| publications-1571 |
PEER REVIEWED ARTICLE |
2017 |
Jaap Schellekens , Emanuel Dutra , Alberto MartÃnez-de la Torre , Gianpaolo Balsamo , Albert van Dijk , Frederiek Sperna Weiland , Marie Minvielle , |
A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset |
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10.5194/essd-9-389-2017 |
Simulation & Modeling |
River Basins |
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Abstract. The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr−1 (334 kg m−2 yr−1), while the ensemble mean of total evaporation was 537 kg m−2 yr−1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070. |
603608 |
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| publications-1572 |
PEER REVIEWED ARTICLE |
2017 |
Clément Albergel , Simon Munier , Delphine Jennifer Leroux , Hélène Dewaele , David Fairbairn , Alina Lavinia Barbu , Emiliano Gelati , Wouter Dori |
Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area |
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10.5194/gmd-10-3889-2017 |
Simulation & Modeling |
Precipitation & Ecological Systems |
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Abstract. In this study, a global land data assimilation system (LDAS-Monde) is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the interactions between soil–biosphere–atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. SSM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 to 100 cm depth). A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and SSM have an impact on the different control variables. From the assimilation of SSM, the LDAS is more effective in modifying soil moisture (SM) from the top layers of soil, as model sensitivity to SSM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. A comprehensive evaluation of the assimilation impact is conducted using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observation-based estimates of upscaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement. |
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| publications-1573 |
PEER REVIEWED ARTICLE |
2017 |
Hélène Dewaele , Simon Munier , Clément Albergel , Carole Planque , Nabil Laanaia , Dominique Carrer , Jean-Christophe Calvet |
Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation |
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10.5194/hess-21-4861-2017 |
Simulation & Modeling |
Precipitation & Ecological Systems |
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Abstract. Soil maximum available water content (MaxAWC) is a key parameter in land surface models (LSMs). However, being difficult to measure, this parameter is usually uncertain. This study assesses the feasibility of using a 15-year (1999–2013) time series of satellite-derived low-resolution observations of leaf area index (LAI) to estimate MaxAWC for rainfed croplands over France. LAI interannual variability is simulated using the CO2-responsive version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) LSM for various values of MaxAWC. Optimal value is then selected by using (1) a simple inverse modelling technique, comparing simulated and observed LAI and (2) a more complex method consisting in integrating observed LAI in ISBA through a land data assimilation system (LDAS) and minimising LAI analysis increments. The evaluation of the MaxAWC estimates from both methods is done using simulated annual maximum above-ground biomass (Bag) and straw cereal grain yield (GY) values from the Agreste French agricultural statistics portal, for 45 administrative units presenting a high proportion of straw cereals. Significant correlations (p value  <  0.01) between Bag and GY are found for up to 36 and 53 % of the administrative units for the inverse modelling and LDAS tuning methods, respectively. It is found that the LDAS tuning experiment gives more realistic values of MaxAWC and maximum Bag than the inverse modelling experiment. Using undisaggregated LAI observations leads to an underestimation of MaxAWC and maximum Bag in both experiments. Median annual maximum values of disaggregated LAI observations are found to correlate very well with MaxAWC. |
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| publications-1574 |
PEER REVIEWED ARTICLE |
2018 |
Emiliano Gelati , Bertrand Decharme , Jean-Christophe Calvet , Marie Minvielle , Jan Polcher , David Fairbairn , Graham P. Weedon |
Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model |
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10.5194/hess-22-2091-2018 |
Simulation & Modeling |
Groundwater |
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Abstract. Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25–75.5∘ N, 11.5∘ W–62.5∘ E, at 0.5∘ resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early-autumn LAI. Forcing uncertainty impacts on simulated river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter–spring high flows and underestimate summer–autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely explained by forcing uncertainty, we suggest several research directions. These range from further investigating the discrepancies between LSMs and remote sensing retrievals to developing new model components to represent physical and anthropogenic processes. |
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| publications-1575 |
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Romas Smilgys |
Formation of stars and stellar clusters in Galactic environment |
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Simulation & Modeling |
Groundwater |
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No abstract available |
603608 |
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| publications-1576 |
PEER REVIEWED ARTICLE |
2016 |
P. Quintana-Seguà |
Meteorological Analysis Systems in North-East Spain: Validation of SAFRAN and SPAN |
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10.3808/jei.201600335 |
AI & Machine Learning |
Soil Moisture |
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No abstract available |
603608 |
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| publications-1577 |
PEER REVIEWED ARTICLE |
2017 |
Md Abul Ehsan Bhuiyan , Efthymios I. Nikolopoulos , Emmanouil N. Anagnostou , Pere Quintana-Seguà , Anaïs Barella-Ortiz |
A Nonparametric Statistical Technique for Combining Global Precipitation Datasets: Development and Hydrological Evaluation over the Iberian Peninsula |
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10.5194/hess-2017-268 |
Data Management & Analytics |
Precipitation & Ecological Systems |
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Abstract. This study investigates the use of a nonparametric, tree-based model, Quantile Regression Forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning eleven years (2000–10). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate rainfall ensembles for these conditions. We then carried an evaluation based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km/1 h resolution and further used generated ensembles to force a distributed hydrological model (the SURFEX land-surface model and the RAPID river routing scheme). To evaluate relative improvements and the overall impact of the combined product in hydrological response, we compared its streamflow simulation results with the results of simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20 %–99 % and 44 %–88 %, respectively, when considering the ensemble mean. |
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| publications-1578 |
PEER REVIEWED ARTICLE |
2017 |
Clara Linés , Micha Werner , Wim Bastiaanssen |
The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets |
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10.5194/hess-21-4747-2017 |
Data Management & Analytics |
Water Distribution Networks |
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Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation–anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions. |
603608 |
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| publications-1579 |
PEER REVIEWED ARTICLE |
2017 |
Alexander Kaune , Micha Werner , Erasmo RodrÃguez , Poolad Karimi , Charlotte de Fraiture |
A novel tool to assess available hydrological information and the occurrence of sub-optimal water allocation decisions in large irrigation districts |
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10.1016/j.agwat.2017.06.013 |
Simulation & Modeling |
Natural Water Bodies |
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No abstract available |
603608 |
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| publications-1580 |
PEER REVIEWED ARTICLE |
2017 |
Ying Fan , Gonzalo Miguez-Macho , Esteban G. Jobbágy , Robert B. Jackson , Carlos Otero-Casal |
Hydrologic regulation of plant rooting depth |
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10.1073/pnas.1712381114 |
Simulation & Modeling |
River Basins |
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Significance Knowledge of plant rooting depth is critical to understanding plant-mediated global change. Earth system models are highly sensitive to this particular parameter with large consequences for modeled plant productivity, water–energy–carbon exchange between the land and the atmosphere, and silicate weathering regulating multimillion-year-timescale carbon cycle. However, we know little about how deep roots go and why. Accidental discoveries of >70-m-deep roots in wells and >20-m-deep roots in caves offer glimpses of the enormous plasticity of root response to its environment, but the drivers and the global significance of such deep roots are not clear. Through observations and modeling, we demonstrate that soil hydrology is a globally prevalent force driving landscape to global patterns of plant rooting depth. |
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