Precipitation Sensitivity to the Uncertainty of Terrestrial Water Flow in WRF-Hydro: An Ensemble Analysis for Central Europe

Arnault, Joël; Rummler, Thomas; Baur, Florian; Lerch, Sebastian; Wagner, Sven; Fersch, Benjamin; Zhang, Zhenyu; Kerandi, Noah M.; Keil, Christian; Kunstmann, Harald

doi:10.1175/jhm-d-17-0042.1

Cited by 39 publications

(51 citation statements)

References 58 publications

(95 reference statements)

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“…Currently, there are three prominent methods to obtain the forcing data. Firstly, all forcings derive from one source directly such as the WRF outputs and the Global Forecast System [24,28,[69][70][71]. Secondly, forcings are from the combination of several products [19,25,72].…”

Section: Forcing Scenarios Designmentioning

confidence: 99%

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Sun

Yao

et al. 2020

Water

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The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).

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Section: Forcing Scenarios Designmentioning

confidence: 99%

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Sun

Yao

et al. 2020

Water

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“…Most of the surplus in humidity is probably transported beyond the domain boundary. If the coupled simulation was extended to a larger area, e.g., Europe, impact above the boundary layer would be expected, at least for weak synoptic forcing periods (Arnault et al, 2018).…”

Section: Evapotranspiration and Soil Moisturementioning

confidence: 99%

High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

Fersch¹,

Senatore²,

Adler³

et al. 2019

Preprint

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Abstract. The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or largely bound to traditional disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. This study examines the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the preAlpine Terrestrial Environmental Observatory (TERENO-preAlpine) for the Ammer (600 km2) and Rott (55 km2) river catchments in southern Germany, covering a five month period (Jun–Oct 2016). The sensitivity of 7 land surface parameters is tested using the Latin-Hypercube One-factor-At-a-Time (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent Parameter Estimation and Uncertainty Analysis software (PEST). The calibration of the offline WRF-Hydro gives Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro, both using the calibrated parameters from the offline model, shows nominal alterations for radiation and precipitation but considerable changes for moisture- and heat fluxes. By comparison with TERENO-preAlpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.

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“…As a side note, the fact that the annual surface evaporation is slightly higher in WRF-Hydro (see section 3.2, and also Senatore et al, 2015, Arnault et al, 2018, Rummler et al, 2019, Zhang et al, 2019 is considered to be related to the primary increasing effect on E tagged , and the relatively high frequency of precipitation events in the study region which masks the secondary decreasing effect on E tagged .…”

Section: Water Resources Researchmentioning

confidence: 99%

“…Annually, WRF overestimates E OBS by 2.7 mm/year (+0.6%) and WRF-Hydro by 7.3 mm/year (+1.5%). The slight increase in surface evaporation induced by WRF-Hydro is the consequence of ponded water infiltration, which increases the soil water storage, as discussed by, for example, Senatore et al (2015), Arnault et al (2018), Rummler et al (2019), and Zhang et al (2019).…”

Section: Water Resources Researchmentioning

confidence: 99%

“…Recent studies showed that considering the horizontal transport of terrestrial water in a climate model has an impact on precipitation (e.g., Arnault, Wagner, et al, ; Arnault et al, ; Kerandi et al, ; Larsen et al, ; Maxwell et al, ; Rahman et al, ; Rummler et al, ; Senatore et al, ; Wagner et al, ; Zhang et al ). Indeed, spatially redistributing soil moisture modifies surface fluxes, which potentially affects boundary layer dynamics, convection initiation and precipitation (e.g., Pielke, ).…”

Section: Introductionmentioning

confidence: 99%

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A Joint Soil‐Vegetation‐Atmospheric Water Tagging Procedure With WRF‐Hydro: Implementation and Application to the Case of Precipitation Partitioning in the Upper Danube River Basin

Arnault

Wei

Rummler

et al. 2019

Water Resources Research

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Atmospheric models such as the Weather Research and Forecasting (WRF) model provide a tool to evaluate the behavior of regional hydrological cycle components, including precipitation, evapotranspiration, soil water storage, and runoff. Recent model developments have focused on coupled atmospheric‐hydrological modeling systems, such as WRF‐Hydro, in order to account for subsurface, overland, and river flow and potentially improve the representation of land‐atmosphere interactions. The aim of this study is to investigate the contribution of lateral terrestrial water flow to the regional hydrological cycle, with the help of a joint soil‐vegetation‐atmospheric water tagging procedure newly developed in the so‐called WRF‐tag and WRF‐Hydro‐tag models. An application of both models for the high precipitation event on 15 August 2008 in the German and Austrian parts of the upper Danube river basin (94,100 km2) is presented. The precipitation that fell in the basin during this event is considered as a water source, is tagged, and subsequently tracked for a 40‐month period until December 2011. At the end of the study period, in both simulations, approximately 57% of the tagged water has run off, while 41% has evaporated back to the atmosphere, including 2% that has recycled in the upper Danube river basin as precipitation. In WRF‐Hydro‐tag, the surface evaporation of tagged water is slightly enhanced by surface flow infiltration and slightly reduced by subsurface lateral water flow in areas with low topography gradients. This affects the source precipitation recycling only in a negligible amount.

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Precipitation Sensitivity to the Uncertainty of Terrestrial Water Flow in WRF-Hydro: An Ensemble Analysis for Central Europe

Cited by 39 publications

References 58 publications

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

A Joint Soil‐Vegetation‐Atmospheric Water Tagging Procedure With WRF‐Hydro: Implementation and Application to the Case of Precipitation Partitioning in the Upper Danube River Basin

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