Recession Analysis Research Articles

Simulating the recession dynamics of Arctic catchments in the context of a thawing permafrost

In cold regions, climate warming is causing permafrost to thaw, which modifies the dynamics of groundwater flow from a local system, constrained by frozen ground, to a regional system of interconnected aquifers. Under the assumption presented in Brutsaert (2005), the recession slope of an arctic catchment hydrograph is linearly related to permafrost thawing depth. The recession analysis of arctic river flow may therefore reflect permafrost thawing dynamics. In areas where permafrost observations are rare, recession analysis appears as a valuable method to be exploited since there are extensive datasets of river-discharge for the Arctic, with an exceptionally large temporal and spatial coverage. Yet, it has been shown that the linear relationship between recession slope and permafrost thaw depth may be complicated by the extent of the permafrost, the landscape topography as well as the hydraulic properties of the aquifer. The integrated surface and subsurface hydrologic model Hydrogeosphere (HGS) is used here to account for hydrological complexifications and mechanisms involved when permafrost extent decreases and landscape hillslope increases. Previous modeling studies have already tested the impacts of these factors on the hydrological signature of arctic catchments. However, few have been able to (1) simulate permafrost extent, catchment hillslope and the resulting groundwater flowpaths in three dimensions, (2) distinguish the impact of permafrost thawing in extent from those of permafrost thawing in thickness and (3) represent the connection between surface and subsurface with a coupled approach. These assets allow to conclude that river-tàlik development and valley incision both clearly increase the vertical connectivity of the subsurface flowpaths as well as the non-linearity of the reservoir. The flexibility and the realism of HGS model allow to compare the modeling outputs with the dynamics of 336 real catchments from the Arctic. The comparison corroborates our results: below continuous, assumptions behind Brutsaert conceptual model are not verified and it is no longer possible to relate recession constant to permafrost thaw depth. Finally, this study gives the keys to start elaborating on the recession analysis method to be able to relate the hydrological signature of Arctic rivers to permafrost thawing rate in any type of permafrost extent and catchment topography.

Catchment response times – understanding runoff dynamics from catchment distances and celerities

ABSTRACT In this study we explore how varying the river network (RN) density affects the distribution of hillslope to RN distances, the subsurface water celerities and hence the response times. Eleven Norwegian catchments (with areas of 0.007 to 500 km2) were used for the analysis, and the Distance Distribution Dynamics (DDD) hydrological model was calibrated for each catchment and RN. Equally good Kling-Gupta efficiency scores suggest a degree of equifinality in that many constellations of RNs and subsurface celerities have equally good model performance. All catchments display a linear relationship between the calibrated mean subsurface water celerity and mean hillslope to RN distance, consistent with a constant mean response time (MRT). The MRTs vary from 1 to 49 days for the different catchments and agree with MRT estimated from recession analysis and regionalized through regression and catchment characteristics. The latter aids in the estimation of model parameters for ungauged basins.

Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine

AbstractFlow recession analysis, relating discharge Q and its time rate of change −dQ/dt, has been widely used to understand catchment scale flow dynamics. However, data points in the recession plot, the plot of −dQ/dt versus Q, typically form a wide point cloud due to noise and hysteresis in the storage‐discharge relationship, and it is still unclear what information we can extract from the plot and how to understand the information. There seem to be two contrasting approaches to interpret the plot. One emphasizes the importance of the ensemble characteristics of many recessions (i.e., the lower envelope or a measure of central tendency), and the other highlights the importance of the event scale analysis and questions the meaning of the ensemble characteristics. We examine if those approaches can be reconciled. We utilize a machine learning tool to capture the point cloud using the past trajectory of daily discharge. Our model results for a catchment show that most of the data points can be captured using 5 days of past discharge. We show that we can learn the catchment scale flow recession dynamics from what the machine learned. We analyze patterns learned by the machine and explain and hypothesize why the machine learned those characteristics. The hysteresis in the plot mainly occurs during the early time dynamics, and the flow recession dynamics eventually converge to an attractor in the plot, which represents the master recession curve. We also illustrate that a hysteretic storage‐discharge relationship can be estimated based on the attractor.

Land cover influence on catchment scale subsurface water storage investigated by multiple methods: Implications for UK Natural Flood Management

Study regionUnited Kingdom (UK). Study Focus‘Natural flood management’ (NFM) schemes manipulating land use and other catchment features to control runoff are increasingly promoted across the UK. Catchment water storage and mixing processes influence runoff, but our understanding of the effects of land cover change on these processes is still limited. This study combined hydrometric, isotopic and geochemical measurements to investigate land cover versus potential topographic, soil and geological controls. It compared storage-discharge dynamics in nine nested catchments within a 67 km2 managed upland catchment in southern Scotland. Storage and mixing dynamics were characterised from hydrometric data using recession analysis and from isotopic data using mean transit time and young water fraction estimates. To give information on water sources, groundwater fraction was estimated from end member mixing analysis based on acid neutralising capacity. New hydrological insightsThe analysis showed low but variable sub-catchment scale dynamic storage (16–200 mm), mean transit times (134–370 days) and groundwater fractions (0.20–0.52 of annual stream runoff). Soil hydraulic conductivity was most significantly positively correlated with storage and mixing measures, whilst percentage forest cover was inversely correlated. Any effects of forest cover on increasing catchment infiltration and storage are masked by soil hydraulic properties even in the most responsive catchments. This highlights the importance of understanding dominant controls on catchment storage when using tree planting as a flood management strategy.

Intelligent neuro-computing to analyze the awareness programs of fractional epidemic system outbreaks

A new model of fractional for the influence of consciousness initiatives on epidemic incidences (ICIEI) is described in this research. The fractional Caputo operator (FCO) is used to expand this system to the typical awareness program model (ICIEI-FCO). Analytically and quantitatively, the properties of the new system are investigated. This article presents an artificial neural network-based fractional model (ANN-FM) for estimating the efficacy of epidemic outbreak awareness initiatives. The reliability, resolution, durability, and robustness of the proposed model are examined using the suggested ANN-BLM approach for five distinct situations. The population of the model is created by utilizing the power of the explicit Runge-Kutta numerical approach, and it is represented by a system of nonlinear ordinary differential equations. The Grunwald–Letnikov (GL) technique is used to numerically evaluate the modeled differential system of the physical issue for multiple scenarios to anticipate numerical data, and these results are utilized as a reference dataset of the networks. The data needed to answer the fractional model's questions on how awareness campaigns affect epidemic outbreaks is broken down as follows: training takes up 80% of the time, testing 10%, and authorization 10%. There are two aspects to the strategy: First, the fundamental ANN-BLM operator performances are displayed. In the meantime, the ANN-BLM execution approach is used to address the fractional-order problem. The GL-mathematical system was used to compare the numerical findings. The TSs, regression, correlation, EHs, and MSE are used to demonstrate the dependability and competency of ANN-BLM as well as their numerical performances in the presented numerical findings, which were developed using ANN-BLM to lower the MSE. The Levenberg-Marquart training (LMT) algorithm is used to optimize network results in terms of mean-square errors, training states graphs, errors of the histogram, recession analysis, auto-correlation and time series responses, which demonstrates the system's accurate and proficient trend acknowledgment. The mean-square error fitness analysis in the ranges of 10-6 to 10-11 validates the authenticity and effectiveness of the designed solver. The suggested AI-based study is expected to pave the way for new, creative approaches to fractional order modeling and analysis of naturally unpredictable dynamic systems.

Uncovering Hidden Insights: Analyzing Recession with Data Science

The global economy is no stranger to recessions, which can have severe impacts on businesses, individuals, and entire nations. As a result, it is essential to analyze economic data to identify and understand recession patterns, allowing for effective policy-making and planning. In this paper, we explore the use of Python as a tool for recession analysis. We discuss various techniques for data collection, processing, and analysis using popular Python libraries such as Pandas, NumPy, and Matplotlib

A new method for effective parameterization of catchment-scale aquifer through event-scale recession analysis

Accurate estimation of aquifer properties is of critical importance in representing the interactions between hydraulically connected surface-subsurface systems. The variability in recession characteristics (i.e., decline rates and slope nonlinearity) shows that each recession event (RE) occurs under different hydraulic diffusivity conditions between the stream-hillslope. Therefore, the streamflow recession characteristics should be considered in the effective parameterization of the catchment-scale aquifer. This study presents a new method for estimating catchment-scale aquifer properties while accounting for distinct recession characteristics through event-scale recession analysis. In this work, we used the recently developed BE3S (Hong et al., 2020) for (1) explicit incorporation of river stage observations as forcing data to account for the distinct recession characteristics and (2) numerical prediction of outflow from a Boussinesq aquifer to comparatively evaluate analytically derived effective groundwater parameters. We also addressed the relevance of the net subsurface discharge (NSD) observations in event-scale recession analysis for the catchment-scale aquifer parameterization. Specifically, we found that the catchment-scale NSD data plays a role in determining the initial saturated aquifer thickness (Dini) and the size of contributing aquifer (Ac), which varies in individual REs. The applicability of the proposed parameterization scheme was successfully validated for individual REs with distinct recession slopes, demonstrating that hydraulic diffusivity parameters can be estimated by explicitly incorporating recession characteristics. As a catchment-scale application of hydraulic groundwater theory, we expect that the proposed scheme for effective groundwater parameterization will contribute to the accurate representation of the catchment-scale stream-hillslope hydrologic continuum.

Evaluation of seasonal catchment dynamic storage components using an analytical streamflow duration curve model

AbstractDynamic storage refers to groundwater storage that is sensitive to rainfall infiltration, streamflow generation, evapotranspiration, and other variables involving groundwater gain or loss. It plays a crucial role in habitat maintenance and the mitigation of environmental impacts on regional hydrological behaviors. Dynamic storage can be separated into direct storage, which contributes to the river channel, and indirect storage, which is insensitive to streamflow. The combination of diverse approaches would provide an estimation of the two storage types. This study estimated optimal baseflow coefficients and direct storage in the wet and dry seasons using an analytical streamflow duration curve model in eight catchments of the Choushui River Basin from 2013 to 2017. The water balance approach was then combined to assess indirect storage for evaluating seasonal dynamic storage components. The model applicability for each catchment of the Choushui River Basin in the wet and dry seasons was assessed using the similarity between observed and simulated flow duration curves, namely Kolmogorov–Smirnov distance. We also applied it to assess the performance difference between model and streamflow recession analysis, which is typically used to estimate baseflow coefficients. The results demonstrated that seasonal differences in baseflow coefficients were related to catchment characteristics as well as the aquifer extent through which groundwater flows. The model utilizing maximum likelihood estimation exhibited superior performance than streamflow recession analysis and was highly applicable in our study area in wet and dry seasons. Dynamic storage components demonstrated a considerable difference in the additional groundwater storage between dry and wet seasons and a loss of direct storage was observed in most catchments during the dry season.

Explaining the natural and anthropogenic factors driving glacier recession in Kashmir Himalaya, India.

Glaciers across the Kashmir Himalayan region are melting at an accelerated pace compared to other regions across the Himalayan arc. This study analyzed the recession patterns of nine glaciers in the Kashmir Himalaya region over 28years between 1992 and 2020 using satellite images and field measurements. The recession patterns were correlated with debris cover, topographic factors, and ambient black carbon (BC) concentration at glacier sites. HYSPLIT model was used to track the air mass sources at a 7-day time-step from September 1, 2014, to September 28, 2014, over the selected region. All nine glaciers revealed high recession as indicated by changes in the area (average recession: 20.8%) and snout position (~ 14m a-1). The relative percentage of debris on each glacier varied between ~ 0% (clean glacier) and 43%. Although the investigated glaciers lie in the same climatological regime, their topographical behavior is dissimilar with mean altitude ranging between 4000 and ~ 4700m asl and the average slope varying from 17 to 24°. All the investigated glaciers are north-facing except G3 (southerly aspect). Our results indicate anomalously high ambient BC concentrations, ranging from 500 to 1364ngm-3, at the glacier sites, higher than previously studied for glaciers in the Himalayas and neighboring Tibetan Plateau. The backward air-mass trajectory modeling indicated both local and global sources of particulate matter in the study area. A comparative analysis of BC measurements and glacier recession with the studies conducted across high Asia indicated the influence of BC in accelerating the melting of glaciers in the Kashmir region.

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.

Groundwater Vulnerability of Halabja-Khurmal Sub-Basin Using Modified DRASTIC Method

Evolving groundwater vulnerability from DRASTIC to modified DRASTIC methods helps choose the most accurate areas that are most delicate toward pollution. This study aims to modify DRASTIC with land use and water quality index for groundwater vulnerability assessment in the Halabja-Khurmal sub-basin, NE/Iraq. The Halabja-Khurmal sub-basin groundwater vulnerability index is calculated from nine hydrogeological parameters by the overlay weighting method. As a result, 1.3% of the total area has a very high vulnerability value and 46.1% with high vulnerability. The regions with high groundwater vulnerability have a high-water table and groundwater recharge. Nitrate concentration was used to validate the result, and the Pearson correlation and recession analysis between the modified DRASTIC index and nitrate concentration depicted a strong relation with 0.76 and 0.7, respectively.

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.

Impact of climate and NDVI changes on catchment storage–discharge dynamics in southern Taiwan

This study used streamflow recession analysis to assess the changes in the groundwater–streamflow relationships using streamflow data. The groundwater response to climate and Normalized Difference Vegetation Index (NDVI) changes was considered as a variable in the recession analysis. The integrated impact could thus be compared to assess the consistency of these effects on the catchment discharge processes. The results showed that the recession characteristics is linked to regional topography and streamflow condition. Variation of storage–discharge sensitivities in most catchments was not linked to the variation of groundwater due to the lower capacity of groundwater contribution. The quantified impacts aided in assessing the relationship between climate, land cover, groundwater storage and discharge. This study provides a simple explanation of effects of environmental coevolution on storage–discharge processes.

Dynamic volume in karst aquifers: Parameters affecting the accuracy of estimates from recession analysis

Reliable estimates of the dynamic volume of karst aquifers, i.e. the drainable volume of groundwater, are important in the context of sustainable management and environmental protection of the karst water resources. Extrapolation of the observed recession of spring hydrographs is commonly used as a basis for the estimation of the dynamic volume. However, our understanding concerning the reliability of this approach is still limited. Therefore, the adequacy of this approach is investigated using flow models of hypothetical karst aquifers to assess the deviation of the extrapolated from the real recession curve and identify parameters controlling the extrapolated recession coefficient. The results show that parameters such as the geometry of the catchment area and point recharge have little effect on the accuracy of the estimated dynamic volume. Low extension of the highly conductive zone and increase in specific yield or catchment area with depth of the aquifer result in the underestimation of the dynamic volume. Groundwater abstraction and decrease of specific yield or catchment area with increasing saturated thickness can lead to an overestimation of the dynamic volume. These findings clarify the potential underestimation or overestimation in dynamic volume estimates on the basis of the observed recession characteristics.

A Decade of Cave Drip Hydrographs Shows Spatial and Temporal Variability in Epikarst Storage and Recharge to Appalachian Karst Systems

We conducted recession analyses on cave drip hydrographs from a 10-year record (2008–2018) of three drip monitoring stations within James Cave (Pulaski County, VA, USA) to examine differences in hydrologic characteristics of the epikarst and quantify the storage volume of the epikarst feeding the drips. We used two recession analysis methods (correlation and matching strip) to calculate recession coefficients for multiple hydrographs at each site. Results show subtle differences between the three drip sites, suggestive of spatial heterogeneity in permeability and storage in the overlying epikarst. Storage volume calculations show that during the recharge season, up to 95% of recharge through the epikarst to the cave occurs through rapid pathways (i.e., fractures), and 5% of recharge occurs through diffuse pathways (i.e., pores). However, during the recession period, recharge through rapid pathways in the epikarst decreases and occurs predominantly through diffuse flow. Combined, these results underscore the importance of documenting spatial and temporal characterization of drip rates and other recharge inputs into karst systems.

Hydrogeological characterization of heterogeneous volcanic aquifers in the Canary Islands using recession analysis of deep water gallery discharge

Aquifers constitute the main freshwater supply of oceanic islands. Maintaining groundwater quantity and quality is of critical concern as demographic and climatic changes place additional pressures on already fragile water resources systems. Islands with heterogeneous volcanic aquifers pose additional difficulties in assessing their water resources. This work proposes an approach for improving the hydrogeological characterization of heterogeneous volcanic aquifer systems by making use of recession coefficients from deep water gallery discharge. To demonstrate the usefulness of this approach, hydrographs and recession curves from groundwater discharge of 30 water galleries on La Palma (Canary Islands) were evaluated. This analysis allowed us to obtain the main hydrogeological parameters of a volcanic aquifer system, in terms of hydraulic diffusivity. A Maillet-Boussinesq model with an exponential decay law was adopted, according to field observations of drainage discharge. The alpha coefficients of recession values ranged between 10−3 and 4·10−4 day−1 and showed significant spatial correlation with insular geology. Additionally, hydraulic diffusivity values of island hydrogeological domains were obtained from recession coefficients using the Rorabaugh-Singh method. Weighted storage coefficients for volcanic materials were in the range of 3% to 7%, with an average transmissivity in the range of 15 to 150 m2·day−1. The methodology proposed has demonstrated its usefulness in coping with local uncertainty in hydraulic characterization of insular aquifers associated with volcanic heterogeneity. This is an improvement compared to standard pumping tests, thus providing hydraulic parameters prior to numerical analysis for water management planning.

Evaluation of Sublimation Kinetics for Phenolic Impregnated Carbon Ablator

Recession analyses of stagnation tests conducted in NASA Ames Research Center arcjet facilities with surface heat fluxes high enough for carbon char to sublimate are performed and compared with data. The axisymmetric recession computations are based on both the finite-rate gas–surface interaction model and the standard chemical equilibrium surface ablation model. The finite-rate gas–surface interaction model is studied using three different sets of sublimation coefficients, which are obtained through direct measurements. The standard chemical equilibrium ablation model is established through the tables. The ablating material used in these arcjet tests is phenolic impregnated carbon ablator. The prediction of axisymmetric surface recession is achieved by implicitly coupling a material thermal response code and a flow code. The material thermal response code used in this study is the Two-Dimensional Implicit Thermal-Response and Ablation program, which predicts charring material thermal response and shape change. The flow code solves the reacting Navier–Stokes equations using the data parallel line relaxation method. The recession predictions for three selected arcjet conditions with various base radii are presented. The effect of sticking coefficient for carbon sublimation on recession prediction is investigated, and the prediction discrepancy between finite-rate and chemical equilibrium models is examined and discussed in detail.

139-OR: Large-Scale Sex-Stratified Additive and Recessive GWAS Identifies Novel Large-Effect Variants and Improves Polygenic Prediction for Type 2 Diabetes

Introduction: Most genome-wide association studies (GWAS) of type 2 diabetes (T2D) assume an additive mode of inheritance and equal effects in men and women. These assumptions can preclude the discovery of variants with effects that are non-additive and/or sex-specific. Focused exploration of these effects may reveal novel genetic variation associated with T2D and improve the predictive performance of polygenic risk scores (PRS) . Methods: We performed a sex-stratified GWAS, using additive and recessive models for T2D, in individuals of European ancestry in the UK Biobank (UKBB) and Genetic Epidemiology Research on Aging cohort. We also generated sex-specific and non-sex-specific T2D PRSs, using PRS-CS, and assessed their performance in Mass General Brigham Biobank (MGBB) . Results: As the largest sex-stratified additive and recessive GWAS of T2D performed to date, this study included 30,625 cases and 223,442 controls. In the recessive analysis, we identified 7 novel variants, of which 1 was female-specific and 1 was male-specific. Among these variants, 4 were associated with over 10-fold increase in risk for T2D. The male-specific variant, rs35725476 (OR = 1.49, p = 3×10- 9) , is associated with higher expression of a long non-coding RNA in pancreatic islets (p = 3×10- 14) . In the additive analysis, we identified a novel female-specific protective variant, rs12109272 (OR = 0.91, p = 3×10-8) , which is associated with lower PCSK1 expression, lower risk of gestational diabetes (p = 1×10- 6) and lower fasting glucose (p = 6×10- 32) in independent cohorts. Finally, the sex-specific PRS outperformed the non-sex-specific PRS at predicting T2D in MGBB (AUC of 0.653 versus 0.643, p [of difference] = 2×10-4 in males; AUC of 0.674 versus 0.656, p [of difference] = 3×10-4 in females) . Conclusions: These findings demonstrate the value of non-additive and sex-stratified analyses for both variant discovery and improving polygenic prediction for T2D. Disclosure P.H.Schroeder: None. J.B.Cole: None. A.Leong: None. J.C.Florez: Consultant; AstraZeneca, Goldfinch Bio, Inc., Other Relationship; AstraZeneca, Merck & Co., Inc., Novo Nordisk. J.M.Mercader: None. Funding American Diabetes Association (1-19-ICTS-068) ; National Human Genome Research Institute (U01HG011723)

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.

Recession curve power-law exponent estimation: is there a perfect approach?

The coefficient (k) of (discharge at time t) power-law relationship is typically computed after fixing the value of the exponent (α). However, recession analysis poses the challenge of finding a suitable α for a basin. Although many recent studies have suggested to consider the median of the α distribution as the representative value, a purely logical reasoning has not been yet provided. In this study, we argue that there is no perfect approach to estimate α and that it should depend on the final objective. Recession flow prediction is considered as the objective in this study. We employ a model to predict recession discharge for 408 USGS basins for a wide range of α values. Considering Nash-Sutcliffe efficiency as the indicator of model performance, we observed that the optimum value of α is substantially lower than the α median for most of the basins. Overall, our study establishes that there is no single value of preferable for all practical purposes.