Modern climate change presents unprecedented challenges, posing critical crises that threaten sustainable development, human well-being, and planetary health. A significant concern is the potential for global warming to cause irreversible disruptions to the water-carbon cycle, a topic that remains underexplored. This study seeks to address a crucial knowledge gap by examining how increasing wet extremes impact ecosystem productivity. The research agenda focuses on three primary questions: 1) How do the intensity and duration of various wet extremes affect evapotranspiration across different watersheds and terrestrial biomes? 2) How do immediate and lagged responses to wet extremes vary across different biomes, and what insights do these temporal patterns provide about the causal and predictive relationships between wet extreme and evapotranspiration? 3) To what extent do watershed characteristics (such as soil properties, hydrological conditions, and vegetation factors) modulate the relationship between wet extremes and ecosystem productivity? As climate change alters precipitation patterns, understanding these complex ecosystem responses becomes crucial for developing adaptive strategies and improving food and water resource management. Significance statement: This study addresses a critical knowledge gap in understanding the impact of increasing wet extremes on ecosystem productivity amid rapidly changing climate. By examining complex relationships between wet climate extremes and evapotranspiration across diverse watersheds and biomes, the research reveals multi-timescale interactions. It highlights both immediate and lagged effects of extreme precipitation on ecosystem productivity, identifying key watershed characteristics that modulate this coupling. The study quantifies thresholds in these characteristics and lagged effects, essential for developing climate-resilient management strategies. By integrating these insights into watershed management approaches, stakeholders can better adapt to and mitigate the impacts of escalating climate extremes on water resources and ecosystem productivity. Anthropogenic climate change is intensifying Earth's hydrologic cycle, leading to more frequent and severe extreme precipitation events (1) (2) (3) (4) (5). As global temperatures rise, the atmosphere's water-holding capacity increases, following the Clausius-Clapeyron relation, with approximately 7% more water vapor per 1 deg C of warming (6) (7) (8). Climate models project a robust increase in extreme precipitation globally and regionally, with heavy precipitation becoming more frequent and intense as warming progresses (9) (10) (11) (12). The consequences of these changes are far-reaching and complex. Extreme precipitation can have devastating direct societal impacts, including flooding (10) (13), soil erosion, and agricultural damage (14), as well as lagged impacts like pests/pathogen outbreaks (15), soil carbon loss (16), and changes in plant phenology (17). Altered rainfall distributions cause ecosystems with high rainfall to experience more frequent soil waterlogging events beyond traditional flood zones, particularly in lowland humid regions. Excessive rainfall can result in tree mortality due to soil waterlogging (18), with maladapted species experiencing higher mortality rates in saturated soils (19) (20). In the Arctic, increased summer precipitation is expected to continue, with a greater proportion falling as liquid rain rather than snow, potentially accelerating permafrost thaw and degradation (21), which could further contribute to significant greenhouse gas emissions (22). As such, many regions are witnessing shifts in their local water and carbon balance, placing substantial stress on both ecological and societal water and food resources. The IPCC SREX (23) special report defines climate extremes as occurrences where weather or climate variables exceed typical threshold values, either above the 90th or below the 10th percentile of their probability density function. (WMO) Commission for Climatology (CCl)/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) has developed a suite of indices to quantify climate extremes, providing a standardized framework for assessing changes in weather patterns. The average annual maximum precipitation amount in a day (RX1Day) has significantly increased since the mid-20th century over land (24) and in the humid and dry regions of the globe (25). The probability of precipitation exceeding 50 mm/day increased during 1961-2018 (26) (27). The globally averaged annual fraction of precipitation from days in the top 5% (R95P) has also significantly increased (24). Extreme climate events are characterized by their great magnitude and short duration compared to average climate conditions. Despite their brevity, these events have a disproportionately large impact on the environment (28). They act as powerful drivers of change in two ways: through their immediate occurrence and through the ecosystem's response to them. When assessing potential ecosystem impacts, these extreme events likely play a more crucial role than gradual climate trends (29). This is because the environmental response to a single extreme event can often be significant enough to alter ecosystem functioning (30). Consequently, extreme events may accelerate the effects of climate change beyond what we might expect from long-term trends alone (28). These unprecedented changes in precipitation extremes have profound implications for ecosystem functioning, particularly in terms of productivity (31) (32) (33). However, the relationship between precipitation and ecosystem productivity is not straightforward, complicated by lag effects and nonlinear responses. This nonlinearity can manifest as concavedown curves, where productivity increases rapidly with initial precipitation but plateaus at higher levels, or concave-up curves, where productivity responds slowly at first but accelerates with increasing precipitation. Legacy effects further complicate this relationship, as dry or wet conditions in one year can influence productivity in subsequent years. For instance, the influence of rainfall on vegetation often accumulates over time, with the full benefits of a significant rainfall event potentially not realized for days or weeks. Recent evidence suggests that extreme precipitation events can trigger lagged responses of ecosystems, substantially affecting ecosystem productivity on a longer timescale (34)(35)(36)(37) (38). Evapotranspiration effectively links water and carbon cycles, serving as a simple way to capture ecosystem productivity (39) (40). Nonlinear changes in evapotranspiration are typically driven by variations in climatic, ecological, geological, and human activities, which reshape the distribution of surface energy, carbon and water within a watershed. Thus, evapotranspiration serves as a versatile tool, effectively representing ecosystem productivity through the interaction between the water, energy, and carbon nexus. As climate change continues to alter precipitation patterns, understanding these complex ecosystem responses becomes increasingly critical for developing adaptive strategies and improving our ability to forecast and manage food and water resources.
Building on this understanding of rapidly changing precipitation patterns and evapotranspiration's role in ecosystem dynamics, as well as the urgent need for adaptive strategies, our study aims to delve deeper into the specific impacts of precipitation extremes by considering follow hypothesis: (i) ecosystem productivity varies with the type and intensity of extremes, (ii) immediate and lagged responses to wet extremes varies by biome type, (iii) watershed characteristics, including soil properties, hydrological conditions, and vegetation factors, will significantly influence how wet extremes impact ecosystem productivity, affecting positive or negative outcomes. To test our hypotheses, we employ machine learning (ML) methods, specifically random forest (RF) and Shapley Additive Explanations (SHAP), for attribution of watershed characteristics. We also use cluster analysis to determine mean lagged behaviors of various precipitation extremes. Through a large-scale and long-term quantitative analysis, we derive specific thresholds for watershed characteristics to guide adaptation and remediation efforts in the face of climate extremes (41). The analysis is conducted utilizing two different evapotranspiration products: Atmosphere-Land Exchange Inverse, ALEXI (2000-2023) (42) and GLEAM (1980-2023) (43), representing distinct methodological approaches. ALEXI is an energy balance model that uses thermal infrared data to estimate land surface temperature, while GLEAM is based on the Priestley-Taylor framework and incorporates multiple satellite data sources. Both products are analyzed at a monthly scale at a common spatial resolution of ~0.5 degrees. While ALEXI offers higher temporal resolution and potential for near-real-time estimates, GLEAM provides a longer historical record. The comparison of these two approaches allows for a comprehensive assessment of evapotranspiration dynamics across different ecosystems and climatic conditions. In this analysis, we use fractional evapotranspiration (fET) which is defined as the ratio of actual evapotranspiration to potential evapotranspiration. This dimensionless index allows for comparison across diverse climatic regions and vegetation types, effectively capturing the relative water stress and productivity of ecosystems. By accounting for both atmospheric demand and water availability, fET serves as a sensitive indicator of ecosystem response to climate variability and extremes. Atmosphere-Land Exchange Inverse (ALEXI) ALEXI is an energy-budget based algorithm that calculates evapotranspiration as the residual of net radiation by subtracting soil heat flux and sensible heat flux. It uses thermal infrared data to estimate surface temperature and couples a two-source energy balance (TSEB) model with an atmospheric boundary layer model. ALEXI is available at 0.05 deg x 0.05 deg and uses land surface temperature observations at two times during the morning to compute the energy balance. The model is less sensitive to biases in instantaneous satellite-based temperature estimates due to its time-differential approach. ALEXI can provide daily evapotranspiration estimates and has been implemented globally, with the capability to be downscaled to higher resolutions through the DisALEXI framework. However, since evapotranspiration estimates are based on thermal information, values used to calculate evapotranspiration depend on clear-sky conditions, meaning only a portion of the ALEXI modeling domain can be updated on any given day. To address this limitation, temporal compositing of clear-sky evapotranspiration is necessary to achieve full domain coverage. Compositing also helps reduce noise in evapotranspiration retrievals, primarily due to incomplete cloud clearing in the land surface temperature inputs to ALEXI. Typically, 28-day composites are generated using a 4-week moving window, with timestamps corresponding to the end date of each period. (GLEAM) GLEAM uses a unique algorithmic approach based on the Priestley-Taylor framework to estimate global evapotranspiration. It incorporates multiple satellite data sources, including surface soil moisture, vegetation optical depth, and land surface temperature observations, along with ERA5 meteorological reanalysis data. GLEAM provides daily evapotranspiration estimates at a global scale with a spatial resolution of 0.25 deg x 0.25 deg . Unlike energy-budget based models, GLEAM does not calculate evapotranspiration as a residual of the energy balance. Instead, it uses a more direct estimation approach that integrates various satellite-derived inputs. GLEAM estimates land evaporation mainly based on parameterized physical processes, with stress conditions parameterized as a function of dynamic vegetation information and available water in the root zone. A key feature of GLEAM is its detailed parameterization of forest interception. GLEAM calculates different components of land evaporation separately, including transpiration, bare-soil evaporation, interception loss, open-water evaporation, and sublimation. This approach allows for a comprehensive representation of evapotranspiration processes across diverse ecosystems. Miralles et al. (2011) (46) found that GLEAM estimates correlated well with eddycovariance measurements across various biomes (R2 = 0.83). Martens et al. (2017) (47) reported that GLEAM v3 showed improved performance over its predecessors, with a mean correlation of 0.78 against in situ observations. However, GLEAM has some limitations. It tends to underestimate evapotranspiration in water-stressed conditions and overestimate in energy-limited environments. Despite these challenges, GLEAM offers comprehensive global long-term coverage. Over the past two decades, significant efforts have been made to assess changes in the spatial and temporal patterns of extreme climate events globally using observational climatic data (48) (49). One such dataset is NASA's for Research and Applications, version 2 (MERRA-2) (50) (51), which includes at spatial resolution of 0.625 deg longitude by 0.5 deg latitude from 1980-2023 (52). Extreme detection indices in MERRA-2 are derived using daily precipitation or daily mean, maximum, or minimum 2-meter temperature. These indices provide long-term characteristics of extreme events, such as their intensity, duration, and frequency, using daily exceedances of percentile thresholds. The dataset consists of 15 wet extreme indices calculated based on daily exceedances of percentiles and aggregated into monthly indices. We use 14 indices related to precipitation extremes, This analysis explores the relationships between Climate Extreme Indices (CEIs) and fractional evapotranspiration (fET) using Spearman and Kendall correlation coefficients (p < 0.1). To ensure robust results, only extreme events affecting at least 40% of the watershed are considered, and correlation values must exceed an absolute value of 0.3 with p < 0.1. The CEIs data were normalized using the Box-Cox transformation, although this did not significantly alter the final results. The purpose of applying these lags is to explore potential temporal relationships between climate extremes and evapotranspiration.
By shifting the data series relative to each other, the analysis can identify whether there are delayed effects or precursor signals in the relationship between climate extremes and evapotranspiration. This approach allows for a comprehensive examination of how fET responds to or potentially influences climate extremes across different time scales, which is crucial for understanding the complex interactions between climate and hydrological processes. Precipitation-related CEIs exhibit complex and varied lagged effects on ecosystem processes. Thus, lagged correlations were computed for durations of +/-6 months. Positive lags might represent delayed ecosystem responses to precipitation events (e.g., increased fET following rainfall). Conversely, negative lags could indicate anticipatory changes in fET before precipitation events or seasonal transitions. A lag of zero represents the simultaneous relationship between fET and CEIs. A cluster analysis using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm ( 54) is further employed to determine mean lag times for causal and predictive relationships between CEIs and fET, identifying groups of similar correlation-lag combinations. This approach highlights whether certain CEIs consistently lead or lag fET changes and quantifies average time differences. The clustering process ensures sufficient data points (watersheds) >50 were used. The algorithm employs a grid search approach to find optimal DBSCAN parameters: epsilon and minimum samples. It iterates through a range of values for both parameters, creating DBSCAN models for each combination. The quality of each clustering is evaluated using the silhouette score, which measures how similar an object is to its own cluster compared to other clusters. This adaptive approach ensures the clustering is optimized for the specific characteristics of the data. The coupling strength between CEIs and fET, determined through Spearman correlation, is further analyzed using a Random Forest (RF) model (55) and Shapley Additive Explanations (SHAP) (56). This approach quantifies the contribution of watershed characteristics in driving the coupling strength. RF, an ensemble learning method, was chosen for its versatility, power in regression tasks, and ability to mitigate overfitting. While XGBoost was also evaluated, RF demonstrated superior performance and efficiency. Both RF and Spearman correlation methods were selected for their robustness to outliers and skewed distributions. The RF model was optimized through hyperparameter tuning, focusing on parameters such as the number of estimators, maximum depth, minimum samples required to split, and learning rate. This optimization aimed to minimize the (RMSE) while maximizing R2 and overall accuracy. To address the interpretability limitations of RF, we employed SHAP, a gametheoretic approach that provides a consistent and interpretable metric for understanding the relative contribution of input features and their interactions. SHAP values were used to rank feature importance and elucidate the nature of these influences-whether positive or negativeon the model output. This method overcomes the limitations of traditional feature importance measures, offering a more nuanced and personalized interpretation. By averaging SHAP values at points where they change sign, inflection points were identified, quantifying thresholds for each significant watershed characteristic that influence the coupling strength between CEIs and fET. Among the wet CEIs, we observe a higher frequency of intensity-based wet CEIs (e.g., R90P, R95P, R99P, RX1DAY, RX5DAY, SDII) compared to duration-based wet CEIs (e.g., R10mm, R20mm, R90D, R95D, R99D, WET, RX5DAYCOUNT, CWD). Throughout this paper, 'coupling strength' and 'correlation' are used interchangeably to represent the relationship between CEIs and fET. The coupling strength is generally positive and more pronounced for intensity-based wet CEIs compared to duration-based ones. Our findings reveal that the influence of watershed characteristics differs markedly across CEIs, highlighting the complex interactions between precipitation extremes, watershed properties, and ecosystem responses as measured by fET. Understanding these nuanced relationships is crucial for predicting and managing ecosystem responses to changing precipitation patterns. The metrics for GLEAM and ALEXI fET from 2001-2023 are shown in A general pattern emerges across most CEIs: positive coupling between CEIs and fET for lags within +/-2 months , and negative coupling for lags exceeding 2 months. Cluster analysis identified mean lag times for causal and predictive relationships between CEIs and fET, grouping similar correlation-lag combinations, In the short term (0-18 days), intensity-based CEIs such as R90P and R99P, which represent precipitation amounts exceeding the 90th and 99th percentiles, respectively, exhibit mean positive lags of ~ 0.168 and ~ 0.491 months, For duration-based CEIs, CWD (consecutive days with precipitation 1mm) shows a short positive lag of ~0.22 months (approximately 7 days), indicating a quick increase in fET following sustained rainfall as plants utilize newly available soil moisture, Intensity-based CEIs exhibit a bimodal distribution in their correlation with fET, For duration-based CEIs, the optimized RF model achieved RMSE=0.125, R 2 = 0.80, and accuracy = 80.50%. The SHAP analysis revealed that the watershed characteristics most significantly influencing this relationship are SOC, GWT, and permafrost %, The coupling strength varies significantly across 13 terrestrial biomes, Climate extremes are projected to increase in frequency and intensity over the coming decades, making it essential to understand their relationship with evapotranspiration (ET), a key process linking water and carbon cycles. This study explores the complex interactions between Climate Extreme Indices (CEIs) and fractional evapotranspiration (fET) across various watersheds and terrestrial biomes. The research hypothesizes that these interactions are influenced by watershed characteristics and biome types. To investigate this, the study utilizes data from MERRA-2 climate indices, GLEAM, and ALEXI for evapotranspiration. While acknowledging limitations such as coarse resolutions and data uncertainties, the study leverages ALEXI's open-source nature, GLEAM's long-term global coverage, and MERRA-2's climatological foundation to provide valuable insights into wet climate extremes' impacts on ET. This comprehensive quantitative approach aims to identify thresholds for watershed characteristics that influence ecosystem productivity in response to climate extremes. The study also considers legacy effects, recognizing that dry or wet conditions in one year can significantly impact productivity in subsequent years. While the quantified thresholds for watershed characteristics may not be universally applicable and lack ground validation, this research serves as an initial effort to investigate and quantify thresholds essential for developing effective climate resilience strategies. The analysis reveals multi-timescale relationships between CEIs and fET.
Short-term positive coupling within +/-2 months suggests rapid ecosystem responses to water availability, while negative coupling at longer lags indicates seasonality and broader ecosystem processes. Importantly, the study distinguishes between intensity-based and duration-based CEIs, as they exhibit distinct lag patterns and correlations with fET. For duration-based CEIs, soil organic carbon (SOC), permafrost %, and silt % significantly influence the CEI-fET coupling. Intensitybased CEIs are primarily affected by SOC, groundwater table depth, and clay %. The analysis reveals complex interactions between these factors and their thresholds, which determine the strength and direction of CEI-fET coupling. Across 13 terrestrial biomes, the relationship shows significant variations, with some biomes exhibiting uniform responses and others displaying more complex behaviors. The key highlights of the study are:  Multi-timescale relationships between CEIs and fET reflect diverse ecosystem responses to climate extremes, with soil organic carbon (SOC) emerging as a primary driver for both duration-based and intensity-based CEI-fET coupling. Soil characteristics, including texture (silt and clay percentages) and groundwater table depth, significantly influence CEI-fET relationships, particularly in intensity-based CEIs. Among biomes, Temperate biomes, especially Broadleaf & Mixed Forests and Grasslands, exhibit heightened sensitivity to wet extremes. These findings are crucial as they demonstrate the complex, time-dependent nature of the relationship between climate extremes and evapotranspiration. Future research should further explore similar studies considering higher-resolution data, ground-based validation, developing region-specific thresholds, and integrating additional ecological parameters. Exploring the impacts of land use changes and human interventions on potential cascading effects of climate extremes on interconnected ecosystems will also be crucial. Ultimately, this work lays the groundwork for more targeted studies and the development of adaptive management strategies to strengthen ecosystem resilience in the face of increasing climate extremes. Acknowledgments: