Conventional calibration methods used in hydrological modelling are based on runoff observations at the basin outlet. However, calibration with only runoff often produces reasonable runoff but poor results for other hydrological variables. Multi-variable calibration with both runoff and remote sensing-based evapotranspiration (ET) is developed naturally, due to the importance of ET and its data availability. This study compares two main calibration schemes: (1) calibration with only runoff (Scheme I) and (2) multi-variable calibration with both runoff and remote sensing-based ET (Scheme II). ET data are obtained from three remote sensing-based ET datasets, namely Penman–Monteith–Leuning (PML), FLUXCOM, and the Global Land Evaporation Amsterdam Model (GLEAM). The aforementioned calibration schemes are applied to calibrate the parameters of the Distributed Hydrology Soil Vegetation Model (DHSVM) through ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII). The results show that all three ET datasets have good performance for areal ET in the study area. The DHSVM model calibrated based on Scheme I produces acceptable performance in runoff simulation (Kling–Gupta Efficiency, KGE = 0.87), but not for ET simulation (KGE < 0.7). However, reasonable simulations can be achieved for both variables based on Scheme II. The KGE value of runoff simulation can reach 0.87(0.91), 0.72(0.85), and 0.75(0.86) in the calibration (validation) period based on Scheme II (PML), Scheme II (FLUXCOM), and Scheme II (GLEAM), respectively. Simultaneously, ET simulations are greatly improved both in the calibration and validation periods. Furthermore, incorporating ET data into all three Scheme II variants is able to improve the performance of extreme flow simulations (including extreme low flow and high flow). Based on the improvement of the three datasets in extreme flow simulations, PML can be utilized for multi-variable calibration in drought forecasting, and FLUXCOM and GLEAM are good choices for flood forecasting.

Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.

Accurately modeling and predicting flood flows across multiple sites within a watershed presents significant challenges due to potential issues of insufficient accuracy and excessive computational demands in existing methodologies. In response to these challenges, this study introduces a novel approach centered around the use of vine copula models, termed RDV-Copula (reduced-dimension vine copula construction approach). The core of this methodology lies in its ability to integrate and extract complex data before constructing the copula function, thus preserving the intricate spatial–temporal connections among multiple sites while substantially reducing the vine copula's complexity. This study performs a synchronization frequency analysis using the devised copula models, offering valuable insights into flood encounter probabilities. Additionally, the innovative approach undergoes validation by comparison with three benchmark models which vary in dimensions and nature of variable interactions. Furthermore, the study conducts stochastic simulations, exploring both unconditional and conditional scenarios across different vine copula models. Applied in the Shifeng Creek watershed, China, the findings reveal that vine copula models are superior in capturing complex variable relationships, demonstrating significant spatial interconnectivity crucial for flood risk prediction in heavy-rainfall events. Interestingly, the study observes that expanding the model's dimensions does not inherently enhance simulation precision. The RDV-Copula method not only captures comprehensive information effectively but also simplifies the vine copula model by reducing its dimensionality and complexity. This study contributes to the field of hydrology by offering a refined method for analyzing and simulating multi-site flood flows.

Many hydrological models incorporate vegetation-related parameters to describe hydrological processes more precisely. These parameters should adjust dynamically in response to seasonal changes in vegetation. However, due to limited information or methodological constraints, vegetation-related parameters in hydrological models are often treated as fixed values, which restricts model performance and hinders the accurate representation of hydrological responses to vegetation changes. To address this issue, a vegetation-related dynamic-parameter framework is applied on the Xinanjiang (XAJ) model, which is noted as Eco-XAJ. The dynamic-parameter framework establishes the regression between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration parameter K. Two routing methods are used in the models, i.e., the unit hydrograph (XAJ-UH and Eco-XAJ-UH) and the Linear Reservoir (XAJ-LR and Eco-XAJ-LR). The original XAJ model and the modified Eco-XAJ model are applied to the Ou River Basin, with detailed comparisons and analyses conducted under various scenarios. The results indicate that the Eco-XAJ model outperforms the original model in long-term discharge simulations, with the NSE increasing from 0.635 of XAJ-UH to 0.647 of Eco-XAJ-UH. The Eco-XAJ model also reduces overestimation and incorrect peak flow simulations during dry seasons, especially in the year 1991. In drought events, the modified model significantly enhances water balance performance. The Eco-XAJ-UH outperforms the XAJ-UH in 9 out of 16 drought events, while the Eco-XAJ-LR outperforms the XAJ-LR in 14 out of 16 drought events. The results demonstrate that the dynamic-parameter model, in regard to vegetation changes, offers more accurate simulations of hydrological processes across different scenarios, and its parameters have reasonable physical interpretations.

There is evidence that climate change and human activities are changing ecohydrological systems, yet the complex relationships among ecological (normalized difference vegetation index, gross primary productivity, and water use efficiency) and hydrological variables (runoff, soil water storage, groundwater storage, etc.) remain understudied. This study develops a novel framework based on network analysis alongside satellite data and in situ observations to delineate the joint evolutions (phenomena) and causal interactions (mechanisms) in complex systems. The former employs correlations, and the latter uses physically constrained causality analysis to construct network relationships. This framework is applied to the Yellow River basin, a region undergoing profound ecohydrological changes. Results suggest that joint evolutions are controlled by compound drivers and direct causality. Different types of network relationships are found – namely, joint evolution with weak causality, joint evolution with high causality, and asynchronous evolution with high causality. The upstream alpine subregions, for example, where the ecological subsystem is more influenced by temperature, while the hydrological one is more driven by precipitation, show relatively high synchronization but with weak and lagged causality between two subsystems. On the other hand, ecohydrological causality can be masked by intensive human activities (revegetation, water withdrawals, and reservoir regulation), leading to distinct evolution trends. Other mechanisms can also be deduced. Reductions in water use efficiency in the growing season are directly caused by the control of evapotranspiration, and the strength of control decreases with the greening land surface in some subregions. Overall, the proposed framework provides useful insight into the complex interactions within the ecohydrological systems for the Yellow River basin and has applicability to broader geographical contexts.

Accurate drought assessment is crucial for effective regional water resource management. While reanalysis and remote sensing products enable high-resolution drought assessment, their regional application requires rigorous local validation. This study evaluates nine data combinations, pairing three precipitation products with three evapotranspiration products, to identify the optimal combination for robust SPEI estimation and subsequently to investigate the spatiotemporal variations in drought conditions during 1980–2020 in Zhejiang Province, China. The results indicate that the choice of precipitation product is the dominant factor influencing SPEI accuracy, with the combination of CMFD V2.0 precipitation and GLEAM v4.2a evapotranspiration identified as the most reliable for SPEI estimation across multiple timescales (SPEI1/3/6/12). The long-term trend analysis of the SPEI derived from this optimal data combination reveals significant spatiotemporal heterogeneity: temporally, a pronounced “wetter winters, drier springs” seasonal pattern emerges, posing a substantial threat to agricultural water security; spatially, a distinct divergence shows central/northeastern areas wetting while southern/southeastern regions experience a significant drying trend, particularly for long-term hydrological drought (SPEI12). Additionally, the prevalence of light droughts across the province suggests a sustained baseline of water stress. Attribution analysis further demonstrates that precipitation is the dominant driver of droughts across all timescales. This study contributes both a validated, high-resolution data foundation for regional drought assessment and a scientific basis for targeted drought adaptation strategies.