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Empirical baseflow filters are widely used for baseflow separation. These filters rely on ad‐hoc parameters that introduce significant uncertainties in the calculation. A recent study by Mei et al. (2024, ) optimized these parameters using environmental tracer data for 1,100 catchments across the Contiguous United States (CONUS). However, optimized parameters are unavailable for most CONUS catchments lacking tracer data. To address this gap, we developed regionalization models for the filter parameters using the random forest (RF) algorithm and 82 catchment‐scale characteristics, including geomorphology, climate, soil properties, and human activities. We demonstrated this approach for the block length parameter N$N$of the smooth minima baseflow filter, one of the optimized filters in Mei et al.’s study, across 855 catchments. Our results show that the prediction of N$N$achieves an R2${R}^{2}$of 0.80. Predictor importance analysis identified catchment area as the most influential factor, followed by climate, hydrology, soil, and water usage characteristics. Using the RF‐predicted N$N$in baseflow separation improves daily baseflow accuracy, with the median Kling‐Gupta Efficiency increasing from 0.62 to 0.80 compared to the literature‐suggested area‐based power function. This study enhances the accuracy of baseflow separation, providing a robust foundation for understanding streamflow partitioning and supporting improved hydrological modeling.

Accurately identifying baseflow-dominant (BFD) periods in streamflow records is crucial for evaluating low-flow conditions, groundwater interactions, and other water resource management issues. While baseflow separation methods are widespread, the definition and identification of BFD flows are relatively new areas. Here, we define BFD periods as flow conditions that occur with minimal contribution from quickflow, including periods dominated by bank flow, groundwater interaction, or residual flow routing through the system. We develop a comprehensive, expert-labeled dataset of BFD periods from 182 USGS stream gages across diverse hydrological settings in the continental United States as ground truth. Using this dataset, we evaluate various automated BFD identification methods, including three new approaches, a machine learning classifier, a gradient-based method, and a statistical method, as well as two established techniques: the BN77 and Strict Baseflow methods. Our results demonstrate that the machine learning model (RF-BFD) outperforms all other approaches, achieving an F1 score of 0.92 and 92% accuracy. This study characterizes challenges in identifying BDF periods and establishes benchmarks for improving BFD identification in large-scale hydrological studies. The findings offer a pathway toward more robust and scalable BFD identification techniques, enhancing low-flow forecasting and groundwater-surface water interaction assessments.

Environmental flow assessment is an essential tool in water resource management. This study employs a holistic approach to evaluate the environmental flow in the Yen Basin, Thanh Hoa, Vietnam. Based on information gathered from a field survey, the Yen River system is divided into five reaches, and environmental objectives and ecological assets are identified in each reach. Hydrological and hydraulic mathematical models are applied to simulate the flow regime in the river, demonstrating their potential to assess environmental flow, especially in basins with limited data. The detailed results from the mathematical model facilitate selecting environmental flow components to address specific objectives for each river reach. By analyzing and selecting the flow regime, this study aims to ensure environmental protection while also considering basin development requirements, laying the groundwork for defining prescribed flow regimes in basin water management.

Previous studies indicate predominantly increasing trends in precipitation across the Western United States, while at the same time, historical streamflow records indicate decreasing summertime streamflow and 25th percentile annual flows. These opposing trends could be viewed as paradoxical, given that several studies suggest that increased annual precipitation will equate to increased annual groundwater recharge, and therefore increased summertime flow. To gain insight on mechanisms behind these potential changes, we rely on a calibrated, integrated surface and groundwater model to simulate climate impacts on surface water/groundwater interactions using 12 general circulation model projections of temperature and precipitation from 2010 to 2100, and evaluate the interplay between snowmelt timing and other hydrologic variables, including streamflow, groundwater recharge, storage, groundwater discharge, and evapotranspiration. Hydrologic simulations show that the timing of peak groundwater discharge to the stream is inversely correlated to snowmelt runoff and groundwater recharge due to the bank storage effect and reversal of hydraulic gradients between the stream and underlying groundwater. That is, groundwater flow to streams peaks following the decrease in stream depth caused by snowmelt recession, and the shift in snowmelt causes a corresponding shift in groundwater discharge to streams. Our results show that groundwater discharge to streams is depleted during the summer due to earlier drainage of shallow aquifers adjacent to streams even if projected annual precipitation and groundwater recharge increases. These projected changes in surface water/groundwater interactions result in more than a 30% decrease in the projected ensemble summertime streamflow. Our findings clarify causality of observed decreasing summertime flow, highlight important aspects of potential climate change impacts on groundwater resources, and underscore the need for integrated hydrologic models in climate change studies. Key Points Baseflows decrease despite higher annual precipitation and groundwater recharge Groundwater discharge to streams inversely correlated to snowmelt runoff Surface and groundwater interactions important for projected hydrologic change

Baseflow is a major component of streamflow during the dry season and has a crucial role in maintaining the stability of river flows in many regions. To investigate the suitability of baseflow separation methods and baseflow characteristics in the Yiluo River basin, seven baseflow separation methods, including the digital filtering method, HYSEP method and UKIH method, were selected to separate the measured daily streamflow data from five stations within the basin. The baseflow separation results were evaluated with reference to a typical recession curve in the Yiluo River to select the most appropriate separation method for the basin. The results show that the Chapman digital filter (F2) method, with an NSE value closest to 1 and the smallest RMSE value, is the most stable and reliable baseflow separation method for use in the basin. The simulated baseflow process line can reflect the receding process and hysteresis effect of runoff. The intra-annual trend of baseflow is the same as the streamflow, with a single-peaked distribution, while it is opposite to the baseflow index (BFI). According to the Seasonal Kendall test, the annual baseflow at the Changshui (II) station showed a highly significant reducing trend during the period from 2000 to 2019. The Longmenzhen and Baimasi stations showed a significant increasing trend in spring. In general, there is no trend in the BFI on a seasonal basis. The study can provide theoretical support for runoff and groundwater management in the basin.

Baseflow plays a vital role in protecting the environment and ensuring a stable water supply for farming. There are still gaps in the current understanding of baseflow convergence rates in the humid region due to the abundance of rainfall and the high‐water table. Therefore, this study focused on the evolution and hysteresis characteristics of baseflow in humid basins of southeastern China. The baseflow ensemble simulation (BES) method was established to improve the reliability and applicability of baseflow simulation. We suggest a way of differentiating the wet and dry seasons based on the multi‐year average monthly baseflow index (BFI) to determine the intra‐annual distribution of water effectively and simply. The hydrological hysteresis effect of baseflow on precipitation is revealed by characterizing baseflow response to precipitation under precipitation events during wet and dry seasons. A methodology for assessing the performance of baseflow simulation was proposed from observations of streamflow and precipitation. We found that the BES method performed better in baseflow simulation than other single separation methods. Using the BES method, the lag time of baseflow to precipitation during the wet and dry seasons was found to be 3.09 and 4.04 days after utilizing the BFI to divide the hydrological situation into wet and dry seasons. Additionally, precipitation had nearly twice as much intensity influence on baseflow during the dry season compared to the wet season. These findings have significant ramifications for the use, management, and planning of water resources in humid areas of China. Plain Language Summary The importance of researching baseflow in humid places is expanding as drought conditions occur more frequently. The lag time effect of baseflow on precipitation varies spatially and temporally, while the applicability of each baseflow simulation method varies in different regions. In this study, we validated the performance of a baseflow ensemble simulation method in the humid region of southeastern China. Humid regions had a shorter lag between baseflow and precipitation than desert, semiarid, and semi‐humid zones. The lag time of baseflow for rainfall simulated by the BES method was in the middle of the four methods. Additionally, compared to the dry season, the baseflow lag time was noticeably shorter during the wet season. This is because the humid region basin receives most of its yearly precipitation during the rainy season, primarily in the form of intense rainfall that lasts just a brief time. In addition, baseflow variations coincided with variations in precipitation during the rainy season, while there was a delay between variations in baseflow and changes in precipitation during the dry season. Understanding the effects of climate change and water use on groundwater‐surface water interactions in humid regions of China is significantly impacted by these findings. Key Points An ensemble‐based baseflow simulation method is proposed to characterize the uncertainty of each baseflow separation method The hydrologic hysteresis between baseflow and rainfall was found to be within 1 week in the humid basins of Southeastern China The influence of precipitation on baseflow in the humid basins is significantly stronger in the dry season than in the wet season

Baseflow is a vital water source for environmental and economic growth. To reveal the changes in baseflow in an arid area, Loss Plateau, China, we analyzed the annual, seasonal, and monthly baseflow fluctuations in 1981–1990 and 2006–2010. We discussed the effects of PET (potential evapotranspiration), precipitation, HI (humidity index), and temperature might have on baseflow in the basin. Results showed that the annual baseflow decreased significantly, and seasonal baseflow and baseflow index (BFI) were distributed differently in the four seasons. Baseflow and BFI were stable during the winter, but during May and June, baseflow was unstable while BFI remained stable. During 1981–1990, January and December exhibited a slight variation in baseflow, while January, May, and June exhibited a slight variation in BFI. From 2006 to 2015, baseflow was stable, with limited fluctuations in January, February, March, April, May, November, and December. The correlations between baseflow and PET, precipitation, HI, and temperature were neither statistically significant nor robust. Increases in PET, precipitation, HI, and temperature did not result in a corresponding increase or decrease in baseflow from the annual, seasonal, and monthly time scales.

Optimizing empirical baseflow filters using environmental tracers (e.g., specific electrical conductance (SEC), turbidity) is an effective and efficient way to quantify the contribution of baseflow to total flow. To execute this baseflow separation, three key components are needed: The tracer, the method to estimate tracer concentration in different flow components, and the empirical baseflow filter. However, a comprehensive evaluation of the various combinations of these components, especially with a large sample of catchments, is currently lacking in the literature. Therefore, our study assembles 16 hybrid baseflow filters from two tracers, two concentration estimation methods, and four empirical baseflow filters, and evaluated their performance in baseflow separation and producing two long‐term baseflow signatures for 1,100 catchments in the Contiguous United States. Our results suggest that SEC is a superior tracer to turbidity for baseflow separation. Additionally, using monthly maximum and minimum values to represent tracer concentration in flow components produces better separation than using a power function relationship between flow rate and concentration. The four empirical baseflow filters offer a similar level of performance, regardless of the other options used. Yet, some of these filters produce inconsistent results in calculating the baseflow signatures for the catchments. Our analysis shed light on the optimization of hybrid baseflow filters for the accurate quantification of baseflow contribution. Plain Language Summary River flow can be broken down into two components: fast flow and slow flow. The latter is usually known as baseflow, and it represents the stable portion of river flow that comes from stored water sources, such as groundwater or snowpack. It is crucial to understand the proportion of baseflow in river flow for effective water resource management. A commonly used method to separate baseflow from river flow is by filtering streamflow data with empirical baseflow filters. These filters contain some parameters that are often optimized using geochemical data, such as specific electrical conductance (SEC) and turbidity, to ensure reasonable performance of baseflow separation. This study examined how SEC and turbidity can be used to optimize four empirical baseflow filters for quantitative assessment of baseflow contribution to streamflow. Our analysis of 1,100 catchments across the Contiguous United States revealed that SEC is a more reliable indicator of baseflow than turbidity. Interestingly, the choice of empirical baseflow filter had minimal impact, though some filters produced inconsistent results for the quantification of baseflow contribution. This research enhances our ability to accurately estimate baseflow, aiding in water resource planning and management. Key Points Evidence suggests that specific electrical conductance is a better tracer for baseflow separation compared to turbidity Using monthly extreme values to describe tracer signature in flow components is better than using a power function relationship The smooth minima method provides the most consistent estimation of baseflow contribution across various combinations

Baseflow is a crucial component of river runoff and river ecological health. Reliable baseflow separation and attribution of its drivers are important for sustainable water management in arid and semi-arid basins. We analyzed 18 tributaries on the north and south banks of the Wei River basin (2006–2020). Nine baseflow separation methods were compared, and performance was evaluated using NSE and KGE. We then assessed trends of hydro-meteorological variables and quantified the contributions of climate change and human activities to baseflow changes. Among the nine methods, F2 performed best, with the highest mean NSE (0.73) and mean KGE (0.76) across the 18 sites. Baseflow on both banks showed a non-significant increasing trend (P > 0.05). Precipitation significantly affected baseflow on both banks, and potential evapotranspiration also had a significant influence on the south bank (P < 0.05). Attributions differed spatially: on the south bank, baseflow changes at Laoyukou, Dayu, and Luolicun were mainly climatedriven (63.26%, 58.81%, and 74.55%), while on the north bank only Fenggeling and Qianyang were mainly climate-driven (72.29% and 53.92%); most other stations were mainly influenced by human activities. The optimal separation method and the contrasting attributions between banks highlight strong spatial heterogeneity in baseflow controls and underscore the importance of considering both climatic drivers and human activities in basin management.

Baseflow, the sustained flow from groundwater, lakes, and snowmelt, is essential for maintaining surface water flow, particularly during droughts. Amid rising global water demands and climate change impacts, understanding baseflow dynamics is crucial for water resource management. This study offers new insights by assessing baseflow controls at finer temporal scales and examining their relationship with hydrological drought flows. We investigate how climatic factors influence seasonal baseflow in 7138 global catchments across five major climate regions. Our analysis identifies precipitation as the primary driver, affecting 58.3% of catchments, though its impact varies significantly across different climates. In temperate regions, precipitation dominates (61.9% of catchments), while in tropical regions, evaporative demand is the leading factor (47.3%). Snow fraction is particularly crucial in both snow-dominated (20.8%) and polar regions (48.5%). Negative baseflow trends generally emerge where the effects of evaporative demand or snow fraction outweigh those of precipitation. Specifically, in northern regions and the Rocky Mountains, where snow fraction predominantly controls baseflow changes, a negative trend is evident. Similarly, in tropical catchments, where evaporative demand drives baseflow changes, this also leads to a negative trend. Additionally, our findings indicate that baseflow changes are closely linked to hydrologic drought severity, with concurrent trends observed in 69% of catchments. These findings highlight the relationship between baseflow changes, the severity of hydrologic drought and shifts in precipitation, evaporative demand, and snow dynamics. This study provides crucial insights for sustainable water resource planning and climate change adaptation, emphasizing the importance of managing groundwater-fed river flows to mitigate drought impacts.