Find research datasets worth reusing

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Glen Annie Canyon (GlenAnnieCanyon309)

Precipitation was collected by the Santa Barbara County Flood Control District at Glen Annie Canyon (GlenAnnieCanyon309) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Goleta Fire Station (GoletaFireStation440)

Precipitation was collected by the Santa Barbara County Flood Control District at Goleta Fire Station (GoletaFireStation440) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Goleta Water District (GoletaWaterDistrict334)

Precipitation was collected by the Santa Barbara County Flood Control District at Goleta Water District (GoletaWaterDistrict334) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Santa Barbara Caltrans Office (SBCaltrans335)

Precipitation was collected by the Santa Barbara County Flood Control District at Santa Barbara Caltrans Office (SBCaltrans335) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Botanic Garden (BotanicGarden321)

Precipitation was collected by the Santa Barbara County Flood Control District at Botanic Garden (BotanicGarden321) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Carpinteria US Forest Service Office (CarpinteriaUSFS383)

Precipitation was collected by the Santa Barbara County Flood Control District at Carpinteria US Forest Service Office (CarpinteriaUSFS383) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Montecito (Montecito325)

Precipitation was collected by the Santa Barbara County Flood Control District at Montecito (Montecito325) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Rancho San Julian (RanchoSJ389)

Precipitation was collected by the Santa Barbara County Flood Control District at Rancho San Julian (RanchoSJ389) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Buellton Fire Station (BuelltonFS233)

Precipitation was collected by the Santa Barbara County Flood Control District at Buellton Fire Station (BuelltonFS233) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

opencc-by-4.0Sep 2020View details →

DC2 High resolution future hydrological data for Sweden

Hourly river flow and total runoff were computed for the southern part of Sweden using the hourly version of a high resolution hydrological model S-HYPE, which is operationally used by SMHI. The model was calibrated and validated using radar based hourly precipitation and an operationally used hourly reanalysis temperature data. Projection of the impact of climate change was performed by running the model with hourly forcing data from an ensemble of EURO-COREX climate model simulations over 1971 - 2100. Four GCM-RCM combinations were used under two emission scenarios, RCP4.5 and RCP8.5. The results can be used to assess the risk of riverine flooding in areas located along a small to meso-scale river basin. The results can, in particular, be used to assess the risk of flash flooding that can result from heavy precipitation of short duration.

opencc-by-4.0Oct 2020View details →

An introduction to the use of Extended Petri Nets to represent Hydrological Dynamical Systems

The video tries to be a quiet introduction to the Extended Petri Nets (EPN), a graphical representations designed to describe Earth Science and Environmental models that results is systems of ordinary differential equations. The video uses an example to exemplify the mechanics of EPN and constitute ancillary material for a paper submitted to Hydrological Processes (https://osf.io/fpx7y/). The main reference for the EPN is: Bancheri,M., Serafin,F. and Rigon, R (2019), "The representation of hydrological dynamical systems using the extended petri nets." Water Resources Research, 8(01), 159–27. In our opinion, EPN improve models representation and help to understand the feedbacks among the various processes.

openodc-odblNov 2020View details →

EstSoil-EH: A high-resolution eco-hydrological modelling parameters dataset for Estonia (dataset)

For the EstSoil-EH dataset, we synthesized more than 20 extended eco-hydrological variables for Estonia  as numerical and categorical values from the original Soil Map of Estonia, the Estonian 5m Lidar DEM, Estonian Topographic Database and EU-HydroSoilGrids layers. The Soil Map of Estonia maps more than 750 000 soil units throughout Estonia at a scale of 1:10 000 and forms the basis for EstSoil-EH. It is the most detailed and information-rich dataset for soils in Estonia, with 75% of mapped units smaller than 4.0 ha, based on Soviet era field mapping. For each soil unit, it describes the soil type (i.e. soil reference group), soil texture, and layer information with a composite text code, which comprises not only of the actual texture class, but also of classifiers for rock content, peat soils, distinct compositional layers and their depths. To use these as eco-hydrological process properties in modelling applications we translated the text codes into numbers. The derived parameters include soil profiles (e.g., layers, depths), texture (clay, silt, sand components), coarse fragments and rock content. In addition, we aggregated and predicted physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content, available water capacity). The developed methodology and dataset will be an important resource for the Baltic region, but possibly also all other regions where detailed field-based soil mapping data is available. Countries like Lithuania and Latvia have similar historical soil records from the Soviet era that could be turned into value-added datasets such as the one we developed for Estonia.   We created an extended eco-hydrological dataset for Estonia, the EstSoil-EH, containing derived numerical values for the following data in all of the mapped soil units in the 1:10 000 soil map: soil profiles (e.g., layers, depths), texture (clay, silt, and sand components), rockiness, and physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content). Ultimately, our objective was to develop a reproducible method for deriving numerical values to support modelling and prediction of eco-hydrological processes in Estonia using the popular Soil and Water Assessment Tool. For more information on the development of this dataset look for "EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia", Alexander Kmoch, Arno Kanal&dagger;, Alar Astover, Ain Kull, Holger Virro, Aveliina Helm, Meelis Pärtel, Ivika Ostonen and Evelyn Uuemaa, 2021, Earth Syst. Sci. Data, 13, 83–97, <a href="https://doi.org/10.5194/essd-13-83-2021">https://doi.org/10.5194/essd-13-83-2021</a>

opencc-by-4.0May 2017View details →

Peak flow identified at selected GRDC stations and the corresponding hydrological and hydrometeorological state variables

Data set contains a list of peak flows at selected GRDC stations as well as the start, peak, and end dates of each selected event. Hydrometeorlogical variables and hydrological state variables simulated by a hydrological model E-HYPE corresponding to each selected event are also listed in the dataset. Further description and content of each data file is available in the included metadata.

opencc-by-4.0Jun 2017View details →

Eco-hydrology Cikapundung Project: Research Sphere

opencc-by-4.0Jun 2017View details →

Eco-hydrology Cikapundung Project: citation connections and research profile building

This image is uploaded as an integrated part of Eco-Hydrology Cikapundung project. This image will be cited across all future publications related to this project as CC-BY image. Therefore it should not be treated as prior publication of any kind. We used www.Draw.io and the source code is available on GIthub (https://github.com/dasaptaerwin/CikapundungProject/blob/master/citationConnection.xml). --- Dokumen ini disusun sebagai pelengkap riset untuk menggambarkan kaitan sitasi antar dokumen dari hulu ke hilir. Setiap dokumen diupayakan ber-DOI agar dapat autosync dengan profil riset yang tersedia: Google Scholar, Sinta, ORCID. Dengan dibuatnya dokumen hubungan sitasi ini, maka diharapkan dapat menjelaskan bahwa tidak terjadi duplikasi dalam publikasi.

opencc-by-4.0May 2022View details →

Caravan - A global community dataset for large-sample hydrology

This is the accompanying dataset to the following paper <a href="https://www.nature.com/articles/s41597-023-01975-w">https://www.nature.com/articles/s41597-023-01975-w</a> Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes from the same data sources in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time. If you use Caravan in your research, it would be appreciated to not only cite Caravan itself, but also the source datasets, to pay respect to the amount of work that was put into the creation of these datasets and that made Caravan possible in the first place. All current development and additional community extensions can be found at <a href="https://github.com/kratzert/Caravan">https://github.com/kratzert/Caravan</a> IMPORTANT: Due to size limitations for individual repositories, the netCDF version and the CSV version of Caravan (since Version 1.6)  are split into two different repositories. You can find the CSV version at <a href="https://zenodo.org/records/15530021">https://zenodo.org/records/15530021</a> Channel Log: <ul> <li>23 May 2022: Version 0.2 - Resolved a bug when renaming the LamaH gauge ids from the LamaH ids to the official gauge ids provided as "govnr" in the LamaH dataset attribute files.</li> <li>24 May 2022: Version 0.3 - Fixed gaps in forcing data in some "camels" (US) basins.</li> <li>15 June 2022: Version 0.4 - Fixed replacing negative CAMELS US values with NaN (-999 in CAMELS indicates missing observation).</li> <li>1 December 2022: Version 0.4 - Added 4298 basins in the US, Canada and Mexico (part of HYSETS), now totalling to 6830 basins. Fixed a bug in the computation of catchment attributes that are defined as pour point properties, where sometimes the wrong HydroATLAS polygon was picked. Restructured the attribute files and added some more meta data (station name and country).</li> <li>16 January 2023: Version 1.0 - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. For the most up to date version, please check https://github.com/kratzert/Caravan</li> <li>10 May 2023: Version 1.1 - No data change, just update data description.</li> <li>17 May 2023: Version 1.2 - Updated a handful of attribute values that were affected by a bug in their derivation. See https://github.com/kratzert/Caravan/issues/22 for details.</li> <li>16 April 2024: Version 1.4 - Added 9130 gauges from the original source dataset that were initially not included because of the area thresholds (i.e. basins smaller  than 100sqkm or larger than 2000sqkm). Also extended the forcing period for all gauges (including the original ones) to 1950-2023. Added two different download options that include timeseries data only as either csv files (Caravan-csv.tar.xz) or netcdf files (Caravan-nc.tar.xz). Including the large basins also required an update in the earth engine code</li> <li>16 Jan 2025: Version 1.5 - Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND"). </li> <li>27 May 2025: Version 1.6 <ul> <li>Updated the CAMELS-AUS data to source from CAMELS-AUS v2. This means more basins (561 compared to 222) and more recent streamflow data (2022 compared to 2014). Note that the gauge id for four basins changed between the original CAMELS-AUS version and v2. Those gauges are ['camelsaus_224213A', 'camelsaus_224214A', 'camelsaus_227225A', 'camelsaus_403213A'] that all lost their trailing "A". To stay synced with CAMELS-AUS (v2), we also adapted the new naming.</li> <li>Added VERSION file to the root directory that contains the current version number.</li> <li>Updated the code to the most recent GitHub snapshot (commit 6eab036).</li> <li>Due to the 50GB repository limit, we had to split the netCDF version and the CSV version into two separate repositories. The CSV version can be found under https://zenodo.org/records/15530021</li> </ul> </li> </ul>

opencc-by-sa-4.0Aug 2024View details →

Bias Correction of CRCM5-LE for Hydrological Bavaria

The frequency and intensity of extreme hydrometeorological events are anticipated to rise as a result of climate change. For precise analysis, especially in low-flow assessments, it is crucial to have data on precipitation and temperature with high spatial and sub-daily resolution. However, such data is often lacking in both density and duration. The <a href="https://www.climex-project.org/">ClimEx-II </a>project (Climate Change and Hydrological Extreme Events 2nd Phase) is dedicated to enhancing our understanding of these shifts in hydrological extremes. The Canadian Regional Climate Model version 5 Large Ensemble (CRCM5-LE; Leduc et al. (2019)) under RCP 8.5 builds the climatic boundary conditions for the hydrological modelling. The ensemble covers a European and a North American domain, each comprising 50 members from 1951 to 2100. The SDCLIREF v2 (Lehr- und Forschungseinheit für physische Geographie und komplexe Umweltsysteme 2024),  a sub-daily (3h), high-resolution (500m) data set for the domain of Bavaria and hydrologically important neighbouring catchments marks the reference data set.  In ClimEx-II, bias correction is a crucial step before downscaling (regional) climate model simulations to higher resolutions as it adjusts local inconsistencies in the climate model. The corrected and downscaled meteorological inputs can be used to drive a hydrological model (Emami and Koch 2018,  Fang et al. 2015). The quality of the corrected data depends on the method used. Therefore, this data set comprises a comparison of the input and output data from three different bias correction methods, UBC (Cannon et al., 2015), MBCn (Cannon 2018) and VBC (Funk et al., 2024) for three diverse climate regions in Bavaria: <ul> <li>Fränkische Saale Salz is a franconian catchment</li> <li>Iller Kempten is a pre-alpine catchment</li> <li>Hart an der Ziller is an alpine catchment</li> </ul> Each catchment comprises six to seven grid cells of a 12 km resolution. Five climate variables of hydrological importance are corrected in a 3-hourly temporal resolution per grid cell: <ul> <li>Near-Surface Dewpoint Temperature in °C (dew)</li> <li>Precipitation in kg/m2 (pr)</li> <li>Surface Downwelling Shortwave Radiation in W/m2 (rsds)</li> <li>Near-Surface Wind Speed in m/s (sfcWind)</li> <li>Near-Surface Air Temperature in °C (tas)</li> </ul> The environment in each file comprises the inputs to the bias correction <ul> <li>mp_dts: CRCM5-LE model data during the projection period (2011-2030) before correction</li> <li>mc_dts: CRCM5-LE model data during calibration period (1991-2010)</li> <li>oc_dts: SDCLIREF v2 reference data during calibration period (1991-2010)</li> </ul> ,the outputs from the bias correction comparison during the projection period <ul> <li>vbc: CRCM5-LE model data during the projection period (2011-2030) after correction by VBC</li> <li>mbcn: CRCM5-LE model data during the projection period (2011-2030) after correction by MBCn</li> <li>ubc: CRCM5-LE model data during the projection period (2011-2030) after correction by UBC</li> </ul> and the held-out reference data for validation <ul> <li>op_dts: SDCLIREF v2 reference data during the projection period (2011-2030).</li> </ul> Each of the above-presented data sets consists of six columns. The five climate variables are indexed by their abbreviations. The sixth column time contains a string marking the respective timestamp. All CRCM5-LE model data contain a seventh column indicating the respective ensemble member. The evaluation results by Wasserstein Distance and Model Correction Inconsistency from Funk et al. (2024) are captured in the three additional files starting with 06_*.

opencc-by-4.0Dec 2022View details →

Single-point CLM simulations with hillslope hydrology at Niwot Ridge, CO

In this study, we ran ecosystem-scale Community Land Model (CLM) simulations with a novel hillslope hydrology configuration to represent topographically heterogeneous alpine tundra vegetation across a moisture gradient at Niwot Ridge, Colorado, USA. We used local observations to evaluate our simulations and investigated the role of topography and aspect in mediating patterns of snow, productivity, soil moisture and temperature, as well as the potential exposure to climate change across an alpine tundra hillslope. This dataset contains output files from single-point CLM simulations with the hillslope hydrology for the manuscript titled 'Topographic Heterogeneity and Aspect Moderate Exposure to Climate Change Across an Alpine Tundra Hillslope'. Local observations from Niwot Ridge, CO were used to force and evaluate these simulations to represent alpine tundra vegetation across a moisture gradient. Our control simulations were modified to represent a site at Niwot Ridge referred to as the 'Saddle', with an east and west facing knoll and a lowland area between them. Three columns represent distinct moist, wet, and dry meadow vegetation communities. We used the same model setup to run additional experiments on north- and south-facing slopes. Simulations were run using input data from 2008-2021 (historical) and then extended to year 2100 (future) using an anomaly forcing protocol.

opencc-by-4.0Oct 2023View details →

Outputs of the WiMMed hydrological model for Sierra Nevada (Spain). Sept2015-Aug2022

Ecosystem  Services related to flood prevention, aquifer recharge and erosion prevention in SIERRA NEVADA (Spain) were quantified through the WiMMed hydrological model (Watershed Integrated Model in Mediterranean Environments; Herrero et al., 2014). WiMMed is a distributed and physically based model that combines hourly and daily meteorological data with soil hydro-physical properties and land use and land cover information to simulate water balance and flow circulation at basin scale (see Herrero et al. (2014) for details). In this study we applied the WiMMed model considering the land use and land cover data for 2020 (according to SIPNA) and the meteorological data from Sept2015 to Aug2022 to evaluate the value of ecosystem services, following the work made by Moreno-Llorca et al. (2020). Specific parameters, expressing the influence of vegetation changes in the hydrological processes of the study area, were considered, namely on evapotranspiration, interception, infiltration, overland flow and soil erodibility. Aquifer recharge (mm/m2/year) was calculated as the total volume of water moving from the soil into the aquifer and becoming groundwater. For that, the model firstly interpolates the precipitation at the cell scale (Herrero et al., 2009), and then calculates rainfall/snowfall partition, reproduces the interception from the vegetation, calculates the snow accumulation and melting (Herrero et al., 2009), and separates surface runoff from infiltration on the ground surface. Vertical and horizontal soil water movement was reproduced by a two-layer soil approach, using Darcy-Buckingham law with Mualem-vanGenuchten parameterization (Muñoz Carpena and Ritter Rodriguez, 2005). Evapotranspiration extract water from soil using a parameterization based on potential evapotranspiration and soil water content (Herrero et al., 2014). Water percolating through the second layer of soil becomes aquifer recharge. Soil erosion prevention (T/ha/year) was calculated by considering the inverse of soil loss by water flow concentration (rill processes) and raindrop impacts (interrill processes). WiMMed uses the variation of different parameters that link soil loss, with changes in vegetation cover and land uses, as described in (Millares et al., 2019). Changes on soil erodibility were estimated from vertical distribution of root biomass, by adapting empirical models (e.g. Gale and Grigal, 1987; Jackson et al., 1996) to Mediterranean environments reported previously (Martinez- Fernandez et al., 1995). From these estimations, distributed soil erodibility was calculated from the empirical model proposed by Flanagan and Livingstone (1995). The calibration and validation of the WiMMed model in Sierra Nevada has been conducted through a series of studies that analysed each hydrological process in the area and designed and corrected each WiMMed module, pertaining to snow (Herrero et al., 2009), soil (Aguilar and Polo, 2011), baseflow (Millares, 2008; Millares et al., 2009), river flow (Pérez-Palazón et al., 2014), or soil loss and sediment transportation (Bergillos et al., 2016; Millares et al., 2020).INPUT DATAThe input data used in the hydrological simulations were:<ul><li>Digital elevation model from national remote sensing program PNOA-LIDAR MDT02 and the topographic features calculated by WiMMed from the DEM: surface drainage system, river delineation, slope, aspect, sky view factor and horizon (sky obstruction in 8 directions).</li><li>Meteorological data from more than 50 weather stations in the area: hourly/daily rainfall (mm), hourly and daily temperature (oC), daily solar radiation (MJ/m2), average daily wind speed (m·s−1), average daily relative humidity (%), average daily barometric pressure (hPa).</li><li>Physico-chemical and hydraulic properties of the soil selected from the available spatial database performed by Rodríguez (2008), in which thematic maps were obtained for Andalusia at a 250-m resolution: hydraulic conductivity (mm·h−1), saturation and residual moisture values (mm·mm−1), air-entry matric potential (mm), retention parameter of the van Genuchten (dimensionless) and soil thickness (mm).</li><li>Land cover and land use information from SIPNA 2020.</li><li>Aquifer regions and information from hydrogeological atlas of Andalusia (ITGE-Junta de Andalucía, 1998; Castillo, 2008).</li></ul>OUTPUT DATAThe results contained in this database are raster files in UTM ETRS89 30S, with a spatial resolution of 30x30 meters, for the whole SIerra Nevada. The raster files are Esri-ASCII ArcGIS (.asc) grids with 3846 columns (X) and 2099 rows (Y). There are different time scales for each variable. The prefix of the file indicates this time scale, namely "Ano" for annual maps, "mes" for monthly maps and "Tot" for the whole simulation. The suffix indicates the variable of interest:<ul><li>Pre: Accumulated precipitation (solid + liquid) in mm</li><li>T_m: Mean temperature in ºC</li><li>P_n: Accumulated snowfall in mm</li><li>ErT: Accumulated total erosion (rill + interrill) in kg/m2</li><li>ET0: Accumulated potential evapotranspiration in mm</li><li>EvC: Accumulated real evaporation from canopy (intercepted precipitation) in mm</li><li>EvN:Accumulated real sublimation from snow in mm</li><li>EvS: Accumulated real evapotranspiration ration from soil in mm</li><li>Exp: Accumulated direct runoff in mm</li><li>Fus: Accumulated snowmelt in mm</li><li>HSol1: Instantaneous soil moisture in surface layer 1 (upper 25 cm) in mm</li><li>HSol2: Instantaneous soil moisture in deep layer 2 in mm</li><li>Inf: Accumulated infiltration from surface into soil in mm</li><li>Per: Accumulated aquifer recharge (from soil to groundwater) in mm</li><li>Qlat: Accumulated lateral flow (horizontal movement of water between cells) in mm</li><li>Tmn: Minimum temperature in ºC</li><li>Tmx: Maximum temperature in ºC</li></ul>There are also some other grid files (Tot_XXX.asc) related to the initial and final conditions of the state variables or internal conditions of the model.ReferencesAguilar, C., Polo, M.J., 2011. Generating reference evapotranspiration surfaces from the Hargreaves equation at watershed scale. Hydrol. Earth Syst. Sci. 15, 2495–2508. doi: 10.5194/hess-15-2495-2011.Bergillos, R.J., Rodríguez-Delgado, C., Millares, A., Ortega-Sánchez, M., Losada, M.A., 2016. Impact of river regulation on a Mediterranean delta: assessment of managed versus unmanaged scenarios. Water Resour. Res. 52 (7), 5132–5148.Castillo, A. 2008. Manantiales de Andalucía. Agencia Andaluza del agua, Consejería de Medio Ambiente, Junta de Andalucía, Sevilla, 410 pp.Herrero, J., Polo, M.J., Moñino, A., Losada, M.A., 2009. An energy balance snowmelt model in a Mediterranean site. J. Hydrol. 371 (1-4), 98–107.Herrero, J., Millares, A., Aguilar, C., Egüen, M., Losada, M.A., 2014. Coupling spatial and time scales in the hydrological modelling of mediterranean regions: WiMMed, in: CUNY Academic Works. In: Presented at the International Conference on Hydroinformatics, p. 8. ITGE-Junta de Andalucía: Atlas Hidrogeológico de Andalucía. Madrid, 216 pp., ISBN: 84-7840-351-5, available at: http: //aguas.igme.es/igme/publica/libros1 HR/libro110/lib110.htm, last access: 18 March 2012, 1998Millares, A., 2008. Integración del caudal base en un modelo distribuido de cuenca. Estudio de las aportaciones subterráneas en ríos de montaña. University of Granada.Millares, A., Polo, M.J., Losada, M.A., 2009. The hydrological response of baseflow in fractured mountain areas. Hydrol. Earth Syst. Sci. 13 (1261–1271), 2009.Millares, A., Díez-Minguito, M., Moñino, A., 2019. Evaluating gullying effects on modeling erosive responses at basin scale. Environ. Modell. Software 111, 61–71. Millares, A., Herrero, J., Bermúdez, M., Leiva, J.F., Cantalejo, M., 2020. Long-term modelling of soil loss and fluvial transport processes in a mountainous semi-arid basin, southern Spain, in: River Flow 2020 - Twentieth International Conference on Fluvial Hydraulic. Delf, Netherlands.Moreno-Llorca, R., Vaz, A. S., Herrero, J., Millares, A., Bonet-García, F. J., & Alcaraz-Segura, D. 2020. Multi-scale evolution of ecosystem services' supply in Sierra Nevada (Spain): An assessment over the last half-century. Ecosystem Services, 46, 101204.Muñoz Carpena, R., Ritter Rodriguez, A., 2005. Hidrología Agroforestal. Mundiprensa.Pérez-Palazón, M. J., Pimentel, R., Herrero, J., & Polo-Gómez, M. J. 2014. Analysis of snow spatial and temporary variability through the study of terrestrial photography in the Trevelez river valley. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI (Vol. 9239, pp. 358-368). SPIE.Rodríguez, J. A. 2008. Sistema de Inferencia Espacial de Propiedades Físico-Químicas e Hidráulicas de los Suelos de Andalucía. Herramienta de Apoyo a la Simulación de Procesos Agro-Hidrológicos a Escala Regional. Informe Final. Empresa Pública Desarrollo Agrario y Pesquero, Consejería de Agricultura y Pesca, Sevilla.