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–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-1531 PEER REVIEWED ARTICLE 2016 P. Quintana-Seguí Meteorological Analysis Systems in North-East Spain: Validation of SAFRAN and SPAN 10.3808/jei.201600335 Data Management & Analytics Precipitation & Ecological Systems No abstract available 603608
publications-1532 PEER REVIEWED ARTICLE 2016 C.M Holgate , R.A.M. De Jeu , A.I.J.M van Dijk , Y.Y Liu , L.J. Renzullo , Vinodkumar , I. Dharssi , R.M. Parinussa , R. Van Der Schalie , A. Gevaert Comparison of remotely sensed and modelled soil moisture data sets across Australia 10.1016/j.rse.2016.09.015 Data Management & Analytics Precipitation & Ecological Systems No abstract available 603608
publications-1533 PEER REVIEWED ARTICLE 2016 Patricia López López , Niko Wanders , Jaap Schellekens , Luigi J. Renzullo , Edwin H. Sutanudjaja , Marc F. P. Bierkens Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations 10.5194/hess-20-3059-2016 Data Management & Analytics Precipitation & Ecological Systems Abstract. The coarse spatial resolution of global hydrological models (typically  >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from  ∼  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research. 603608
publications-1534 PEER REVIEWED ARTICLE 2016 Rogier Westerhoff , Paul White , Zara Rawlinson Application of global models and satellite data forsmaller-scale groundwater recharge studies 10.5194/hess-2016-410 Data Management & Analytics Precipitation & Ecological Systems Abstract. Large-scale models and satellite data are increasingly used to characterise groundwater and its recharge at the global scale. Although these models have the potential to fill in data gaps and solve trans-boundary issues, they are often neglected in smaller-scale studies, since data are often coarse or uncertain. Large-scale models and satellite data could play a more important role in smaller-scale (i.e., national or regional) studies, if they could be adjusted to fit that scale. In New Zealand, large-scale models and satellite data are not used for groundwater recharge estimation at the national scale, since regional councils (i.e., the water managers) have varying water policy and models are calibrated at the local scale. Also, some regions have many localised ground observations (but poor record coverage), whereas others are data-sparse. Therefore, estimation of recharge is inconsistent at the national scale. This paper presents an approach to apply large-scale, global, models and satellite data to estimate rainfall recharge at the national to regional scale across New Zealand. We present a model, NGRM, that is largely inspired by the global-scale WaterGAP recharge model, but is improved and adjusted using national data. The NGRM model uses MODIS-derived ET and vegetation satellite data, and the available nation-wide datasets on rainfall, elevation, soil and geology. A valuable addition to the recharge estimation is the model uncertainty estimate, based on variance, covariance and sensitivity of all input data components in the model environment. This research shows that, with minor model adjustments and use of improved input data, large-scale models and satellite data can be used to derive rainfall recharge estimates, including their uncertainty, at the smaller scale, i.e., national and regional scale of New Zealand. The estimated New Zealand recharge of the NGRM model compare well to most local and regional lysimeter data and recharge models. The NGRM is therefore assumed to be capable to fill in gaps in data-sparse areas and to create more consistency between datasets from different regions, i.e., to solve trans-boundary issues. This research also shows that smaller-scale recharge studies in New Zealand should include larger boundaries than only a (sub-)aquifer, and preferably the whole catchment. This research points out the need for improved collaboration on the international to national to regional levels to further merge large-scale (global) models to smaller (i.e., national or regional) scales. Future research topics should, collaboratively, focus on: improvement of rainfall-runoff and snowmelt methods; inclusion of river recharge; further improvement of input data (rainfall, evapotranspiration, soil and geology); and the impact of recharge uncertainty in mountainous and irrigated areas. 603608
publications-1535 PEER REVIEWED ARTICLE 2015 R.S. Westerhoff Using uncertainty of Penman and Penman–Monteith methods in combined satellite and ground-based evapotranspiration estimates 10.1016/j.rse.2015.07.021 Data Management & Analytics Precipitation & Ecological Systems No abstract available 603608
publications-1536 PEER REVIEWED ARTICLE 2016 A.I. Gevaert , R.M. Parinussa , L.J. Renzullo , A.I.J.M. van Dijk , R.A.M. de Jeu Spatio-temporal evaluation of resolution enhancement for passive microwave soil moisture and vegetation optical depth 10.1016/j.jag.2015.08.006 Data Management & Analytics Precipitation & Ecological Systems No abstract available 603608
publications-1537 PEER REVIEWED ARTICLE 2016 Dejene Sahlu , Efthymios I. Nikolopoulos , Semu A. Moges , Emmanouil N. Anagnostou , Dereje Hailu First Evaluation of the Day-1 IMERG over the Upper Blue Nile Basin 10.1175/jhm-d-15-0230.1 Simulation & Modeling Precipitation & Ecological Systems Abstract This work presents a first evaluation of the performance of the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product over the upper Blue Nile basin of Ethiopia. One of the unique features of this study is the availability of hourly rainfall measurements from an experimental rain gauge network in the area. Both the uncalibrated and calibrated versions of IMERG are evaluated, and their performance is contrasted against another high-resolution satellite product, which is the Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH). The analysis is performed for hourly and daily time scales and at spatial scales that correspond to the nominal resolution of satellite products, which is 0.1° spatial resolution. The period analyzed is focused on a single wet season (May–October 2014). Evaluation is performed using several statistical and categorical error metrics, as well as spatial correlation analysis to assess the ability of satellite products to represent spatial variability of precipitation in the area. Results show that both IMERG products have a better bias ratio and correlation coefficient on both time scales as compared to CMORPH. Comparison statistics show a slight improvement in the skill of detecting rainfall events in IMERG products compared to CMORPH. Results also show a decreasing trend in the detection ability of satellite products for increasing threshold values, highlighting the need to further improve detection during heavy precipitation. 603608
publications-1538 PEER REVIEWED ARTICLE 2017 Pere Quintana-Seguí , Marco Turco , Sixto Herrera , Gonzalo Miguez-Macho Validation of a new SAFRAN-based gridded precipitation productfor Spain and comparisons to Spain02 and ERA-Interim 10.5194/hess-21-2187-2017 Uncategorized Precipitation & Ecological Systems Abstract. Offline land surface model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/1980–2013/2014). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate regional climate models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The validation of SAFRAN with independent data shows that this is a robust product. SAFRAN and Spain02 have very similar scores, although the latter slightly surpasses the former. The scores are robust with altitude and throughout the year, save perhaps in summer when a diminished skill is observed. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model. 603608
publications-1539 PEER REVIEWED ARTICLE 2016 Maria Jose Escorihuela , Pere Quintana-Seguí Comparison of remote sensing and simulated soil moisture datasets in Mediterranean landscapes 10.1016/j.rse.2016.02.046 Uncategorized Precipitation & Ecological Systems No abstract available 603608
publications-1540 PEER REVIEWED ARTICLE 2016 P. Quintana-Seguí Meteorological Analysis Systems in North-East Spain: Validation of SAFRAN and SPAN 10.3808/jei.201600335 Hydrological modeling River Basins No abstract available 603608