Diagnostic classification of flash drought events reveals distinct classes of forcings and impacts

Recent years have seen growing appreciation that rapidly intensifying “flash droughts” are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility-based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the Contiguous United States (CONUS) for the period 1979-2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture “flash droughts” are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways.

Subseasonal Meteorological Drought Development over the Central United States during Spring

Diagnosis of rapidly developing springtime droughts in the central U.S. has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal “meteorological drought” evolution, here defined as persistent precipitation minus evapotranspiration (P-ET) deficits, revealed in early (April 1-May 15) and late (May 16-June 30) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958-2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1-2 weeks, and generally has a greater impact on the moisture budget than does reduced poleward moisture transport. The resulting “dynamically driven” evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases ∼40 percentiles within ∼10 days. Anomalous near-surface warmth develops only after P-ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P-ET deficits, it still appears to capture much of the springtime development of so-called “flash droughts” as well.

Bark Transpiration Rates Can Reach Needle Transpiration Rates Under Dry Conditions in a Semi-arid Forest

Drought can cause tree mortality through hydraulic failure and carbon starvation. To prevent excess water loss, plants typically close their stomata before massive embolism formation occurs. However, unregulated water loss through leaf cuticles and bark continues after stomatal closure. Here, we studied the diurnal and seasonal dynamics of bark transpiration and how it is affected by tree water availability. We measured continuously for six months water loss and CO2 efflux from branch segments and needle-bearing shoots in Pinus halepensis growing in a control and an irrigation plot in a semi-arid forest in Israel. Our aim was to find out how much passive bark transpiration is affected by tree water status in comparison with shoot transpiration and bark CO2 emission that involve active plant processes, and what is the role of bark transpiration in total tree water use during dry summer conditions. Maximum daily water loss rate per bark area was 0.03–0.14 mmol m−2 s−1, which was typically ~76% of the shoot transpiration rate (on leaf area basis) but could even surpass the shoot transpiration rate during the highest evaporative demand in the control plot. Irrigation did not affect bark transpiration rate. Bark transpiration was estimated to account for 64–78% of total water loss in drought-stressed trees, but only for 6–11% of the irrigated trees, due to differences in stomatal control between the treatments. Water uptake through bark was observed during most nights, but it was not high enough to replenish the lost water during the day. Unlike bark transpiration, branch CO2 efflux decreased during drought due to decreased metabolic activity. Our results demonstrate that although bark transpiration represents a small fraction of the total water loss through transpiration from foliage in non-stressed trees, it may have a large impact during drought.

Determination of the Hydrodynamic Parameters of Two Types of Soil in the Senegal River Delta. Simulation of Hydro-saline Transfers: Application to the Wind Deflation Phenomenon

In the Senegal River delta, the presence of a shallow salt water table associated with a strong evaporative demand sometimes leads to an upwelling of salts that crystallize on the surface. This phenomenon can be observed in the vicinity of the Diawling Basin, where a powdery structure sensitive to wind deflation and a massive structure with a fractionation into platelets that cannot be transported by the wind are noted. To understand the hydrodynamic characteristics of these soils, we used numerical simulation of water and solute transfers. The hydrodynamic parameters were determined in the laboratory using Wind's method on undisturbed samples. The experimental retention h() and hydraulic conductivity K(h) curves were fitted using the Van Genuchten model. The simulations show that the soil with a powdery structure has hydrodynamic characteristics that favour the ascent of salts from the water table to the surface. For the soil with a massive structure, the hydrodynamic conditions impose a deposition of salts in the subsurface.

Potential impacts of stratospheric aerosol injection on drought risk managements over major river basins in Africa

Most socio-economic activities in Africa depend on the continent’s river basins, but effectively managing drought risks over the basins in response to climate change remains a big challenge. While studies have shown that the stratospheric aerosol injection (SAI) intervention could mitigate temperature-related climate change impacts over Africa, there is a dearth of information on how the SAI intervention could influence drought characteristics and drought risk managements over the river basins. The present study thus examines the potential impacts of climate change and the SAI intervention on droughts and drought management over the major river basins in Africa. Multi-ensemble climate simulation datasets from the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) Project were analysed for the study. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) were used to characterize the upper and lower limits of future drought severity, respectively, over the basins. The SPEI is a function of rainfall and potential evapotranspiration, whereas the SPI is only a function of rainfall, so the difference between the two indices is influenced by atmospheric evaporative demand. The results of the study show that, while the SAI intervention, as simulated in GLENS, may offset the impacts of climate change on temperature and atmospheric evaporative demand, the level of SAI that compensates for temperature change would overcompensate for the impacts on precipitation and therefore impose a climate water balance deficit in the tropics. SAI would narrow the gaps between SPEI and SPI projections over the basins by reducing SPEI drought frequency through reduced temperature and atmospheric evaporative demand while increasing SPI drought frequency through reduced rainfall. The narrowing of this gap lowers the level of uncertainty regarding future changes in drought frequency, but nonetheless has implications for future drought management in the basins, because while SAI lowers the upper limit of the future drought stress, it also raises the lower limit of the drought stress.

High-throughput phenotyping reveals a link between transpiration efficiency and transpiration restriction under high evaporative demand and new loci controlling water use-related traits in African rice, Oryza glaberrima Steud.

Because water availability is the most important environmental factor limiting crop production, improving water use efficiency, the amount of carbon fixed per water used, is a major target for crop improvement. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand, remain unknown. These traits were measured in a panel of 147 African rice Oryza glaberrima genotypes, known as potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly differentiated genotypes confirmed these associations and suggested that the root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency and its residuals, that links to genes involved in water transport and cell wall patterning. Our data suggest that root shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.

Analysis of Flash Drought in China using Artificial Intelligence models

The term “Flash drought” describes a type of drought with rapid onset and strong intensity, which is co-affected by both water-limited and energy-limited conditions. It has aroused widespread attention in related research communities due to its devastating impacts on agricultural production and natural system. Based on a global reanalysis dataset, we identify flash droughts across China during 1979~2016 by focusing on the depletion rate of weekly soil moisture percentile. The relationship between the rate of intensification (RI) and nine related climate variables is constructed using three artificial intelligence (AI) technologies, namely, multiple linear regression (MLR), long short-term memory (LSTM), and random forest (RF) models. On this basis, the capabilities of these algorithms for estimating RI and droughts (flash droughts and traditional slowly-evolving droughts) detection were analyzed. Results showed that the RF model achieved the highest skill in terms of RI estimation and flash droughts identification among the three approaches. Spatially, the RF-based RI performed best in the southeastern China, with an average CC of 0.90 and average RMSE of 2.6th percentile per week, while the poor performances were found in Xinjiang region. For drought detection, all three AI technologies presented a better performance in monitoring flash droughts than in conventional slowly-evolving droughts. Particularly, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of flash drought derived from RF were 0.93, 0.15, and 0.80, respectively, indicating that RF technology is preferable to estimate the RI and monitoring flash droughts by considering multiple meteorological variable anomalies in adjacent weeks of drought onset. In terms of the meteorological driving mechanism of flash drought, the negative precipitation (P) anomalies and positive potential evapotranspiration (PET) anomalies exhibited a stronger synergistic effect on flash droughts comparing to slowly-developing droughts, along with asymmetrical compound influences in different regions over China. For the Xinjiang region, P deficit played a dominant role in triggering the onset of flash droughts, while in the southwestern China, the lack of precipitation and enhanced evaporative demand almost contributed equally to the occurrence of flash drought. This study is valuable to enhance the understanding of flash drought and highlight the potential of AI technologies in flash droughts monitoring.

Bridging knowledge gaps with hybrid machine-learning forest ecosystem models (ML-FEMs): inferential simulation of past understory light regimes

Soil moisture is a key limiting factor of plant productivity in boreal and montane regions, producing additional climate feedbacks through evaporation, regeneration, mortality, and respiration. Understory solar irradiation – the primary driver of surface temperature and evaporative demand – remains poorly represented in vegetation models due to a lack of 3-D canopy geometry. Existing models are further unable to represent processes lacking sufficient parameterization and/or knowledge, with no land model to date utilizing machine learning (ML) to represent vegetation processes. Here, we developed the first hybrid forest ecosystem model using ML (ML-FEM), a specific case of hybrid AI land model (a concept also invented here). In this approach, ML models are trained and validated with a ground-truth dataset, whether observations or high-fidelity simulations, before being applied to vegetation model parameters for inference, internally or externally to the model. Using this approach, we simulated annual understory global solar irradiation (Iu) across 25.2 Mha in southwestern Canada at 1-ha resolution under historical climate and fire scenarios. In cross-validation, we found that linear and ML regression models performed comparably well in the prediction of angular canopy cover (ACC), due to the linearity of its relationship to predictors (linear R2 = 0.938, RMSE = 0.079; ML R2 = 0.939, RMSE = 0.074). Reduced area burned, increased ignitions, and reduced regeneration potential for recent periods resulted in stable or reduced Iu. This suggests that diminished disturbance may reduce Iu through forest aging, masking latent regeneration decline. Only in the most extreme and unconstrained scenarios did Iu increase. In these experiments, conducted in late 2015, we demonstrated an entirely new class of hybrid models that we anticipated to be of vital importance to understanding and representing pattern-based processes in Earth system models.

Reviewing Mini-lysimeter Controlled Irrigation in Container Crop Systems

This study reviews how mini-lysimeters have been used effectively to optimize irrigation control in container horticulture production. Lysimeters are devices that measure evapotranspiration (ET) from the water balance of a fixed soil volume. The primary components of lysimeter-controlled irrigation are load cell sensors, a multiplexer, a data logger, a controller, and solenoid valves. The two common mini-lysimeter systems are platform lysimeters and suspension lysimeters. In these systems, a bending-beam single-point load cell is fastened between two plates, and a container is placed directly on the top platform. Platform lysimeters are commonly used for smaller pot sizes, and suspension lysimeters have been used for large shade trees up to 2.8 m tall and weighing 225 kg. Mini-lysimeters have been used for decades to calibrate ET models and create on-demand irrigation control programs that replenish plant daily water use or maintain deficit conditions. Research has demonstrated that lysimeter-based irrigation can respond more effectively to seasonal and diurnal variations in water demand, increasing irrigation cycles when evaporative demand is high, and decreasing irrigation cycles when demand is low. A strength of these systems is that for containerized plants, such as nursery production systems, mini-lysimeters capture whole-plant water use, which presents a more holistic measure compared with soil moisture sensors or leaf moisture sensors.

A proxy-model comparison for mid-Pliocene warm period hydroclimate in the Southwestern US

Hydroclimate proxy reconstructions and paleoclimate models of the mid-Pliocene warm period provide insight into how, under a moderate greenhouse warming scenario, Earth-system feedbacks may impact regional hydroclimate. However, in the Southwestern United States there is discord between these two types of information: proxy data have been interpreted to indicate much wetter conditions, while the most recent generation of mid-Pliocene warm period climate models simulates drying. We use a water and energy balance framework to directly compare paleoclimate model output to a refined compilation of proxy records of the presence and areal extent of mid-Pliocene lakes. Within this framework, we quantify uncertainties in the proxy system model parameters and in the interpretation of available proxy records. We find that despite these significant uncertainties, most paleoclimate models simulate a regional balance between precipitation and evaporative demand that could not have sustained the extent of recorded lakes from this time. Moreover, the extensive lakes included as boundary conditions in mid-Pliocene warm period climate models are inconsistent with the regional climate simulated by those same models. This study identifies and quantifies the remaining unknowns in our picture of regional mid-Pliocene warm period hydroclimate, with implications for analyses of climate dynamics during this time.