This work examines the characteristics and prevalent life cycle of agricultural flash droughts globally. Using ERA5 data, the study introduces a flash drought indicator based on soil water availability. This approach integrates root-zone soil moisture and hydraulic soil properties, such as field capacity and wilting point, to couple the rapid soil moisture depletion and plant water stress. Our findings reveal that agricultural flash droughts present their higher frequency predominantly during the critical growth periods of crops. Notably, these droughts exhibit a similar life cycle regardless of the location or climatic regime. The primary cause of the rapid soil moisture depletion is the precipitation deficit, but evapotranspiration also plays a significant role. In an energy-limited environment, evapotranspiration rapidly increases before the onset and decreases rapidly during the intensification period as the system becomes water-limited. Upon concluding the intensification period, most crops experience water stress, diminishing their yields.

There is high confidence that global warming intensifies all components of the global water cycle. This work investigates the possible effects of global warming on river flows worldwide in the coming decades. We conducted 18 global hydrological simulations to assess how river flows are projected to change in the near future (2015–2050) compared to the recent past (1950–2014). The simulations are forced by runoff from the High Resolution Model Intercomparison Project (HighResMIP) CMIP6 global climate models (GCMs), which assume a high-emission scenario for the projections. The assessment includes estimating the signal-to-noise (S/N) ratio and the time of emergence (ToE) of all the rivers in the world. Consistently with the water cycle intensification, the hydrological simulations project a clear positive global river discharge trend from ∼2000 that emerges beyond the levels of natural variability and becomes “unfamiliar” by 2017 and “unusual” by 2033. Simulations agree that the climate change signal is dominated by strong increases in the flows of rivers originating in central Africa and South Asia and those discharging into the Arctic Ocean, partially compensated for by the reduced flow projected for Patagonian rivers. The potential implications of such changes may include more frequent floods in central African and South Asian rivers, driven by the projected magnification of the annual cycles with unprecedented peaks, a freshening of the Arctic Ocean from extra freshwater release, and limited water availability in Patagonia given the projected drier conditions of its rivers. This underscores the critical need for a paradigm shift in prioritizing water-related concerns amidst the challenges of global warming.

Extreme hydrometeorological events (EHE) negatively affect ecosystems, human settlements, food production, water resources, and public health worldwide. In southeastern South America (SESA), the frequency and intensity of temperature and precipitation extremes have increased over recent decades. SESA is particularly vulnerable to EHE due to its high population rates and an economy heavily reliant on agricultural activities; therefore, advancing towards a climate-resilient development is a key goal for the region. This study presents a multi-hazard analysis of EHE and their changes over SESA. Our study assesses the frequency, duration, and intensity of short- and long-term EHE for the 1961-1990 and 1991-2020 periods. ERA5 precipitation, soil moisture, and temperature data at multiple time scales (from daily to monthly) are used, with a spatial resolution of 0.25°×0.25° latitude-longitude grid. Long-term EHE are studied using nonparametric standardized indices—specifically, the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI)—at 3- and 18-month timescales to analyze agricultural and hydrological impacts. Short-term EHE are characterized by heavy precipitation, flash droughts, and heat waves events to analyze immediate impacts in urban areas and in agriculture. Individual hazard components are derived by multiplying the frequency, duration, and intensity of the identified events, followed by a rescaling to a 0-1 range using range normalization (with minimum and maximum values). The long-term and short-term EHE hazard indices are formulated by aggregating the rescaled individual hazard components and dividing by the total number of components. Changes in observed EHE hazard components are determined by comparing the EHE hazard indices for the 1991-2020 and 1961-1990 periods. Our findings underscore significant precipitation excess hazard, mainly concentrated in agriculture-prone areas spanning central-eastern Argentina, Uruguay, and southern Brazil across both 3- and 18-month timescales. In contrast, precipitation deficit hazard predominantly manifests in the western regions of SESA. Regarding short-term EHE, the highest hazard magnitudes are observed in northeastern Argentina, southern Brazil, and southeastern Paraguay. Heat waves occur frequently in the region, with hazardous intensities over the northern part of SESA. Additionally, heavy precipitation events constitute a significant hazard component for urban and rural infrastructure primarily in northeastern Argentina. Flash droughts also affect agriculture-prone areas, particularly with high intensity in southern Brazil, northeastern Argentina, and Uruguay. Our results reveal that the most significant changes are observed in short-term hazard indices in northeastern SESA. This region, which includes eastern Paraguay, northeastern Argentina, southern Brazil, and Uruguay, has experienced an increase in heat wave hazard, primarily due to a significant rise in the frequency of heat waves. Hazards associated with heavy precipitation and flash drought events have also increased, with a rise in their frequency and duration observed mainly over northeastern Argentina and southern Brazil. In contrast, long-term hazard indices exhibit non-uniform patterns of change. Our findings suggest that weather-related hazards have undergone changes over the last decades. We expect that these findings provide valuable insights to enhance SESA's hydroclimatic risk management systems by identifying areas susceptible to both short- and long-term hazards.

The Gran Chaco ecoregion is South America's largest remaining continuous stretch of dry forest. It has experienced intensive deforestation, mainly in the western part known as the Dry Chaco, resulting in the highest rate of dry-forest loss globally between 2000 and 2012. The replacement of natural vegetation with other land uses modifies the surface's biophysical properties, affecting heat and water fluxes and modifying the regional climate. This study examines land use and land cover changes (LULCCs) in the Dry Chaco from 2001 to 2015 and their effects on local and non-local climate and explores the potential impacts of future agricultural expansion in the region. To this end, Weather Research and Forecasting (WRF) model simulations are performed for two scenarios: the first one evaluates the observed land cover changes between 2001 and 2015 that covered 8 % of the total area of the Dry Chaco; the second scenario assumes an intensive agricultural expansion within the Dry Chaco. In both scenarios, deforestation processes lead to decreases in leaf area index (LAI), reductions in stomatal resistance, and increases in albedo, thus reducing the net surface radiation and, correspondingly, decreasing the turbulent fluxes, suggesting a decline in available energy in the boundary layer. The result is an overall weakening of the water cycle in the Dry Chaco and, most prominently, implying a reduction in precipitation. A feedback loop develops since dry soil absorbs significantly less solar radiation than moist soil. Finally, the simulations suggest that the Dry Chaco will intensify its aridity, extending drier and hotter conditions into the Humid Chaco.

<p>Flash droughts negatively impact agriculture and natural ecosystems worldwide. However, there is still no flash drought indicator that can couple rapid soil moisture depletion and its impacts on vegetation health. Moreover, the literature describes several indicators that may not reliably represent flash drought evolution or its drivers in varying climatic regimes. Our study introduces a robust and straightforward approach to identify flash droughts based on ERA5 root-zone soil moisture along with soil properties (field capacity and wilting point) to reflect the soil moisture deficit and plant water stress conditions across different climate regions. Using this approach, we identify the regions in the world prone to flash droughts, and their seasonal frequency. This study also analyzes the processes involving atmospheric and surface drivers during the drought lifecycle.</p> <p>The proposed indicator captures the rapid soil moisture depletion by assessing the decline in the soil water deficit index (SWDI) from an upper threshold to a lower threshold over a period of 20 days (4 pentads). The upper threshold (SWDI=-3) ensures an increase in evaporative stress as it is close to or at the critical soil moisture value, which differentiates energy- and soil moisture-limited evapotranspiration regimes. The lower threshold (SWDI=-5) is the readily available soil water limit for vegetation growth. Below this point, plants begin to experience water stress. Although soil water is theoretically available for plants before reaching the wilting point, the plant water uptake is reduced well before that value.</p> <p>Our findings show that the main flash drought hotspots are found to be located in southeastern South America, southern China, India, central-eastern Europe and southern Russia, and the central-eastern United States. Most flash drought occurrence hotspots are found in agricultural regions. Additionally, the analysis of the seasonal flash drought frequency indicates that most flash drought events impact the critical growth periods of crops.</p> <p>Our results reveal that all flash drought hotspots exhibit similar evolution of key atmospheric and surface variables regardless of the location or climatic regime. Thus, the physical processes involved in flash droughts appear to be similar worldwide. As expected, a precipitation deficit is the main driver for rapid soil moisture depletion. Temperature also plays an important role in the persistence of flash drought events. The evolution of evapotranspiration during flash droughts modulates the precipitation effects: evapotranspiration increases until the onset of the flash drought (energy-limited regime) and then decreases during the intensification period due to water stress (soil moisture-limited regime).</p>

Thermal processes on the Tibetan Plateau (TP) influence atmospheric conditions on regional and global scales. Given this, previous work has shown that soil moisture–driven surface flux variations feed back onto the atmosphere. Whilst soil moisture is a source of atmospheric predictability, no study has evaluated soil moisture–atmosphere coupling on the TP in general circulation models (GCMs). In this study, we use several analysis techniques to assess soil moisture-atmosphere coupling in CMIP6 simulations including: instantaneous coupling indices; analysis of flux and atmospheric behaviour during dry spells; and a quantification of the preference for convection over drier soils. Through these metrics we partition feedbacks into their atmospheric and terrestrial components. Consistent with previous global studies, we conclude substantial inter-model differences in the representation of soil moisture–atmosphere coupling, and that most models underestimate such feedbacks. Focusing on dry spell analysis, most models underestimate increased sensible heat during periods of rainfall deficiency. For example, the model-mean bias in anomalous sensible heat flux is 10 W m −2 (≈25%) smaller compared to observations. Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response. We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection. The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture–driven predictability. To improve the representation of land–atmosphere feedbacks requires developments in not only atmospheric modelling, but also surface processes, as we find weak relationships between rainfall biases and coupling indexes.

Current land surface schemes in weather and climate models make use of the so-called coupled photosynthesis–stomatal conductance ( A–g s ) models of plant function to determine the surface fluxes that govern the terrestrial energy, water and carbon budgets. Plant physiology is controlled by many environmental factors, and a number of complex feedbacks are involved, but soil moisture control on root water uptake is primary, particularly in sub-tropical to temperate ecosystems. Land surface models represent plant water stress in different ways, but most implement a water stress factor, β , which ranges linearly (more recently also curvilinearly) between β = 1 for unstressed vegetation and β = 0 at the wilting point, expressed in terms of volumetric water content ( θ ). β is most commonly used to either limit A or g s , and hence carbon and water fluxes, and a pertinent research question is whether these treatments are in fact interchangeable. Following Egea et al. (Agricultural and Forest Meteorology, 2011, 151 (10), 1,370–1,384) and Verhoef et al. (Agricultural and Forest Meteorology, 2014, 191, 22–32), we have implemented new β treatments, reflecting higher levels of biophysical complexity in a state-of-the-art LSM, Joint UK Land Environment Simulator, by allowing root zone soil moisture to limit plant function non-linearly and via individual routes (carbon assimilation, stomatal conductance, or mesophyll conductance) as well as any (non-linear) combinations thereof. The treatment of β does matter to the prediction of water and carbon fluxes: this study demonstrates that it represents a key structural uncertainty in contemporary LSMs, in terms of predictions of gross primary productivity, energy fluxes and soil moisture evolution, both in terms of climate means and response to a number of European droughts, including the 2003 heat wave. Treatments allowing ß to act on vegetation fluxes via stomatal and mesophyll routes are able to simulate the spatiotemporal variability in water use efficiency with higher fidelity during the growing season; they also support a broader range of ecosystem responses, e.g., those observed in regions that are radiation limited or water limited. We conclude that current practice in weather and climate modelling is inconsistent, as well as too simplistic, failing to credibly simulate vegetation response to soil water stress across the typical range of variability that is encountered for current European weather and climate conditions, including extremes of land surface temperature and soil moisture drought. A generalized approach performs better in current climate conditions and promises to be, based on responses to recently observed extremes, more trustworthy for predicting the impacts of climate change.

We have updated the soil properties used in JULES (Joint UK Land Environment Simulator), which is the land-surface component of the UM (Unified Model, the UK Met Office’s climate model). JULES models the interaction of the land surface with the atmosphere, and simulates the energy, water, and carbon fluxes. JULES allows either: (i) the Brooks & Corey (BC) model for estimating soil hydraulic properties, or (ii) the van Genuchten (VG) model but using hydraulic parameters translated from the BC model. One advantage of the VG model over the BC model is the smoother dependence of water retention upon matric potential for nearly saturated soils. Herein, we report on our work towards fully implementing the VG model in JULES and in the UM, through the implementation and evaluation of several VG pedotransfer functions (PTFs) for estimating the soil hydraulic parameters used in the hydraulic functions. We have tested three VG PTFs in global offline JULES runs (driven with WFDEI data over 1979-2012): the combination of Tóth et al. PTFs 17 & 20, the Weynants et al. PTF, and the Zhang & Schaap ROSETTA3 H1 PTF (modified for sandy soils). We also modernized the soil basic properties that are conventionally used for JULES and the UM, from the UM version of the Harmonized World Soil Database (HWSD) to the SoilGrids database. Evaluation of JULES simulations shows (i) that the modified version of the Zhang & Schaap ROSETTA3 H1 PTF is the best VG option, and (ii) that it compares favorably with the BC control model (which uses the Cosby et al. PTF and the UM/HWSD soils), in terms of the surface energy balance and the mitigation of near-surface temperature biases over mid-latitude continental regions. This modified version of the Zhang & Schaap ROSETTA3 H1 PTF with SoilGrids soils is also currently being used in coupled land-atmosphere UM runs.

Previous studies showed that high-resolution GCMs overestimate land precipitation when compared against observation-based data. Particularly, high-resolution HadGEM3-GC3.1 shows a significant precipitation increase in mountainous regions, where the scarcity of gauge stations increases the uncertainty of gridded observations and reanalyses. This work evaluates such precipitation uncertainties indirectly through the assessment of river discharge, considering that an increase of similar to 10% in land precipitation produces similar to 28% more runoff when the resolution is enhanced from 1 degrees to 0.25 degrees, and -50% of the global runoff is produced in 27% of global land dominated by mountains. We diagnosed the river flow by routing the runoff generated by HadGEM3-GC3.1 low- and high-resolution simulations. The river flow is evaluated using a set of 344 monitored catchments distributed around the world. We also infer the global discharge by constraining the simulations with observations following a novel approach that implies bias correction in monitored rivers with two methods, and extension of the correction to the river mouth, and along the coast. Our global discharge estimate is 47.4 +/- 1.6 x 10(3) km(3) yr(-1), which is closer to the original high-resolution estimate (50.5 X 10(3) km(3) yr(-1)) than to the low-resolution (39.6 x 10(3) km(3) yr(-1)). The assessment suggests that high-resolution simulations perform better in mountainous regions, either because the better-defined orography favors the placement of precipitation in the correct catchment, leading to a more accurate distribution of runoff, or the orographic precipitation increases, reducing the dry runoff bias of coarse-resolution simulations. However, high-resolution slightly increases wet biases in catchments dominated by flat terrain. The improvement of model parameterization and tuning may reduce the remaining errors in high-resolution simulations.

Capturing soil moisture-atmosphere feedbacks in a weather or climate model requires realistic simulation of various land surface processes. However, irrigation and other water management methods are still missing in most global climate models today, despite irrigated agriculture being the dominant land use in parts of Asia. In this study, we test the irrigation scheme available in the land model JULES (Joint UK Land Environment Simulator) by running land-only simulations over South and East Asia driven by WFDEI (WATCH Forcing Data ERA-Interim) forcing data. Irrigation in JULES is applied on a daily basis by replenishing soil moisture in the upper soil layers to field capacity, and we use a version of the irrigation scheme that extracts water for irrigation from groundwater and rivers, which physically limits the amount of irrigation that can be applied. We prescribe irrigation for C3 grasses in order to simulate the effects of agriculture, albeit retaining the simpler, widely used 5-PFT (plant functional type) configuration in JULES. Irrigation generally increases soil moisture and evapotranspiration, which results in increasing latent heat fluxes and decreasing sensible heat fluxes. Comparison with combined observational/machine-learning products for turbulent fluxes shows that while irrigation can reduce biases, other biases in JULES, unrelated to irrigation, are larger than improvements due to the inclusion of irrigation. Irrigation also affects water fluxes within the soil, e.g. runoff and drainage into the groundwater level, as well as soil moisture outside of the irrigation season. We find that the irrigation scheme, at least in the uncoupled land-atmosphere setting, can rapidly deplete groundwater to the point that river flow becomes the main source of irrigation (over the North China Plain and the Indus region) and can have the counterintuitive effect of decreasing annual average soil moisture (over the Ganges plain). Subsequently, we will explore the impact of irrigation on regional climate by conducting coupled land-atmosphere simulations.

This study evaluates the ability of 19 models of CMIP phase 6 (CMIP6) to simulate Paraguay's climate features. Historical multi-member simulations of single models and their multi-model ensembles are bias-corrected and evaluated with statistical metrics. Future projections of precipitation and temperature are generated with the ensembles for three integrated scenarios of socio-economic development and greenhouse gas emissions (SSP1–2.6, SSP2–4.5, and SSP5–8.5). The 19 models simulate well the observed mean temperature. The bias-corrected multi-model ensemble reaches the highest skill scores and accurately reproduces the mean spatial field and annual cycle. The bias-corrected multi-model ensemble of precipitation represents the annual cycle weakly, missing the sharp onset and decay of the South American Monsoon. Some individual models and the multi-model ensemble correctly reproduce the west-east gradient, although they underestimate its pronounced spatial variability. Ensembles of future simulations project that by 2100, the annual mean temperature will increase for the three scenarios. On average, the increases are almost 1.7 °C in the sustainable development and low emissions scenario (SSP1–2.6), 3 °C in the middle-of-the-road development and medium emissions scenario (SSP2–4.5), and 5.5 °C in the fossil-fueled development and high emissions scenario (SSP5–8.5). Models project a slight decrease in annual precipitation towards the northwest (less than 50 mm) and an increase towards the southeast (more than 200 mm). Paraguay's humid eastern part is projected to have a small growth in temperature and an increase in precipitation. In contrast, the western arid Chaco region would experience a substantial increase in temperature, while rainfall would slightly decrease.

Previous studies showed that high resolution GCMs overestimate land precipitation when compared against gridded observations or reanalysis (Demory et al. 2014, Vannière et al. 2019). In particular, grid point models (eg. HadGEM3) show a significant increase of precipitation on regions dominated by complex orography, where the scarcity of gauge stations increase the uncertainty of gridded observations. The goal of this work is to assess the effect of such differences in precipitation on river discharge, considering it as an integrator of the water balance at catchment scale. A set of JULES and CLM simulations have been conducted turning rivers on with Total Runoff Integrating Pathways (TRIP) and the River Transport Model (RTM) respectively. The simulations form three ensembles for each land surface model (LSM) which main difference is given by the forcing dataset. The forcings are WFDEI (reanalysis), LR (~1° resolution in meteorological data from GCMs) and HR (~0.25° resolution in meteorological data from GCMs). These ensembles are evaluated in a set of 280 catchments distributed around the world.In terms of correlation between simulated and observed river discharge observations, the results show that LSMs forced by reanalysis have higher performance than LSMs forced by GCMs as expected. In terms of biases, the river discharge is underestimated in eight out of eleven major basins when LSMs are forced by reanalysis. On those basins, the extra precipitation estimated by GCMs help to simulate an amount of river discharge closer to observations (Eg. Yenisey and Lena). Moreover, 37 small basins with a strong component of orographic precipitation over the Andes, the Rocky Mountains, the Alps and in the Maritime Continent were evaluated. In most cases HR offers notably better results than LR and WFDEI, suggesting that high resolution models produce orographic precipitation in the correct place and time.In future works offline TRIP simulations will be carried out directly forced by runoff and subsurface runoff from GCMs. It will allow to discard errors in evapotranspiration produced by JULES or CLM when they are used to simulate river discharge. This work is part of the European Process-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment (PRIMAVERA) Project. PRIMAVERA is a collaboration between 19 funded by the European Union’s Horizon 2020 Research & Innovation Programme.Demory, M. E., Vidale, P. L., Roberts, M. J., Berrisford, P., Strachan, J., Schiemann, R., & Mizielinski, M. S. (2014). The role of horizontal resolution in simulating drivers of the global hydrological cycle. CLIM DYNAM, 42(7-8), 2201-2225.Vannière, B., Demory, M. E., Vidale, P. L., Schiemann, R., Roberts, M. J., Roberts, C. D., ... & Senan, R. (2018). Multi-model evaluation of the sensitivity of the global energy budget and hydrological cycle to resolution. CLIM DYNAM, 1-30.

Land–atmosphere interactions are often interpreted as local effects, whereby the soil state drives local atmospheric conditions and feedbacks originate. However, nonlocal mechanisms can significantly modulate land–atmosphere exchanges and coupling. We make use of GCMs at different resolutions (low ~1° and high ~0.25°) to separate the two contributions to coupling: better represented local processes versus the influence of improved large-scale circulation. We use a two-legged metric, complemented by a process-based assessment of four CMIP6 GCMs. Our results show that weakening, strengthening, and relocation of coupling hot spots occur at high resolution globally. The northward expansion of the Sahel hot spot, driven by nonlocal mechanisms, is the most notable change. The African easterly jet’s horizontal wind shear is enhanced in JJA due to better resolved orography at high resolution. This effect, combined with enhanced easterly moisture flux, favors the development of African easterly waves over the Sahel. More precipitation and soil moisture recharge produce strengthening of the coupling, where evapotranspiration remains controlled by soil moisture, and weakening where evapotranspiration depends on atmospheric demand. In SON, the atmospheric influence is weaker, but soil memory helps to maintain the coupling between soil moisture and evapotranspiration and the relocation of the hot spot at high resolution. The multimodel agreement provides robust evidence that atmospheric dynamics determines the onset of land–atmosphere interactions, while the soil state modulates their duration. Comparison of precipitation, soil moisture, and evapotranspiration against satellite data reveals that the enhanced moistening at high resolution significantly reduces model biases, supporting the realism of the hot-spot relocation.