Lateral terrestrial water fluxes in the LSM of WRF‐Hydro: benefits of a 2D groundwater representation

The interactions between the atmosphere and the land surface are characterized by complex, non-linear processes on varying time scales. The Noah-MP is a medium complexity land-surface model (LSM), which was recently selected as the new default LSM for the hydrologically enhanced Weather Research and Forecasting modelling system (WRF-Hydro). Compared to its predecessor, several parameterizations were considerably improved and new ones added, inter alia more sophisticated groundwater descriptions, which aim to replace the traditional free-drainage lower boundary condition. This study investigates the benefits that can be obtained from a two-dimensional groundwater representation within the WRF-Hydro modelling system by performing two offline simulations for the upper Danube river basin. In comparison to the free-drainage reference simulation, the lateral routing of groundwater and the two-way interaction with the water table greatly enhances small scale variability in simulated fields of soil moisture content and evapotranspiration (ET). The representation of upward fluxes from the aquifer helps to maintain higher soil moisture contents and thus ET during prolonged dry periods. These differences are rather small though (<2%) and explained by the fact that the study region is considered to be limited by radiative energy and not water availability. The most striking difference however is the performance gap in simulating streamflow. WRF-Hydro with 2d groundwater scheme clearly outperforms the reference simulation in terms of performance metrics. A comparison with hourly streamflow observations for the water year of 2016 yields average Kling-Gupta efficiencies of 0.79 versus 0.57 for the reference. Given that both model configurations were not calibrated beforehand, we conclude that the two-dimensional groundwater option is especially beneficial for applications in poorly or even ungauged catchments. Furthermore, the inclusion of a so far missing compartment of the water cycle in the WRF-Hydro modelling system allows for a more holistic representation of interactions between atmosphere land surface and subsurface, which will be advantageous in feedback studies with the fully coupled WRF-Hydro

Lateral terrestrial water fluxes in the LSM of WRF‐Hydro: Benefits of a 2D groundwater representation

The interactions between the atmosphere and the land surface are characterized by complex, non-linear processes on varying time scales. The Noah-MP is a medium complexity land-surface model (LSM), which was recently selected as the new default LSM for the hydrologically enhanced Weather Research and Forecasting modelling system (WRF-Hydro). Compared to its predecessor, several parameterizations were considerably improved and new ones added, inter alia more sophisticated groundwater descriptions, which aim to replace the traditional free-drainage lower boundary condition. This study investigates the benefits that can be obtained from a two-dimensional groundwater representation within the WRF-Hydro modelling system by performing two offline simulations for the upper Danube river basin. In comparison to the free-drainage reference simulation, the lateral routing of groundwater and the two-way interaction with the water table greatly enhances small scale variability in simulated fields of soil moisture content and evapotranspiration (ET). The representation of upward fluxes from the aquifer helps to maintain higher soil moisture contents and thus ET during prolonged dry periods. These differences are rather small though (<2%) and explained by the fact that the study region is considered to be limited by radiative energy and not water availability. The most striking difference however is the performance gap in simulating streamflow. WRF-Hydro with 2d groundwater scheme clearly outperforms the reference simulation in terms of performance metrics. A comparison with hourly streamflow observations for the water year of 2016 yields average Kling-Gupta efficiencies of 0.79 versus 0.57 for the reference. Given that both model configurations were not calibrated beforehand, we conclude that the two-dimensional groundwater option is especially beneficial for applications in poorly or even ungauged catchments. Furthermore, the inclusion of a so far missing compartment of the water cycle in the WRF-Hydro modelling system allows for a more holistic representation of interactions between atmosphere land surface and subsurface, which will be advantageous in feedback studies with the fully coupled WRF-Hydro

Role of Runoff–Infiltration Partitioning and Resolved Overland Flow on Land–Atmosphere Feedbacks: A Case Study with the WRF-Hydro Coupled Modeling System for West Africa

The analysis of land–atmosphere feedbacks requires detailed representation of land processes in atmospheric models. The focus here is on runoff–infiltration partitioning and resolved overland flow. In the standard version of WRF, runoff–infiltration partitioning is described as a purely vertical process. In WRF-Hydro, runoff is enhanced with lateral water flows. The study region is the Sissili catchment (12 800 km$^{2}$) in West Africa, and the study period is from March 2003 to February 2004. The WRF setup here includes an outer and inner domain at 10- and 2-km resolution covering the West Africa and Sissili regions, respectively. In this WRF-Hydro setup, the inner domain is coupled with a subgrid at 500-m resolution to compute overland and river flow. Model results are compared with TRMM precipitation, model tree ensemble (MTE) evapotranspiration, Climate Change Initiative (CCI) soil moisture, CRU temperature, and streamflow observation. The role of runoff–infiltration partitioning and resolved overland flow on land–atmosphere feedbacks is addressed with a sensitivity analysis of WRF results to the runoff–infiltration partitioning parameter and a comparison between WRF and WRF-Hydro results, respectively. In the outer domain, precipitation is sensitive to runoff–infiltration partitioning at the scale of the Sissili area (~100 × 100 km2), but not of area A (500 × 2500 km$^{2}$). In the inner domain, where precipitation patterns are mainly prescribed by lateral boundary conditions, sensitivity is small, but additionally resolved overland flow here clearly increases infiltration and evapotranspiration at the beginning of the wet season when soils are still dry. The WRF-Hydro setup presented here shows potential for joint atmospheric and terrestrial water balance studies and reproduces observed daily discharge with a Nash–Sutcliffe model efficiency coefficient of 0.43

Assimilation of GNSS and synoptic data in a convection permitting limited area model: improvement of simulated tropospheric water vapor content

The assimilation of observations in limited area models (LAMs) allows to find the best possible estimate of a region’s meteorological state. Water vapor is a crucial constituent in terms of cloud and precipitation formation. Its highly variable nature in space and time is often insufficiently represented in models. This study investigates the improvement of simulated water vapor content within the Weather Research and Forecasting model (WRF) in every season by assimilating temperature, relative humidity, and surface pressure obtained from climate stations, as well as geodetically derived Zenith Total Delay (ZTD) and precipitable water vapor (PWV) data from global navigation satellite system (GNSS) ground stations. In four case studies we analyze the results of high-resolution convection-resolving WRF simulations (2.1 km) between 2016 and 2018 each in every season for a 650 × 670 km domain in the tri-border-area Germany, France and Switzerland. The impact of 3D VAR assimilation of different variables and combinations thereof, background error option, as well as the temporal and spatial resolution of assimilation is evaluated. Both column values and profiles derived from radiosondes are addressed. Best outcome was achieved when assimilating ZTD and synoptic data at an hourly resolution and a spatial thinning distance of 10 km. It is concluded that the careful selection of assimilation options can additionally improve simulation results in every season. Clear effects of assimilation on the water budgets can also be seen

Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape

The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R 2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops

Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively

Precipitation sensitivity to the uncertainty of terrestrial water flow in WRF-Hydro: An ensemble analysis for central Europe

Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April–October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%–20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%–0.7%. The reproduction of observed daily discharge with Nash–Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science

Disentangling effects of climate and land use on biodiversity and ecosystem services - a multi‐scale experimental design

Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs