Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Abstract

Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.

Comment on egusphere-2024-978

“…Several past studies also indicated the underestimation of ET and GPP in CLM5 compared to observations (Boas et al, 2023;Strebel et al, 2023;Cheng et al, 2021;Birch et al, 2021), which we confirm in this study. Parameter improvements could also alleviate these general underestimations of GPP and ET across PFTs, especially during summer (Dagon et al, 2020).…”

“…Recent studies already found discrepancies between LSM simulations of ET and GPP and observations collected in the field and from remote sensing. For instance, these discrepancies are evident in their magnitude and variability (De Pue et al, 2023;Boas et al, 2023;Cheng et al, 2021;Strebel et al, 2023) and their response to drought (Ukkola et al, 2016;Wu et al, 2020;Green et al, 2024). Therefore, assessing the accuracy of LSMs in representing observed GPP and ET fluxes is crucial to test and improve our current understanding of ecosystem process variability and identify the limitations of state-of-the-art LSMs.…”

“…In the most recent version, CLM5 solves the biogeochemistry (BGC), i.e., the carbon and nitrogen cycles between the atmosphere, vegetation, and soil. CLM5 has been widely employed for quantifying and examining ecosystems at various scales, including global (Xie et al, 2020;Sitch et al, 2015;Lawrence et al, 2019), regional (Cheng et al, 2021;Boas et al, 2023) and site-scale (Strebel et al, 2023;Umair et al, 2020;Song et al, 2020;Fisher et al, 2019a) applications. Several studies have highlighted the ability of CLM5 to simulate ecosystem processes close to the observations (Wozniak et al, 2020;Lawrence et al, 2019;Cheng et al, 2021;Zhang et al, 2023;Boas et al, 2023).…”

“…CLM5 has been widely employed for quantifying and examining ecosystems at various scales, including global (Xie et al, 2020;Sitch et al, 2015;Lawrence et al, 2019), regional (Cheng et al, 2021;Boas et al, 2023) and site-scale (Strebel et al, 2023;Umair et al, 2020;Song et al, 2020;Fisher et al, 2019a) applications. Several studies have highlighted the ability of CLM5 to simulate ecosystem processes close to the observations (Wozniak et al, 2020;Lawrence et al, 2019;Cheng et al, 2021;Zhang et al, 2023;Boas et al, 2023). However, they have also emphasized an underestimated magnitude and variability in the simulations across different time scales and under various conditions.…”

Hydrometeorological Insights into the Forecasting Performance of Multi-Source Weather over a Typical Hill-Karst Basin, Southwest China

“…These products typically use partial differential formulations to describe atmospheric states and then apply numerical approximations to solve for the states at a specific resolution. Meanwhile, these products have provided valuable seasonal precipitation forecasts to support various applications, including those related to agriculture [7], hydrology [8], and the environment [9].…”