Recession Parameters Research Articles

MASTER RECESSION CURVE VISUALIZATION USING SEVEN BASEFLOW RECESSION MODELS IN PAIRED WATERSHEDS

River flow recession analysis plays a crucial role in understanding how watersheds release water during dry periods. Consequently, modeling baseflow recession is closely related to the characteristics of unconfined aquifers, storage behavior, and the discharge properties of the watershed. While several theories exist on modeling recession curves, limited research has compared different approaches regarding baseflow recession characteristics. This study aims to model seven baseflow recession equations in paired watersheds in Ambon City. The research methodology involves calibrating seven baseflow recession models using the Recession Curve (RC) 4.0 Hydro Office software. The tested models include Linear Reservoir, Exponential Reservoir, Double Exponential Horton, Dupuit-Boussinesq Aquifer Storage, Depression Storage, Turbulent Flow Model, and Hyperbolic Function Model. The calibration results yield optimal combinations of recession parameters. The parameterization order from highest to lowest is as follows: Depression Storage, followed by the Hyperbolic Function, Exponential Reservoir, Turbulent Flow Model, Double Exponential Horton, Linear Reservoir, and Dupuit-Boussinesq Aquifer Storage. Quantifying baseflow recession constants and coefficients is essential for understanding baseflow behavior. Visualizing the slope of the Recession Curve (MRC) reveals that models with high recession constants tend to have gradual MRCs, while low recession constants result in steep MRCs. The MRC slope further describes the relationship between storage conditions and discharge from the watershed. The advantage of creating MRCs from discontinuous recession segments lies in their ability to appropriately describe the MRC process and provide quantitative parameters relevant to drainage mechanisms. MRCs also serve as an optimal automated computational tool.

On the regional-scale variability in flow duration curves in Peninsular India

Abstract. Peninsular India is a unique region with major mountain ranges that govern regional atmospheric circulation and precipitation variability, the monsoons, and regional geology at range of timescales and process scales. However, the landscape and climatic feature controls on streamflow variability at a regional scale using flow duration curves (FDCs) – compact descriptions of streamflow variability that offer a window into the multiple, interacting processes that contribute to streamflow variability – have received little attention. This study examines the suitability of the partitioning of (1) an annual streamflow FDC into seasonal FDCs and (2) a total streamflow FDC into fast- and slow-flow FDCs to unravel the process controls on FDCs at a regional scale, with application to low-gradient rivers flowing east from the Western Ghats in Peninsular India. The results indicate that bimodal rainfall seasonality and subsurface gradients explain the higher contribution of slow flow to total flow across the north–south gradient of the region. Shapes of fast and slow FDCs are controlled by recession parameters, revealing the role of climate seasonality and geological profiles, respectively. Systematic spatial variation across the north–south gradient is observed, highlighting the importance of the coherent functioning of landscape–hydroclimate settings in imparting a distinct signature of streamflow variability. The framework is useful to discover the role of time and process controls on streamflow variability in a region with seasonal hydro-climatology and hydro-geological gradients.

Landscape structures regulate the contrasting response of recession along rainfall amounts

Abstract. Streamflow recession, shaped by hydrological processes, runoff dynamics, and catchment storage, is heavily influenced by landscape structure and rainstorm characteristics. However, our understanding of how recession relates to landscape structure and rainstorm characteristics remains inconsistent, with limited research examining their combined impact. This study examines this interplay in shaping recession responses upon 291 sets of recession parameters obtained through the decorrelation process. The data originate from 19 subtropical mountainous rivers and cover events with a wide spectrum of rainfall amounts. Key findings indicate that the recession coefficient (a) increases while the exponent (b) decreases with the L/G ratio (the median of ratios between flow-path length and gradient), suggesting that longer and gentler hillslopes facilitate flow accumulation and aquifer connectivity, ultimately reducing nonlinearity. Additionally, in large catchments, the exponent (b) increases with increasing rainfall due to greater landscape heterogeneity. Conversely, in small catchments, it declines with rainfall, indicating that these catchments have less landscape heterogeneity and thus reduced runoff heterogeneity. Our findings underscore the necessity for further validation of how L/G and drainage area regulate recession responses to varying rainfall levels across diverse regions.

Analysis of the Behavior of Groundwater Storage Systems at Different Time Scales in Basins of South Central Chile: A Study Based on Flow Recession Records

Understanding the groundwater storage and release (S-Q) process and its contribution to river flows is essential for different hydrological applications, especially in periods of water scarcity. The S-Q process can be characterized based on recession parameter b, which is the slope of the power–law relationship −dQ/dt = aQb of the recession flow analysis, where recession parameter b represents the linearity of the S-Q process. In various studies, it has been found that this parameter can present high variability, which has been associated with the approach or spatial variability of basin characteristics. However, the variability of parameter b and its relationship with geology and the behavior of groundwater storage over time (evolution over time) have not been sufficiently studied. The objective of this study is to analyze the variability of recession parameter b and its relationship with geological and morphological characteristics and climate variability at different time scales. To this end, 72 drainage basins located in south central Chile were examined via recession flow analysis, considering five different time scales (5 years, 10 years, 15 years, 20 years, and 25 years). In addition, to analyze spatial variability patterns and generate groups of basins with similar characteristics, a cluster analysis was carried out. Clusters were obtained using the principal component analysis (PCA) and K-means methods. The results show that in wet periods, the slope of recession parameter b tends to increase (fast drainage process), while in dry periods, the recession slope tends to decrease (slow drainage processes). In general, the results suggest that the variability of recession coefficient b indicates changes in S-Q behavior; therefore, it could be used as an indicator of the sensitivity of a basin to climate variability.

Comments about selected recession parameters

AbstractRecession analysis can be used for qualitative comparisons between basins and for quantitative analysis involving the slope and the shape of the recession curve. The groundwater‐level recession hydrograph can be used to derive an estimate of the hydraulic diffusivity of a surficial aquifer. The recession index can be obtained from streamflow data and from a formulation that includes aquifer properties. The analysis of the method reveals nonlinear recession which can result from the down‐valley flow component, a dual‐aquifer effect, and leakage to or from the aquifer. Important concepts include the dominant recession index and secondary recession characteristics which can affect low flow. Under conditions of down‐valley flow, the cross‐valley flow component tends to be important for determining recession characteristics, including hydraulic diffusivity and the recession index. These findings are relevant to another recession parameter a which is the distance from the stream to the hydrologic divide and results are supported by test simulations of a technique for estimating recharge.

Advanced Platelet-Rich Fibrin and Connective Tissue Graft for Treating Marginal Tissue Recessions: A Randomized, Controlled Split-Mouth Study.

This study aimed to evaluate and compare the clinical outcomes of advanced platelet-rich fibrin (A-PRF) and connective tissue graft (CTG) in treating marginal tissue recessions. Fifteen patients with isolated bilateral maxillary gingival recessions were recruited for the study, with 30 defects. The defects were classified as Miller's class I/II gingival recession on the canine or premolar region. Patients were randomly divided into two groups, each receiving one of the two treatment techniques (A-PRF or CTG) on a different side of the maxilla in a split-mouth design. Clinical parameters such as recession height (RH), recession width (RW), probing pocket depth (PPD), clinical attachment level (CAL), a width of attached gingiva (WAG), and keratinized tissue height (KTH) were evaluated at baseline, 3, and 6 months. Changes in biotype, Recession Esthetic Score (RES), and Visual Analogue Score-Esthetics (VAS-E) were also evaluated at 6 months. Ethics approval number (Helsinki): PHRC/HC/877/21 and registered at the Clinical Trials Registry under the number NCT05267015 Results:At the end of 6 months, there was a statistically significant reduction in RH and RW in both groups, with the mean RC% of 69.2±22.91, and 88.66±33.18 in Groups I and II, respectively. Intergroup analysis showed statistically significant differences in recession parameters between groups at 3 and 6 months, with better outcomes for the CTG group. This study demonstrates that A-PRF and CTG effectively manage gingival recession defects. However, CTG resulted in better clinical outcomes in terms of reduction in recession height and width.

Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components

Abstract. Analysis of karst spring recession hydrographs is essential for determining hydraulic parameters, geometric characteristics, and transfer mechanisms that describe the dynamic nature of karst aquifer systems. The extraction and separation of different fast- and slow-flow components constituting a karst spring recession hydrograph typically involve manual and subjective procedures. This subjectivity introduces a bias that exists, while manual procedures can introduce errors into the derived parameters representing the system. To provide an alternative recession extraction procedure that is automated, fully objective, and easy to apply, we modified traditional streamflow extraction methods to identify components relevant for karst spring recession analysis. Mangin's karst-specific recession analysis model was fitted to individual extracted recession segments to determine matrix and conduit recession parameters. We introduced different parameter optimization approaches into Mangin's model to increase the degree of freedom, thereby allowing for more parameter interaction. The modified recession extraction and parameter optimization approaches were tested on three karst springs under different climate conditions. Our results showed that the modified extraction methods are capable of distinguishing different recession components and derived parameters that reasonably represent the analyzed karst systems. We recorded an average Kling–Gupta efficiency KGE > 0.85 among all recession events simulated by the recession parameters derived from all combinations of recession extraction methods and parameter optimization approaches. While there are variabilities among parameters estimated by different combinations of extraction methods, optimization approaches, and seasons, we found much higher variability among individual recession events. We provided suggestions to reduce the uncertainty among individual recession events and raised questions about how to improve confidence in the system's attributes derived from recession parameters.

Comparing Master Recession Curve Shapes Between Linear and Exponential Reservoir Models

The behaviour of river flows during periods of recession can be better identified than in other periods. The Master Recession Curve (MRC) approach is a technical approach that is quite effective and efficient in modelling baseflow. This study aims to compare the shape of the MRC between linear and exponential reservoir models. The research method uses two linear reservoir models, the Depuit-Boussinesq equation and an exponential model based on exponential hydraulic conductivity. The results showed that the combination of recession parameters (initial recession discharge, constant and coefficients) for MRC manually linear and exponential reservoir models, and hybridization of genetic algorithm processes, showed that MRC visualization for linear reservoir models was more optimal compared to exponential reservoir models. These results are closely related to the slope of the MRC, where the linear reservoir model is gentler, and the exponential reservoir model is relatively steeper. The slope of the MRC for both reservoir models relates to the storage capabilities of the baseflow and the hydraulic conductivity properties of the study area. The gentle slope of the MRC has the properties of relatively slow storage and is relatively long stored. In contrast, the steep slope of the MRC determines the somewhat wasteful nature of storage.

A statistical approach for identifying factors governing streamflow recession behaviour

AbstractCatchment storage‐release relation has been widely studied using the parameters of streamflow recession analysis. The governing factors of the recession parameters are poorly understood, particularly in snow‐dominated regions. Here, we tailor a newly developed statistical approach, marginal contribution feature importance, to a hydrologic context, and couple it with random forests, in order to investigate the governing physical and climatic factors of catchment recession behaviour. The coupled approach can incorporate the interactions among catchment climatic and physical attributes. In a large sample hydrology study, we identify and compare the governing factors of recession parameters, in rainfall versus snowmelt‐dominated catchments, and in medium‐size versus large catchments, across more than 1000 catchments in United States and Canada. Results show that streamflow recession behaviour, particularly recession nonlinearity, strongly depend on belowground attributes and slope in rain‐dominated medium size catchments, and strongly depend on slope and annual maximum snow water equivalent in snow‐dominated medium size catchments. As catchment scale increases (>1000 km2), the attributes related to the magnitude and timing of input water (e.g., water surplus, aridity index, maximum snow water equivalent) dictates the streamflow recession behaviour and the importance of belowground attributes is dropped. Furthermore, recession nonlinearity generally increases with an increase in catchment size. The findings of this study help improve our understanding of the governing factors and the interpretation of the spatial variability of recession behaviours. Such understanding could inform the development of a generalizable process‐based framework for estimating the sensitivity of catchment storage‐release relation to climate change in different environmental settings.

Upscaling Hillslope‐Scale Subsurface Flow to Inform Catchment‐Scale Recession Behavior

AbstractThe subsurface flow contribution to a basin hydrograph is often conceptualized as a single lumped reservoir parameterized by a power law function fit through the observed recession behavior of the basin. However, basins may be better represented as multiple independent subsurface hillslope reservoirs characterized with variable, transient recession behavior as a function of the time history of recharge. In this work, we unify the latter, more complex understanding of the subsurface with the large‐scale power law recession behavior convenient in hydrologic modeling applications. This unification is achieved by deriving upscaling relationships capable of converting simple metrics of basin hillslope topography into a signature basin response characterizing the subsurface‐flow‐induced recession from a single recharge event. Event superposition may then be used to simulate the application of a time series of recharge and a simple scaling relationship may be used to handle basins with homogeneous conductivity. The signature response is informed by numerical simulations of the hillslope‐storage Boussinesq equation for unconfined saturated flow through sloping hillslopes applied to 30 basins in the CAMELS database. The efficacy of the upscaled signature response in replicating numerical simulations in 50 CAMELS basins is demonstrated, as is the practical efficacy of the upscaling relationships for predicting transient recession in 17 of these basins. The upscaled signature response method thus provides a tool for hydrologic modelers to estimate transient recession parameters using the insights of subsurface flow modeling, although the predictions made by this method are unable to capture the full variability of observed recession behavior.

Characterizing the groundwater storage–discharge relationship of small catchments in China

Abstract Baseflow recession is an essential part of the hydrological cycle, as it transfers unconfined aquifer storage to runoff. This study derived the parameterization of the baseflow recession from recession curves extracted from 382 catchments in China. The recession parameters of recession curves in power-law form, which control the shape of the curves and reflect the net effects of hillslope on the runoff decline process, are estimated by the baseflow recession analysis. The results show that the ranges of recession coefficient α and recession exponent β across China are 0–0.70 and 0.57–3, respectively. Most of the α values range between 0 and 0.20, and most of the β values range between 1 and 2. Generally, the α values of relatively dry catchments are higher than that of the wet catchments, and the distribution pattern of β values is opposite to that of α values. Statistical analysis is used to construct parametric equations for the recession parameters, indicating that catchment area, field capacity, etc., are essential for predicting α and β. In addition, the transplantation results of parametric equations show that equations can be applied to the estimation of α and β in other catchments. The results provide data support for storage estimation of data-scarce catchments.

Inference of Parameters for a Global Hydrological Model: Identifiability and Predictive Uncertainties of Climate‐Based Parameters

AbstractCalibration of global hydrological models (GHMs) has been attempted for over two decades; however, an effective and generic calibration method has not been explored. We present a novel framework for calibrating GHMs assuming that parameters can be regionalized by climate similarities. We calibrated four sensitive parameters of the H08 global hydrological model by aggregating the results of 5,000 simulations with randomly generated parameters into 11 Köppen climate classes and using an objective function Nash–Sutcliffe Efficiency (NSE) with random sampling from the proposed parameter distribution. From a 100‐fold split‐sampling test, we found that both the representativeness and robustness of the transferred parameter sets were guaranteed when the upper 5% of the samples were accepted and assign the median of each accepted parameter distribution for the climate class. The simulation with the climate‐based parameters yielded satisfactory (NSE > 0.0) and good (NSE > 0.5) performances at 480 and 234 stations (61.7% and 30.1% of 777 stations), respectively. The storage capacity (SD) and the conductive coefficient (CD) were sensitive to the climate classes and exhibited well‐constrained distributions of the accepted samples, whereas the recession parameters for the subsurface storage (γ and τ) showed little or no explanatory power to climate. The identified parameters for climate classes exhibited consistency with the physical interpretation of soil formation and efficiencies in vapor transfer. The consistency of the identified parameter values with physical underpinnings indicates that the appropriate parameters were determined, which ensured the robustness of parameters, especially when they are transferred to ungauged watersheds.

An improved method to estimate the rate of change of streamflow recession and basin synthetic recession parameters from hydrographs

Recession analysis of -dQ/dt ∼ Q is popularly used for determining catchment storage-discharge relationship, estimating the aquifer hydraulic properties, and predicting low flow processes. However, there are uncertainties arising from the numerical approximation of -dQ⁄dt by the finite difference (ΔQ/Δt) for individual recession and the fitting cloud-like data of -dQ⁄dt ∼ Q for many recession events in a catchment. In this study, we proposed an improved variable time step increment method (IVTS) to minimize the effects of the noisy observations on estimating -dQ⁄dt, and a fitting method in terms of the binning-average of -dQ⁄dt ∼ Q data points to determine the basin synthetic recession parameters. The proposed methods are validated by using numerical generation of recessions with different noises and recession behaviors and the observed hydrographs from twenty catchments in Huai River Basin in China. The results from our proposed method are compared with those from the other popularly used methods. The IVTS method is proven to be robust for approximation of -dQ⁄dt, which can significantly reduce errors of the estimated -dQ⁄dt and the recession slope (b). Combining with the IVTS method, the fitting method in terms of the binning average by splitting range of -dQ⁄dt, instead of Q, could overcome the shortage of traditional recession analysis methods that underestimate the recession slope. Our study indicates that the more accurate and reliable calculation of -dQ⁄dt ∼ Q data and estimation of basin synthetic recession parameters could increase the objectivity in interpretation of recession behaviors and accuracy in perdition of low flow processes.

A comparative evaluation of automated recession extraction methods to determine the late-time recession characteristics of karst spring hydrographs

Recession Curve Analysis is a common method to characterize karstic aquifers and their discharge dynamics. Although this technique provides crucial information on quantifying system hydrodynamic properties, the manually selected recession curves analysis is neither a practical technique to cover all candidate recession curves, nor it allows extracting the entire hydrological diversity of the recession behavior. This study aimed to comparatively evaluate the applicability of automated recession selection procedures to the late-time recession analysis of karst spring hydrograph. For the comparative evaluation of the three automated recession extraction methods (Vogel Method, Brutsaert Method, and Aksoy and Wittenberg Method), we quantified the late-time recession parameters of spring hydrographs by combining three extraction methods with four recession analysis methods (Maillet, 1905; Boussinesq, 1904; Coutagne, 1948; and Wittenberg, 1999). By applying our experimental design into the five karst springs located in Austria, we identified the possible weaknesses of the automated recession extraction procedures for the late-time recession analysis for spring hydrographs. To explore the value of the karst spring’s physicochemical data (electrical conductivity and water temperature) as a completion data for the recession curve analysis, we carried out the hydro-chemograph analysis to examine the recession time and its duration. The research provides a research direction as to how the automated recession extraction procedures for the karst spring hydrographs could be improved by the physicochemical signatures of karst springs.

Evolution of Arctic rivers recession flow: Global assessment and data-based attribution analysis

Due to polar amplification of climate change, high latitudes are warming up twice as fast as the rest of the world. This warming leads to permafrost thawing, which increases the thickness of the overlying active layer and modifies the subsurface hydrologic regime of the draining watershed, therefore affecting baseflow to surface water and modifying recession characteristics. The active layer thickening and the subsurface flow modification are assumed to be linearly correlated. The objective of this study is to test this assumption by quantifying the correlation between the temporal evolution of hydrologic parameters (recession slope and initial recession outflow) and 11 controlling factors (all linked to surface, subsurface and climatic conditions) for 336 Arctic catchments from 1970 to 2000. Contrary to previous studies, we demonstrate a clear decrease in recession slope and initial recession outflow over 1970–2000 for a majority of catchments at any significance level. We explain this result by identifying high topography and low permafrost extent as controlling factors that complexify the relationship between trends in recession parameters and active layer thickness evolution. The study goes further by identifying the mechanisms behind the complexification of the relationship: permafrost-extent loss, hydrologic-connectivity increase, flow-path-diversity increase, contributing drainage area multiplication. The novel aspect of the study lay behind the large number of studied catchments and the large range of controlling factors tested.

Hydrograph recession extraction algorithm (HYDRA): Minimizing influence of stage uncertainty in identification of recession events

Fine temporal resolution streamflow records offer valuable information to further our understanding of watershed function. Among the many tools to decipher watershed processes, recession analysis can be used to gain insight into storage-discharge properties and watershed storage by analyzing portions of stream hydrographs whose behavior is attributed to the relationship between aquifer structure and groundwater outflow to the steam channel. Although numerous methods have been employed to advance recession analysis methodology, no comprehensive approach to identify the cessation or continuation of recession events from fine temporal resolution data has been established. A new extraction algorithm, the HYDrograph Recession extraction Algorithm (HYDRA), is proposed to improve identification of recession events from fine temporal resolution hydrographs. HYDRA exploits site-specific stage-discharge relations to qualify the beginning or end of a hydrograph recession by accounting for stage measurement error which translates to uncertainty in the stream hydrograph. Experiments compared HYDRA to previous published methods, revealing that HYDRA led to significant improvements in identifying the continuation of recession events, resulting in superior estimation of theoretical recession parameter values. Additional case studies assert that HYDRA robustly identifies recession events across a spectrum of temporal resolution discharge records, affirming that the extraction algorithm provides a functional tool to identify recession events for further recession-based theoretical analysis.

Runoff recession features in an analytical probabilistic streamflow model

The development of run-of-river hydropower requires the flow duration curve (FDC) to estimate the electricity production. As obtaining an FDC based on gauging station streamflow requires significant cost and time (i.e., more than several years), it is important to predict the FDC in the absence of streamflow data. An analytical probabilistic model of streamflow dynamics (p(Q) model) can calculate the FDC, which requires several rainfall and runoff recession parameters. To estimate the recession parameters in the absence of discharge data, we selected two approaches: the geomorphic recession flow model (GRFM) and geological recession approach (GRA). The GRFM and GRA associate the river network and geology, respectively, with runoff recession features. This study aims to comprehend the characteristics of the GRFM and GRA employed in the p(Q) model by targeting 57 basins throughout Japan. We found different performance trends between the GRFM and GRA that corresponded to specific basin characteristics. While the GRFM performed well in high precipitation basins, the GRA performed well in basins with abundant unconfined groundwater contributions. These results agree with the features in the derivations of the GRFM and GRA. In addition, we found a complementary relationship between the GRFM and GRA based on the baseflow index (BFI), which suggests that the complementary relationship is one method to improve the performance of the p(Q) model in the absence of streamflow data.

Lag in Hydrologic Recovery Following Extreme Meteorological Drought Events: Implications for Ecological Water Requirements

Hydrological regimes, being strongly impacted by climate change, play a vital role in maintaining the integrity of aquatic river habitats. We investigated lag in hydrologic recovery following extreme meteorological drought events, and we also discussed its implications in the assessment of ecological environment flow. We used monthly anomalies of three specific hydrometeorological variables (precipitation, streamflow, and baseflow) to identify drought, while we used the Chapman–Maxwell method (the CM filter) with recession constant calculated from Automatic Baseflow Identification Technique (ABIT) to separate baseflow. Results showed that: (i) Compared to the default recession parameter (α = 0.925), the CM filter with the ABIT estimate (α = 0.984) separated baseflow more accurately. (ii) Hydrological drought, resulting from meteorological drought, reflected the duration and intensity of meteorological drought; namely, longer meteorological drought periods resulted in longer hydrological drought periods. Interestingly, the time lag in streamflow and baseflow indicated that aquatic ecosystem habitat recovery also lagged behind meteorological drought. (iii) Assessing environmental flow by quantifying drought provided greater detail on hydrological regimes compared to abrupt changes, such as the increased hydrological periods and the different environment flows obtained. Taken together, our results indicated that the hydrological response in streamflow and baseflow (e.g., the time lag and the precipitation recovery rate (Pr)) played a vital role in the assessment of environmental flow.

Recession analysis revisited: impacts of climate on parameter estimation

Abstract. Recession analysis is a classical method in hydrology to assess watersheds' hydrological properties by means of the receding limb of a hydrograph, frequently expressed as the rate of change in discharge (-dQ/dt) against discharge (Q). This relationship is often assumed to take the form of a power law -dQ/dt=aQb, where a and b are recession parameters. Recent studies have highlighted major differences in the estimation of the recession parameters depending on the method, casting doubt on our ability to properly evaluate and compare hydrological properties across watersheds based on recession analysis of -dQ/dt vs. Q. This study shows that estimation based on collective recessions as an average watershed response is strongly affected by the distributions of event inter-arrival time, magnitudes, and antecedent conditions, implying that the resulting recession parameters do not represent watershed properties as much as they represent the climate. The main outcome from this work highlights that proper evaluation of watershed properties is only ensured by considering independent individual recession events. While average properties can be assessed by considering the average (or median) values of a and b, their variabilities provide critical insight into the sensitivity of a watershed to the initial conditions involved prior to each recharge event.

Estimation of baseflow nitrate loads by a recursive tracing source algorithm in a rainy agricultural watershed

AbstractBaseflow has become an important source of nitrate nonpoint source pollution in many intensive agricultural watersheds. Uncertainties in baseflow nutrient load separation are caused by the effects of hydrometeorological factors on both baseflow recession and baseflow nutrient load recession. These uncertainties have not been addressed well in the existing separating algorithms, which are based on simple baseflow rate–load relationships. In the present study, a recursive tracing source algorithm (RTSA) was developed based on a nonlinear reservoir algorithm and hydrometeorology‐corrected baseflow nutrient load recession parameter. This approach was used to reduce the uncertainty of baseflow nitrate load estimation caused by variations in different load recessions under varying climate conditions. RTSA validation in a typical rainy agricultural watershed yielded Nash–Sutcliffe efficiency, root mean square error‐observation standard deviation ratio, and R2 values of 0.91, 0.30, and 0.91, respectively. The baseflow nitrate–nitrogen (N─NO3–) loads from 2003 to 2012 in the Changle River watershed of eastern China were estimated with the RTSA. The results indicated that baseflow nitrate export accounted for 62.0% of the mean total annual N─NO3– loads (18.0 kg/ha). The total baseflow N─NO3– export was highest in spring (3.6 kg/ha), followed by summer (3.2 kg/ha), winter (2.3 kg/ha), and autumn (2.1 kg/ha). The contribution of baseflow to total nitrate in the stream decreased in the order of winter (69.88%) >spring (66.59%) >autumn (60.36%) >summer (54.04%). The monthly baseflow N─NO3– loads and flow‐weighted concentrations greatly increased during the research period (Mann–Kendall test, Zs > 2.56, p < .01). Without proper countermeasures, baseflow nitrate may represent a serious long‐term risk for water surfaces in the future.