## 1 Introduction  
Vegetation plays a vital role in regulating the terrestrial water cycle because of intrinsic connections between water and vegetation across various spatial and temporal scales [1, 2, 3]. Water availability controls the distribution and productivity of plant communities in both water-limited and humid environments [4, 5]. Conversely, changes in vegetation driven by climate change and land use change influence water availability by regulating evapotranspiration, infiltration, and runoff patterns [6, 7, 8]. Incorporating realistic plant-water feedbacks into hydrological simulations effectively improves model performance [9, 10]. Thus, understanding plant-water interactions is crucial for assessing ecosystem sustainability under changing environmental conditions. Traditional hydrological models primarily represent vegetation effects through canopy interception and transpiration processes [11; 12]. However, these models often employ oversimplified vegetation parameterizations and make excessive assumptions, leading to unrealistic estimations of vegetation impacts--particularly in ecosystems with complex surface characteristics [13]. Moreover, they struggle to capture the temporal dynamics of hydrological responses to vegetation and climate change, as they lack explicit linkages between water availability and plant physiological processes [1; 14; 15; 16]. In contrast, recent developments in dynamic vegetation models and their coupling to land surface models allow for more comprehensive representation of phenology, photosynthesis, respiration, and carbon partitioning [17; 18]. These coupled models have demonstrated improved capability in simulating long-term hydrological responses to climate and vegetation changes [19; 20; 21]. For example, the Noah-MP model effectively captures seasonal variations in evapotranspiration, runoff, and terrestrial water storage across the continental United States [22]. However, many land surface models still exhibit deficiencies in representing drought responses, often underestimating ecosystem resilience because of unrealistically low vegetation productivity and greenness [23]. For instance, CLM4.0 simulated a more rapid decline in the leaf area index than observed by MODIS during drought events [24], and Noah-MP underestimated both evapotranspiration and gross primary productivity in water-limited regions, particularly during the 2011 Texas drought and the 2012 Central United States drought [22; 25; 26]. A key limitation is static rooting depths and vertical root distributions in simulations, which fail to capture the dynamic interactions between water availability and plant carbon allocation [26]. Root-soil interactions are critical for accurately representing plant-water interactions in land surface models [27; 28]. Root systems directly influence soil water dynamics and distribution, which in turn shape root growth and architecture. Both lateral and vertical root distributions exhibit spatial heterogeneity due to hydrotropism [29] and hydport-terning [30], where roots preferentially grow toward wetter soil layers. Plants in various ecosystems, including tropical forests, temperate woodlands, and arid or semi-arid regions, often develop deep root systems or higher root-to-shoot ratios to cope with water stress [31; 32; 33]. These plants also display flexible water uptake strategies: during dry periods, deep-rooted species increasingly rely on deep soil moisture [31; 34; 35], whereas under wet conditions, they preferentially utilize shallower water sources [36]. However, static root distributions in land surface models fail to capture such adaptive strategies, leading to unrealistic representations of water fluxes and vegetation growth under drought conditions.