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.
Drought is a major natural hazard for societies in semi-arid climates (Van Loon, 2015) and demands increasing levels of adaptation and resilience measures to guarantee water supply (Watts et al., 2012), particularly in water-stressed environments. Rainfed agriculture (Tigkas and Tsakiris, 2015) and even the enduring natural vegetation are very exposed to drought, especially under climate change, which has long-lasting implications for the local environment (Gudmundsson et al., 2014). Knowing that complex interactions take place in the land–atmosphere system under drought, the traditional meteorological or hydrologic approach may overlook drought-relevant interactions between evapotranspiration and soil moisture (Teuling et al., 2013).
To track drought status and to analyze the interactions of the land–atmosphere system, modern drought monitoring combines evapotranspiration, soil moisture and even vegetation anomalies in composite drought indices, such as the Objective Blends of Drought Indicators (OBDI) integrated in the U.S. Drought Monitor (USDM; Svoboda et al., 2002) or the Combined Drought Indicator (CDI) of the European Drought Observatory (Sepulcre-Canto et al., 2012). The use of this combined approach to monitoring drought is on an upward trend because even parsimonious composite drought indices like the probabilistic precipitation vegetation index (PPVI) (Monteleone et al., 2020) outperform the capabilities of common indices to characterize drought. One of the major advantages of composite indices is that they facilitate the characterization of drought from multiple perspectives (e.g., meteorological, hydrological or agricultural). Conversely, composite indices can be impractical to explore the mechanisms of drought, whose understanding may require focusing on key variables of the system. Unfortunately, evapotranspiration and soil moisture are still challenging to monitor compared to the meteorological, hydrological or vegetation variables currently regularly recorded. Despite the relevance of these two variables in the recurrence of drought and heat waves (Zampieri et al., 2009; Dasari et al., 2014), even at short timescales (Teuling, 2018), relatively few studies have evaluated their anomalies due to the limited availability of data of sufficient spatial and temporal resolution.
Well-known drought indices such as the standardized precipitation index (SPI) (McKee et al., 1993) and the Palmer drought severity index (PDSI) (Palmer, 1965), primarily defined on the monthly scale, can lack detail to identify short-term anomalies of temperature, wind or radiation-originating “flash droughts” (Otkin et al., 2013). Rainfed agriculture and natural vegetation are particularly sensitive to quickly evolving droughts in specific moments of the growing season (Saini and Westgate, 1999), which subsequently generate evapotranspiration and soil moisture anomalies of short- and long-term impact (Jiménez et al., 2011). Recently, there has been more interest in using drought indices with high temporal resolution for short-term drought monitoring, such as the SPI and other indices at the weekly scale (Otkin et al., 2015). Indices with this short-term timescale include the weekly-scale evapotranspiration deficit index (ETDI) and the soil moisture deficit index (SMDI) (Narasimhan and Srinivasan, 2005). The ETDI and SMDI are variable-specific, enabling full characterization of anomalies at specific levels of the land–atmosphere system. This is especially useful in the Mediterranean climates where drought originates from not only rainfall anomalies (Vicente-Serrano et al., 2004).
This study focuses on the Ebro basin, which is an important Mediterranean
river basin of the Iberian Peninsula (IP). In view of the increase in the
frequency of drought events (Sousa et al., 2011) and the number of
consecutive dry spells (Turco and Llasat, 2011) identified in the area, we
can expect consequences in the long-term environmental state and the balance
between water availability and demands. Furthermore, being placed in a
semi-arid climate where most of the rainfall evaporates (68 %, Table 15
of
Space agencies have released multiple RS products in the last decade facilitating the distributed analysis of drought (AghaKouchak et al., 2015). Optical spectrometry of the atmospheric (rainfall, temperature, water vapor) and surface (vegetation reflectance) variables has often been the basis for distributed characterization of drought indicators. Surface vegetation indices such as the widespread normalized difference vegetation index (NDVI; Liu and Kogan, 1996) pioneered the application of RS data to assess the impacts of drought, but thereafter the increasing availability of RS data for multiple meteorological variables has increased their usage in drought indices (West et al., 2019). Currently, common indices like the SPI can rely on RS data (Sahoo et al., 2015) because integrating the increasing resolution of RS data into drought indicators enables short-term drought monitoring at least at the weekly scale (USDM – Svoboda et al., 2002; CDI – Sepulcre-Canto et al., 2012; Monteleone et al., 2020). However, unlike precipitation, temperature, and other directly observable and densely monitored meteorological variables, the measurement of evapotranspiration and soil moisture on the ground is still challenging and often costly or impractical at sufficient spatial resolution. Overcoming this gap is possible now thanks to the increasing availability of RS-based evapotranspiration databases such as the global dataset included in GLEAM (Miralles et al., 2011; Martens et al., 2017) or the soil moisture global database CCI (Dorigo et al., 2017). Despite the coarse spatial resolution of these global datasets, the recent developments in RS processing and downscaling improve their applicability at regional spatial scales and short timescales (Wagner et al., 2007). Aiming to gain insight into drought mechanisms, the availability of high-resolution datasets focused on such relevant variables of the land–atmosphere facilitates the use of single-variable drought indices such as the SPI, ETDI and SMDI, which is advantageous to analyze the interactions between variables during droughts.
On this basis, there are soil moisture datasets of increasingly high resolution available from a combination of passive microwave sensors such as those from SMOS and Soil Moisture Active Passive (SMAP) missions (Kerr et al., 2010, and Entekhabi et al., 2010, respectively) and active microwave sensors such as ASCAT or Sentinel-1 (Bartalis et al., 2007, and Hornacek et al., 2012, respectively). This is the case of the high-resolution soil moisture product SMOS1km (Merlin et al., 2013; Molero et al., 2016; Escorihuela and Quintana-Seguí, 2016; Escorihuela et al., 2018), which has been tested in the area and has been shown to outperform ASCAT and AMSR-E due to its lack of roughness and vegetation effects. SMAP and Sentinel-1 options are of similar resolution to SMOS1km and accurate in the study area (Dari et al., 2021), but they are of much shorter series length and are consequently not selected. Similarly, high-resolution RS evapotranspiration products such as MOD16A2 (Mu et al., 2013) used in this study are currently available. Therefore, it is worth exploring the capabilities and limitations of high-resolution RS evapotranspiration data for drought monitoring at the regional scale. High-resolution RS data are most suitable for analysis at the basin scale where the resolution of alternative reanalysis or modeled datasets such as ERA5-Land (Muñoz-Sabater et al., 2021), LISFLOOD (Van Der Knijff et al., 2008) or GLEAM v3 (Miralles et al., 2011; Martens et al., 2017) lack detail. To date, relatively few works have used high-resolution satellite data for drought analysis in the IP (Vicente-Serrano, 2006; Scaini et al., 2015; Martínez-Fernández et al., 2016; Sánchez et al., 2016; Ribeiro et al., 2019), especially at the spatial and temporal resolution of this study (Pablos et al., 2017).
Another source of high-temporal-resolution and high-spatial-resolution data is land-surface models (LSMs). Used in atmospheric models to simulate the interactions between soil, vegetation and the atmosphere, LSMs represent a suitable alternative to RS to evaluate the surface water and energy balances at regional to local scales. The development of LSMs was initiated with one-layer models such as TOPUP (Schultz et al., 1998) or PROMET (Mauser and Schadlich, 1998). Avissar and Pielke (1989) inaugurated the mosaic approach, applying just one-layer models to the different fractions of land-use type. One of the mosaic models able to distinguish between soil evaporation and transpiration is the Météo-France-developed model SURFEX (Masson et al., 2013), which, fed by the atmospheric analysis SAFRAN (Durand et al., 1999), uses the ISBA scheme for natural surfaces (Noilhan and Mahfouf, 1996). SURFEX has been improved to study the continental water cycle in applications such as SIM and SIM2 (Habets et al., 2008; Le Moigne et al., 2020), often in combination with the hydrologic model MODCOU (Ledoux et al., 1989). The modeling chain called SASER (SAFRAN–SURFEX–Eaudyssée–RAPID) used in this study has been applied to Spain before (Barella-Ortiz and Quintana-Seguí, 2019; Quintana-Seguí et al., 2020). This LSM provides the precipitation required for the SPI and the evapotranspiration and soil moisture necessary to generate LSM-based ETDI and SMDI series comparable to the ones generated using RS data. Despite the limitations of this LSM when applied as an offline model, it has been validated and shown to provide useful evaluations of water resources in the study area (Escorihuela and Quitana-Seguí, 2016; Barella-Ortiz and Quintana-Seguí, 2019) and nearby Portugal and France (Nogueira et al., 2020; Le Moigne et al., 2020).
This study aims at evaluating the suitability of high-resolution RS (SMOS1km and MOD16A2) and LSM (SURFEX-ISBA) data for generating rainfall (SPI), soil moisture (SMDI) and evapotranspiration (ETDI) drought (single-variable) indices to better understand the mechanisms behind the temporal evolution of drought in semi-arid climates. The comparison of RS and LSM data results is a main aim of the study to detect the factors impacting drought indices based on RS and LSM data. The study further evaluates the advantage of the barely explored weekly temporal scale to capture the short-term anomalies of evaporation and soil moisture decisive for drought in semi-arid areas. The study has an agricultural scope focused on drought in rainfed environments given its importance to land–atmosphere feedbacks (Herrera-Estrada et al., 2017) and regional socioeconomic sustainability.
The study area is the Ebro basin, located in the northeast of the Iberian
Peninsula (IP). Placed in between Atlantic and Mediterranean climatic
influences, the vast area (85 534 km
SURFEX, the land-surface modeling platform originally developed and
currently maintained by Météo-France (Masson et al., 2013; Le Moigne et al., 2020), has been chosen to perform the LSM simulation used in this
study. Simulations for the IP and Balearic Islands developed within the
HUMID project have
ISBA (Noilhan and Mahouf, 1996) is the SURFEX module in charge of simulating natural surfaces. There are different versions of ISBA. In this study, we have used the diffusion version (ISBA-DIF; Boone, 1999; Decharme et al., 2011), which performs better in the study area than the simpler three-layer force restore version (Quintana-Seguí et al., 2020). In this version of ISBA, the leaf area index (LAI) has a prescribed annual cycle (constant every year), which may limit the ability of the model to reproduce the long-term effects of drought on vegetation. The model simulates the soil column, but it is unable to simulate groundwater, which despite its impact on soil moisture memory, is not very relevant in the Ebro basin. SURFEX-ISBA requires additional physiographic information that is incorporated from the ECOCLIMAP-II land cover database (Faroux et al., 2013), which includes topographic, soil and land cover information at high resolution.
The available SAFRAN forcing data allow us to simulate the period
1979–2017, but the period used for this study is restricted by the
relatively short length of the RS SMOS data coverage (2010–present) compared to the
model. To ensure the comparability of RS-based and LSM-based drought
indices, the study period is 2010–2017, for which both RS and LSM data are
available. To ensure that the RS soil moisture and the LSM soil moisture are comparable,
we have averaged the first three soil layers of the model according to their
discretization in the first 5 cm of the soil (1, 3 and 10 cm of depth,
respectively). The simulation is performed using a regular
To evaluate evapotranspiration, barely measured on the ground and not
directly measurable from space, we adopt a product based on multiple
evaporation-related variables observed by MODIS (Moderate Resolution Imaging
Spectroradiometer on NASA's Terra satellite): the MOD16A2 dataset. This is a
level-4 product providing 8 d evapotranspiration (ET) and potential
evapotranspiration (PET) based on daily meteorological forcing and 8 d RS
data of vegetation dynamics from MODIS (Mu et al., 2013). The datasets of
MOD16A2 are published in a sinusoidal projection at a resolution of 500 m
(Running et al., 2017). In this study, we have reprojected and interpolated
all RS products to the same
Because of the relatively few years of data currently available from the
Soil Moisture Active Passive (SMAP) mission (Entekhabi et al., 2010), the
study adopts SMOS data (Kerr et al., 2010), in particular, the high-resolution SMOS1km dataset (Merlin et al., 2013). This dataset
downscales the original coarse-resolution SMOS data using the Disaggregation
based on Physical And Theoretical scale Change (DisPATCh) algorithm (Merlin et al., 2012) and the C4DIS algorithm (Molero et al., 2016). The algorithm enables the downscaling of the 40 km resolution of the SMOS soil moisture data available from 2010 into 1 km resolution using two products at 1 km
resolution from MODIS, the NDVI and land surface temperature (LST), and an elevation map at the same resolution. Precisely because the scale of interest to study relevant interactions to droughts is the weekly scale, the data are primarily used on a weekly scale. The spatial scale of interest for the study is that of a regular
Drought indices allow quantifying several aspects of drought, like the magnitude and duration, and may also focus on particular variables depending on the scope of interest (i.e., precipitation, soil moisture, aridity, etc.). In view of the convenience to combine several single-variable indicators to describe most of the drought mechanisms and our focus on rainfed environments, we adopt the use of the standardized precipitation index (SPI) (McKee et al., 1993), the evapotranspiration deficit index (ETDI) and the soil moisture deficit index (SMDI) (Narasimhan and Srinivasan, 2005). Using these three indices, the study aims to investigate the interaction between the two main water fluxes (rainfall and evapotranspiration) and the main storage (soil moisture) involved in the water balance of the land–atmosphere system. The aggregation periods of the SPI inform us about the different responding times of rainfall, soil moisture, streamflow and groundwater anomalies. By evaluating the evolution of these indices along with their interactions, this study aims to characterize drought mechanisms in the Ebro basin. In this study, the indices have been computed using gridded datasets, thus generating a time series for each grid point.
The standardized precipitation index (SPI) (McKee et al., 1993) is an index
of precipitation anomalies, which is calculated by transforming the
accumulated precipitation from its original distribution (usually gamma or
Pearson type III) to the normal distribution with zero mean and unit
standard deviation. As a result, we obtain a time series that shows, for
each time step, the departure from the expected value in terms of standard
deviations. The calculation of the index is usually made based on monthly
time series of rainfall, aggregated over multiple accumulation periods,
typically at 3, 6 and 12 months. However, the SPI can also be calculated on a
weekly basis, provided that the accumulation periods are at least 4 weeks (1 month). In this way, this study uses the notation SPIm-
The second index incorporated into the analysis is the evapotranspiration
deficit index (ETDI) defined by Narasimhan and Srinivasan (2005). The first
step for calculating this index implies defining the water stress (WS) ratio
for each week, which is the difference between potential evapotranspiration
(PET) and actual evapotranspiration (AET) divided by PET. Then the water
stress anomaly (WSA) is computed as follows:
The third index incorporated into the analysis is the soil moisture deficit
index (SMDI) also defined by Narasimhan and Srinivasan (2005). The sequence
to calculate this index follows the same procedure as the ETDI. We first
calculate a weekly soil moisture deficit (SD) as follows:
Given the relatively short availability of data for the calculation of ETDI and SMDI series, which depend on the maximum, minimum and median values of the available series, we conducted a sensitivity analysis of the indices in reference to the length of the series and the subset of spatial data. Results shown in Table S1 illustrate the relatively low impact of the length of the series thanks to the high spatial resolution of the dataset. The shortening of the series by half or a quarter barely alters the ETDI series compared to the case of using the full temporal length. The subset of the dataset to a fraction of its spatial resolution increasingly impacts the robustness of ETDI and SMDI series. Therefore, the high-resolution spatial and temporal datasets such as the RS and LSM used for this study support the consistency of drought indices even when data availability remains under a decade long.
To evaluate the similarity between the series of the drought indices (SPI,
ETDI and SMDI), we use a variant of the procedure applied by Barella-Ortiz
and Quintana-Seguí (2019) and Quintana-Seguí et al. (2020), based
on Barker et al. (2016). The method consists of computing the
Following the correlation analysis between the series, we perform a lag
analysis of the correlation of the pairs of drought indices at a weekly
scale, introducing lags from
The first two aims of the study are to evaluate the suitability of the SPI, ETDI and SMDI to characterize the main anomalies in water exchanges of
the land–atmosphere system and to evaluate the suitability of adopting the
weekly scale for the analysis of drought indices compared to the use of the
monthly scale. Regarding the first, results shown in Fig. 2 indicate a
general agreement of the SPI, ETDI and SMDI (computed at either a
monthly or a weekly scale) on the major events of dry and wet anomalies of the period 2010–2017. The dry period of 2011–2012 and the wet period from the end of 2012 to 2015 were properly depicted. However, we identified
differences, especially in the case of the SMDI. This index tends to show a
generally lower variability than other indices when calculated with the LSM.
RS results of the SMDI differ from the other indices during the start of 2010
due to the uncertainties during the test period of the SMOS mission. The
left column of Fig. 2 shows the monthly SPIm-
Time series of the SPI, ETDI and SMDI at the monthly scale (left
column of subplots identified with sub-index 1) and weekly scale (right
column of subplots identified with sub-index 2). Panels
Matrices of significant (in bold) correlation coefficients
considering a
Table 1 also reveals differences in the moderate correlation of SPIm-
The monthly correlation analysis is additionally conducted for the subsets
of dry periods (those with negative signs of SPIm, ETDIm and SMDIm).
Using the dry subset instead of the entire period primarily decreases all
correlations. Compared to the correlations of the entire period, the RS
ETDIm–SMDIm values decrease a little while the LSM ones increase a little. Also the SPIm-
Figure 2 provides an overview of the effect of adopting the weekly scale
(right column) instead of the monthly scale (left column). The weekly scale
substantially improves the temporal resolution of the plots. Subplots of
SPI-
The right columns of Table 1 indicate the correlations between indices on a
weekly scale compared to those on a monthly scale (the “w” sub-index of
ETDIw, SMDIw and SPIw denotes the weekly scale). There is an overall decrease
in correlations at the weekly scale compared to the monthly scale, accentuated by the increasing period of aggregation (from SPIw-3 to
SPIw-12). Correlations increase compared to the monthly scale at the lowest period of aggregation of SPIw-
The analysis of correlations between pairs of temporally lagged time series
of drought indices provides valuable insights into the interactions between
indices (e.g., in terms of reciprocity) as indicators of synchronicity of
one variable with respect to the other. The analysis addresses the
characterization of the interactions between rainfall, evapotranspiration
and soil moisture while also aiming to examine whether the land–atmosphere
exchange of semi-arid areas under drought events is a quickly evolving or
rather inertial system. Plots of the fraction of the area affected by each
correlation level (from
The lag analysis of SPIw–ETDIw and SPIw–SMDIw shown in Figs. 3–6
aims at diagnosing the reciprocity, synchronicity and memory in the interaction between rainfall and evapotranspiration and between rainfall and
surface soil moisture. Each subplot of Figs. 3–6 shows the correlation
between the ETDIw and SMDIw indices with SPIw-
In the case of the ETDIw–SPIw-
Lag plots of SPI-1, SPI-3, SPI-6 and SPI-12 (expressed as SPIw-4, SPIw-13, SPIw-26 and SPIw-52, respectively) with remote sensing (RS) ETDIw at the weekly scale for the period 2010–2017. Lags are calculated for the
Results from the LSM SURFEX-ISBA (Fig. 4) show a less lasting and more
concentrated cluster of significant positive correlations around lag 0
(ST
Lag plots of SPI-1, SPI-3, SPI-6 and SPI-12 (expressed as SPIw-4, SPIw-13, SPIw-26 and SPIw-52, respectively) with the land-surface model (LSM) ETDIw index at the weekly scale for the period 2010–2017. Lags are
calculated for the
The interaction of SPIw-
Lag plots of SPI-1, SPI-3, SPI-6 and SPI-12 (expressed as SPIw-4, SPIw-13, SPIw-26 and SPIw-52, respectively) with the remote sensing (RS) SMDIw index at the weekly scale for the period 2010–2017. Lags are calculated for the
Lag plots of SPI-1, SPI-3, SPI-6 and SPI-12 (expressed as SPIw-4, SPIw-13, SPIw-26 and SPIw-52, respectively) with the land-surface model (LSM) SMDIw index
at the weekly scale for the period 2010–2017. Lags are calculated for the
The LSM results for the SMDIw–SPIw-
The remaining interaction in this analysis is the ETDI–SMDI. The results
show a less asymmetric relationship between the ETDI and the SMDI compared
to the ones of SPIw with the ETDI and SMDI. The significant moderate positive correlation values (red bars in Fig. 7a) between lag
Lag plots of ETDIw–SMDIw at the weekly timescale in the period 2010–2017 for
The results of ETDI–SMDI interaction based on LSM data show less evident
periods of interaction between the indices compared to the RS results. The
expected strong correlation around lag 0 is largely diminished. The
strongest cluster appears from lag
Results require careful discussion regarding three main aspects: firstly, the effect of adopting the weekly scale for drought indices and analyses, secondly the meaning behind the complex interactions between drought indices, and thirdly the comparison of RS and LSMs as tools for high-resolution monitoring of drought. All comments refer to the results on a weekly scale.
Analyzing the differences in correlations between indices at monthly and weekly scales (Fig. 2), we support the necessity of adopting the weekly scale to study lags. The monthly scale preferred for drought assessment from a hydrological perspective may overlook the quick response of the land–atmosphere interactions. The clusters of moderate to high correlation between indices mostly occur within the first month preceding or following an anomaly (Figs. 3–7), particularly in the short to very short term. High correlations tend to peak and plunge in an interval of a few weeks. This short-term response recommends the use of the weekly scale and aggregation periods below the seasonal scale, such as SPIw-13 (equivalent to SPI3), to evaluate the delay or precedence between indices.
Our results showing soil moisture response to rainfall (
Adopting specific drought indices for rainfall, evapotranspiration and soil moisture allows exploring the interactions between variables of different levels of the land–atmosphere system. The pertinence of using the SPI, ETDI and SMDI to evaluate the interactions between the variables' anomalies can be the subject of discussion depending on the specific advantages, drawbacks and applicability of each index. There can be alternative drought indices even for soil moisture and evapotranspiration of better stability and statistical characteristics worth exploring. Furthermore, assessing the interactions of anomalies may be possible without using drought indices. However, since the commonplace in the analysis of the anomalies behind drought has been widely based on drought indices for comparable interpretation, we consider the SPI, ETDI and SMDI to represent a set of comprehensive indices to flexibly evaluate interactions at different timescales.
Figure 8a and graphically Fig. 8b summarize the annual mode of interactions between the SPI, ETDI and SMDI. In the short to middle term, both the ETDI and the SMDI interactions with the SPI concur on having moderate significance, with only a few negative and positive low interactions in the middle to long term. Positive correlations around lag 0 of the ETDI and SMDI with the SPI indicate direct precedent dependence of the indices, which means changes on the ETDI and SMDI correlate positively (negatively) to positive (negative) changes on the SPI. The short-term correlations after rainfall for both the ETDI and the SMDI (lagged response of these indices to the SPI) are straightforward and have been reported before in similar Iberian regions (Martínez-Fernandez et al., 2016). Sustained dry or wet anomalies in both variables favored by a positive correlation between indices are primarily restricted to a length of three seasons. Correlations beyond the year may represent the multi-annual persistence of anomalies common in Mediterranean climates.
The strong aggregation impact occurring when adopting SPIw-26 and
SPIw-52 indicates the analysis of interactions may be uncertain when indices
are aggregated beyond the seasonal scale. It is evaluated if the magnifying
effect of clusters with long aggregation periods (SPIw-26 and SPIw-52) is
the autocorrelation of the indices. Significant autocorrelated values always extend for less than the period of aggregation of SPIw-
The existence of the precedent influence of the ETDI and SMDI on the SPI (clusters of the negative range of lags) implies some unequal reciprocity (feedback) between evapotranspiration and soil moisture with rainfall. This precedent influence is weaker than the influence of the SPI on subsequent ETDI and SMDI anomalies (lagged response) but still remarkable. It is reasonable that the lagged response of evapotranspiration and soil moisture to rainfall is stronger and longer-lasting than the precedent influence (Fig. 8a). Furthermore, the precedent influence period between the ETDI and SPI is stronger and of longer duration than the one of the SMDI on the SPI. This asymmetry suggests that the ETDI, more than the SMDI, has a weekly to seasonal precedent influence on rainfall (Fig. 8b). We expected a longer period of positive correlations of the SMDI influencing rainfall, given the multiple reports of soil moisture inducing memory at the near-surface atmosphere (Manning et al., 2018).
One reason why the ETDI shows a longer influence on the SPI than the SMDI may be that the ETDI from MOD16A2 is fed by the whole depth of soil moisture, while the SMDI based on SMOS1km is limited to the top 5 cm of soil moisture, a very exposed soil level in semi-arid climates. The complexity of soil moisture dynamics, which barely follow a cyclic interaction (Rodriguez-Iturbe et al., 1991), can also explain a weaker relationship between the SMDI and SPI compared to the ETDI. Other reasons for this may lie in the prevalence of maritime advection as the main contributor to evapotranspiration in the IP (Gimeno et al., 2010) compared to the prevalence of local soil moisture recycling common in more continental areas of Europe (Bisselink and Dolman, 2008). The advective explanation is supported by the contrast between the few weeks of precedent influence of soil moisture on rainfall we observe in the Ebro basin and the up to 250 d of precedent influence of continental areas prone to soil moisture recycling (Rowntree and Bolton, 1983; Bisselink and Dolman, 2008). Some studies focused on continental climates of relevant summer rainfall have described the implications of the alteration of the recycling due to soil moisture depletion during heat waves and drought which can eventually alter the atmosphere (Rasmijn et al., 2018; Miralles et al., 2019). In the Mediterranean climate of the Iberian Peninsula characterized by a lack of summer rainfall, soil moisture annually reaches such low levels that we can expect annual summer alterations in the near atmosphere. Differences between areas where soil moisture plays a role, like central Europe, and areas where soil moisture is unable to control the evolution of the system under high-energy conditions, like the Iberian Peninsula, have been reported before in Mediterranean-like Western Australia (Herold et al., 2016).
In consequence, our results at the Ebro basin seem compatible with the
frequent activation of a reinforcing or self-intensification loop (Brubaker
and Entekhabi, 1996), by which the precedent influence of negative
(eventually positive) anomalies of evapotranspiration reducing (increasing)
rainfall cascades into a depletion (rise) in soil moisture that further
limits (enhances) the response of evapotranspiration and restarts the cycle
(Fig. 8c, right column). The weak precedence of soil moisture on rainfall
compared to that of evapotranspiration expresses the limited duration of the
control capacity of the soil moisture over evapotranspiration in semi-arid
climates of the Mediterranean type (left column of Fig. 8c). Negative
correlations when indices differ in sign (
In this way, the annual cycle can be modeled as the seasonal succession of two sequences: one under the low-energy conditions of winter when evapotranspiration no longer outweighs the inhibiting of soil moisture due to rainfall (left column of Fig. 8c) and the other under high-energy conditions driven by evapotranspiration (right column of Fig. 8c). The shift between the long period of interactions dominated by evapotranspiration (right column of Fig. 8c) and a short period of interactions controlled by rainfall and soil moisture (lower sequence of Fig. 8c) is generally driven by an energy threshold. However, certain levels of rainfall and soil moisture anomalies may temporally advance or delay the shift. This reason explains why under high-energy conditions drought may terminate due to heavy rainfall, while soil moisture deficits under low-energy conditions may cause an anticipated onset of the self-intensification loop of evapotranspiration.
The conceptualization of the interactions illustrated in Fig. 8 aims to raise awareness about the power of evapotranspiration anomalies to alter the land–atmosphere system, year-round, beyond hydrometeorological extremes (Seneviratne et al., 2006; Otkin et al., 2013; Teuling, 2018; Miralles et al., 2019). Another reason for supporting the year-round implications of the dominance of evapotranspiration over soil moisture is that rainfall mostly transfers to evapotranspiration in semi-arid climates (Rodriguez-Iturbe et al., 2001), where the often underestimated interception (Savenije et al., 2004) further increases evaporation at the expense of soil moisture. Additionally, as the semi-arid Mediterranean climate likely presents thresholds of rainfall, evapotranspiration and soil moisture anomalies different from those triggering hydrometeorological extremes in other areas (Tramblay et al., 2021), the evapotranspiration-dominated sequence may initiate not only more often but also more abruptly than in regions of lower-energy inputs. All these aspects, together with the increasing chance of extremes in the Mediterranean area due to climate change (Samaniego et al., 2018), recommend assessing changes in the balance of land–atmosphere interactions from the basis of this study.
However, we bear in mind that our results may oversimplify the causality, since processes not analyzed in this study may also play a role. The multiple periods showing neither prevalently positive nor prevalently negative correlations between indices indicate a loss of linear interaction. A source of non-linearity is vegetation due to its mediating role in water exchanges of the land–atmosphere system. Plants can control evapotranspiration and soil moisture in adaptation to water stress in non-linear manners that depend more on the type of vegetation (Katul et al., 2012), particularly within the Mediterranean floras (Boulet et al., 2020), than on the evapotranspiration or soil moisture status. Vegetation can also modulate the partitioning of energy governing evapotranspiration (Lansu et al., 2020) but similar processes are reported with soil moisture (Barbeta et al., 2015). In consequence, the quick response to drought of rainfed crops and sclerophyllous vegetation (Vicente-Serrano et al., 2019) may obscure the interpretation of the links between rainfall, evapotranspiration and soil moisture. This is of concern regarding the ETDI and SMDI results because interactions of vegetation integrate the status of the atmospheric and the land-surface variables (Peters et al., 1991). Nonetheless, additional factors of uncertainty may arise from teleconnections such as the well-known North Atlantic Oscillation (NAO) or Western Mediterranean Oscillation (WeMO; Barnston and Livezey, 1987; Conte et al., 1989) or the oceanic ones like the Atlantic Multidecadal Oscillation (AMO; Kerr, 2000) altering the land–atmosphere system at large scales.
The RS and LSM results of the lag analysis of the ETDI–SPI interactions show consistently comparable results in contrast to the remarkable disagreement between RS and LSM results for the SMDI–SPI interaction. Results of the SMDI obtained with the LSM show substantially lower correlations than the ones of RS while also differing in the timing of the clusters of correlation. We expected the opposite, i.e., that the LSM, being simpler than reality, has stronger SPI–ETDI–SMDI correlations than the RS dataset. We assume the implicit accumulation of uncertainties in modeling (Rodriguez-Iturbe et al., 1991), partly inherited from inputs but also from the LSM structure, is the cause of the decrease in correlations. This is particularly true for soil moisture, a variable integrating exchanges between climate, soil and vegetation (Rodriguez-Iturbe et al., 2001). Secondly, this is an offline simulation, where the atmosphere (SAFRAN) is forcing the land surface (SURFEX-ISBA) without explicit feedback of SURFEX-ISBA influencing SAFRAN back. SAFRAN estimates real conditions by ingesting observations, so the feedback is implicit in results, which may be insufficient to represent reality. Thirdly, the model itself does not consider important processes like the interactive response of vegetation. ISBA has an interactive vegetation module (ISBA-A-gs), but Mediterranean vegetation can be particularly challenging for it. We expect to test the capabilities of interactive modeling vegetation in a follow-up study. Uncertainties in ISBA with vegetation also have roots in the use of the ECOCLIMAP-II database, which shows inaccuracies in cover type and the LAI. ECOCLIMAP assumes the maximum/minimum LAI occurs in June/February in contrast with the early spring and autumn LAI maximums characteristic of the Mediterranean environment (Queguiner et al., 2011). All in all, the differences between the LSM and RS datasets are already an important result to improve the LSM and comprise a useful insight into the use of offline LSM drought simulations.
Our results positively verify that RS represents an effective tool to overcome the problem of sparsely observed soil moisture or evapotranspiration, whose crucial role in drought evolution requires high-resolution data similarly to precipitation (AghaKouchak and Nakhjiri, 2012). Including high-resolution evapotranspiration products from MODIS (MOD16A2) and soil moisture from SMOS missions (SMOS1km) together with the distributed rainfall reanalysis data allows dedicated interpretation of the interactions between these two drought-relevant variables and rainfall and their role in the water balance of the land–atmosphere interface (Dai, 2011). Especially for evapotranspiration, the maps and series of the LSM SURFEX-ISBA are comparable to those of RS, which supports the reliability of LSMs despite their limited capability in arid regions (De Kauwe et al., 2015).
The temporal and spatial patterns of the anomalies are overly identified by both RS and the LSM. The RS data seem able to capture a more complex scheme of interactions than the LSM, despite the intrinsic data issues of the RS sensors and the performance of the algorithms used to generate the products. Conversely, the LSM seems sensitive to uncertainties from input data, especially surface properties, and the offline forcing. The parametrization of the model assumes a semi-distributed approach by sub-basins of the catchment on which each sub-basin is defined based on average values of land cover and soil characteristics of the ECOCLIMAP database, which may induce some patchiness of LSM results compared to the RS results. The offline run means that the meteorological data force the LSM, but the feedbacks are lost beyond the meteorological observations included as observation in the model. Additional aspects can be, for instance, the impact of groundwater redistributing soil moisture depending on topography, which is underrepresented in the LSM. Limitations of LSMs have been reported in multiple works before and are the subject of improvement efforts (Teuling et al., 2006; Samaniego et al., 2018). Either way, uncertainties are causes of major concern in the lag analysis where they can alter the correlations between indices and obscure the interpretation of the interactions.
These aspects together can be critical to improving evapotranspiration and soil moisture estimates (Ukkola et al., 2016). Solving these inaccuracies would increase the value of RS and LSM estimates. This study exemplifies the potential of high-resolution RS and LSM products for a wide range of applications, such as drought analysis.
The analysis of droughts in the Ebro basin using dedicated evapotranspiration and soil moisture drought indices based on high-resolution data from MOD16A2, SMOS1km and the LSM SURFEX-ISBA provides the following insights.
The monthly scale commonly adopted for drought evaluation (e.g., SPI-3) may overlook the quick evolution of drought from an agricultural and environmental perspective, especially in the high-energy climates of the Mediterranean basin where the anomalies of rainfall, evapotranspiration and soil moisture can vary in a matter of days. The ETDI shows the strongest response at a weekly scale while it also remains influential in the mid-term. The SMDI can also quickly evolve with anomalies of evapotranspiration and particularly with lasting anomalies of rainfall. The weekly scale is advantageous to describe trends and shifts in the evolution of the indices and to identify disregarded interactions such as the preceding influence of the ETDI on the SPI.
The ETDI and SMDI, together with the SPI adapted to the weekly scale, allow tracking of the evolution of the anomalies of evapotranspiration, soil moisture and rainfall, as well as their interactions driving water anomalies in the region. There is great consistency between the time series of the ETDI, SMDI and SPI. Lag analysis between these indices clarifies the interactions between anomalies on different levels of the surface–atmosphere system, information that is neglected when using multivariable indices or indices aggregated beyond the seasonal scale. The lag analysis also identifies sequences of interactions defining reinforcing or inhibiting feedbacks. Evapotranspiration dominates the water balance of the Iberian semi-arid climate, especially during high-energy periods. This dominance frequently exceeds the controlling action of rainfall and soil moisture, inducing the reinforcing dry loop. Because of the relevance of evapotranspiration, heat waves further fueling dry events deserve further attention. The weak influence of soil moisture on subsequent evapotranspiration and rainfall limits its capability to control the propagation of anomalies.
RS datasets of MOD16A2 and SMOS-1km accurately estimate the temporal and spatial anomalies in the basin. Evapotranspiration from the LSM SURFEX-ISBA closely resembles the RS one of MOD16A2. Results differ substantially between SMOS1km and SURFEX-ISBA estimates of soil moisture. RS uncertainties arise mainly from data gaps. Land-surface model's estimates can extend the evaluation of soil moisture beyond the surface towards the root zone but face notable challenges from offline simulation neglecting feedback, as well as from the quality of input data that define surface characteristics. RS outcompetes the LSM in its ability to integrate information about challenging processes, such as vegetation dynamics. Assimilation seems the way forward to integrate the best aspects of both kinds of data. For as long as ground-based observations remain sparse, RS and LSMs represent effective tools to assess the water anomalies of the land–atmosphere system and their interaction mechanisms.
Codes (as Python scripts) used to analyze the data are available upon request.
Several datasets were used for this article.
The satellite-derived evapotranspiration data of MOD16A2 are available at the Land Processes Distributed Active Archive Center (LP-DAAC) of NASA USGS at The satellite-derived soil moisture data of SMOS1km will be available soon in repositories but are currently available upon request. The authors refer interested readers to the following publications for a detailed description of the data: Escorihuela and Quintana-Seguí (2016) and Merlin et al. (2013). The SAFRAN dataset forcing the SURFEX LSM simulations for Spain is being updated for ongoing studies related to this article but can be made available upon request. The last version of the SAFRAN dataset in repositories is available at the MISTRALS HyMeX database:
The study followed standard statistical routines that can be easily reproduced by the methodological explanations in the text.
The supplement related to this article is available online at:
PQS defined the research aim and together with JG collected, curated and analysed the data. MJE provided the SMOS1km dataset and supervised its integration in the study. JG and PQS analyzed the interactions between indices and the lag analysis. JG, PQS and MJE evaluated and discussed the results. JG and PQS prepared the manuscript. MJE, AB and MCLL provided feedback and improvements to the text and to the interpretation and discussion of results.
At least one of the (co-)authors is a member of the editorial board of
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The authors acknowledge support from the Spanish State Research Agency (AEI) to the Hydrology and Climate Change lab of Pere Quintana-Seguí at Ebro Observatory.
This research has been supported by the Spanish State Research Agency (Agencia Estatal de Investigación, AEI) within the HUMID project (AEI/FEDER EU grant no. CGL2017-85687-R).
This paper was edited by David J. Peres and reviewed by Emanuele Romano and one anonymous referee.