Critical Role of Soil Moisture Memory in Predicting the 2012 Central United States Flash Drought

The unprecedented 2012 summer drought over the central United States was characterized by rapid intensification and severe impact and was known as a flash drought. Since then, flash drought has raised a wide concern, with considerable progresses on the definition, detection of anthropogenic footprints, and assessment of ecological impact. However, physical mechanisms related to the flash drought predictability remain unclear. Here, we show that the severity of the 2012 flash drought will be heavily underestimated without realistic initial soil moisture condition. The global Weather Research and Forecasting (GWRF) model was employed during the summers of 1979–2012, driven by observed sea surface temperature but without lateral boundary controls, which is similar to two-tier global seasonal prediction. The 2012 United States drought pattern was roughly captured by the GWRF ensemble global simulations, although with obvious underestimation of the severity. To further diagnose the role of soil moisture memory, dry and wet simulations that decrease and increase initial soil moisture by 10% were conducted. While the dry case does not significantly differ from the control case, the wet case totally missed the drought over the Central and Southern Great Plains by changing the anticyclonic circulation anomaly to a cyclonic anomaly and simulating a northward anomaly of meridional wind that brought anomalous moisture from the Gulf of Mexico and finally resulted in a failure to predict the drought. This study highlights the importance of soil moisture memory in predicting flash drought that often occurred without strong oceanic signal.

Land-atmosphere coupling in operational NCUM forecasts during the 2020 monsoon season

<p>The onset, persistence and variability of summer monsoon rainfall impacts over a billion people. Advance knowledge is critical for agricultural planning and hazard mitigation, yet forecasting remains a challenge. Sources of error that have been identified in forecast models include the representation of the land surface and subsequent coupling with the boundary layer and convection. This study presents an analysis of land-atmosphere coupling in the operational Indian 4km convective scale regional model configuration of the Unified Model (NCUM-R), used by NCMRWF to provide daily forecasts. An earlier study (Barton et al, QJRMS 2019) analysed the coupling in this model for a single forecast when research aircraft observations were available. It revealed rapidly evolving biases in the monsoon trough linked to errors in the representation of soil moisture. Our current work aims to understand whether this behavior is typical of the monsoon season. This matters because the trough is an important dynamical feature and a key driver of regional rainfall. Here we provide a more comprehensive analysis by assessing the impact of initial soil moisture state on a full season of operational three day forecasts. NCUM-R output is evaluated by comparison to ERA5 reanalysis (atmospheric temperature and pressure) and satellite observations from AMSR2 (land surface temperature) and SMAP (soil moisture).  Correlations between surface and atmospheric variables in the model are computed using linear regression. Our results suggest that systematic biases in the evolution of atmospheric temperature and pressure over three days are indeed linked to errors in the initial soil moisture state. These biases likely impact rainfall predictions derived from the forecasts throughout the monsoon season. This work highlights the importance for realistic soil moisture initialisation in high resolution operational forecasts.</p>

Exploring the potential of soil moisture reanalysis data for improving the identification of regional landslide triggering thresholds

<p>Landslide triggering thresholds provide the rainfall conditions that are likely to trigger landslides, therefore their derivation is key for prediction purposes. Different variables can be considered for the identification of thresholds, which commonly are in the form of a power-law relationship linking rainfall event duration and intensity or cumulated event rainfall. The assessment of such rainfall thresholds generally neglects initial soil moisture conditions at each rainfall event, which are indeed a predisposing factor that can be crucial for the proper definition of the triggering scenario. Thus, more studies are needed to understand whether and the extent to which the integration of the initial soil moisture conditions with rainfall thresholds could improve the conventional precipitation-based approach. Although soil moisture data availability has hindered such type of studies, yet now this information is increasingly becoming available at the large scale, for instance as an output of meteorological reanalysis initiatives. In particular, in this study, we focus on the use of the ERA5-Land reanalysis soil moisture dataset. Climate reanalysis combines past observations with models in order to generate consistent time series and the ERA5-Land data actually provides the volume of water in soil layer at different depths and at global scale. Era5-Land project is, indeed, a global dataset at 9 km horizontal resolution in which atmospheric data are at an hourly scale from 1981 to present. Volumetric soil water data are available at four depths ranging from the surface level to 289 cm, namely 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm. After collecting the rainfall and soil moisture data at the desired spatio-temporal resolution, together with the target data discriminating landslide and no-landslide events, we develop automatic triggering/non-triggering classifiers and test their performances via confusion matrix statistics. In particular, we compare the performances associated with the following set of precursors: a) event rainfall duration and depth (traditional approach), b) initial soil moisture at several soil depths, and c) event rainfall duration and depth and initial soil moisture at different depths. The approach is applied to the Oltrepò Pavese region (northern Italy), for which the historical observed landslides have been provided by the IFFI project (Italian landslides inventory). Results show that soil moisture may allow an improvement in the performances of the classifier, but that the quality of the landslide inventory is crucial.</p>

Simulation analysis of local land atmosphere coupling in rainy season over a typical underlying surface in the Tibetan Plateau

The local land–atmosphere coupling (LoCo) investigates the interactions between soil conditions, surface fluxes, planetary boundary layer (PBL) growth, and the formations of convective clouds and precipitation. Studying LoCo over the Tibetan Plateau (TP) is of great significance for understanding the TP's role in the Asian water tower. A series of real-case simulations, using the Weather Research and Forecasting (WRF) model with different combinations of land surface model (LSM) schemes and PBL schemes, has been carried out to investigate the LoCo characteristics over a typical underlying surface in the central TP in the rainy season. The LoCo characteristics in the study area are analyzed by applying a mixing diagram to the simulation results. The analysis indicates that the WRF simulations, using the Noah with BouLac, Mellor-Yamada Nakanishi and Niino Level-2.5 PBL (MYNN), and Yonsei University (YSU) produce closer results to the observation in terms of curves of Cp⋅θ and Lv⋅q, surface fluxes (Hsfc and LEsfc), entrainment fluxes (Hent, and LEent) at site BJ of Nagqu Station (BJ/Nagqu) than those using the Community Land Model (CLM) with BouLac, MYNN, and YSU. The frequency distributions of Hsfc, LEsfc, Hent, and LEent in the study area confirm this result. The spatial distributions of simulated Hsfc, LEsfc, Hent, and LEent, using WRF with Noah and BouLac, suggest that the spatial distributions of Hsfc and LEsfc in the study area are consistent with that of soil moisture, but the spatial distributions of Hent and LEent are quite different from that of soil moisture. A close examination of the relationship between entrainment fluxes and cloud water content (QCloud) reveals that the grids with small Hent and large LEent tend to have high QCloud and Hsfc, suggesting that high Hsfc is conducive to convective cloud formation, which leads to small Hent and large LEent. A sensitivity analysis of LoCo to the soil moisture at site BJ/Nagqu indicates that, on a sunny day, an increase in soil moisture leads to an increase in LEsfc but decreases in Hsfc, Hent, and LEent. The sensitivity of the relationship between simulated maximum daytime PBL height (PBLH) and mean daytime evapotranspiration (ET) in the study area to soil moisture indicates the rate at which the maximum daytime PBLH decreases with the mean ET increase as the initial soil moisture goes up. The analysis of simulated Hsfc, LEsfc, Hent, and LEent under different soil moisture conditions reveals that the frequency of Hent ranging from 80 to 240 W m−2 and the frequency of LEent ranging from −240 to −90 W m−2 both increase as the initial soil moisture increases. Coupled with the changes in QCloud, the changes in Hent and LEent as the initial soil moisture increases indicate that the rise in soil moisture leads to an increase in the cloud amount but a decrease in QCloud.

Chickpea (Cicer arietinum L.) root system architecture adaptation to initial soil moisture improves seed development in dry-down conditions

Soil water deficit (WD) impacts vascular plant phenology, morpho-physiology, and reproduction. Chickpea, which is mainly grown in semi-arid areas, is a good model plant to dissect mechanisms involved in drought resistance.We used a rhizobox-based phenotyping system to simultaneously and non-destructively characterise root system architecture (RSA) dynamics and water use (WU) patterns. We compared the drought-adaptive strategies of ‘Teketay’ to the drought-sensitive genotype ICC 1882 in high and low initial soil moisture without subsequent irrigation.WD restricted vegetative and reproductive organ biomass for both genotypes. Teketay displayed greater adaptability for RSA dynamics and WU patterns and revealed different drought adaptive strategies depending on initial soil moisture: escape when high, postponement when low. These strategies were manifested in distinct RSA dynamics: in low initial soil moisture, its reduced root growth at the end of the vegetative phase was followed by increased root growth in deeper, wetter soil strata, which facilitated timely WU for seed development and produced better-developed seeds.We demonstrate that RSA adaptation to initial soil moisture is one mechanism by which plants can tolerate WD conditions and ensure reproduction by producing well-developed seeds. Our approach will help in identifying the genetic basis for large plasticity of RSA dynamics which enhances the resilience with which crops can optimally adapt to various drought scenarios.HighlightRoot system architecture and water use patterns change dynamically for distinct drought adaptation strategies in chickpea.

Predictability of Seasonal Streamflow and Soil Moisture in National Water Model and a Humid Alabama–Coosa–Tallapoosa River Basin

This study investigates the potential predictability of streamflow and soil moisture in the Alabama–Coosa–Tallapoosa (ACT) river basin in the southeastern United States. The study employs the state-of-the-art National Water Model (NWM) and compares the effects of initial soil moisture condition with those of seasonal climate anomalies on streamflow and soil moisture forecast skills. We have designed and implemented seasonal streamflow forecast ensemble experiments following the methodology suggested by Dirmeyer et al. The study also compares the soil moisture variability in the NWM with in situ measurements and remote sensing data from the Soil Moisture Active and Passive (SMAP) satellite. The NWM skillfully simulates the observed streamflow in the ACT basin. The soil moisture variability is 46% smaller in the NWM compared with the SMAP data, mainly due to a weaker amplitude of the seasonal cycle. This study finds that initial soil moisture condition is a major source of predictability for the seasonal streamflow forecast. The contribution of the initial soil moisture condition is comparable or even higher than that of seasonal climate anomaly effects in dry seasons. In the boreal summer season, the initial soil moisture condition contributes to 65% and 48% improvements in the seasonal streamflow and soil moisture forecast skills, respectively. This study attributes a greater improvement in the streamflow forecast skill to the lag effects between the soil moisture and streamflow anomalies. The results of this study can inform the development and improvement of the operational streamflow forecasting system.