Soil moisture (SM) is a critical variable for global ecosystems, playing a key role in water cycles, climate regulation and agricultural productivity. While numerous remote sensing and reanalysis products provide global SM, their representation of long-term trends often exhibits significant discrepancies, creating uncertainty for climate studies and applications. To better evaluate the trend performance of these products, this study analyzes the temporal trends (2000-2023) of four seamless global SM products (ERA5-Land, ESA CCI, GLDAS, and GLEAM) and validates them against an amount of ground station data. Furthermore, a controlled gap-simulation experiment incorporating discontinuous sensor-based products (SMAP, SMOS, AMSR2 and NNsm) into a dualbaseline framework is conducted to disentangle the statistical artifacts caused by data gaps from the broader context of trend uncertainty. The results show a dominant global drying trend, though substantial discrepancies in trend magnitude and direction persist among products. Among the evaluated datasets, GLEAM demonstrates outstanding performance, achieving the highest agreement with in-situ observations in both trend magnitude (R = 0.57) and directional consistency (66.0%). Further analysis reveals that on a continuous reference time series, data missingness primarily leads to the missed detection of trend signals rather than causing actual trend reversals, which are mainly attributed to sensor physics and algorithmic limitations. In addition, the long-term trends of ESA CCI are heavily influenced by its scaling data (GLDAS), which may limit the product's trend independence and requires further investigation. This study provides new methodological perspectives for the attribution of SM trend discrepancies, offering valuable guidance for user selection by recommending the prioritization of physically robust datasets (e.g., GLEAM) and the use of multi-product ensembles to mitigate specific biases.

The roadmap to enable operational soil moisture (SM) monitoring for meteorologic and hydrological early warning depends on the capabilities of the available remote sensing and modelling products. Since each type of soil moisture product shows specific strengths and limitations due to their technical restrictions over certain environments, the detection of impactful anomalies across a wide range of conditions and scales is often challenging and incomplete without a combination of complemental data types of sufficient resolution, revisit time and coverage. This study evaluates the capabilities of SM products of different nature and their compatibility for combination, with special attention to their uncertainties in spatial consistency and in residual trends. While the first has been often revisited to validate remote sensing and modelling products against in situ data, the last is often overlooked in studies addressing SM changes despite its potential to disrupt the outcomes.To meet the demands of operational monitoring this study evaluated three SM products: (1) the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) active Advanced SCATterometer (ASCAT)-derived dataset, (2) the passive subset of the European Space Agency (ESA) - Climate Change Initiative (CCIp), and (3) the modelled dataset from the European Drought Observatory (EDO). The analysis was carried out over Europe in the period 2007-2022 at 10 d temporal scales and 5 km x 5 km spatial sampling. First, Pearson's correlation coefficient (R) is used to measure the correspondence between H120, H121, CCIp and EDO SM products. Then triplets of the active, passive and model-based products are applied triple collocation analysis (TCA) to assess their performance based on TCA metrics such as the correlation, error variance, sensitivity and signal-to-noise ratio.We obtained that these popular well-validated datasets are increasingly capable in view of the notable TCA scores obtained but still subject to patches of spatial inconsistency and residual trends when compared against in situ SM data of the International Soil Moisture Network (ISMN). These uncertainties have minimal impact on drought monitoring in most of Europe, except in snow prone regions and for the assessment of long-term soil moisture trends used to design climate adaptation policies. Furthermore, each type of soil moisture product prevails in terms of triple collocation scores over the others under specific environmental conditions of the European continent. In view of the synergies shown by the active and passive remote sensing and the modelled SM estimates, two merged products are proposed and tested against the in situ data. The merging of the products is conducted by combining the various products based on weights calculated proportionally to the R_TCA scores of the triplets equalized in dynamic range matching their cumulative distribution functions. Results indicate that combining H SAF ASCAT, CCIp and EDO equals or surpasses the spatial and temporal consistency of the individual SM products alone, even when only the near-real-time products of H SAF ASCAT and EDO are combined. The evaluation of the trends of the individual products also indicates that small residual trends remain despite the improved filtering of the uncertainties, but given their differing sign of the trend, once combined into merged products can provide improved temporal stability of the series. Thus, merging remote sensing and modelled SM products enhances spatial consistency, resolution, temporal coverage and near-real-time capabilities for better European-scale drought monitoring, strengthening the early warning and risk management systems devoted to improving societal and environmental resilience.

Soil moisture is a crucial state variable for understanding the water cycle. The increasingly available soil moisture data from remote sensing and models is rapidly facilitating improved hydrological analysis and evaluation of climate change impacts. To discern the degree of alteration of soil moisture, the patterns of spatiotemporal anomalies must be considered, but often product-specific uncertainties are overlooked. Such limitations are of particular concern for the operational monitoring and long-term evaluation of soil moisture. Among the sources of uncertainty jeopardizing remotely sensed and modeled soil moisture, this study evaluates over Europe (1) the heterogeneous spatial patches of validity, (2) the residual trends in the series, and (3) the sensitivity of anomaly detection to the baseline period of popular soil moisture products such as the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF), the passive subset of the Climate Change initiative on SM (CCIp) and the European Drought Observatory (EDO) datasets. The inter-comparison of these remotely sensed and modeled soil moisture products by triple collocation analysis and against data of the international soil moisture network (ISMN) provides insightful results regarding (1) the contrasting patches of accurate soil moisture estimates, (2) the existence of residual temporal trends in the series, and (3) the differing sensitivity of the products to the baseline period for anomaly analysis. The factors impacting products are subject to debate, particularly concerning spatial and temporal consistency. Merged products combining H SAF, EDO and CCIp are also assessed to elucidate their potential and limitations for operational monitoring in comparison to individual products. Overall, the combined products equal or exceed the performance of individual products while incorporating specific benefits and drawbacks. Outcomes also inform about the best-performing product by area and period. All in all, the study illustrates the notable degree of consistency of commonly available soil moisture databases for multiple applications, despite some constraints, while highlighting the potential of merged soil moisture products for the operational monitoring of droughts within the European Drought Observatory (EDO) system.

High -resolution soil moisture data is crucial in the development of hydrological applications as it provides detailed insights into the spatiotemporal variability of soil moisture. The emergence of advanced remote sensing technologies, alongside the widespread adoption of machine learning, has facilitated the creation of continental and global soil moisture products both at fine spatial (1 km) and temporal (daily) scales. Some of these products rely on several data sources as input (satellite, in situ, modelling), and therefore an evaluation of their actual spatial and temporal resolution is required. Nevertheless, the absence of appropriate ground monitoring networks poses a significant challenge for this assessment. In this study, five high -resolution (1 km) soil moisture products (S1-RT1, S1 -COP, SMAP-Planet, SMAP-NSIDC, and ESACCI-Zheng) were analysed and evaluated throughout the Italian territory, together with a coarse resolution (12.5 km) dataset for comparison (ASCAT-HSAF). The main objective is to investigate their actual spatial and temporal resolution, and accuracy. Firstly, a cross -comparison of the products in space and time is carried out, including the use of triple collocation analysis. Secondly, an application -based assessment is implemented, considering irrigation, fire, drought, and precipitation case studies. The results clearly indicate the limitations and the potential of each product. Sentinel -1 based products (S1COP and S1-RT1) are found able to reproduce high -resolution spatial patterns by detecting localised events for irrigation, fire, and precipitation. Their lower temporal resolution leads to accuracies lower than that of the SMAP-Planet product, and comparable with SMAP-NSIDC and ESACCI-Zheng products. However, SMAP-Planet is found to have an actual spatial resolution coarser than 1 km. The study highlights the need for further research to improve the high -resolution soil moisture products, and particularly to determine accurately the spatial resolution represented in soil moisture products. At the same time, the analysed products are found able to address high -resolution applications for the first time, opening promising activities for their operational use in hydrology and water resources management.

Rainfed cereal yields show high variability depending on the varying conditions of concurrent factors during the crop year. Among them, hydrometeorological factors such as maximum temperature, rainfall, and notably, soil moisture, strongly affect crop production, but the greatest source of uncertainty on yield estimates stems from their interaction. This is of special interest in water-limited regions where climate change is expected to affect more intensely, but also in others where water is increasingly limited. Despite the highly non-linear nature of the interactions, simple statistic models such as multilinear regression accurately explore a notable proportion of the variability of cereal yields. To describe the impacts behind interactions, we perform stepwise multilinear regression of meteorological factors derived from E-OBSv23 database and soil moisture from ERA5-Land against annual wheat and barley yields for the period 1981–2019 in the main cereal regions of Spain. The multivariate approach characterizes the temporal shifts of factors’ influence. Beyond the temporal shifts on the synchrony of the factors, some of them tend to co-dominate the impact during the critical period of crop development, with soil moisture exceeding all others in relevance. Multivariate analysis fosters discussion about the impact of the choice of variables on the model fit, as well as on the pertinence of monthly and annual scales for explorative and predictive purposes. Monthly models perform particularly well during the critical period of growth and reproduction of crops and consistently better than univariate estimates. The annual model built using the data of the months of maximum impact of key variables outperforms the model at a monthly scale, which underlines the decisive role of the critical period. Similarly, results highlight the worth of parsimony in modelling. Soil moisture stands out as the principal concurrent variable to improve yield estimates from environmental data, which governs yields of rainfed water-limited croplands.

Given the growing interest in drought impacts on crops, this work studied the impact of agricultural drought on wheat and barley during the period 2001–2020. The study was carried out in the Spanish regions of Castilla y León and Castilla–La Mancha, with approximate areas of 94,000 km2 and 79,000 km2, respectively, and in the German regions of Nordrhein-Westfalen, Niedersachsen and Bayern, with approximate areas of 34,000 km2, 48,000 km2 and 71,000 km2, respectively. These are the main cereal-growing regions of Spain and Germany. Soil moisture (SM) in the root zone was extracted from the LISFLOOD model database, and SM anomalies were used as the agricultural drought index. Gross primary productivity (GPP) and leaf area index (LAI) variables were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the month in which SM is most influential on these crop state variables was identified. Crop yields in Spain and Germany were obtained from the Spanish Ministry of Agriculture, Fisheries and Food and the German Federal Statistical Office, respectively. Agricultural drought years and their impact on cereal yields were determined on a regional scale using three approaches based on the critical month with different time periods. These approaches were the use of the critical month and the two (before or after) and the three months (before and after) around the critical month. Two different analyses were used to identify the critical month, depending on the different environmental conditions in each country. These two approaches consisted of a monthly correlation analysis between SM anomalies and cereal yield in Spain and a monthly trend analysis of SM anomalies in Germany. The results showed a dependence of crop variables on SM in spring months in both countries and in summer months in Germany. Differences were found depending on the environmental conditions. A considerable reduction in cereal yields was obtained in Spain which exceeded 30%. Similarly, a worrying sign was observed in Germany, with a positive agricultural drought trend and a yield reduction of almost 5% in cereal crops. In view of future forecasts of the negative impact of climate change on global food production, this study provides valuable information for water and agricultural management under climate change scenarios. Both in regions that are already threatened and in those that until recently were not affected, it is necessary to study adaptation measures to avoid aggravating the impact of agricultural drought on crops, which could improve water productivity and future food security.

This study investigates the relationship between soil moisture and the growth of Pinus halepensis, P. nigra, P. sylvestris and P. uncinata, which are some of the main pine species of the Iberian Peninsula, and the response of these species to soil drought. The role played by climatic and geographic factors in the resilience of these species to drought events is also evaluated. A total of 110 locations of the four species studied were selected, with data ranging from 1950 to 2007. The results show that the species that are less dependent on soil moisture best withstood droughts, while those more dependent on it showed better adaptability. Additionally, climatic and geographic factors had a stronger influence on the species' resilience to soil drought at higher altitudes. The results of this study can help us to better understand forest ecosystem dynamics and their reaction to droughts in Mediterranean areas, where this phenomenon will be much more severe in the future due to climate change.

<p>The seasonal variation of precipitation intensity in continental semi-arid flatlands determines the shifting influence of interception in the throughfall and soil moisture regimes under distinct vegetation covers including conifer, broadleaved, grassland and rainfed croplands (i.e., wheat). In a study case located at Sierra de Atapuerca, in the high plains of North Spain, where continental climate defines a very contrasting precipitation intensity between the cold and warm season, the study analyzes the seasonal difference between the low and high energy rain drops affecting throughfall and soil moisture recharge levels along the year. Results identify the distinct response of throughfall, and the subsequent soil moisture change to distinct rainfall events and its consequences for the sustainability of surface conditions afterwards. The study outcomes highlight the major role of vegetation type on modulating the throughfall and soil moisture evolution which influences the exposure of the surface to soil erosion. Snow remarkably distorts the throughfall/interception balance between seasons, representing a third type of alteration, particularly for soil moisture, concerning the vegetation cover. Secondary atmospheric variables such as relative humidity and radiation also seem influential in the soil moisture anomalies and soil surface developing under the different vegetation covers of this environment. The type of canopy cover additionally influences the interaction between different levels of the soil moisture profile which subsequently determines the resilience to drought of the vegetation cover. Consequently, the study contributes to understanding the reciprocal interaction between vegetation and hydrology in the definition of surface processes and land-surface sustainability.</p>

Many upper reaches of important catchments around the world locate in sub-humid to semi-arid environments. The water balance of these areas experiences remarkable variability. Beyond precipitation anomalies, water and energy variables such as relative humidity and radiation can have notable impact on these fluctuations, with consequences at hydrological, agricultural, and environmental level. This study aims to characterize the role of several secondary atmospheric variables such as relative humidity and radiation in the generation of dry anomalies in sub-humid to semi-arid environments. Lag analysis of the interactions between anomalies allows the interpretation of the interplay between energy- and water-related factors. The study case focus on the area of the Critical zone Observatory BUREAUX-Sierra de Atapuerca, located in the basin of the River Vena, a tributary of Duero River in North Spain, a basin comprising from humid to arid conditions in the hydrological division between upper Duero and Ebro River. Results highlight the notable role of secondary factors such as radiation in driving dry anomalies. Radiation shows significant long-lasting influence over water-related variables, particularly over rainfall, given the low contribution of heavy rains to the water balance of the region, especially in winter, the productive season for soil water recharge. Interactions are overly reciprocal showing a notable role of precedent surface soil moisture on the latter conditions of atmospheric variables. Deeper levels of soil moisture are less responsive to the quicks changes of atmospheric anomalies which indicates an important resilience of deep soils to dry anomalies, at least when not affected by radiation. These results suggest distinct evolution of the surface and deep levels of the land-atmosphere system, with implications to the water available for agriculture and hydrology.

Mediterranean climates experience important climatic variability often causing droughts, whose consequences are especially worrisome in highly human-altered basins such as the Ebro Basin. An accurate understanding of the governing interactions of the water cycle is crucial in this area, which is a basin representative of water-related issues of the Mediterranean area. The HUMID project (CGL2017-85687-R) studies how remote sensing data and models (Quintana-Seguí et al., 2019; Barella-Ortiz and Quintana-Seguí, 2019) can improve our understanding of the alterations of rainfall-evapotranspiration-soil moisture interactions, which is essential to characterize the water cycle in drought-prone regions. Climates in these areas are driven by radiative factors while controlled by water-related ones, but the dominance of certain feedbacks such as the one of evapotranspiration-rainfall can locally modify the water balance and interactions. Within the complex climatic mosaic of the Ebro basin, there are areas with interesting high levels of local water recycling due to storm tracks of relevance at Iberian and even European scale. However, other areas of the basin barely show any moisture recycling. Since recycling suggests enhanced interaction between evapotranspiration and rainfall, this study explores the differences in the magnitude of rainfall anomalies with evapotranspiration and soil moisture anomalies between areas with low and high recycling. The comparison of the dominance of evapotranspiration-rainfall interaction over the other interactions of the water cycle is evaluated over areas of storm tracks compared to those barely affected by recycling. The comparison is conducted over three climatic types of the Köppen-Geiger classification: BSk, Cfa and Cfb in order to distinguish the relevance of recycling, mostly of local scale, in comparison to the climatic type, influential at the synoptic scale. High-resolution remote sensing products such as SMOS 1km and MODIS16 A2 ET enable evaluating rainfall, evapotranspiration and soil moisture anomalies with a level of detail suitable for local-scale analysis. Standardized drought indices such as soil moisture deficit index (SMDI) or the evapotranspiration deficit index (ETDI) can be calculated based on SMOS 1km data (2010-2019) and MODIS16 A2 ET 500m. The SPI index is used for rainfall anomalies. To assess the impact of recycling on the rainfall-evapotranspiration and soil moisture interactions we compare the distribution and magnitude of lags between these three variable-specific drought indices at the contrasting regions. The method allows identifying differences in the distribution of lags between the SPI, ETDI and SMDI that differ depending on the vicinity to the storm track. The type of climate shows certain interaction with the effects of local recycling. The study illustrates the worth of high-resolution remote sensing data to evaluate recycling mechanisms and the anomalies of the land-atmosphere system propagating drought across feedbacks, even at the local scale. This advantage facilitates a better understanding of the climatic variability in semi-arid Mediterranean climates while encouraging developing monitoring tools integrating the particularities of water-limited types of climate.

The relationship between modelled root zone soil moisture (SM) and black truffle production in the Iberian Peninsula was studied. Previous works have investigated the influence that precipitation exerts on truffle yield highlighting the importance of water for the growth of black truffle. However, SM had not been used until now due to the lack of suitable databases. The SM series from the LISFLOOD hydrological rainfall–runoff model was used in this study. Annual black truffle yield series from 175 locations in Spain was correlated with SM for the period 1991–2012. For this, different approaches were applied considering daily, weekly and monthly temporal scales. The same analysis was carried out using precipitation data to compare the behaviors of both variables related to truffle production variability. The results obtained show critical periods in terms of soil water content in summer (June–September) and during October–November months. Moreover, a clear delay between precipitation and SM influence on black truffle was observed. The results obtained in this study highlight the importance of SM for black truffle production, since this variable truly expresses the available water for this fungus, which completes its entire life cycle living below ground.

Soil moisture (SM) plays a key role in the water cycle, and its variability is intimately linked to coupled land-atmosphere processes. Having a good knowledge of soil-atmospheric interactions is thus essential to assess the impact of climate change on SM; however, many aspects of how water and energy exchanges occur in the soil-atmosphere continuum are still uncertain. In particular, it is known that atmospheric circulation patterns influence climate conditions over Europe but their impact on SM has only rarely been studied. This study provides insight into how atmospheric patterns influence soil moisture dynamics in Europe, where an increase in temperature and agricultural droughts are expected as an impact of climate change. To do so, we analysed the influence of the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the El Niño Southern Oscillation (ENSO) on European SM, including lagged responses, for the period 1991–2020 at a monthly scale. Two methods have been used: a lagged correlation analysis and a more sophisticated causality approach using the PCMCI (PC method combined with the momentary conditional independence (MCI) test). SM series from two different databases were considered: the hydrological model LISFLOOD and the reanalysis dataset ERA5-Land. The results from the correlation analysis showed a significant, predominantly negative relationships of SM with NAO and AO over almost all of Europe and no significant relation with ENSO. With the causality analysis, similar patterns are obtained for NAO and AO; however, the PCMCI analysis revealed clear patterns of ENSO influencing SM with a delayed response of one-to-two months in central and northwest Europe. The results obtained in this work highlight that there are causal relations between the main modes of interannual climate oscillations and SM variations in Europe, underlining the importance of accounting for global atmospheric circulations to study current changes in regional soil water-related processes.

The impact of climate change on soil moisture (SM) dynamics is uncertain. Changes in the Earth's SM during recent decades have been studied globally and in different regions, but little attention has been given to Europe. In addition, most previous works have just relied on a monotonic behavior of SM changes, which is a strong assumption and not always valid. We argue that this fact, together with the use of large temporal scales, has prevented the observation of clear patterns of SM trends over the continent. In this work, we study European SM trends for a 30-year period, from 1991 to 2020, using two complementary databases, one from reanalysis project ERA5-Land and the other from the model Lisflood. Both rank-based and empirical decomposition approaches have been considered and applied to monthly and annual series of SM anomalies. The Köppen-Geiger classification allowed us to analyze the distribution of SM anomaly trends in the separate European climates. The results obtained with both databases, methods and temporal scales were consistent, with the empirical decomposition method generally detecting more significant trends. Our results show a general decreasing trend of SM, regardless of climate type but more intense in Eastern and Central Europe. In addition, the rank-based method detected fewer positive trends, suggesting a non-monotonic behavior in changes to wetter conditions. The most notable differences were obtained with the empirical decomposition method when comparing the different temporal scales. Hence, an intramonthly analysis was conducted to provide insight into the different patterns. An increase in significant trends was observed in April and the autumn (September–October-November). Furthermore, we conducted a similar analysis to study trends in extreme drought characteristics (annual duration, intensity and onset) and we obtained consistent results, with the empirical decomposition method detecting more significant trends. Our investigations show a general increase in the duration and intensity of extreme droughts over the European continent, tending to be delayed a few days per year in arid and temperate regions.

The Iberian Peninsula is prone to drought due to the high variability in the Mediterranean climate with severe consequences for drinking water supply, agriculture, hydropower and ecosystem functioning. Because of the complexity and relevance of droughts in this region, it is necessary to increase our understanding of the temporal interactions of precipitation, evapotranspiration and soil moisture that originate from drought within the Ebro basin, in northeastern Spain, as the study region. Remote sensing and land-surface models provide high-spatial-resolution and high-temporal-resolution data to characterize evapotranspiration and soil moisture anomalies in detail. The increasing availability of these datasets has the potential to overcome the lack of in situ observations of evapotranspiration and soil moisture. In this study, remote sensing data of evapotranspiration from MOD16A2 and soil moisture data from SMOS1km as well as SURFEX-ISBA land-surface model data are used to calculate the evapotranspiration deficit index (ETDI) and the soil moisture deficit index (SMDI) for the period 2010–2017. The study compares the remote sensing time series of the ETDI and SMDI with the ones estimated using the land-surface model SURFEX-ISBA, including the standardized precipitation index (SPI) computed at a weekly scale. The study focuses on the analysis of the time lags between the indices to identify the synchronicity and memory of the anomalies between precipitation, evapotranspiration and soil moisture. Lag analysis results demonstrate the capabilities of the SPI, ETDI and SMDI drought indices computed at a weekly scale to give information about the mechanisms of drought propagation at distinct levels of the land–atmosphere system. Relevant feedback for both antecedent and subsequent conditions is identified, with a preeminent role of evapotranspiration in the link between rainfall and soil moisture. Both remote sensing and the land-surface model show capability to characterize drought events, with specific advantages and drawbacks of the remote sensing and land-surface model datasets. Results underline the value of analyzing drought with dedicated indices, preferably at a weekly scale, to better identify the quick self-intensifying and mitigating mechanisms governing drought, which are relevant for drought monitoring in semi-arid areas.

Cereals are keystone crops for achieving food security and socioeconomic equilibrium, but rainfed cropland is highly sensitive to environmental anomalies that impact yields. The impact of soil moisture on cereal yield is particularly overlooked. This study evaluates the impact of root-zone soil moisture on yield compared to nine common climatic variables: maximum and minimum temperature, diurnal temperature range, growing degree days, accumulated rainfall, radiation, photothermal quotient, relative humidity of the air, and vapor pressure deficit. This study used the climatic database E-OBSv23 and the soil moisture databases ERA5-Land and LISFLOOD, focused on wheat and barley over the main cereal areas of Spain. Correlation analysis between annual yield and daily soil moisture and climatic data provided indicated the prevalence and concurrence of the impact factors on phenological stages of the Zadoks scale. Critical periods of impact on wheat and barley yields primarily concentrate during the growth and reproductive phases of spring. Soil moisture exceeds all other factors in magnitude and duration of influence, and our results suggest a complex interplay of factors during the critical spring period. This study highlights the preeminent role of soil moisture over climatic factors on the variability of rainfed cereal yields in water-limited areas.

Droughts in the Iberian Peninsula are a natural hazard of great relevance due to their recurrence, severity and impact on multiple environmental and socioeconomic aspects. The Ebro Basin, located in the NE of the Iberian Peninsula, is particularly vulnerable to drought with consequences on agriculture, urban water supply and hydropower. This study, performed within the Project HUMID (CGL2017-85687-R), aims at evaluating the influence of the climatic, land cover and soil characteristics on the interactions between rainfall, evapotranspiration and soil moisture anomalies which define the spatio-temporal drought patterns in the basin. The onset, propagation and mitigation of droughts in the Iberian Peninsula is driven by anomalies of rainfall, evapotranspiration and soil moisture, which are related by feedback processes. To test the relative importance of such anomalies, we evaluate the contribution of climatic, land-cover and geologic heterogeneity on the definition of the spatio-temporal patterns of drought. We use the Köppen-Geiger climatic classification to assess how the contrasting climatic types within the basin determine differences on drought behavior. Land-cover types that govern the partition between evaporation and transpiration are also of great interest to discern the influence of vegetation and crop types on the anomalies of evapotranspiration across the distinct regions of the basin (e.g. forested mountains vs. crop-dominated areas). The third physical characteristic whose effect on drought we investigate is the impact of soil properties on soil moisture anomalies. The maps and time series used for the spatio-temporal analysis are based on drought indices calculated with high-resolution datasets from remote sensing (MOD16A2ET and SMOS1km) and the land-surface model SURFEX-ISBA. The Standardized Precipitation Index (SPI), the EvapoTranspiration Deficit Index (ETDI) and the Soil Moisture Deficit Index (SMDI) are the three indices chosen to characterize the anomalies of the corresponding rainfall (atmospheric), evapotranspiration (atmosphere-land interface) and soil moisture (land) anomalies (components of the water balance). The comparison of the correlations of the indices (with different time lags) between contrasting regions offers insights about the impact of climate, land-cover and soil properties in the dominance, the timing of the response and memory aspects of the interactions. The high spatial and temporal resolution of remote sensing and land-surface model data allows adopting time and spatial scales suitable to investigate the influence of these physical factors with detail beyond comparison with ground-based datasets. The spatial and temporal analysis prove useful to investigate the physical factors of influence on the anomalies between rainfall, evapotranspiration and soil moisture. This approach facilitates the physical interpretation of the anomalies of drought indices aiming to improve the characterization of drought in heterogeneous semi-arid areas like the Ebro River Basin.

The Mediterranean climate of the Iberian Peninsula defines high spatial and temporal variability of drought at multiple scales. These droughts impact human activities such as water management, agriculture or forestry, and may alter valuable natural ecosystems as well. An accurate understanding and monitoring of drought processes are crucial in this area. The HUMID project (CGL2017-85687-R) is studying how remote sensing data and models (Quintana-Seguí et al., 2019; Barella-Ortiz and Quintana-Seguí, 2019) can improve our current knowledge on Iberian droughts, in general, and in the Ebro basin, more specifically. The traditional ground-based monitoring of drought lacks the spatial resolution needed to identify the microclimatic mechanisms of drought at sub-basin scale, particularly when considering relevant variables for drought such as soil moisture and evapotranspiration. In situ data of these two variables is very scarce. The increasing availability of remote sensing products such as MODIS16 A2 ET and the high-resolution SMOS 1km facilitates the use of distributed observations for the analysis of drought patterns across scales. The data is used to generate standardized drought indexes: the soil moisture deficit index (SMDI) based on SMOS 1km data (2010-2019) and the evapotranspiration deficit index (ETDI) based on MODIS16 A2 ET 500m. The study aims to identify the spatio-temporal mechanisms of drought generation, propagation and mitigation within the Ebro River basin and sub-basins, located in NE Spain where dynamic Atlantic, Mediterranean and Continental climatic influences dynamically mix, causing a large heterogeneity in climates. Droughts in the 10-year period 2010-2019 of study exhibit spatio-temporal patterns at synoptic and mesoscale scales. Mesoscale spatio-temporal patterns prevail for the SMDI while the ETDI ones show primarily synoptic characteristics. The study compares the patterns of drought propagation identified with remote sensing data with the patterns estimated using the land surface model SURFEX-ISBA at 5km. The comparison provides further insights about the capabilities and limitations of both tools, while emphasizes the value of combining approaches to improve our understanding about the complexity of drought processes across scales. Additionally, the periods of quick change of drought indexes comprise valuable information about the response of evapotranspiration to water deficits as well as on the resilience of soil to evaporative stress. The lag analysis ranges from weeks to seasons. Results show lags between the ETDI and SMDI ranging from days to weeks depending on the precedent drought status and the season/month of drought’s generation or mitigation. The comparison of the lags observed on remote sensing data and land surface model data aims at evaluating the adequacy of the data sources and the indexes to represent the nonlinear interaction between soil moisture and evapotranspiration. This aspect is particularly relevant for developing drought monitoring aiming at managing the impact of drought in semi-arid environments and improving the adaptation to drought alterations under climate change.

Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate. Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs. Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015. A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc. The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013). In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.

Rivers are important ecosystems under continuous anthropogenic stresses. The hyporheic zone is a ubiquitous, reactive interface between the main channel and its surrounding sediments along the river network. We elaborate on the main physical, biological, and biogeochemical drivers and processes within the hyporheic zone that have been studied by multiple scientific disciplines for almost half a century. These previous efforts have shown that the hyporheic zone is a modulator for most metabolic stream processes and serves as a refuge and habitat for a diverse range of aquatic organisms. It also exerts a major control on river water quality by increasing the contact time with reactive environments, which in turn results in retention and transformation of nutrients, trace organic compounds, fine suspended particles, and microplastics, among others. The paper showcases the critical importance of hyporheic zones, both from a scientific and an applied perspective, and their role in ecosystem services to answer the question of the manuscript title. It identifies major research gaps in our understanding of hyporheic processes. In conclusion, we highlight the potential of hyporheic restoration to efficiently manage and reactivate ecosystem functions and services in river corridors.