Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–Č Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-1561 PEER REVIEWED ARTICLE 2015 Ted I.E. Veldkamp , Yoshihide Wada , Hans de Moel , Matti Kummu , Stephanie Eisner , Jeroen C.J.H. Aerts , Philip J. Ward Changing mechanism of global water scarcity events: Impacts of socioeconomic changes and inter-annual hydro-climatic variability 10.1016/j.gloenvcha.2015.02.011 AI & Machine Learning Soil Moisture No abstract available 603608
publications-1562 PEER REVIEWED ARTICLE 2017 Daniele Casella , Lia Martins Costa do Amaral , Stefano Dietrich , Anna Cinzia Marra , Paolo Sano , Giulia Panegrossi The Cloud Dynamics and Radiation Database Algorithm for AMSR2: Exploitation of the GPM Observational Dataset for Operational Applications 10.1109/jstars.2017.2713485 Predictive Analytics River Basins No abstract available 603608
publications-1563 PEER REVIEWED ARTICLE 2017 Daniele Casella , Giulia Panegrossi , Paolo SanĂČ , Anna Cinzia Marra , Stefano Dietrich , Benjamin T. Johnson , Mark S. Kulie Evaluation of the GPM-DPR snowfall detection capability: Comparison with CloudSat-CPR 10.1016/j.atmosres.2017.06.018 Predictive Analytics River Basins No abstract available 603608
publications-1564 PEER REVIEWED ARTICLE 2017 Giulia Panegrossi , Jean-François Rysman , Daniele Casella , Anna Marra , Paolo SanĂČ , Mark Kulie CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities 10.3390/rs9121263 Simulation & Modeling River Basins The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m−2, IWP > 0.24 kg·m−2 over land, and SIC > 57%, TPW > 5.1 kg·m−2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms. 603608
publications-1565 PEER REVIEWED ARTICLE 2018 Yagmur Derin , Emmanouil Anagnostou , Marios N. Anagnostou , John Kalogiros , Daniele Casella , Anna Cinzia Marra , Giulia Panegrossi , Paolo Sano Passive Microwave Rainfall Error Analysis Using High-Resolution X-Band Dual-Polarization Radar Observations in Complex Terrain 10.1109/tgrs.2017.2763622 Predictive Analytics River Basins No abstract available 603608
publications-1566 PEER REVIEWED ARTICLE 2017 Hylke E. Beck , Noemi Vergopolan , Ming Pan , Vincenzo Levizzani , Albert I. J. M. van Dijk , Graham P. Weedon , Luca Brocca , Florian Pappenberger , Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling 10.5194/hess-21-6201-2017 Data Management & Analytics Soil Moisture Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates. 603608
publications-1567 PEER REVIEWED ARTICLE 2017 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&amp;deg; global gridded precipitation (1979&amp;ndash;2015) by merging gauge, satellite, and reanalysis data 10.5194/hess-21-589-2017 Simulation & Modeling Precipitation & Ecological Systems 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) version 1.1, 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 timescale 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 vs. 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 (Hydrologiska ByrĂ„ns Vattenbalansavdelning) 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 vs. 0.29–0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org. 603608
publications-1568 PEER REVIEWED ARTICLE 2017 M. M. Miglietta , D. Cerrai , S. Laviola , E. Cattani , V. Levizzani Potential vorticity patterns in Mediterranean “hurricanes” 10.1002/2017gl072670 Simulation & Modeling River Basins AbstractThe potential vorticity (PV) anomalies due to the intrusion of dry stratospheric air and those generated by the tropospheric diabatic latent heating are qualitatively analyzed for five Mediterranean tropical‐like cyclones (also known as Medicanes). Model simulations show the presence of an upper level PV streamer in the early stages of the cyclone, located on the left exit of a jet stream, and a middle‐low level PV anomaly generated by the convection developing around the low‐level vortex. In the mature stage, the upper level PV anomaly around the cyclone evolves differently for each case and appears somehow dependent on the lifetime. Only for the 2006 Medicane, the PV anomalies form an intense PV tower extending continuously from the lower troposphere to the lower stratosphere. 603608
publications-1569 PEER REVIEWED ARTICLE 2017 F.J. Tapiador , A. Navarro , V. Levizzani , E. GarcĂ­a-Ortega , G.J. Huffman , C. Kidd , P.A. Kucera , C.D. Kummerow , H. Masunaga , W.A. Petersen , R Global precipitation measurements for validating climate models 10.1016/j.atmosres.2017.06.021 Simulation & Modeling River Basins No abstract available 603608
publications-1570 PEER REVIEWED ARTICLE 2018 V. Levizzani , C. Kidd , K. Aonashi , R. Bennartz , R. R. Ferraro , G. J. Huffman , R. Roca , F. J. Turk , N.-Y. Wang The activities of the International Precipitation Working Group 10.1002/qj.3214 Simulation & Modeling River Basins The International Precipitation Working Group (IPWG) is a permanent International Science Working Group (ISWG) of the Coordination Group for Meteorological Satellites (CGMS), co‐sponsored by CGMS and the World Meteorological Organization (WMO). The IPWG provides a focal point and forum for the international scientific community to address the issues and challenges of satellite‐based quantitative precipitation retrievals, and for the operational agencies to access and make use of precipitation products. Through partnerships and biennial meetings, the group supports the exchange of information on techniques for retrieving and measuring precipitation and for enhancing the impact of space‐borne precipitation retrievals in numerical weather and hydrometeorological prediction and climate studies. The group furthers the refinement of current estimation techniques and the development of new methodologies for improved global precipitation measurements, together with the validation of the derived precipitation products with ground‐based precipitation measurements. The IPWG identifies critical issues, provides recommendations to the CGMS and supports upcoming precipitation‐oriented missions. Training activities on precipitation retrieval from space are also part of the IPWG mandate in cooperation with WMO and other bodies. 603608