existing in a grid cell and allow multiple plant functional types (PFTs) to exist in one soil column in which the dynamics for soil water, soil organic carbon, litter, etc. are represented [Lawrence et al., 2011] | 0.5° | subdaily: met forcing is temporarily interpolated to drive the model at hourly time steps using the algorithm described in Leng,G., and Q. Tang (2014), Modeling the impacts of future climate change on irrigation over China: sensitivity to adjusted projections, J. Hydrometeor., 15, 2085–2103. | constant (no effect on the simulation) | constant | constant | temperature, precipitation, humidity, pressure, wind, shortwave radiation, longwave radiation | aerosol deposition simulated by CCSM, key for snow albedo simulations | no, but we did not finish the LULCC runs | yes | start the spinup from an existing CLM initial condition provided by NCAR. Then the watch dataset was cycled for 200 years to stablized the state variables again. The final state was then used as the initial conditon for the historical simulation | based on satellite data provided by NCAR. Details can be found at Oleson et al. (2010) | irrigation. See Leng et al., 2015 for details | ||||||||||
8 | Water | global | DBH | Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences | Qiuhong Tang | tangqh@igsnrr.ac.cn | Tang, Q., Oki, T., Kanae, S., and Hu, H.: The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation, J. Hydrometeor, 8, 499-512, 10.1175/jhm589.1, 2007. | Tang, Q., Oki, T., and Kanae, S.: A distributed biosphere hydrological model (DBHM) for large river basin, Annual Journal of Hydraulic Engineering, 50, 37-42, 2006. | regular grid | 0.5° | daily | annual | annual | constant | tasmax, tasmin, tas, pr, ps, rhs, rsds, rlds, wind | crop area, reservoirs | FAO Digital Soil Map of the World | spin-up | 20 years (1951-1970) | SiB2 global land cover dataset | no | ||||||||||||
9 | Water | global | H08 | National Institute for Environmental Studies, Japan | Naota Hanasaki | hanasaki@nies.go.jp | H08 | [1] Hanasaki et al. (2008a), Hydrol. Earth Syst. Sci., 12, 1007--1025. [2] Hanasaki et al. (2008b), Hydrol. Earth Syst. Sci., 12, 1027--1037. | regular grid | 0.5° | daily | constant (no effect on the simulation) | annual | NA (H08 doesn't specify soil texture) | 8 elements (temperature, rainfall, snowfall, humidity, pressure, wind, shortwave radiation, longwave radiation) For WATCH and WFDEI forcings, precipitation was separated into rainfall and snowfall according to Yasutomi et al. (2011). | NA | spin-up | We started hydrological simulation since 1901, long enough for stabilizing initial conditions. | Planting date was determined to obtain maximum yield under meteorological conditions for 1960-1999. The planting date was fixed throughout the simulation period. Harvesting date was calculated in the model and changed with years according to meteorological conditions. | ||||||||||||||
10 | Water | global | JULES-TUC | Aristeidis Koutroulis | aris@hydromech.gr | version 4.3 | Best, M.J., Pryor, M., Clark, D.B., Rooney, G.G., Essery, R., Ménard, C.B., Edwards, J.M., Hendry, M.A., Porson, A., Gedney, N. and Mercado, L.M., 2011. The Joint UK Land Environment Simulator (JULES), model description–Part 1: energy and water fluxes. Geoscientific Model Development, 4(3), pp.677-699. | regular grid | 0.5 deg | 1 hour | annual | constant | constant | climate variables: precip, tas, rsds, rlds, huss, ps, wind, tas_range (tasmax-tasmin) | spinup | 10 spinup cycles (1969-1970), plus year 1970 | static vegetation (5 types) | No | |||||||||||||||
11 | Water | global | JULES-UOE | Catherine Morfopoulos | C.Morfopoulos@exeter.ac.uk | ||||||||||||||||||||||||||||
12 | Water | global | Mac-PDM.09 | Simon Gosling | simon.gosling@nottingham.ac.uk | ||||||||||||||||||||||||||||
13 | Water | global | MATSIRO | University of Tokyo, IIASA, Michigan state University | Yusuke Satoh | yu_sato@rainbow.iis.u-tokyo.ac.jp | HiGW-MAT | Pokhrel, Y. N., S. Koirala, P. J.-F. Yeh, N. Hanasaki, L. Longuevergne, S. Kanae, and T. Oki (2015), Incorporation of groundwater pumping in a global Land Surface Model with the representation of human impacts, Water Resour. Res., 51, 78–96, doi:10.1002/2014WR015602. | POKHREL et al. (2012) Incorporating Anthropogenic Water Regulation Modules into a Land Surface Model, Journal of Hydrometeorology, Volume 13, Issue 1 (February 2012) Kumiko Takata et al. (2003) Development of the minimal advanced treatments of surface interaction and runoff, Global and Planetary Change, Volume 38, Issues 1–2, July 2003, Pages 209–222 | regular grid | 0.5deg | 3hrly | constant | constant | constant | 8 parameters: Tmean, Rain, Snow, AH, long and short wave RAD, WS, Surface pressure (3hrly) | GSWP2 | spin-up | 1951-1970 | SiB2 global land cover dataset. fixed vegetation characteristics for 12 types of natural land cover | no | ||||||||||||
14 | Water | global | MPI-HM | Max Planck Institute for Meteorology | Tobias Stacke | tobias.stacke@mpimet.mpg.de | R44 | Stacke & Hagemann,Hydrol. Earth Syst. Sci., http://dx.doi.org/10.5194/hess-16-2915-2012 | regular lat/lon grid, sub-grid heterogeneity taken into account for some processes | 0.5deg | daily | not used | constant | constant | Only climatic forcing: Tair, total precip and potential ET, the latter computed from the forcing data using Penman-Montheith equation | LSP2 (Hagemann, 2002) + update based on data from Kleidon (2004) | GLWD for lakes and wetlands | none | spin-up | Simulations were started from the first time step provided by the forcing data. Thus, all storages are well initialialized at the time of the reporting period. | Prescibed natural vegetation climatology based on LSP2, only differentiation is between natural vegatation and irrigated crops | no | No, all remaining parameters are used in the default mode which are set in the run script | Probably missing impacts of reservoirs on the mitigation of extreme flow events. Extreme values are probably also affected by the missing soil layering (bucket only) as well as using a reference PET method | |||||||||
15 | Water | global | ORCHIDEE | LSCE | Jinfeng Chang | jiclod@locean-ipsl.upmc.fr | rev3013 | Traore, A. K., Ciais, P., Vuichard, N., Poulter, B., Viovy, N., Guimberteau, M., Jung, M., My30 neni, R., and Fisher, J. B.: Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite-based water and carbon measurements, J. Geophys. Res.- Biogeociences, 119, 1554–1575, doi:10.1002/2014JG002638, 2014b. | Guimberteau, M., A. Ducharne, P. Ciais, J. P. Boisier, S. Peng, M. De Weirdt, and H. Verbeeck (2014), Testing conceptual and physically based soil hydrology schemes against observations for the Amazon Basin, Geosci. Model Dev., 7, 1115–1136. | regular grid | 0.5deg | daily | annual | annual | constant | tasmax, tasmin, ps, huss, pr, rsds, rlds, wind | USDA soil texture classification based on HWSD | spin-up | We use the 1901-1910 climate condition, pre-industry CO2 (287.14 ppm), land cover map of 1860 to do the spin-up until the soil carbon to be equilibrium. Then a simulation from 1861 to 1900 was performed with varied CO2 and land-cover/land-use change, and climate of 1901-1910 cycled. The final transient simulation of 1901-2012 was forced by varied climate, CO2 and land-cover/land-use change. | Prescribe natural vegetation cover. The land cover map is derived from GLC2000, and classified according to Poulter et al., 2011. The land-cover change is derived from Hurtt et al., dataset. | no | ||||||||||||
16 | Water | global | PCR-GLOBWB | Utrecht University | Yoshihide Wada | Y.Wada@uu.nl | version 2 | Wada, Y., Wisser, D., and Bierkens, M. F. P.: Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources, Earth Syst. Dynam., 5, 15-40, doi:10.5194/esd-5-15-2014, 2014. | Wada, Y., Flörke, M., Hanasaki, N., Eisner, S., Fischer, G., Tramberend, S., Satoh, Y., van Vliet, M. T. H., Yillia, P., Ringler, C., Burek, P., and Wiberg, D.: Modeling global water use for the 21st century: the Water Futures and Solutions (WFaS) initiative and its approaches, Geosci. Model Dev., 9, 175-222, doi:10.5194/gmd-9-175-2016, 2016. | regular lat/lon grid with sub-grid variability for vegetation, land cover, etc | 0.5deg | daily | not used | annual | constant | precipitation, mean temperature, population, reservoirs, land use (irrigation, rainfed, etc) | FAO Digital Soil Map of the World, ISRIC-WISE (Batjes, 2006) | GLWD for lakes and wetlands combined with GRanD reservoir dataset | spin-up | 50 year spin-up before the model simulation starts at 1901 with climate forcing provided | Land use and land cover prescribed by HYDE dataset and MIRCA, GLOBCOVER (annually prescribed, not dynamic) | no | Discharge variability is relatively well reproduced (in relative sense of max-min) but absolute amounts of low flows in arid regions tend to be less well reproduced due to model-specific uncertainty. | ||||||||||
17 | Water | global | SiBUC | Kenji Tanaka | tanaka.kenji.6u@kyoto-u.ac.jp | ||||||||||||||||||||||||||||
18 | Water | global | SWBM | ETH Zurich | Sonia Seneviratne | sonia.seneviratne@ethz.ch | Orth, R., and S.I. Seneviratne, 2015: Introduction of a simple-model-based land surface dataset for Europe. Env. Res. Lett., 10, 044012, doi: 10.1088/1748-9326/10/4/044012 | regular grid | 0.5deg | daily | not used | not used | constant | precipitation, temperature, net radiation | none | none | spin-up | 5 years with climate data provided | The model performs well in capturing dry extremes but has difficulties with wet extremes. The reasons are not yet fully understood. | ||||||||||||||
19 | Water | global | WaterGAP2 | Institute of Physical Geography (IPG), Goethe-University Frankfurt Center of Environmental Systems Research (CESR), University of Kassel | Hannes Mueller Schmied (IPG), Martina Flörke (CESR) | hannes.mueller.schmied@em.uni-frankfurt.de, floerke@usf.uni-kassel.de | WaterGAP 2.2 (ISI-MIP2.1) | Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P.: Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2015-527, in review, 2016. | Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T., Flörke, M., and Döll, P.: Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration, Hydrol. Earth Syst. Sci., 18, 3511-3538, doi:10.5194/hess-18-3511-2014, 2014. | regular grid | 0.5° | daily time steps | Not represented in the model | Not used (used instead IGBP-classification based on MODIS land cover from the year 2004) | Not used (used instead WISE Available Water Capacity (Batjes, 1996)) | precipitation, air temperature, shortwave downward radiation, longwave downward radiation | WISE Available Water Capacity (Batjes, 1996) | GLWD for lakes and wetlands as well as GRanD for lakes and reservoirs | we have additionally uploaded pressocall and varsocall scenarios, where all water sectors (not only irrigation) are included | We spin-up our model. | Spin-up is in our case only to make sure that water storages behaves correctly with respect to the initialisation. We started the simulations in 1901 with 5 initial years. We have no CO2 concentration included in the model. | We only distinguishing IGBP classes and have a very simplified LAI development model for canopy evaporation. | No | No, details to the model and used input data are described in the reference papers. | Correct simulation of low flows and high flows, from a climatic perspective modelling in dry regions; (climate) input data uncertainty, process representation in the model(s) | ||||||||
20 | Water | global, regional | SWAP | Institute of Water Problems of the Russian Academy of Sciences | Olga Nasonova, Yeugeny Gusev | olniknas@yandex.ru, sowaso@yandex.ru | Gusev Ye.M., Nasonova O.N. Modelling heat and water exchange in the boreal spruce forest by the land-surface model SWAP. J. Hydrology. 2003. V. 280. № 1-4. P.162-191. | Gusev, E.M., Nasonova, O.N., Kovalev, E.E. Modeling the components of heat and water balance for the land surface of the globe, Water Resources. 2006. 33 (6), 616-627. Gusev Ye.M., Nasonova O.N. The simulation of heat and water exchange at the land-atmosphere interface for the boreal grassland by the land-surface model SWAP. Hydrological Processes. 2002. V. 16, No 10. P. 1893-1919. | Grid cells with sub-grid accounting for heterogeneity of hydraulic conductivity at saturation | Here, 0.5 deg | Here, daily time steps | - | time-constant | precipitation, air temperature, shortwave downward radiation, longwave downward radiation, air humidity, wind speed, air pressure | Soil parameters were derived (by Cosby et al., 1984) from Clay and Sand taken from ECOCLIMAP | Vegetation parameters were taken or derived from ECOCLIMAP | spin-up | We started the simulations from 1July 1969 and the first year was simulated 4 times for spin-up (according to our experience it is enough to reach equilibrium by our model). | We aggregated parameters taken from ECOCLIMAP for 0.5 deg grid cells | no | Time step should be finer to reproduce high flow more accurately. | ||||||||||||
21 | Water | regional | SWIM | Potsdam Institute for Climate Impact Research | Valentina Krysanova | krysanova@pik-potsdam.de | v. 7 | Krysanova, V., Mueller-Wohlfeil, D.I., Becker, A., 1998. Development and test of a spatially distributed hydrological / water quality model for mesoscale watersheds. Ecological Modelling, 106, 261-289. | Krysanova, F. Wechsung, J. Arnold, R. Srinivasan, J. Williams, 2000. PIK Report Nr. 69 "SWIM (Soil and Water Integrated Model), User Manual", 239p. mesoscale watersheds. Ecological Modelling, 106, 261-289. | Subbasins and hydrotopes | Hydrotopes within subbasins | daily time step | Precipitation, air temperature (min, max, average daily), solar radiation, air humidity; and data on water management | 11 soil parameters derived from HWSD | DEM, Land use map, soil map, subbasin map, river network, discharge data | starting in 1970 | Natural vegetation and crops are simulated using a simplified EPIC approach and the vegetation parameter database attached to the model (as in SWAT | Water management can be included if data are available | If the input data are of a good quality, SWIM is able to reproduce hydrological extreme events: floods and droughts. | ||||||||||||||
22 | Water | regional | VIC | Tao Yang | yang.tao@ms.xjb.ac.cn | ||||||||||||||||||||||||||||
23 | Water | regional | WaterGAP3 | Martina Floerke | floerke@usf.uni-kassel.de | Grid cells (5 arc min.) with elevation subgrid (1 arc min.) | |||||||||||||||||||||||||||
24 | Water | regional | mHM | Luis Samaniego | luis.samaniego@ufz.de | v. 5.4 | Samaniego, Luis, Rohini Kumar, Sabine Attinger. 2010. Multiscale Parameter Regionalization of a Grid-Based Hydrologic Model at the Mesoscale. Water Resources Research 46 (5): n/a–n/a. doi:10.1029/2008WR007327. | Kumar, Rohini, Luis Samaniego, Sabine Attinger. 2013. Implications of Distributed Hydrologic Model Parameterization on Water Fluxes at Multiple Scales and Locations. Water Resources Research 49 (1): 360–79. doi:10.1029/2012WR012195. Rakovec, O., Kumar, R., Mai, J., Cuntz, M., Thober, S., Zink, M., et al. (2015). Multiscale and multivariate evaluation of water fluxes and states over European river basins. Journal of Hydrometeorology, 150918163128001–21. http://doi.org/10.1175/JHM-D-15-0054.1 | Grid cells with sub-grid heterogeneity accounting method | regular or irregular grid, any size from 100 m to 100 km | hourly or daily | yearly | soil texture time-constant. Soil porosity, hydraulic conductivity, Van Gennugten constants are time-dependent and depend on land cover (organic matter) | Minimun requirements: precipitation., min, max and mean temperature. If avaliable: relative humidity, pressure, wind speed, net radiation | Any. For Pan-EU/global: HWSD. For USA: stasgo. For Germany BUEK 1000 or better. | LAI time-dependent | spin-up or restart file | Spin-up is made to generat a restart file at any time point. Spin-ups are made with the longest time series available. It the time series are short, the available data is use until steady-state is reached. Another form of initialization is to estimate the climatology of a given day using available data. Min timeseries to reach stable results is five years. | Corine land cover and LAI (both time-dependent) | No (In version 5.4). Will be included in future versions. | mHM uses a multiscale parameter regionalization (MPR) technique to ease transferability of model constants across scales and locations. Model constants are those scalars (like those in the pedothnasfer fucntions) that are valid for all grid cells and time points, thus can be transfered. Model parameters, on the other, hand are not transferable becuase they are cell specific e.g., soil porosity. See Samaniego et al. WRR 2010 for dertails. | If the input data is of good quality, mHM is able to reproduce extreme events and demostrated in the provided references. | |||||||||||
25 | Water | regional | HBV | Alejandro Chamorro-Chavez | Alejandro.Chamorro-Chavez@umwelt.uni-giessen.de | Grid cells | In principle any size of defined regular or irregular grids | daily | The model needs as basic information: Temperature, Precipitation (mean values, min and max values). Information can be added as vegetation, radiation, wind speed, humidity. | spin-up | no | ||||||||||||||||||||||
26 | Water | regional | HBV-IWW_WFDisi | Stefan Plötner | ploetner@iww.uni.hannover.de | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | not uploaded yet | - | - | |||||||||
27 | Water | regional | HBV-IWW_WFDown | Stefan Plötner | ploetner@iww.uni.hannover.de | - | Wallner, M., Haberlandt, U., Dietrich, J., 2013. A one-step similarity approach for the regionalization of hydrological model parameters based on Self-Organizing Maps. Journal of Hydrology 494, 59–71. doi:10.1016/j.jhydrol.2013.04.022 | - | subbasins | subbasins | daily | - | time-constant | time-constant | precipitation, mean temperature, gras-reference-evapotranspiration | HWSD | - | - | spin-up | same as protocol | mean areal crop-coefficient per subbasins | - | original WFD data used instead of ISIMIP server data | calibrated on hydrograph, extreme events can be reproduced if calibrated on | - | ||||||||
28 | Water | regional | HBV-JLU | Alejandro Chamorro-Chavez | Alejandro.Chamorro-Chavez@umwelt.uni-giessen.de | Same of HBV before mentioned | |||||||||||||||||||||||||||
29 | Water | regional | HYMOD | Alejandro Chamorro-Chavez | Alejandro.Chamorro-Chavez@umwelt.uni-giessen.de | Grid cells | In principle any size of defined regular or irregular grids | daily | The model needs as basic information: Temperature, Precipitation (mean values, min and max values). Information can be added as vegetation, radiation, wind speed, humidity. | spin-up | |||||||||||||||||||||||
30 | Water | regional | HYMOD-UFZ | Rohini Kumar | rohini.kumar@ufz.de | version 1 | Boyle D.P. (2001). Multicriteria calibration of hydrological models. PhD Dissertation, Dep. of Hydrol. and Water Resour., Univ. of Arizona, Tucson. | Moore R. J. (1985). The probability-distributed principle and runoff production at point and basin scales. Hydrological Sciences Journal 30 (2): 273–297. Vrugt, J. A., C. G. H. Diks, H. V. Gupta, W. Bouten, and J. M. Verstraten (2005), Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, doi:10.1029/2004WR003059. | Lumped model, a single basin structure | Basin | daily | - | time-constant | time-constant | precipitation, temperature mean, temperature min, temperature max | - | - | - | spin-up | Same as protocol | - | - | - | It is a lumped hydrologic model and may be therefore rely more on parameter calibration than other spatially explicit model. | - | ||||||||
31 | Water | regional | SWAT | Ann van griensven, Sandhya Rao | ann.vangriensven@gmail.com, sandhya.mrigasira@gmail.com | SWATV637 | Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Williams. 1998. Large-area hydrologic modeling and assessment: Part I. Model development. J. American Water Res. Assoc. 34(1): 73-89 | Neitsch, S. L., J. G. Arnold, J. R. Kiniry, J. R. Williams, and K. W. King. 2002a. Soil and Water Assessment Tool - Theoretical Documentation (version 2000). Temple, Texas: Grassland, Soil and Water Research Laboratory, Agricultural Research Service, Blackland Research Center, Texas Agricultural Experiment Station. | Subbasins and hydrologic response units (HRU) | Subbasin | Daily | Onetime each (30 years) for Baseline, Mid century and end century | Static data | Staticdata | Precipitation, Temperature maximum, minimum, Relative humidity, wind speed and solar radiation | ISIMIP | yes | Static Landuse | Management for agriculture crop (Irrigation and fertilization, both at auto) | Elevation bands (10 bands) was used and Temperature lapse and precipitation lapse rate were used | Outputs are at daily time step (as the inputs), therefore reproducing flood extreme may not be meaningful | ||||||||||||
32 | Water | regional | HYPE | SMHI | Ilias Pechlivanidis | ilias.pechlivanidis@smhi.se | The model version depend on the regional application | Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., & Arheimer, B. (2010). Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research, 41(3-4), 295–319. doi:10.2166/nh.2010.007 | Donnelly, C., Andersson, J. C. M., & Arheimer, B. (2015). Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe. Hydrological Sciences Journal, 1–19. doi:10.1080/02626667.2015.1027710 Pechlivanidis, I. G., & Arheimer, B. (2015). Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case. Hydrology and Earth System Sciences, 19, 4559–4579. doi:10.5194/hess-19-4559-2015 | Subbasins and hydrological response units (HRU) | depends on the region, mean area for the European basins is 215 km2, for the Arctic basins is 715 km2, for the Ganges is 800 km2, and for the Niger is 2650 km2. | daily | - | time-constant | time-constant | precipitation, temperature mean, temperature min, temperature max | HWSD | yes | Same as in protocol | we do not include dynamic modelling in the model | no | A good model performance would result in higher confidence on model robustness to reproduce extremes | |||||||||||
33 | Water | regional | ECOMAG | Water Problems Institute of RAS | Alexander Gelfan, Yuri Motovilov | hydrowpi@mail.ru, motol49@yandex.ru | The model version depend on the regional application | Motovilov Yu.G., L.Gottschalk, K.Engeland and A.Rodhe. Validation of a distributed hydrological model against spatial observation. Agricultural and Forest Meteorology. 1999, 98-99, pp.257-277. | Motovilov Yu.G., L.Gottschalk, K.Engeland and A.Belokurov. ECOMAG – regional model of hydrological cycle. Application to the NOPEX region. Department of Geophysics, University of Oslo, Institute Report Series no.105, 1999, ISBN 82-91885-04-4, ISSN 1501-6854, 88 p. | Subbasins, soil and land-use classes within them | mean area of subbasins 915 km2 | daily | - | time-constant | time-constant | precipitation, temperature, humidity | HWSD | GRanD for lakes and reservoirs | - | no spin-up | - | Static Landuse (GLCC) | no | no | model describes in detail the hydrological processes in the cold season, which accounts for extreme events in the basins at high latitudes | ||||||||
34 | Agriculture | global | DayCent | regular grid | 0.5° | ISRIC-WISE (Batjes, 2006) | |||||||||||||||||||||||||||
35 | Agriculture | global | EPIC-Boku | BOKU; University of Natural Resources and Life Sciences, Vienna | Erwin Schmid | erwin.schmid@boku.ac.at | epic0810 | Williams, 1995; Izaurralde et al., 2006 | regular grid | 5’ – 0.5° | daily | annual | time-constant, unless crop roatations are used; total cropland cover (GLC2000) | constant; | min. and max. temperature, rainfall (incl. snowfall), humidity, wind speed, solar radiation (short wave) | ISRIC-WISE (Batjes, 2006), ROSETTA (Shaap et Bouten,1996), Available Water Capacity (van Genuchten, 1988), ALBEDO (Dobos, 2006), hydraulic soil parameters (USDA-NRCS, 2007) | no spin-up | The crops are simulated for three management/input systems (AI, AN, and SS): AN: automatic nitrogen fertilization – N-fertilization rates based on crop specific N-stress levels (N-stress free days in 90% of the vegetation period). The upper limit of N application is 200 kg ha-1 a-1. AI: automatic nitrogen fertilization and irrigation – N and irrigation rates are based on crop specific stress levels (N and water stress free days in 90% of the vegetation period. N and irrigation upper limits of 200 kg ha-1 a-1 and 500 mm a-1. SS: subsistence farming – no N fertilizations and irrigation. | hail, and rainfall intensity e.g. mm/h | ||||||||||||||
36 | Agriculture | global | GEPIC | LMU Munich, EAWAG (Swiss Federal Institute of Aquatic Science and Technology) | Christian Folberth, Hong Yang | Christian.Folberth@geographie.uni-muenchen.de, Hong.yang@eawag.ch | EPIC0810; partly modified at EAWAG | Liu et al., 2007; Folberth et al., 2012 | Williams et al., 1989; Izaurralde et al., 2006 | regular grid | 0.5° | daily | annual | time-constant | constant | min. and max. temperature, rainfall (incl. snowfall), humidity, wind speed, solar radiation (short wave) | ISRIC-WISE (Batjes, 2006); Digital Soil Map of the World (FAO, 1995) | N and P fertilizer application rates based on FertiStat (2007) | spin-up | Simulations were run for each decade separately with 20 years spin-up | Planting dates were estimated similar to Waha et al. (2012). Time until maturiy was calculated as grid-specific average PHU for the whole simulation period. Hence, maturity in each will depend on the specific growing season temperature. After harvest, 80% of crop residue were removed from the field. | EPIC does not take floods and any physical damage to plants (e.g. hail or extreme winds) into account. Crops are not killed by extreme drought or temperatures, but only limtied in growth and yield formation. | |||||||||||
37 | Agriculture | global | LPJmL | Potsdam Institute for Climate Impact Research | Christoph Müller | cmueller@pik-potsdam.de | Bondeau et al., 2007, Fader et al. 2010, Waha et al. 2012 | Müller C, Robertson R (2014): Projecting future crop productivity for global economic modeling. Agricultural Economics, 45, 1, 37-50, doi:10.1111/agec.12088 | regular grid | 0.5° | daily | annual | all crops everywhere | constant | lwnet (derived from tas and rlds), pr, rsds, tas | HWSD, soil texture classification (USDA, http://edis.ifas.ufl.edu/ss169), hydraulic soil parameters (Cosby et al.,1984), thermal parameters (Lawrence and Slater, 2008) | GGCMI harmonized planting and maturity datasets (for a subset of simulations) | spin-up | 200 year spinup for soil temperatures and soil moisture, recycling the first 30 years of the time series | NA | crop sowing dates computed internally but fixed after the first simulation year in DEFAULT crop simulations, fixed at prescribed dates for HARMNON crop simulations | ||||||||||||
38 | Agriculture | global | pDSSAT | University of Chicago, Computation Institute | Joshua Elliott | joshuaelliott@uchicago.edu | pDSSAT2.0 (DSSAT4.6) | Elliott et al. The parallel system for integrating impact models and sectors (pSIMS), Environmental Modelling & Software, Volume 62, December 2014, Pages 509-516, ISSN 1364-8152, http://dx.doi.org/10.1016/j.envsoft.2014.04.008 | Jones et al., 2003 (for DSSAT) | regular grid | 5’ – 0.5° | Daily | Monthly or annual | Various | Constant | Tmax, Tmin, Precip, Rsds | GSDE (http://onlinelibrary.wiley.com/doi/10.1002/2013MS000293/abstract) | GGCMI harmonized planting, maturity and fertlizer dataset. | Planting window; harvest at maturity. | Depends on the time scale you're talking about. Do you mean single extreme flood, storm or heatwave events? Or are you talking about extreme drought/hot seasons? Assuming the latter, models are able to capture effects of extremes pretty well. If you mean the former, extreme events are not well represented (especially flood impacts). | |||||||||||||
39 | Agriculture | global | PEGASUS | University of Chicago, Computation Institute | Delphine Deryng | deryng@uchicago.edu | V1.1 | Deryng et al., 2014, Deryng et al., 2011 (for PEGASUS V.1.0) | regular grid | 5'-0.5º | Daily | Annual | Annual | Constant | pr, tas, cloud cover (or rsds & rlds) | ISRIC-WISE (Available Water Capacity only) (Batjes, 2006) | GGCMI harmonized planting, maturity and fertlizer dataset. | 4 years spin-up | 4-years based on 1971 | Prescribed planting window; fully irrigation assumes water is available to reduce water stress; fix fertiliser inputs from Ag-GRID harmonised dataset | PEGASUS simulates crop responses to extreme temperature events occuring around anthesis | ||||||||||||
40 | Agriculture | global | LPJ-GUESS | Lund University, department for Physical Geography and Ecosystem Scince, IMK-IFU, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany | Stefan Olin, Thomas Pugh | Stefan.Olin@nateko.lu.se, thomas.pugh@imk.fzk.de | Version 2.1 with crop module | Lindeskog et al., 2013 | Smith et al 2001, Bondeau et al., 2007 | regular grid | 0.5° | Daily | annual | Tmean, Precip, Rsds | HWSD, soil texture classification (USDA, http://edis.ifas.ufl.edu/ss169), hydraulic soil parameters (Cosby et al.,1984), thermal parameters (Lawrence and Slater, 2008) | GGCMI harmonized planting and maturity datasets (for a subset of simulations) | spin-up | 30 year spinup, using climate and [CO2] from the first simulation year. | With and without adapting growing periods. | ||||||||||||||
41 | Agriculture | global | CLM-Crop | National Center for Atmospheric Research | Peter Lawrence | lawrence@ucar.edu | CLM 4.5 with mods by slevis and lawrence | Drewniak et al. (2013) | regular grid | 1° | IGBP Global Soil Data Task 2000 | ||||||||||||||||||||||
42 | Agriculture | global | EPIC-IIASA | International Institute for Applied Systems Analysis (IIASA) | Juraj Balkovic, Nikolay Khabarov | balkovic@iiasa.ac.at, khabarov@iiasa.ac.at | EPIC0810 | Williams, 1995 | regular grid | 5' - 0.5° | daily | annual | time constant | Constant | min. and max. temperature, precipitation (incl. snowfall), humidity, wind speed, solar radiation (short wave) | ISRIC-WISE (Batjes, 2006); Digital Soil Map of the World (FAO, 1995) | N and P fertilizer application rates based on Mueller et al. (2012) | spin up | 20-yr spin up | Planting dates and length of the growing season were estimated based on Sacks et al. (2010). Harvest day was scheduled automatically as a fraction of accumulated PHU. Hence, maturity in each year depends on the specific growing season temperature. No residue removal. P-fertilization scheduled together with tillage, N-fertilization scheduled based on N stress. | EPIC does not take floods and any physical damage to plants (e.g. hail or extreme winds) into account. Crops are not killed by extreme drought or temperatures, but only limtied in growth and yield formation. | ||||||||||||
43 | Agriculture | global | pAPSIM | University of Chicago Computation Institute | Joshua Elliott | joshuaelliott@uchicago.edu | pAPSIM1.0 (APSIM V7.5) | Elliott et al. The parallel system for integrating impact models and sectors (pSIMS), Environmental Modelling & Software, Volume 62, December 2014, Pages 509-516, ISSN 1364-8152, http://dx.doi.org/10.1016/j.envsoft.2014.04.008 | Keating et al., 2003; Holwzorth et al., 2014 for APSIM | regular grid | 5’ – 0.5° | Daily | Monthly or annual | Various | Constant | Tmax, Tmin, Precip, Rsds | GSDE (http://onlinelibrary.wiley.com/doi/10.1002/2013MS000293/abstract) | GGCMI harmonized planting, maturity and fertlizer dataset. | Fixed planting date; harvest at maturity. | ||||||||||||||
44 | Agriculture | global | ORCHIDEE-CROP | IPSL ( Institute Pierre Simon Laplace) | Philippe Ciais/Xuhui Wang | Philippe.ciais@lsce.ipsl.fr, xuhui.wang@pku.edu.cn | V1.1 | Wu et al. (2015) | regular grid | 0.5° | half-hourly | annual | Various | constant | tasmax, tasmin, ps, huss, pr, rsds, rlds, wind | USDA soil texture classification based on HWSD | GGCMI harmonized planting, maturity and fertlizer dataset. | no | time-invariant planting/transplanting date, fertilizer aaplication | ||||||||||||||
45 | Agriculture | global | EPIC-TAMU | Department of Geographical Sciences, University of Maryland, College Park; Texas AgriLife, Texas A&M University | Cèsar Izaurralde | cizaurra@umd.edu | EPIC1102 | Williams, 1995; Izaurralde et al., 2006 | regular grid | 0.5° | ISRIC-WISE (Batjes, 2006) | ||||||||||||||||||||||
46 | Agriculture | global | PEPIC | EAWAG (Swiss Federal Institute of Aquatic Science and Technology) | Wenfeng Liu/Hong Yang | Wenfeng.liu@eawag.ch, Hong.yang@eawag.ch | Eawag (EPIC0810) | Liu et al. Global investigation of impacts of PET methods on simulating crop-water relations for maize. Agricultural and Forest Meteorology. 221: 164-175. http://dx.doi.org/10.1016/j.agrformet.2016.02.017 | Williams et al., 1989; Izaurralde et al., 2006 | regular grid | 0.5° | daily | annual | time constant | constant | precipitation, max and min temperature, humidity, wind speed, solar radiation | ISRIC-WISE (Batjes, 2006) | N and P fertilizer application rates based on FertiStat (2007) | Spin up | 20 years | Planting and harvesting dates were based on SAGE dataset. PHU was estiamted based on planting and harvesting dates. After harvest, 75% of crop residue were removed from the field. | ||||||||||||
47 | Agriculture | global | IMAGE | Netherland Environmental Assessment Agency (PBL) | Elke Stehfest | Elke.stehfest@pbl.nl.nl | 2.4 (used in ISI-MIP 1) | MNP, 2006 | regular grid, categorial land use | 0.5° | daily | annual | constant | constant | precipitation, max and min temperature | ISRIC-WISE (Batjes, 2006) | yes, spin-up | 270 years | internally determined growing season, therefore autonomous adaptation | ||||||||||||||
48 | Agriculture | global | IMAGE | Netherland Environmental Assessment Agency (PBL) | Elke Stehfest | Elke.stehfest@pbl.nl.nl | 3 | Stehfest, E., van Vuuren, D., Kram, T., Bouwman, L., Alkemade, R., Bakkenes, M., Biemans, H., Bouwman, A., den Elzen, M., Janse, J., Lucas, P., van Minnen, J., Muller, C., Prins, A. (2014), Integrated Assessment of Global Environmental Change with IMAGE 3.0. Model description and policy applications, The Hague: PBL Netherlands Environmental Assessment Agency. | regular grid, land use on 5 arcmin, crop model on 30 arcmin | 30 arcmin | daily | annual | constant | constant | precipitation, max and min temperature | HSWD | yes, spin-up | internally determined growing season, therefore autonomous adaptation | |||||||||||||||
49 | Energy | global | GCAM-IMM | Council on Energy, Environment and Water | Vaibhav Chaturvedi | vaibhav.chaturvedi@ceew.in | Chaturvedi V, Eom J, Clarke L and Shukla PR. 2014. Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework. Energy Policy 64, 226-242 | Chaturvedi V and Sharma M. 2015. Modelling long term HFC emissions from India's residential air-conditioning sector. Climate Policy, In Press, DOI: 10.1080/14693062.2015.1052954 | |||||||||||||||||||||||||
50 | Energy | global | MESSAGE-Brazil | Roberto Schaeffer | roberto@ppe.ufrj.br | ||||||||||||||||||||||||||||
51 | Energy | global | FEEM Stat Model | Enrica de Cian | enrica.decian@feem.it | ||||||||||||||||||||||||||||
52 | Energy | global | KASIM | Stefan Voegle | s.voegele@fz-juelich.de | ||||||||||||||||||||||||||||
53 | Energy | global | VIC-RBM | Michelle van Vliet | michelle.vanvliet@wur.nl | ||||||||||||||||||||||||||||
54 | Energy | global | AIM | Shinishiro Fujimori | fujimori.shinichiro@nies.go.jp | ||||||||||||||||||||||||||||
55 | Energy | global | POLES | Silvana Mima | Silvana.MIMA@upmf-grenoble.fr | ISI-MIP | Mima S, Criqui P. (2015). The Costs of Climate Change for the European Energy System, an Assessment with the POLES Model, Environmental Model Assess, Published online 14 March 2015. | Criqui P., Mima S, Menanteau Ph, Kitous A. (2014). Mitigation strategies and energy technology learning: an assessment with the POLES model. Technological Forecasting and Social Change, vol. 90 Part A, Jan. 2015. | country and region level | annual | HDD, CDD, precipitations | 2010 | |||||||||||||||||||||
56 | Energy | global | GCAM | Katherine Calvin | katherine.calvin@pnnl.gov | ||||||||||||||||||||||||||||
57 | Energy | global | TIAM-UCL | Olivier Dessens | o.dessens@ucl.ac.uk | ||||||||||||||||||||||||||||
58 | Energy | regional | CLIMIX | Robert Vautard | robert.vautard@lsce.ipsl.fr | ||||||||||||||||||||||||||||
59 | Permafrost | IAPRAS-DSS | A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences (IAP RAS) | Alexey Eliseev, Maxim Arzhanov | eliseev.alexey.v@gmail.com, arzhanov@ifaran.ru | standard | Eliseev, A.V., M.M.Arzhanov, P.F.Demchenko, and I.I.Mokhov, 2009: Changes in climatic characteristics of Northern Hemisphere extratropical land in the 21st century: Assessments with the IAP RAS climate model. Izvestiya, Atmos. Ocean. Phys., v.45, no.3, p.271-283, doi: 10.1134/S0001433809030013 | Arzhanov, M.M., A.V.Eliseev, P.F.Demchenko, and I.I.Mokhov, 2007: Modeling of changes in temperature and hydrological regimes of subsurface permafrost, using the climate data (reanalysis). Earth Cryosphere, v.XI, no.4, p.65-69 [in Russian]. Arzhanov, M.M., P.F.Demchenko, A.V.Eliseev, and I.I.Mokhov, 2008: Simulation of characteristics of thermal and hydrologic soil regimes in equilibrium numerical experiments with a climate model of intermediate complexity. Izvestiya, Atmos. Ocean. Phys., v.44, no.5, p.279-287, doi: 10.1134/S0001433808050022 | regular lat*lon grid points | variable, standard is 2.5*2.5 | daily | none | annual | time-constant | precipitation, air temperature | global data set of derived soil properties, 0.5-degree grid (ORNL DAAC) | none | none | spin-up | 200 years of spin-up repeating the data for year 1971 | none | none | none | none | none | ||||||||
60 | Permafrost | JULES | Eleanor Burke | eleanor.burke@metoffice.gov.uk | |||||||||||||||||||||||||||||
61 | Permafrost | ORCHIDEE | Philippe Ciais | philippe.ciais@lsce.ipsl.fr | |||||||||||||||||||||||||||||
62 | Permafrost | SEIB-DGVM | Hisashi Sato | hsato@jamstec.go.jp | |||||||||||||||||||||||||||||
63 | Permafrost | climate-aquifer-permafrost interactions | Svet Milanovskiy | svetmil@mail.ru | |||||||||||||||||||||||||||||
64 | Marine fisheries & ecosystems | global | APECOSM | Olivier Maury | olivier.maury@ird.fr | ||||||||||||||||||||||||||||
65 | Marine fisheries & ecosystems | global | BOATS | Eric Galbraith | eric.galbraith@icrea.cat | ||||||||||||||||||||||||||||
66 | Marine fisheries & ecosystems | global | DBEM | William Cheung | w.cheung@fisheries.ubc.ca | ||||||||||||||||||||||||||||
67 | Marine fisheries & ecosystems | global | DBPM | Julia Blanchard | julia.blanchard@utas.edu.au | ||||||||||||||||||||||||||||
68 | Marine fisheries & ecosystems | global | EcoOcean | Villy Christensen | v.christensen@fisheries.ubc.ca | ||||||||||||||||||||||||||||
69 | Marine fisheries & ecosystems | global | Macroecological model | Simon Jennings | simon.jennings@cefas.co.uk | ||||||||||||||||||||||||||||
70 | Marine fisheries & ecosystems | global | Madingley | Derek Tittensor | derekt@mathstat.dal.ca | ||||||||||||||||||||||||||||
71 | Marine fisheries & ecosystems | global | POEM | James Watson | james.watson@su.se | ||||||||||||||||||||||||||||
72 | Marine fisheries & ecosystems | global | SEAPODYM | Collecte Localisation Satellites (CLS) Marine Ecosystem Department | Patrick Lehodey | plehodey@cls.fr | Lehodey P., Senina I., Murtugudde R. (2008). A Spatial Ecosystem And Populations Dynamics Model (SEAPODYM) - Modelling of tuna and tuna-like populations. Progress in Oceanography, 78: 304-318. | Lehodey P., Senina I., Nicol S., Hampton J. (2015). Modelling the impact of climate change on South Pacific albacore tuna. Deep Sea Research. 113: 246–259. Lehodey P., Senina I., Calmettes B, Hampton J, Nicol S. (2013). Modelling the impact of climate change on Pacific skipjack tuna population and fisheries. Climatic Change, 119 (1): 95-109. | regular grid | 2°x2°; 1°x1° | monthly | Temperature, currents, primary production; euphotic depth, dissolved oxygen concentration, pH (IPSL, France). Historical tuna fishing data (ICCAT; IOTC; WCPFC, IATTC) | initial conditions from optimization experiments over the historical period | no | |||||||||||||||||||
73 | Marine fisheries & ecosystems | global | SS-DBEM | Jose A. Fernandes | jfs@pml.ac.uk | Fernandes, J.A., Cheung, W.W., Jennings, S., Butenschön, M., Mora, L., Frölicher, T.L., Barange, M. and Grant, A., 2013. Modelling the effects of climate change on the distribution and production of marine fishes: accounting for trophic interactions in a dynamic bioclimate envelope model. Global change biology, 19(8), pp.2596-2607. | Queirós, A.M., Fernandes, J.A., Faulwetter, S., Nunes, J., Rastrick, S.P., Mieszkowska, N., Artioli, Y., Yool, A., Calosi, P., Arvanitidis, C. and Findlay, H.S., 2015. Scaling up experimental ocean acidification and warming research: from individuals to the ecosystem. Global change biology, 21(1), pp.130-143. | 0.5x0.5 cells | Yes | annual data for bottom and top temperature, salinity, O2, pH, currents and primary production | |||||||||||||||||||||||
74 | Marine fisheries & ecosystems | regional | Atlantis (NE USA) | Jason Link, Robert Gamble | jason.link@noaa.gov, Robert.Gamble@noaa.gov | v1.0 | Link, J.S., E.A. Fulton, and R.J. Gamble. 2010. The Northeast US Application of ATLANTIS: A full system model exploring marine ecosystem dynamics in a living marine resource management context. Progress in Oceanography. 87:214-234. | Link, J.S., Gamble, R.J., and Fulton, E.A. 2011. NEUS – ATLANTIS: Construction, Calibration and Application of an Ecosystem Model with Ecological Interactions, Physiographic Conditions, and Fleet Behavior. NOAA Tech. Memo. NMFS NE-218. 247 p. | 30 polygons | Yes | daily | ||||||||||||||||||||||
75 | Marine fisheries & ecosystems | regional | Atlantis (SE Australia) | Beth Fulton | beth.fulton@csiro.au | ||||||||||||||||||||||||||||
76 | Marine fisheries & ecosystems | regional | EwE (Adriatic Sea) | Marta Coll | martacoll@yahoo.com | ||||||||||||||||||||||||||||
77 | Marine fisheries & ecosystems | regional | EwE (Baltic Sea) | Stockholm Resilience Centre | Susa Niiranen | susa.niiranen@su.se | Niiranen S., Yletyinen J., Tomczak M. T., Blenckner T., Hjerne O., MacKenzie B., Müller-Karulis B., Neumann T. and Meier H. E. M. (2013). Combined effects of global climate change and regional ecosystem drivers on an exploited marine food web. Global Change Biology, 19:3327-3342. | Tomczak M. T., Niiranen S., Hjerne O. and Blenckner T. (2012). Ecosystem flow dynamics in the Baltic Proper – using a multi-trophic dataset as a basis for food-web modelling. Ecological Modelling, 230:123-147. | area box model | monthly/annual | n/a | n/a | n/a | primary producitivity of large and small phytoplankton, temperature, salinity, oxygen | fishing mortality on sprat, herring and cod; cod RV | n/a | start 1974 | n/a | n/a | n/a | |||||||||||||
78 | Marine fisheries & ecosystems | regional | EwE (Benguela) | Lynne Shannon | lynne.shannon@uct.ac.za | ||||||||||||||||||||||||||||
79 | Marine fisheries & ecosystems | regional | EwE (Catalan Sea) | Marta Coll | martacoll@yahoo.com | ||||||||||||||||||||||||||||
80 | Marine fisheries & ecosystems | regional | EwE (China) | Xiaochun Zhang | xzhang.ciw@gmail.com | ||||||||||||||||||||||||||||
81 | Marine fisheries & ecosystems | regional | EwE (Gulf of Mexico) | Cameron Ainsworth | ainsworth@usf.edu | ||||||||||||||||||||||||||||
82 | Marine fisheries & ecosystems | regional | EwE (NE USA) | Jason Link, Sean Lucey | jason.link@noaa.gov, Sean.Lucey@noaa.gov | v5 | Link, J., Overholtz, W., O’Reilly, J., Green, J., Dow, D., Palka, D., Legault, C., Vitaliano, J., Guida, V., Fogarty, M., Brodziak, J., Methratta, E., Stockhausen, W., Col, L., Waring, G., & Griswold, C. 2008. An Overview of EMAX: The Northeast U.S. Continental Shelf Ecological Network. J. Mar Sys. 74:453-474. | Link, J.S., Griswold, C.A. Methratta, E.M. & Gunnard, J. (eds). 2006. Documentation for the Energy Modeling and Analysis eXercise (EMAX). Northeast Fisheries Science Center Reference Document, 06-15. 166 pp. | 4 boxes | No | |||||||||||||||||||||||
83 | Marine fisheries & ecosystems | regional | EwE (New Zealand) | Tyler Eddy | tyler.eddy@dal.ca | v1 | Eddy TD, Pitcher TJ, MacDiarmid AB, Byfield TT, Jones T, Tam J, Bell JJ, Gardner JPA. 2014. Lobsters as keystone: Only in unfished ecosystems? Ecological Modelling 275: 48-72. | Cornwall CE, Eddy TD. 2015. Effects of near-future ocean acidification, fishing, and marine protection on a temperate, coastal ecosystem. Conservation Biology 29: 207-215. Eddy TD, Coll M, Fulton EA, Lotze HK. 2015. Trade-offs between invertebrate fisheries catches and ecosystem impacts in coastal New Zealand. ICES Journal of Marine Science 72: 1380-1388. | area box model | monthly | n/a | n/a | n/a | primary producitivity of large and small phytoplankton | fisheries mortality of lobster | n/a | simulations started in 1945 | n/a | n/a | n/a | finding observational data during these events | ||||||||||||
84 | Marine fisheries & ecosystems | regional | EwE (North Sea) | Steve Mackinson | steve.mackinson@cefas.co.uk | ||||||||||||||||||||||||||||
85 | Marine fisheries & ecosystems | regional | EwE (Scotian Shelf) | Alida Bundy | alida.bundy@dfo-mpo.gc.ca | ||||||||||||||||||||||||||||
86 | Marine fisheries & ecosystems | regional | EwE (SE Australia) | CSIRO Oceans & Atmosphere | Cathy Bulman | cathy.bulman@csiro.au | Johnson P, Bulman C, Fulton B, & Smith T (2010) MSC Low Trophic Level Project: South Eastern Australian case study. Marine Stewardship Council Science Series 1:111-170. | Bulman, C., Condie, S., Furlani, D., Cahill, M., Klaer, N., Goldsworthy, S., and Knuckey, I. (2006) Trophic dynamics of the eastern shelf and slope of the South East Fishery: impacts of and on the fishery. FRDC Project no. 2002/028. (CSIRO Marine and Atmospheric Research: Hobart, Tas.) Bulman, C.M., Condie, S.A., Neira, F.J., Goldsworthy, S.D., and Fulton, E.A. (2011) The trophodynamics of small pelagic fishes in the southern Australian ecosystem and the implications for ecosystem modelling of southern temperate fisheries. Final Report for FRDC Project 2008/023. (CSIRO Marine and Atmospheric Research and Fisheries Research and Development Corporation: Hobart, Tasmania.) | area box model | monthly | n/a | n/a | n/a | primary producitivity of large and small phytoplankton | fisheries catch and effort from 1994-2005, status quo from 2005+ | no | start 1994 | n/a | n/a | no | |||||||||||||
87 | Marine fisheries & ecosystems | regional | OSMOSE (Gulf of Mexico) | Arnaud Gruss | arnaud.gruss@noaa.gov | ||||||||||||||||||||||||||||
88 | Marine fisheries & ecosystems | regional | OSMOSE (N Humboldt) | Universidad Peruana Cayetano Heredia | Ricardo Oliveros-Ramos | ricardo.oliveros@gmail.com | osmose 3.0 | Oliveros-Ramos, R (2014) End-to-end modelling for an ecosystem approach to fisheries in the Northern Humboldt Current Ecosystem. PhD Thesis Universitè de Montpellier 2. | - | regular grid | 1/6º | monthly | - | - | - | plankton biomass | downscaling of the plankton biomass, nearest-neighbor approach not used, biological effects were fixed to the climatology not to 2005 values | started in 1992, no spin-up | - | fishing constant in 2005 levels | We estimated time series for larval mortality , a key parameter for the model, in order to reproduce the impact of ENSO in the population dynamics. | Forecast was done using climatologies for main time-varying parameters. | |||||||||||
89 | Marine fisheries & ecosystems | regional | OSMOSE (Benguela) | Yunne Shin | yunne-jai.shin@ird.fr | ||||||||||||||||||||||||||||
90 | Marine fisheries & ecosystems | regional | Size spectrum (North Sea) | Julia Blanchard | julia.blanchard@utas.edu.au | ||||||||||||||||||||||||||||
91 | Marine fisheries & ecosystems | regional | Size spectrum (NW Atlantic) | Phil Neubauer | neubauer.phil@gmail.com | ||||||||||||||||||||||||||||
92 | Health | Air quality-related mortality | Bertil Forsberg | bertil.forsberg@envmed.umu.se | |||||||||||||||||||||||||||||
93 | Health | Heat-related mortality | Yasushi Honda | honda@taiiku.tsukuba.ac.jp | |||||||||||||||||||||||||||||
94 | Health | LSHTM model | Sari Kovats | Sari.Kovats@lshtm.ac.uk | |||||||||||||||||||||||||||||
95 | Health | VECTRI | Adrian Tompkins | tompkins@ictp.it | |||||||||||||||||||||||||||||
96 | Health | WHO CCRA Dengue | Joacim Rocklov | joacim.rocklov@envmed.umu.se | |||||||||||||||||||||||||||||
97 | Health | WHO CCRA Malaria | Joacim Rocklov | joacim.rocklov@envmed.umu.se | |||||||||||||||||||||||||||||
98 | Health | Communicable diseases | Jan Semenza | Jan.Semenza@ecdc.europa.eu | |||||||||||||||||||||||||||||
99 | Health | Ozone-related mortality | Hans Orru | hans.orru@ut.ee | |||||||||||||||||||||||||||||
100 | Health | Lyme disease | Nicholas Ogden | Nicholas.Ogden@phac-aspc.gc.ca | |||||||||||||||||||||||||||||