genRE: A Method to Extend Gridded Precipitation Climatology Data Sets in Near Real‐Time for Hydrological Forecasting Purposes

Osnabrugge, Bart van; Weerts, Albrecht; Uijlenhoet, R.

doi:10.1002/2017wr021201

Cited by 22 publications

(25 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A meteorological forcing data set is available for the Rhine basin, consisting of gauged precipitation interpolated with the genRE method (Van Osnabrugge et al, ), potential evapotranspiration, and temperature data (Van Osnabrugge et al, ), on a 1,200 m grid. Since not all meteorological stations were continuously delivering high‐quality data, we refer to Van Osnabrugge et al (, ) for an indication of the yearly availability and quality of the forcing data. These forcing data sets are available for the period 1996 through 2015.…”

Section: Study Area and Datamentioning

confidence: 99%

Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River

Imhoff

Verseveld

Osnabrugge

et al. 2020

Water Resources Research

Self Cite

View full text Add to dashboard Cite

Moving toward high-resolution gridded hydrologic models asks for novel parametrization approaches. A high-resolution conceptual hydrologic model (wflow_sbm) was parameterized for the Rhine basin in Europe based on point-scale (pedo)transfer functions, without further calibration of effective model parameters on discharge. Parameters were estimated on the data resolution, followed by upscaling of parameter fields to the model resolution. The method was tested using a 6-hourly time step at four model resolutions (1.2, 2.4, 3.6, and 4.8 km), followed by a validation with discharge observations and a comparison with actual evapotranspiration (ET act ) estimates from an independent model (DMET Land Surface Analysis Satellite Application Facility). Additionally, the scalability of parameter fields and simulated fluxes was tested. Validation of simulated discharges yielded Kling-Gupta Efficiency (KGE) values ranging from 0.6 to 0.9, except for the Alps where a volume bias caused lower performance. Catchment-averaged temporal ET act dynamics were comparable with independent ET estimates (KGE ≈ 0.7), although wflow_sbm model simulations were on average 115 mm yr −1 higher. Spatially, the two models were less in agreement (SPAEF = 0.10), especially around the Rhine valley. Consistent parameter fields were obtained, and by running the model at the different resolutions, preserved ET act fluxes were found across the scales. For recharge, fluxes were less consistent with relative errors around 30% for regions with high drainage densities. However, catchment-averaged fluxes were better preserved. Routed discharge in headwaters was not consistent across scales, although simulations for the main Rhine River were. Better processing (scale independent) of the river and drainage network may overcome this issue.Plain Language Summary Hydrologic models are used for flood and drought predictions. Most models have parameters, and to increase model performance, hydrologists often tune these parameters by calibration. State-of-the-art gridded hydrologic models have parameter sets per grid cell, leading to many parameters and making current calibration procedures far from ideal. Here, we tested the use of well-known (pedo)transfer functions from literature to estimate these parameter values, something which can reduce the calibration burden. By using parameter-specific upscaling rules to derive seamless parameter maps for the wflow_sbm model, which explicitly takes subsurface lateral flows into account, this gives a model which is scalable to different grid cell sizes. We assessed the approach on multiple model resolutions, and we found consistent parameter fields and the preservation of vertical fluxes. Only routed discharge, a key output, deteriorates for headwater catchments on coarser resolutions. We attribute this to model structure and the derivation procedure of the river network on different scales, resulting in the loss of lateral flow representation on coarser resolutions. Nevertheless, discharge and evapotranspiration simulatio...

show abstract

Section: Study Area and Datamentioning

confidence: 99%

Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River

Imhoff

Verseveld

Osnabrugge

et al. 2020

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pathways for facing these challenges have recently merged. For instance, in IMPREX, a high-resolution (both temporally and spatially) dataset of area-average precipitation, temperature and potential evapotranspiration (based on satellite downwelling shortwave radiation) was developed for the Rhine River and used to verify the ECMWF ensemble weather forecasts [21,22]. These high-resolution datasets have the advantage of better representing the heterogeneities that are not captured by the relatively coarse grid scale of the atmospheric model.…”

Section: Gaps In Global Observed Datamentioning

confidence: 99%

A Vision for Hydrological Prediction

et al. 2020

View full text Add to dashboard Cite

IMproving PRedictions and management of hydrological EXtremes (IMPREX) was a European Union Horizon 2020 project that ran from September 2015 to September 2019. IMPREX aimed to improve society’s ability to anticipate and respond to future extreme hydrological events in Europe across a variety of uses in the water-related sectors (flood forecasting, drought risk assessment, agriculture, navigation, hydropower and water supply utilities). Through the engagement with stakeholders and continuous feedback between model outputs and water applications, progress was achieved in better understanding the way hydrological predictions can be useful to (and operationally incorporated into) problem-solving in the water sector. The work and discussions carried out during the project nurtured further reflections toward a common vision for hydrological prediction. In this article, we summarized the main findings of the IMPREX project within a broader overview of hydrological prediction, providing a vision for improving such predictions. In so doing, we first presented a synopsis of hydrological and weather forecasting, with a focus on medium-range to seasonal scales of prediction for increased preparedness. Second, the lessons learned from IMPREX were discussed. The key findings were the gaps highlighted in the global observing system of the hydrological cycle, the degree of accuracy of hydrological models and the techniques of post-processing to correct biases, the origin of seasonal hydrological skill in Europe and user requirements of hydrometeorological forecasts to ensure their appropriate use in decision-making models and practices. Last, a vision for how to improve these forecast systems/products in the future was expounded, including advancing numerical weather and hydrological models, improved earth monitoring and more frequent interaction between forecasters and users to tailor the forecasts to applications. We conclude that if these improvements can be implemented in the coming years, earth system and hydrological modelling will become more skillful, thus leading to socioeconomic benefits for the citizens of Europe and beyond.

show abstract

“…The study area, the Moselle River, is a tributary of the Rhine River. The Rhine River Basin covers an area of 165,000 km 2 in eight countries, that is, the Netherlands, Germany, Switzerland, Luxemburg, Belgium, France, Austria and Liechtenstein [28,29]. The Moselle River Basin covers the mountains of the middle reaches in Germany, including the Black Forest (Schwarzwald) and the Vosges, reaching elevations over 1000 m (3280 feet) in the south, to around 600 m (1968 feet) towards the north.…”

Section: Study Areamentioning

confidence: 99%

Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

Demirel

Özen

Orta

et al. 2019

Water

View full text Add to dashboard Cite

Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.

show abstract

genRE: A Method to Extend Gridded Precipitation Climatology Data Sets in Near Real‐Time for Hydrological Forecasting Purposes

Cited by 22 publications

References 36 publications

Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River

Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River

A Vision for Hydrological Prediction

Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

Contact Info

Product

Resources

About