| publications-1541 |
PEER REVIEWED ARTICLE |
2017 |
Nikolaos S. Bartsotas , Efthymios I. Nikolopoulos , Emmanouil N. Anagnostou , Stavros Solomos , George Kallos |
Moving toward Subkilometer Modeling Grid Spacings: Impacts on Atmospheric and Hydrological Simulations of Extreme Flash Flood–Inducing Storms |
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10.1175/jhm-d-16-0092.1 |
Simulation & Modeling |
River Basins |
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Abstract Flash floods develop over small spatiotemporal scales, an attribute that makes their predictability a particularly challenging task. The serious threat they pose for human lives, along with damage estimates that can exceed one billion U.S. dollars in some cases, urge toward more accurate forecasting. Recent advances in computational science combined with state-of-the-art atmospheric models allow atmospheric simulations at very fine (i.e., subkilometer) grid scales, an element that is deemed important for capturing the initiation and evolution of flash flood–triggering storms. This work provides some evidence on the relative gain that can be expected from the adoption of such subkilometer model grids. A necessary insight into the complex processes of these severe incidents is provided through the simulation of three flood-inducing heavy precipitation events in the Alps for a range of model grid scales (0.25, 1, and 4 km) with the Regional Atmospheric Modeling System–Integrated Community Limited Area Modeling System (RAMS–ICLAMS) atmospheric model. A distributed hydrologic model [Kinematic Local Excess Model (KLEM)] is forced with the various atmospheric simulation outputs to further evaluate the relative impact of atmospheric model resolution on the hydrologic prediction. The use of a finer grid is beneficial in most cases, yet there are events where the improvement is marginal. This underlines why the use of finer scales is a step in the right direction but not a solitary component of a successful flash flood–forecasting recipe. |
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| publications-1542 |
PEER REVIEWED ARTICLE |
2016 |
Yagmur Derin , Emmanouil Anagnostou , Alexis Berne , Marco Borga , Brice Boudevillain , Wouter Buytaert , Che-Hao Chang , Guy Delrieu , Yang Hong , Yu |
Multiregional Satellite Precipitation Products Evaluation over Complex Terrain |
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10.1175/jhm-d-15-0197.1 |
Simulation & Modeling |
Irrigation Systems |
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Abstract An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network. |
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| publications-1543 |
PEER REVIEWED ARTICLE |
2016 |
Yiwen Mei , Efthymios Nikolopoulos , Emmanouil Anagnostou , Davide Zoccatelli , Marco Borga |
Error Analysis of Satellite Precipitation-Driven Modeling of Flood Events in Complex Alpine Terrain |
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10.3390/rs8040293 |
Hydrological modeling |
River Basins |
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The error in satellite precipitation-driven complex terrain flood simulations is characterized in this study for eight different global satellite products and 128 flood events over the Eastern Italian Alps. The flood events are grouped according to two flood types: rain floods and flash floods. The satellite precipitation products and runoff simulations are evaluated based on systematic and random error metrics applied on the matched event pairs and basin-scale event properties (i.e., rainfall and runoff cumulative depth and time series shape). Overall, error characteristics exhibit dependency on the flood type. Generally, timing of the event precipitation mass center and dispersion of the time series derived from satellite precipitation exhibits good agreement with the reference; the cumulative depth is mostly underestimated. The study shows a dampening effect in both systematic and random error components of the satellite-driven hydrograph relative to the satellite-retrieved hyetograph. The systematic error in shape of the time series shows a significant dampening effect. The random error dampening effect is less pronounced for the flash flood events and the rain flood events with a high runoff coefficient. This event-based analysis of the satellite precipitation error propagation in flood modeling sheds light on the application of satellite precipitation in mountain flood hydrology. |
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| publications-1544 |
PEER REVIEWED ARTICLE |
2017 |
AnaĂŻs Barella-Ortiz , Jan Polcher , Patricia de Rosnay , Maria Piles , Emiliano Gelati |
Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula |
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10.5194/hess-21-357-2017 |
Uncategorized |
Uncategorized |
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Abstract. L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i)Â ORCHIDEE and (ii)Â H-TESSEL. The radiative transfer model used is the CMEM. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies. |
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| publications-1545 |
PEER REVIEWED ARTICLE |
2016 |
Hylke E. Beck , Albert I. J. M. van Dijk , Vincenzo Levizzani , Jaap Schellekens , Diego G. Miralles , Brecht Martens , Ad de Roo |
MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data |
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10.5194/hess-2016-236 |
Uncategorized |
Uncategorized |
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Abstract. Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP), a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of time scale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13,762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 versus 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50,000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV against daily Q observations with P from each of the different datasets. For the 1058 sparsely-gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 versus 0.29–0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org. |
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| publications-1546 |
PEER REVIEWED ARTICLE |
2016 |
Gennadii Donchyts , Jaap Schellekens , Hessel Winsemius , Elmar Eisemann , Nick van de Giesen |
A 30 m Resolution Surface Water Mask Including Estimation of Positional and Thematic Differences Using Landsat 8, SRTM and OpenStreetMap: A Case Study in the Murray-Darling Basin, Australia |
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10.3390/rs8050386 |
AI & Machine Learning |
Precipitation & Ecological Systems |
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Accurate maps of surface water are essential for many environmental applications. Surface water maps can be generated by combining measurements from multiple sources. Precise estimation of surface water using satellite imagery remains a challenging task due to the sensor limitations, complex land cover, topography, and atmospheric conditions. As a complementary dataset, in the case of hilly landscapes, a drainage network can be extracted from high-resolution digital elevation models. Additionally, Volunteered Geographic Information (VGI) initiatives, such as OpenStreetMap, can also be used to produce high-resolution surface water masks. In this study, we derive a high-resolution water mask using Landsat 8 imagery and OpenStreetMap as well as (potential) a drainage network using 30 m SRTM. Our approach to derive a surface water mask from Landsat 8 imagery comprises the use of a lower 15% percentile of Landsat 8 Top of Atmosphere (TOA) reflectance from 2013 to 2015. We introduce a new non-parametric unsupervised method based on the Canny edge filter and Otsu thresholding to detect water in flat areas. For hilly areas, the method is extended with an additional supervised classification step used to refine the water mask. We applied the method across the Murray-Darling basin, Australia. Differences between our new Landsat-based water mask and the OpenStreetMap water mask regarding positional differences along the rivers and overall coverage were analyzed. Our results show that about 50% of the OpenStreetMap linear water features can be confirmed using the water mask extracted from Landsat 8 imagery and the drainage network derived from SRTM. We also show that the observed distances between river features derived from OpenStreetMap and Landsat 8 are mostly smaller than 60 m. The differences between the new water mask and SRTM-based linear features and hilly areas are slightly larger (110 m). The overall agreement between OpenStreetMap and Landsat 8 water masks is about 30%. |
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| publications-1547 |
PEER REVIEWED ARTICLE |
2016 |
M. Kummu , J. H. A. Guillaume , H. de Moel , S. Eisner , M. Flörke , M. Porkka , S. Siebert , T. I. E. Veldkamp , P. J. Ward |
The world’s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability |
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10.1038/srep38495 |
Predictive Analytics |
River Basins |
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AbstractWater scarcity is a rapidly growing concern around the globe, but little is known about how it has developed over time. This study provides a first assessment of continuous sub-national trajectories of blue water consumption, renewable freshwater availability, and water scarcity for the entire 20th century. Water scarcity is analysed using the fundamental concepts of shortage (impacts due to low availability per capita) and stress (impacts due to high consumption relative to availability) which indicate difficulties in satisfying the needs of a population and overuse of resources respectively. While water consumption increased fourfold within the study period, the population under water scarcity increased from 0.24 billion (14% of global population) in the 1900s to 3.8 billion (58%) in the 2000s. Nearly all sub-national trajectories show an increasing trend in water scarcity. The concept of scarcity trajectory archetypes and shapes is introduced to characterize the historical development of water scarcity and suggest measures for alleviating water scarcity and increasing sustainability. Linking the scarcity trajectories to other datasets may help further deepen understanding of how trajectories relate to historical and future drivers, and hence help tackle these evolving challenges. |
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| publications-1548 |
PEER REVIEWED ARTICLE |
2016 |
Brecht Martens , Diego G. Miralles , Hans Lievens , Robin van der Schalie , Richard A. M. de Jeu , Diego FĂ©rnandez-Prieto , Hylke E. Beck , Wouter A. |
GLEAM v3: satellite-based land evaporation and root-zone soil moisture |
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10.5194/gmd-2016-162 |
Uncategorized |
Irrigation Systems |
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Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include: (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 35-year data set spanning the period 1980–2014 (v3.0a, based on satellite-observed soil moisture, vegetation optical depth and snow water equivalents, reanalysis air temperature and radiation, and a multi-source precipitation product), and two fully satellite-based data sets. The latter two share most of their forcing, except for the vegetation optical depth and soil moisture products, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative data sets) for the first data set (v3.0b, spanning the period 2003–2015) and observations from the Soil Moisture and Ocean Salinity satellite in the second data set (v3.0c, spanning the period 2011–2015). These three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 64 eddy-covariance towers and 2338 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in case of the v3.0a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the two fully satellite-based data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar as the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements between 0.78 and 0.80 for the three different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at http://GLEAM.eu and may be used for large-scale hydrological applications, climate studies and research on land-atmosphere feedbacks. |
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| publications-1549 |
PEER REVIEWED ARTICLE |
2017 |
Filipe Aires , LĂ©o Miolane , Catherine Prigent , Binh Pham , Etienne Fluet-Chouinard , Bernhard Lehner , Fabrice Papa |
A global dynamic long-term inundation extent dataset at high spatial resolution derived through downscaling of satellite observations |
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10.1175/jhm-d-16-0155.1 |
Simulation & Modeling |
Precipitation & Ecological Systems |
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Abstract A new procedure is introduced to downscale low-spatial-resolution inundation extents from Global Inundation Extent from Multi-Satellites (GIEMS) to a 3-arc-s (90 m) dataset (known as GIEMS-D3). The methodology is based on topography and hydrography information from the HydroSHEDS database. A new floodability index is introduced and an innovative smoothing procedure is developed to ensure a smooth transition, in the high-resolution maps, between the low-resolution boxes from GIEMS. Topography information is pertinent for natural hydrology environments controlled by elevation but is more limited in human-modified basins. However, the proposed downscaling approach is compatible with forthcoming fusion of other, more pertinent satellite information in these difficult regions. The resulting GIEMS-D3 database is the only high-spatial-resolution inundation database available globally at a monthly time scale over the 1993–2007 period. GIEMS-D3 is assessed by analyzing its spatial and temporal variability and evaluated by comparisons to other independent satellite observations from visible (Google Earth and Landsat), infrared (MODIS), and active microwave (synthetic aperture radar). |
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| publications-1550 |
PEER REVIEWED ARTICLE |
2017 |
Luca Ciabatta , Anna Cinzia Marra , Giulia Panegrossi , Daniele Casella , Paolo Sanò , Stefano Dietrich , Christian Massari , Luca Brocca |
Daily precipitation estimation through different microwave sensors: Verification study over Italy |
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10.1016/j.jhydrol.2016.12.057 |
Data Management & Analytics |
Precipitation & Ecological Systems |
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No abstract available |
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