Hydroclimatic Data Research Articles

Water cycle changes in Czechia: a multi-source water budget perspective

Abstract. The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein, we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then, we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor as consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia occur during spring, while the spatial pattern of the change in median values stems from summer changes in the water cycle, which are the seasons within the months with statistically significant changes.

El impacto combinado de la sequía y los cambios en el uso del suelo sobre los recursos hídricos en el Tabular Atlas Medio, Marruecos

The quantitative degradation of water resources in the Tabular Middle Atlas is linked to climate and changes in agricultural practices. The relationship between these variables is identified through the analysis of hydroclimatic data, fieldwork, and the processing of Landsat satellite images in two representative sectors of the Middle Atlas. Interviews with local populations and piezometric measurements performed in the Guigou depression show that the groundwater table has dropped from a few meters deep to over 45 meters in several locations. Additionally, monitoring of the surface water area of Aoua lake between September 1984 and September 2022 from satellite images indicates remarkable variations, with periods of prolonged drying. The critical situation of water resources in this sector of Morocco is explained by the recurrence of drought periods of varying severity, increasing temperatures, and the progressive extension of irrigated areas across the depressions. In three decades, irrigated areas have multiplied by more than five in the Guigou depression and by more than three in the Imouzzer-Aoua depressions. Changes in climatic parameters associated with modifications in agricultural practices have strongly influenced water resources in the Tabular Middle Atlas.

Environmental flow assessment, evaluation, and suggestions for dying riverine ecosystem of the transboundary Amudarya River, Central Asia

The Amudarya River (ADR) is the largest source of freshwater in Central Asia providing livelihood to millions of people by extracting water for agriculture. However, the over-exploitation has created some critical environmental issues. For example, almost no water for the delta and the Aral Sea, which was once the 4th largest lake in the world. The present study focused on the assessment of environmental flow requirements (E-flows) in the basin. However, the main challenges in the accomplishment were the unavailability of natural streamflow, which is necessary for E-flow assessment, and sparse and limited hydroclimatic data. A hydrologic model was configured to simulate naturalized streamflow using the meteorological data from the Climate Research Unit. Four hydrological methods (i.e., Tennant, low flow index (7Q10), flow duration curve analysis (Q90, and Q95), and flow duration curve shifting (FDCS)) and all-method mean were applied to estimate E-flows at 34 sites on all tributaries of the ADR. According to FDCS, 7Q10, Q90, and Q95, E-flows should be 46%, 37%, 30%, and 25% of naturalized mean annual flow (NMAF), respectively, and on average, it should be 35% of NMAF, assessed by the all-methods mean. For low-flow (October–March) and high-flow (April–September) months, E-flows were determined to be 20–30% and 40–98% of NMAF, respectively. E-flow evaluation with the current environmental conditions showed very serious concerns because no sites met the environmental flow requirements below Kerki. This study will be guidelines to improve the riverine ecosystem and for future sustainable development in the region.

Google Trends indicators to inform water planning and drought management

AbstractIndicators are important tools for tracking the socio‐environmental impacts of droughts and building resilience to climate change. We begin with an overview of metrics used for water planning and drought management, with particular emphasis on the UK. We explain how considerations of cost, immediacy, access, consistency, relevance, reliability and others denote the suitability of information for developing new indicators. We then demonstrate the potential of Google Trends (GT) online search data as drought indicators for England and Ireland. We show that search terms such as ‘drought’, ‘water butt’ and ‘hosepipe ban’ correlate significantly with conventional hydroclimatic data as well as with newspaper reports of various drought impacts during the period 2011–2022. GT data also show evidence of rising interest in water saving technologies, especially for outdoor water use. Meanwhile, online searches for ‘Defra’ and ‘Environment Agency’ have declined and are more often associated with flood episodes than droughts. Interest in water companies in England is more likely around hosepipe bans than water leakage (although this varies by company). We discuss the implications of these findings for targeting information campaigns, plus prospects for monitoring drought impacts and public sentiment in near real‐time.

Groundwater level forecasting in a data-scarce region through remote sensing data downscaling, hydrological modeling, and machine learning: A case study from Morocco

Study regionThe upstream part of the Essaouira basin, a data-scare region in Morocco, Northwestern Africa. Study focusThe scarcity of hydro-climate data is a significant challenge found in several regions worldwide, where qualitative and quantitative water resource information remains limited. Estimating and predicting groundwater levels (GWL) in such areas is a significant challenge in producing knowledge for effective water resource management. To address this issue, the present study aimed to use the Soil and Water Assessment Tool (SWAT) model in conjunction with downscaled total water storage (TWS) data (9 km) obtained from Gravity Recovery And Climate Experiment (GRACE) and machine learning techniques, specifically random forest (RF) and support vector machine (SVM), to estimate and predict the variation in GWL. New hydrological insights for the regionThis study constitutes a first of its kind in the study area; the SWAT model was set up for 10 years, with a warm-up period from 2000 to 2001, calibration from 2002 to 2007, and validation from 2008 to 2010. The statistical indices (Coefficient of Determination (R²) ≥ 0.73, R² ≥ 0.78, Nash–Sutcliffe model efficiency coefficient (NSE) ≥ 0.67, NSE ≥ 0.80 respectively for calibration and validation) highlight a significant correlation, implying the model's capability to faithfully reproduce the streamflow. The downscaled TWS demonstrates an impressive ability to identify and monitor fluctuations in GWL. Using machine learning algorithms (RF and SVR), the prediction of GWL yielded satisfactory results, NSE = 0.78 and root mean square error (RMSE) = 0.33, NSE = 0.51 and RMSE = 0.49 for the RF and SVR, respectively. Despite some limitations, our approach provided promising results in GWL prediction, with the possibility of expanding to other data-scarce regions.

Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction

Enhancing the generalization capability of time-series models for streamflow prediction using dimensionality reduction (DR) techniques remains a major challenge in water resources management (WRM). In this study, we investigated eight DR techniques and their effectiveness in mitigating the curse of dimensionality, which hinders the performance of machine learning (ML) algorithms in the field of WRM. Our study delves into the most non-linear unsupervised representative DR techniques, including principal component analysis (PCA), kernel PCA (KPCA), multi-dimensional scaling (MDS), isometric mapping (ISOMAP), locally linear embedding (LLE), t-distributed stochastic neighbor embedding (t-SNE), Laplacian eigenmaps (LE), and autoencoder (AE), examining their effectiveness in multi-step ahead (MSA) streamflow prediction. In this study, we conducted a conceptual comparison of these techniques. Subsequently, we focused on their performance in four different case studies in the USA. Moreover, we assessed the quality of the transformed feature spaces in terms of the MSA streamflow prediction improvement. Through our investigation, we gained valuable insights into the performance of different DR techniques within linear/dense/convolutional neural network (CNN)/long short-term memory neural network (LSTM) and autoregressive LSTM (AR-LSTM) architectures. This study contributes to a deeper understanding of suitable feature extraction techniques for enhancing the capabilities of the LSTM model in tackling high-dimensional datasets in the realm of WRM.

A graphical non-parametric hydrologic alteration test using flow duration curves

ABSTRACT Utilizing annual flow duration curves (FDCs), the modified Mood’s median test (MMMT) was applied to quantiles of the median annual FDCs at 122 disturbed sites in the conterminous US. A simple graphical tool based on the MMMT was combined with a false discovery rate multiple comparison procedure to illustrate the field significance of hydrologic alteration over a wide range of streamflow conditions. Hydrologic disturbance was mostly caused by dams, although other forms of alteration were explored. In addition, 249 minimally regulated Hydro-Climatic Data Network (HCDN) sites were investigated to assess regional climatic drivers of hydrologic alteration. Results indicate that dams can have varying effects on the hydrologic regime, but generally impact low flows more often than high flows in the US. At HCDN sites, we observe significant changes to hydrologic regimes in many regions, especially to low and median flows in the upper midwestern and eastern US.

Comparison of improved relevance vector machines for streamflow predictions

AbstractThis study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned by particle swarm optimization, whale optimization, marine predators algorithms, and single relevance vector machine methods. Various lagged values of hydroclimatic data (e.g., precipitation, temperature, and streamflow) are used as inputs to the models. The relevance vector machine tuned with dwarf mongoose optimization algorithm improved the efficiency of single method in monthly streamflow prediction. It is found that the integrating metaheuristic algorithms into single relevance vector machine improves the prediction efficiency, and among the input combinations, the lagged streamflow data are found to be the most effective variable on current streamflow whereas precipitation has the least effect.

Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data

Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data

Impact of suspicious streamflow data on the efficiency and parameter estimates of rainfall–runoff models

ABSTRACT Many sources of error in hydroclimatic data can affect hydrological modelling, yet the impact of streamflow data quality is poorly quantified. This work aims to investigate whether inconsistencies found in streamflow time series commonly available for hydrological studies (typically in national streamflow archives) have an impact on the efficiency and the parameter estimates of rainfall–runoff models. Hydroclimatic data were gathered at the hourly time step over the period 1998–2018 for a set of 30 catchments in France. Hydrological modelling was carried out with the lumped conceptual GR5H (standing for modèle du Génie Rural à 5 paramètres Horaire, i.e. Hourly 5-parameter rural engineering model) model. A typology of “realistic” suspicious streamflow was established to set up several error models in order to corrupt the data. Our results suggest that common suspicious streamflow data do not have a strong impact on model efficiency and parameter estimates overall, but may be an important source of instability and lack of robustness when working on a single catchment.

Performance Assessment Comparison between Physically Based and Regression Hydrological Modelling: Case Study of the Euphrates–Tigris Basin

This paper presents two hydrological models developed for the Euphrates–Tigris Basin in Turkey. The first model is a linear regression-based model allowing the estimation of streamflow based on available hydroclimatic data (precipitation, temperature, evapotranspiration, etc.) with the use of clustering analysis. The second model consists of an elevation-based semi-distributed hydrological model (HBV model), allowing process-based modelling of the watershed. A set of performance metrics identified the HBV model as the best performance in terms of predicting streamflow (NSE = 0.752), while the RCA4-EU regression model of CORDEX showed the most robust performance. The results show the potential of regression models from a computational and data point of view in being integrated into physically based models wherein a hybrid approach might be beneficial. The comparison of conceptual models with statistical analyses of streamflow shows the potential of regression analysis when the regions are clustered in hydro-meteorologically homogeneous groups. The employment of the conceptual model HBV also provides significantly robust streamflow estimation for the region, which is especially important in estimating the hydropower potential of the region’s near future.

Prospective analysis of low flows in a climate change context in West Africa: case of the N’zo-Sassandra watershed (West of Côte d’Ivoire)

The objective of this study is to analyze the prospective of the low water flows of the N'zo-Sassandra River located in the West of Ivory Coast. The hydroclimatic data used (rainfall, ETP, flow) extend on the one hand over the historical period (1961-2017) and on the other hand over the future period 2071-2100 (CORDEX data). The methodological approach adopted is based on the analysis of past low flows, the climate prospective analysis and the hydrological prospective analysis. The hydrological impact variable used is the annual minimum monthly mean flow (MAMF). Thus, a pessimistic climate forecast scenario was used (RCP 8.5.). This climate scenario was then fed into a rainfall-runoff hydrological model (GR2M model) calibrated to the 1961-1990 reference period and validated over the 1991-2017 period. In response to the climate forcing, future monthly low-flow rates (MAMF) were simulated. An increase in the number of flows in the class ]0-4] m3/s was observed, reflecting low flows in the 2071–2100-time frame. The best statistical law retained following the frequency analysis of the low-water flows is the lognormal law. By 2071-2100, a decrease in rainfall (-19.2%) and an increase in temperature (+3.9°C) are predicted in the N'zo-Sassandra watershed. Frequent low-water flows estimated from the lognormal law have shown a bias of -50.38%, which translates into a decrease of half of the frequent low-water flows by the end of the 21st century compared to the current period. This decrease in flows will have consequences on the availability of water resources for irrigation, fishing, breeding, drinking water supply, navigation, etc. This is why we propose as alternative resources for agricultural needs, groundwater which is less impacted by global changes.

Prediction of climate change impacts on availability of surface water resources in the semi-arid Tugwi Mukosi catchment of Zimbabwe

Climate change impact drivers includes increased rainfall variability, increased extreme events, and changes in the mean behaviour of hydro-meteorological variables such as air temperature, rainfall, and runoff. As such, this study assessed the potential impacts of climate change on surface water resources in a semi-arid Tugwi Mukosi catchment of Zimbabwe. The Mann-Kendall trend analysis, climate change downscaling and streamflow modelling were used as the primary methodology. The analysis of historical hydro-climatic data trends were performed using the Mann-Kendall trend test and Sen's slope method. Equally, observed daily rainfall was extended using bias-corrected CHIRPS satellite rainfall. Thus, a total of 9 Global Climate Models including (CCCma-CanESM2, Miroc Miroc 5, MOHCHadGEM2, MPI-M-MPI-ESM-LR, and NCCNorESM1-M) on the Sveriges Meteorologiska och Hydrologiska Institut (SMHI) Rosby Centre Atmosphere model version 4 (RCA4) RCM under the Representative Concentrated Pathways 4.5 and 8.5 were extracted and analysed for the 2030s (2021–2050) and 2060s (2051–2080) period. The results show significant (p<0.05) decreasing rainfall trends at the analysed stations, as well as significant decrease in rainfall for the 2030s and the 2060s under GCMs CCCma-CanESM 2 model. The Hydrologic Engineering Centre-Hydrological modeling System (HECHMS) (1997–1999) was calibrated and validated for the period 1981–2016. Calibration results on gauged catchment runoff and peak flows are satisfactory (Ngezi observed peak discharge and simulated peak discharge at 42.4 m3/s and 34.8 m3/s respectively, NSE 62.8%, Upper Muzhwi 30 m3/s and 19.5 m3/s, NSE 53.55%, Musogwezi 18.7 m3/,s and 16.5 m3/s, NSE 58.4%). The GCM-CanESM RCP 4.5 model showed a decrease in the runoff predicted of above 17% in both the 2030s and a significant decrease in the 2060s of 28.41% and 5% respectively. The combination of climate downscaling and water resources modelling is paramount to the management of water resources in a semi-arid area. Hydrological modelling and climate downscaling techniques gives important insights for water resources planning and development to support the water sector adaptation to climate change. The methodology applies to other data-scarce and semi-arid areas whose climate change impacts require further investigation.

Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years

Runoff is a crucial water cycle component that contributes to the water resources to sustain human life. Historical trends in runoff, when examining climate change scenarios, provide vital information about past variability and support the design of adaptation measures. However, hydrological models based on climate data, such as the Budyko model, can be biased in estimating annual runoff due to input data uncertainty. Therefore, it is vital to utilize advanced machine learning-based computing models to reduce uncertainty and reconstruct climate variables over a long period of time and sufficiently large spatial coverage, preferably at a continental scale. We propose and test a novel machine learning-based framework called Hybrid Ensemble Multi-Model Framework (HEMMF) to reconstruct the gridded runoff of Europe over a 500-year historical period (1500 to 1999). The HEMMF combines non-parametric extended data pattern recognition and data-driven methods. The extended data patterns are computed using Moran’s spatial autocorrelation (SPA) index of the climate variable fields and the Budyko models output, whereas the data-driven methods contain nine different machine learning (ML) algorithms and four ensembles of ML. The extended data patterns are jointly ingested with climate-reconstructed data (precipitation, temperature, Palmer’s drought severity index) as predictor variables, which serve as input for the data-driven methods. To assess the impact and contribution of SPA, the runoff is simulated based on three different input training datasets in the HEMMF: (1) a dataset containing only precipitation, temperature, Palmer’s drought severity index, and four different estimates of runoff from the Budyko model, (2) a dataset containing only SPA of the first input datasets, and (3) a dataset created by merging the first and second datasets. The HEMMF offers the best reconstruction performance when using the third input dataset. This reconstructed runoff helps to explain the runoff trend, drought propagation, and runoff’s link with the climate variables. The proposed methodology has the potential to be applied to past hydroclimatic data and related analyses across different temporal periods, climate scenarios, and geographical scales.

The Spatio-Temporal Dynamic Patterns of Shallow Groundwater Level and Salinity: The Yellow River Delta, China

Shallow groundwater in coastal aquifers is a highly dynamic and complex system with a high risk of seawater intrusion. Analyzing the spatio-temporal dynamic patterns of groundwater can help to manage the groundwater resource and prevent it from degradation. Based on the groundwater level (GWL) and electrical conductivity (EC) monitoring data of 18 observation wells in the Yellow River Delta (YRD) from 2004 to 2010, this research analyses the groundwater dynamics using a robust seasonal trend decomposition technique (STL) and spatial interpolation method to detect the groundwater spatio-temporal dynamic patterns of groundwater level and salinity. Combined with hydro-climatic data, the Pearson correlation method and the Mann-Kendall (MK) trend analysis were used to further reveal the impacts that induce their trends and seasonal variations. Our analyses show that the risk of seawater intrusion into local shallow aquifers in this region is high, with the mean groundwater level over 42% of the region lower than the local sea level, and the mean groundwater EC over 96% of the region met the standards for seawater intrusion. In addition, the trends of groundwater level generally declined by 0.01~0.45 m/a and salinity increased by 1.153~25.608 μs/cm.a, which are consistent with the trend of precipitation decline. The seasonal dynamics of groundwater level and salinity are highly correlated with the seasonal components of rainfall and evaporation. It can be concluded that the extent of seawater intrusion will increase in the future with sea level rise. The approaches used in this study proved to be effective and can certainly serve as an example for the analysis of the spatio-temporal dynamics of groundwater in other coastal regions.

Changing Seasonality of Annual Maximum Floods over the Conterminous US: Potential Drivers and Regional Synthesis

Understanding the flood-generating mechanisms that influence flood seasonality in a region provides information on setting up relevant contingency measures. Although former studies estimated flood seasonality at regional/continental scale, limited/no studies have investigated the climate/basin drivers that influence the changes in flood seasonality. Considering this, the current study performed two analyses: (1) estimated the changes in the seasonality of annual maximum floods (AMF) between pre- and post-1970 across Hydroclimate Data Network basins over the conterminous US, and (2) identified the predictors that influence the change in the seasonality from a set of climate and geomorphic variables. Significant changes in the AMF seasonality were noted for approximately half of the basins in the eastern US, but low to no change was found in most basins in the central/western US. We found, except in the Northeast and mid-Atlantic basins, a decrease in the seasonality index, indicating floods arriving more uniformly is typically associated with an increase in the precipitation days in basins. On the other hand, increase in the seasonality index, indicating floods occurring more concentrated in time, is typically associated with an increase in the extreme precipitation in basins. Among the basin characteristics, elevation has a more dominant role than the drainage area in changing the flood seasonality. Elevation affects the form of precipitation, particularly in the western US, because floods arrive more distributed over the year (i.e., decrease in flood seasonality index), which potentially indicates increased warming resulting in early snowmelt.

Modeling seasonal vegetation phenology from hydroclimatic drivers for contrasting plant functional groups within drylands of the Southwestern USA

In dryland ecosystems, vegetation within different plant functional groups exhibits distinct seasonal phenologies that are affected by the prevailing hydroclimatic forcing. The seasonal variability of precipitation, atmospheric evaporative demand, and streamflow influences root-zone water availability to plants in water-limited environments. Increasing interannual variations in climate forcing of the local water balance and uncertainty regarding climate change projections have raised the potential for phenological shifts and changes to vegetation dynamics. This poses significant risks to plant functional types across large areas, especially in drylands and within riparian ecosystems. Due to the complex interactions between climate, water availability, and seasonal plant water use, the timing and amplitude of phenological responses to specific hydroclimate forcing cannot be determined a priori, thus limiting efforts to dynamically predict vegetation greenness under future climate change. Here, we analyze two decades (1994–2021) of remote sensing data (soil adjusted vegetation index (SAVI)) as well as contemporaneous hydroclimate data (precipitation, potential evapotranspiration, depth to groundwater, and air temperature), to identify and quantify the key hydroclimatic controls on the timing and amplitude of seasonal greenness. We focus on key phenological events across four different plant functional groups occupying distinct locations and rooting depths in dryland SE Arizona: semi-arid grasses and shrubs, xeric riparian terrace and hydric riparian floodplain trees. We find that key phenological events such as spring and summer greenness peaks in grass and shrubs are strongly driven by contributions from antecedent spring and monsoonal precipitation, respectively. Meanwhile seasonal canopy greenness in floodplain and terrace vegetation showed strong response to groundwater depth as well as antecedent available precipitation (aaP = P − PET) throughout reaches of perennial and intermediate streamflow permanence. The timings of spring green-up and autumn senescence were driven by seasonal changes in air temperature for all plant functional groups. Based on these findings, we develop and test a simple, empirical phenology model, that predicts the timing and amplitude of greenness based on hydroclimate forcing. We demonstrate the feasibility of the model by exploring simple, plausible climate change scenarios, which may inform our understanding of phenological shifts in dryland plant communities and may ultimately improve our predictive capability of investigating and predicting climate-phenology interactions in the future.

Fossil Diatoms Reveal Natural and Anthropogenic History of Jackson Lake (Wyoming, USA)

Jackson Lake supplies valuable cultural and provisioning ecosystem services to the Upper Snake River watershed in Wyoming and Idaho (western USA). Construction of Jackson Lake Dam in the early 20th century raised lake level by ∼12 m, generating an important water resource supporting agriculture and ranching, as well as tourism associated with Grand Teton National Park. Outlet engineering drastically altered Jackson Lake’s surface area, morphology, and relationship with the inflowing Snake River, yet the consequences for nutrient dynamics and algae in the lake are unknown. Here, we report the results of a retrospective environmental assessment completed for Jackson Lake using a paleolimnological approach. Paleoecological (diatoms) and geochemical datasets were developed on a well-dated sediment core and compared with available hydroclimate data from the region, to assess patterns of limnological change. The core spans the termination of the Little Ice Age and extends to the present day (∼1654–2019 CE). Diatom assemblages prior to dam installation are characterized by high relative abundances of plankton that thrive under low nutrient availability, most likely resulting from prolonged seasonal ice cover and perhaps a single, short episode of deep convective mixing. Following dam construction, diatom assemblages shifted to planktic species that favor more nutrient-rich waters. Elemental abundances of sedimentary nitrogen and phosphorous support the interpretation that dam installation resulted in a more mesotrophic state in Jackson Lake after ∼1916 CE. The data are consistent with enhanced nutrient loading associated with dam emplacement, which inundated deltaic wetlands and nearshore vegetation, and perhaps increased water residence times. The results of the study highlight the sensitivity of algal composition and productivity to changes in nutrient status that accompany outlet engineering of natural lakes by humans and have implications for water resource management.

Drought increase since the mid-20th century in the northern South American Altiplano revealed by a 389-year precipitation record

Abstract. Given the short span of instrumental precipitation records in the South American Altiplano, longer-term hydroclimatic records are needed to understand the nature of climate variability and to improve the predictability of precipitation, a key natural resource for the socioeconomic development in the Altiplano and adjacent arid lowlands. In this region grows Polylepis tarapacana, a long-lived tree species that is very sensitive to hydroclimatic changes and has been widely used for tree-ring studies in the central and southern Altiplano. However, in the northern sector of the Peruvian and Chilean Altiplano (16–19∘ S) still exists a gap of high-resolution hydroclimatic data based on tree-ring records. Our study provides an overview of the temporal evolution of the late-spring–mid-summer precipitation for the period 1625–2013 CE at the northern South American Altiplano, allowing for the identification of wet or dry periods based on a regional reconstruction from three P. tarapacana chronologies. An increase in the occurrence of extreme dry events, together with a decreasing trend in the reconstructed precipitation, has been recorded since the 1970s in the northern Altiplano within the context of the last ∼4 centuries. The average precipitation over the last 17 years stands out as the driest in our 389-year reconstruction. We reveal a temporal and spatial synchrony across the Altiplano region of dry conditions since the mid-1970s. Independent tree-ring-based hydroclimate reconstructions and several paleoclimatic records based on other proxies available for the tropical Andes record this synchrony. The influence of El Niño–Southern Oscillation (ENSO) on the northern Altiplano precipitation was detected by our rainfall reconstruction that showed past drier conditions in our study region associated with ENSO warm events. The spectral properties of the rainfall reconstruction showed strong imprints of ENSO variability at decadal, sub-decadal, and inter-annual timescales, in particular from the Pacific NIÑO 3 sector. Overall, the recent reduction in precipitation in comparison with previous centuries, the increase in extreme dry events and the coupling between precipitation and ENSO variability reported by this work is essential information in the context of the growing demand for water resources in the Altiplano. This study will contribute to a better understanding of the vulnerability and resilience of the region to the projected evapotranspiration increase for the 21st century associated with global warming.

Hyposensitive canopy conductance renders ecosystems vulnerable to meteorological droughts.

Increased meteorological drought intensity with rising atmospheric demand for water (hereafter vapor pressure deficit [VPD]) increases the risk of tree mortality and ecosystem dysfunction worldwide. Ecosystem-scale water-use strategy is increasingly recognized as a key factor in regulating drought-related ecosystem responses. However, the link between water-use strategy and ecosystem vulnerability to meteorological droughts is poorly established. Using the global flux observations, historic hydroclimatic data, remote-sensing products, and plant functional-trait archive, we identified potentially vulnerable ecosystems, examining how ecosystem water-use strategy, quantified by the percentage bias (δ) of the empirical canopy conductance sensitivity to VPD relative to the theoretical value, mediated ecosystem responses to droughts. We found that prevailing soil water availability substantially impacted δ in dryland regions where ecosystems with insufficient soil moisture usually showed conservative water-use strategy, while ecosystems in humid regions exhibited more pronounced climatic adaptability. Hyposensitive and hypersensitive ecosystems, classified based on δ falling below or above the theoretical sensitivity, respectively, achieved similar net ecosystem productivity during droughts, employing different structural and functional strategies. However, hyposensitive ecosystems, risking their hydraulic system with a permissive water-use strategy, were unable to recover from droughts as quickly as hypersensitive ones. Our findings highlight that processed-based models predicting current functions and future performance of vegetation should account for the greater vulnerability of hyposensitive ecosystems to intensifying atmospheric and soil droughts.