Assessment of compound hot-dry events in lakes across global drylands

When weather and climate events occur concurrently in various locations, their combined impacts pose significant threats to connected socio-economic systems. Compound dry and hot events have become major natural disasters that affect production and daily life under global warming. However, the patterns of synchronized compound dry and hot events remain unclear. This study uses temperature data and drought indices to identify compound dry and hot events and adopts the climate network approach to explore their spatial synchronization patterns and temporal change. The findings indicate a significant increase in global compound dry and hot events, with a notable expansion in the extent of their spatial synchronization. However, there is no significant trend in the average distance of synchronization. Spatial synchronization of compound dry and hot events exhibits heterogeneity, with hotspots in Central and Southern Europe, the Middle East, and Central South America. Additionally, some regions exhibit teleconnections of compound hot and dry events, such as the Western United States and Southern Europe. These insights could support adaptation and risk management for compound dry and hot events under climate change.

We investigate the representation of compound hot-dry events in decadal predictions and their relationship with their univariate hot and dry components. We use a CMIP6 multi-model ensemble (MME) of 125 members from the Decadal Climate Prediction Project (DCPP) hindcast simulations and compare it with different observational references. Our analysis focuses on the first five lead years of the simulations, with the different ensemble members initialised every year from 1960 to 2014. We analyse the skill of predicting hot, dry and hot-dry events in the multi-model ensemble. Specifically, we select the days above the 90th percentile of the daily maximum temperature for hot events. For dry events, we use two indicators, the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI), with accumulation periods of 3, 6 and 12 months, and we consider a dry event a month that shows an SPI or an SPEI value ≤1. Finally, we identify days that present both hot and dry conditions according to these criteria as compound hot-dry days. Preliminary results for the observations show a strong correlation between precipitation and the occurrence of compound events, especially for long accumulation periods, suggesting the importance of dryness as a driver for compound hot-dry events. In the DCPP hindcasts, the hot events show some robust predictive skill, mainly as a consequence of the increasing trend in temperature. On the other hand, dry events show sparse skill, concentrated in dry areas of the world and especially for extended accumulation periods. Further analysis of the skill of compound events and their relationship to their univariate counterparts in DCPP hindcasts will shed light on the representation of such events in decadal forecasts. However, these initial results underline the importance of precipitation in both the occurrence of present hot-dry compound events and the prediction of such events in the future.

Lake surface water temperature (LSWT) plays a fundamental role in the lake energy budget. However, direct observations of LSWT require considerable effort for acquisition and hence are rare relative to a large number of lakes. In lakes where LSWT has not been covered sufficiently by in situ measurements, remote sensing and lake modeling can be used to produce a fine spatio-temporal record of LSWTs. In our study, the Moderate-Resolution Imaging Spectroradiometer (MODIS) LSWT was used to compare with in situ data at the overpass times over the six sites in Lake Chaohu, a large shallow lake in China. MODIS-derived LSWT reflected the variation of lake surface temperature well, with a correlation coefficient of 0.96 and a cool bias of 1.25 °C. The bias was modified by an “Upper Envelop” smoothing method and then employed to evaluate the general lake model (GLM) performance, a one-dimensional hydrodynamic model. The GLM simulations showed good performance compared with MODIS LSWT data at an interannual time scale. A 57-year record of simulated LSWT was hindcast by the well-calibrated GLM for Lake Chaohu. The results showed that LSWT decreased by 0.08 °C/year from 1960 to 1981 and then increased by 0.05 °C/year. These trends were most likely caused by a cooling effect of decreased surface incident solar radiation and a warming effect of reduced wind speed. Our study promoted the use of MODIS-derived LSWT as an alternative data source, and then combined with a numerical model for inland water surface temperature, and also further provided an understanding of climate warming effect on such a shallow eutrophic lake. Key points: (1) Moderate-Resolution Imaging Spectroradiometer (MODIS) lake water surface temperature (LSWT) was validated with real-time in situ data collected at Lake Chaohu with high accuracy; (2) MODIS LSWT was modified by the bias correction and employed to evaluate a one-dimensional lake model at interannual and intraannual scale; The LSWT hindcast by a well-calibrated model at Lake Chaohu decreased by 0.08 °C/year from 1960 to 1981 and increased by 0.05 °C/year from 1982 to 2016.

Persistent hot and dry conditions play an important role in vegetation dynamics, being generally associated with reduced activity. In the Mediterranean region, ecosystems are adapted to such conditions. However, prolonged and intense heat and drought or the occurrence of compound hot and dry events may still have a negative impact on vegetation activity. This work aims to study how the productivity of Mediterranean vegetation is affected by hot and dry events, examining a set of severe episodes that occurred in three different regions (Iberian Peninsula, Eastern Mediterranean and Western Europe) between 2001 and 2019. The analysis relies on remote sensing products, namely Gross Primary Production from MODIS to detect and monitor vegetative stress and LST from MODIS and SM from ESA CCI to evaluate the influence of temperature and soil water availability on stressed vegetation. Of all events, the 2005 episode in the Iberian Peninsula was the most significant, affecting large sectors of low tree cover areas and crops and leading to reductions of annual plant productivity in affected vegetation of ~47 TgC/year. The obtained results highlight the influence of land-atmosphere coupling on vegetation productivity and clarified the role of warm springs on vegetation activity and soil moisture that may amplify summer temperatures. The functional recovery of affected vegetation productivity after these episodes varied across events, ranging from months to years. This work highlights the influence of hot and dry events on vegetation productivity in the Mediterranean basin and the usefulness of remote-sensing products to assess the response of different land covers to such episodes.

Heatwaves and dry spells are major climate hazards that severely impact human health, economy, agriculture, and natural ecosystems. Compound hot and dry summers have become more frequent and intense in recent years in Europe. What remains unclear is, however, to which extent the observed trend can be explained by climate change or as a feature of internal climate variability. In this study, we assess the frequency and intensity of compound hot and dry events in Europe by analyzing recent historical events from reanalysis data 1960-2022 and comparing it to i) a counterfactual reference (corresponding to pre-industrial climate conditions), and ii) model data derived from a Single Model Initial-condition Large Ensemble (SMILE).We use data from the fifth generation of the European Reanalysis (ERA5) to assess the current frequency of the compound hot and dry summers like 2003, 2015, 2018, and 2022 and analyze the intensity of the events. We use the data from the 50-member SMILE Canadian Regional Climate Model Large Ensemble (CRCM5-LE) and calculate the probability of event occurrence for those events in Europe’s current climate. Employing the ensemble allows us to assess the influence of internal climate variability vs. climate change for those events. Additionally, we use pre-industrial conditions (pi-control runs) simulated with CRCM5 to compare the probability of recent hot and dry compound events to a counterfactual world without climate change. Our analysis shows that climate change increases the frequency and intensity of compound hot and dry events. We see a substantial increase in occurrence probabilities compared to a pre-industrial world and draw to emerging hotspots of new compound extremes in several European regions. We illustrate the added value of using pi-control runs in a regional SMILE as a novel approach for impact quantification. It provides the means to understand better the already prominent role of climate change on the occurrence, frequency, and intensity of extreme events in a world of still limited warming.

ABSTRACTClimate extremes have garnered considerable attention recently because of their devastating effects on both water resources and vegetation health. The vegetation responses to climate extremes, such as high temperatures (hot events), droughts (dry events) and compound dry and hot events (CDHEs), have been extensively evaluated. However, the risk of vegetation drought considering different severity levels of individual and compound climate extremes is not well assessed. In this study, we employed the meta‐Gaussian (MG) model, a multivariate approach, to evaluate the response of vegetation drought [characterized by the Standardized Normalized Difference Vegetation Index (SNDVI)] to dry events, hot events and CDHEs. The study found that the dominant factor of vegetation drought, in the central and northwestern parts of Northwestern China (NWC), was the dry events. Conversely, in the southern NWC, temperature exerted a substantial influence on vegetation drought. Relative to individual dry events (hot events), the conditional probability of vegetation drought under CDHEs had decreased (increased) by approximately 24% (17%). Furthermore, the response of grassland to both individual and compound climate extremes was sensitive, whereas forests demonstrated greater resilience to droughts. These findings help us better understand the influence that various severity levels of climate extremes exert on vegetation dynamics.

Depth-resolved water temperature data on the thermal environment of lakes are often hindered by sparse temporal frequency, limited depth resolution, or short duration that create many challenges for long-term analysis. Where high frequency and depth-resolved data exist, they can provide a wealth of knowledge about how lakes are responding to a changing climate. In this study, we analyzed around 950 profiles of summer mean water temperature (July to September), which includes about 30,600 unique observations, from a subarctic lake (Lake Konnevesi, Finland) to understand the changes in lake surface water temperature (LSWT), lake deepwater temperature (LDWT), and lake volumetrically weighted mean temperature (LVWMT) from 1984 to 2021. Statistical analysis of this dataset revealed a substantial warming of LSWT (0.41 °C decade−1) and LVWMT (0.32 °C decade−1), whilst LDWT remained unchanged (0.00 °C decade−1). Our analysis using a generalized additive model suggested the inter-annual variability in LSWT and LVWMT correlated significantly with the upward trends of summer mean air temperature and solar radiation, but suggested no significant effect of observed changes in ice departure dates and near-surface wind speed. None of the investigated predictors correlated with the change in the LDWT. Due to the variable response of lake surface and bottom water temperature to climate change in this subarctic lake, our data suggest a substantial increase in lake thermal stability. Our study supports the growing literature on lake thermal responses to climate change, and illustrates the unique contrast of climate change impacts at the surface and at depth in lake ecosystems, with deep waters acting as a potential thermal refuge to aquatic organisms within a warming world.

Hot and dry extreme events in Europe have become more frequent and pose serious threats to human health, agriculture, infrastructure, and ecology. Single and compound hot and dry extremes in Europe have been attributed to synoptic atmospheric circulation variations and land-atmosphere interactions. However, the exact causal pathways and their strength, as well as their historical trends, have not been quantified. An accurate understanding of the mechanisms behind these land-atmosphere extremes is crucial to improving S2S forecasts and implementing appropriate adaptation measures. Here, we use the Peter and Clark momentary conditional independence (PCMCI) based Causal Effect Networks (CENs) to detect and quantify dynamic and thermodynamic causal precursors of extremely high 2m temperature (T2m) and extremely low soil water deficit and surplus (WSD) in central Europe (CEU).Our analysis reveals that the single hot events are driven mainly by anomalous atmospheric patterns and soil water deficiency, while single dry events are mainly driven by the soil moisture memory, and anomalous atmospheric patterns, and only marginally by temperature changes. The atmospheric circulation patterns preceding both single hot and dry events show a high-pressure system over central Europe, with a low-pressure system over the Atlantic Ocean, and partly explain the occurrence of the compound events. This atmospheric pattern is also linked to an anomalous zonal cold-warm-cold SST pattern over the Atlantic Ocean and a warmer eastern Pacific Ocean.The identified causal links vary with temperature and humidity conditions, that is, the impact of soil moisture memory on the WSD variation is sensitive to T2m and WSD, while the influence of soil moisture condition on T2m changes is strengthened by reduced WSD. Moreover, during compound hot and dry extremes, the effect of reduced soil moisture on temperature is significantly higher than during single events, reaching twice the magnitude under moderate conditions. When historical trends are analyzed, we show that the impact of dry soil on temperature is amplified by 42% (46%) for single (compound) extremes during 1979-2020, while the influence of atmospheric drivers on soil moisture is intensified by 28% (43%).This work emphasizes (i) the intensification of the strength of the thermodynamic causal pathways for warmer and dryer CEU over time and (ii) the stress on the varying forcing strength of the drivers, which can lead to non-linear variations of weather stressors under climate changes and thus add extra challenges to extreme adaptations.  

Spatial and temporal variations in the relationship between lake water surface temperatures and water quality - A case study of Dianchi Lake

Lake surface water temperature (LSWT) is a physical property of lakes. LSWT is a critical parameter for evaluating lakes' water quality and biodiversity. The change in LSWT can also be an indicator of climate change. Therefore, it is crucial to monitor LSWT to improve our understanding of the spatiotemporal dynamics of LSWT for many applications. Conventionally and ideally, we can install in-situ gauge stations or monitoring sites to measure surface water temperature in lakes, and these in-situ measurements are generally the most accurate. However, in-situ measurements in lakes are often sparse and limited in terms of spatial coverage and temporal length, which leaves many lakes with no measurements or lacking long-term continuous measurements. For example, Lake Vänern (surface area of about 5,655 km2, the largest lake in the European Union) has only two operational stations for measuring LSWT. The existing in-situ measurements are at irregular intervals (approximately bi-weekly) and have many data gaps. Many lakes globally have the same data situation as Lake Vänern. As a result, in-situ measurements cannot sufficiently capture the spatiotemporal dynamics of LSWT in large lakes.Satellite remote sensing has emerged as an essential method to monitor LSWT. Thermal infrared satellite data have been widely used to estimate the surface temperature at relatively high spatial resolution (higher and up to 1 km resolution). One of the most used satellite products for surface temperature is the MODIS (Moderate Resolution Imaging Spectroradiometer) global land surface temperature product, which is available from 2000 at 1 km-daily spatial-temporal resolutions. However, many studies stressed that cloud influence could significantly degrade the quality and availability of satellite-derived surface temperature for certain lakes, calling for a dedicated investigation to address this issue. Besides MODIS data, there are many other satellite-derived LSWT products at different spatial-temporal resolutions and spatial coverage, e.g., the ones available at http://www.laketemp.net. In addition, the recent ERA5-Land, a state-of-the-art reanalysis dataset, can also provide spatially complete and temporally continuous land surface variables, including the lake temperature at 0.1 degree-hourly spatial-temporal resolutions from 1950 to present. Each of the aforementioned products has its own advantages and limitations.Our initial analysis showed a significant data gap in LSWT from MODIS product for Lake Vänern due to cloud influence, which motivates us to conduct this study. This study aims to evaluate multiple existing LSWT products and, more importantly, to combine them with the advanced data fusion and bias correction method to develop a new spatially complete and temporally continuous LSWT dataset for Lake Vänern, Sweden. New in-situ measurements of LSWT will be collected from boats and drones at many locations of Lake Vänern; such measurements, together with existing data from the two stations, will be used to evaluate multiple LSWT products, the developed method, and the merged dataset. The newly developed LSWT dataset for Lake Vänern will benefit many applications, such as lake evaporation estimation, water balance analysis, air-lake interactions, and local climate forecasting.

An important aspect of inevitable surprises, for the climate system, is the potential of occurrence of compound extreme events. These can be events that occur at the same time over the same geographic location or at multiple locations within a given country or around the world. In this study, we investigate the spatio-temporal variability of summer compound hot and dry (CHD) events at European level and we quantify the relationship between the occurrence of CHDs and the large-scale atmospheric circulation. Here we show that summer 1955 stands out as the year with the largest spatial extent characterized by hot and dry conditions (~21.2% at European level), followed by 2015 (~20.3%), 1959 (~19.4%), and 1950 (~16.9%). By employing an Empirical Orthogonal Function (EOF) analysis we show that there are three preferred centers of action of CHDs over Europe: Fennoscandia, the central part of Europe, and the south-eastern part of Europe. Overall, hot and dry summers are, in general, associated with persistent high-pressure systems over the regions affected by CHDs, which in turn reduces the zonal flow and diverts the storm tracks southward. The high-pressure systems associated with each mode of variability largely suppresses ascending motions, reduces water vapor condensation and precipitation formation, leading to drought conditions below this atmospheric system. This study may help improve our understanding of the spatio-temporal variability of hot and dry summers, at European level, as well as their driving mechanisms.

Water temperature is an important ecological variable that affects the functioning of lakes. Unfortunately, for many lakes there are no long-term observations enabling the assessment of changes in water temperatures. This makes it difficult to include this aspect in research into the biology, ecology and chemistry of such lakes. This paper presents a literature review related to changes of surface water temperatures in lakes and in particular describing the response of water temperatures and stratification to changing climate in Polish lakes. On this basis, a model based on the available data on water temperature in 931 Polish lakes in the years 1951–1968 was proposed, which allows to estimate the baseline water temperature on any day of the year. This model is calculated using the complementary error peak function on the 0–3 m water temperature dataset, which provides the best reduction of diurnal temperature fluctuations. It can be an alternative to the average temperature of surface waters, which are calculated on the basis of systematically collected data. Based on the average water temperature data obtained from 56 thermal profiles in 10 lakes in 2010–2019, the equation was analogically calculated. The average monthly water temperatures in June, July, August and September and the change in water temperature (0.24–0.30 °C decade−1) in the period 1951–1968/2010–2019 were estimated then. Similar regional or single lake trends have been found in studies by other authors covering a similar or longer period of time. The proposed method, which is suitable for simulating temperatures, especially in summer, enables the determination of the value of changes in surface water temperature in Polish lakes when only thermal profiles data from different dates are available, which can be especially helpful when analyzing hydrobiological results.

Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input into geophysical models. In this study, the Multi-scale Ultra-high Resolution (MUR) analysis of LSWT is evaluated between 2007 and 2015 over a small (Lake Oneida), medium (Lake Okeechobee), and large (Lake Michigan) lake. The advantages of the MUR LSWT analyses include daily consistency, high-resolution (~1 km), near-real time production, and multi-platform data synthesis. The MUR LSWT versus in situ measurements for Lake Michigan (Lake Okeechobee) have an overall bias (MUR LSWT-in situ) of −0.20 °C (0.31 °C) and a RMSE of 0.86 °C (0.91 °C). The MUR LSWT versus in situ measurements for Lake Oneida have overall large biases (−1.74 °C) and RMSE (3.42°C) due to a lack of available satellite imagery over the lake, but performs better during the less cloudy 15 July–30 September period. The results of this study highlight the importance of calculating validation statistics on a seasonal and annual basis for evaluating satellite-derived LSWT.

Detection of surface water temperature variations of Mongolian lakes benefiting from the spatially and temporally gap-filled MODIS data

Cascading and compounding multi-hazard events pose increasing challenges, presenting serious direct and indirect threats to people, the environment, and economic assets. Addressing these events and building disaster risk reduction capacity is crucial. This requires not only leveraging novel technologies such as modern Earth Observation (EO) platforms and AI, but also integrating them into effective multi-risk assessment frameworks. This study, conducted within the ESA EO4MultiHazard project, aims to exploit EO data to deepen our understanding of how multi-hazard cascading impacts unfold in affected areas. Specifically, it focuses on cascading and compounding hot and dry events—namely, heatwaves and droughts—and their impacts on crop vegetation in the lower Adige River Basin, located in northeastern Italy. The Adige River serves as a critical resource for the area's intensive agriculture, as its waters supply a dense irrigation network, making it especially vulnerable to reduced water availability during hot and dry conditions. Multi-risk assessment methodologies involve several key steps, including the spatiotemporal identification of hazards and the assessment of exposure and vulnerability. The ultimate goal of this study is to use high-resolution EO data to enhance the understanding of the different risk dimensions and identify risk susceptible areas. The multi-hazard identification methodology was adapted from the Myriad-EU project and applied to the Adige River Basin to analyze hot and dry events over the past 74 years (1950–2023) using the E-Obs gridded dataset. This analysis enabled the identification of general drought and heatwave trends, as well as the most severe and relevant events to inform a more detailed EO analysis. The 2022 drought, a recent and highly severe event, was selected as a case study period. In situ data—such as information on the irrigation network, irrigation districts, river discharge, and crop species at the field level—were combined with EO data from Sentinel-2. This integration of high-resolution satellite imagery (up to 10 meters) with detailed ground information allowed for the detection of vegetation stress responses to hot and dry events, serving as proxies for crop impacts. This approach not only identifies the most susceptible areas to inform multi-risk assessments, but also lays the groundwork for applying AI methodologies to predict future impacts under various climate scenarios. By creating past and present-day susceptibility maps, this study advances our understanding of hot and dry event dynamics on crops, and it demonstrates the potential of integrating advanced analytical tools and EO data into a multi-hazard framework to pave the way for machine learning applications for future climate multi-risk assessment and adaptation strategies.