Recession Analysis Research Articles

Can storage-discharge characteristics of karst matrix system quantified through recession analysis be reliable?

Storage and subsequent release of water in matrix system is a key function of karst catchments that controlling baseflow variation. Hydrograph recession analysis is currently the economic way to assess storage-discharge characteristics broadly at the catchment scale. However, there is large uncertainty in the related quantification due to recession data extraction. This study, by combining recursive digital filters with different automatic extraction methods, slow flow recession segments were extracted for hydrograph recession analysis and the further dynamic matrix storage (DMS) and related recession time (RT) assessment. The diversity and consistence among DMS and RT derived from different recession extraction methods (REMs) were analyzed, using hydrometric data in 20 catchments in the Wujiang river Basin in southwest China. Results indicate that the estimates of DMS and RT remarkedly varied between different REMs, however the order in which they ranked was mostly consistent. Moreover, the relationships between the derived DMS and RT with catchment physical features that potentially control storage and release processes are mostly consistent. Larger DMS are strongly associated with catchments featured as lower soil saturated hydraulic conductivity and smoother hydrographs. Longer RT are mostly related with drier catchments characterized as less variation of elevation and lower soil saturated hydraulic conductivity. This study highlights not only the uncertainty in quantifying storage and accompanied release characteristics, but also the reliability of storage-discharge characteristics quantified through recession analysis in terms of catchment comparison. Considering the diversity among catchments, ensemble of multi-method estimates of DMS and RT can enhance our understanding of the storage-discharge processes in the karst matrix system.

Inherited infertility: Mapping loci associated with impaired female reproduction

Female infertility is a common and complex health problem affecting millions of women worldwide. While multiple factors can contribute to this condition, the underlying cause remains elusive in up to 15%-30% of affected individuals. In our large genome-wide association study (GWAS) of 22,849 women with infertility and 198,989 control individuals from the Finnish population cohort FinnGen, we unveil a landscape of genetic factors associated with the disorder. Our recessive analysis identified a low-frequency stop-gained mutation in TATA-box binding protein-like 2 (TBPL2; c.895A>T [p.Arg299Ter]; minor-allele frequency [MAF]= 1.2%) with an impact comparable to highly penetrant monogenic mutations (odds ratio [OR]= 650, p= 4.1×10-25). While previous studies have linked the orthologous gene to anovulation and sterility in knockout mice, the severe consequence of the p.Arg299Ter variant was evidenced by individuals carrying two copies of that variant having significantly fewer offspring (average of 0.16) compared to women belonging to the other genotype groups (average of 1.75 offspring, p= 1.4×10-15). Notably, all homozygous women who had given birth had received infertility therapy. Moreover, our age-stratified analyses identified three additional genome-wide significant loci. Two loci were associated with early-onset infertility (diagnosed before age 30), located near CHEK2 and within the major histocompatibility complex (MHC) region. The third locus, associated with late-onset infertility, had its lead SNP located in an intron of a long non-coding RNA (lncRNA) gene. Taken together, our data highlight the significance of rare recessive alleles in shaping female infertility risk. The results further provide evidence supporting specific age-dependent mechanisms underlying this complex disorder.

Hydrological dynamics of the shire river: Insights from baseflow and recession analysis

The Shire River, a vital waterway in Malawi, plays a pivotal role in the country's economy, serving as a source of electrical power and irrigation for surrounding communities. This study investigates the hydrological dynamics of the Shire River by examining its flow components and the influence of baseflow under varying climatic conditions. Through meticulous baseflow separation using the Eckhardt recursive digital filter and recession analysis, Lake Malawi emerges as the primary contributor to the river's baseflow, sustaining approximately 80%–82% of total flows annually of which about 78% is contributed during wet seasons. Moreover, an inverse relationship between baseflow and rainfall is observed, highlighting the significance of baseflow augmentation during drier periods. Baseflow analysis of wettest and driest years further elucidates precipitation's impact on baseflow fluctuations, emphasizing the intricate interplay between precipitation patterns and baseflow dynamics. Therefore, to ensure the sustainable management of the Shire River, it is crucial to implement policies that support the conservation of groundwater resources and the efficient allocation of water during varying climatic conditions. Overall, this study provides valuable insights into the hydrological processes of the Shire River, contributing to enhanced understanding and sustainable management of this essential water resource.

Прогнозирование характеристик половодья 2024 года на реках Ишим, Тобол и Урал

The Hydrometeorological Center of Russia has developed a system of methods for forecasting spring flood characteristics, which was implemented for eight stations on the Ishim, Tobol, and Ural rivers in 2024. Specific features of the methods are the simplicity of their obtaining and implementation for a particular river reach and a possibility of the fast correction of forecasts as the current information is available. The peak water level at the station was predicted based on its dependence on the peak water level at the upstream station. The date of the flood peak was predicted with account of the data of the peak at the upstream station and probable values of the travel time from the upstream gauge to the predicted downstream one. The date of the water level drop to the level of a severe event was forecasted taking into account the height and date of the flood peak using the calculated recession curve obtained by the statistical analysis of flood recessions in the previous years. The date of the water level drop to the level of an adverse event was predicted by extrapolating the flood recession observed during 10–15 days after the flood peak. The mean forecast lead time is 9 days. The forecasts were issued in a deterministic and probabilistic form and gave quite satisfactory results, which were used in the organization and implementation of measures to protect the population and economic facilities from the floods observed in 2024. The efficiency of the developed methods allows recommending them for use in predicting flood characteristics in various regions of Russia.

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>

Economic challenges and GDP dynamics in Ukraine from 1991 to 2023: Analysis of growth and recessions

Economic challenges and GDP dynamics in Ukraine from 1991 to 2023: Analysis of growth and recessions

Coercive and Noncoercive Mixed Generalized Complementarity Problems

Impressed with the very recent developments of noncoercive complementarity problems and the use of recession sets in complementarity problems, here, we discuss mixed generalized complementarity problems in Hausdorff topological vector spaces. We used the Tikhonov regularization procedure, as well as arguments from the recession analysis, to establish the existence of solutions for mixed generalized complementarity problems without coercivity assumptions in Banach spaces.

Repercussions of the Recent Economic Downturn on Birth Rates in Greece

A comprehensive analysis of historical economic recessions in developed nations confirms the significant impact of social and economic crises on fertility rates. Across different countries, these effects exhibit common characteristics, such as a modest influence on overall fertility rates, delays in the timing of first childbirth linked to later marriages or unions, and a correlation between unemployment and age-specific fertility. Countries with established family policies and robust social security systems tend to experience less pronounced changes in fertility behavior during economic crises. The recent economic downturn in Greece unfolded amidst unique social conditions, including increased female labor force participation, widespread use of reliable contraception allowing for delayed childbearing, and escalating social security and healthcare costs due to an aging population. These factors may influence reproductive decisions and exacerbate the adverse effects of the recession on fertility rates. This study utilizes the latest official data from Greece to examine the impact of the current economic crisis on fertility levels and their evolution over time.

Simple relational contracts and the dynamics of social capital

This article proposes a dynamic model to examine the structure of “simple” relational contracts, obeying realistic properties that can be easily understood and audited by both parties. In such relationships, the need to offer each supplier a large enough share of future business to deter cheating limits the number of relationships a buyer can sustain. Trade is thus restricted to durable relationships, a form of social capital. Nevertheless, exogenous stochastic shocks sometimes prevent suppliers from fulfilling their promises and relationships are constantly dissolving and later renewed. Moreover, the coming of a crisis, where stochastic shocks are more probable, can lead to the quick rupture of some relationships as there is less expected future business to incentivize all suppliers. New relationships can later be formed, but this takes time due to search frictions. This suggests new connections between the theory of relational contracting and the macroeconomic analysis of recessions.

Identifying baseflow source areas using remotely sensed and ground‐based hydrologic data

AbstractUnderstanding how rainfall and snowmelt influence baseflow, the groundwater‐fed component of streamflow, is essential for sound water resources management. Current approaches to understand the spatial couplings between these processes and baseflow are limited. The most commonly used methods include geochemical tracers and hydrologic models. A key limitation of the first is cost, while the second is limited by the need for simplifying assumptions. This study developed a data‐driven approach which leverages satellite Earth observation data and ground‐based data to assess the degree to which baseflow is influenced by upstream rainfall and snowmelt in California's Sierra Nevada. The procedure involved: (1) separation of baseflow from streamflow time series using a low‐pass filtering technique, (2) quantification of aquifer drainage timescales through baseflow recession analysis, (3) application of time series and information theory methods to identify the areas which have the greatest impacts on baseflow through both rainfall and snowmelt, and (4) characterization of the elevation zones which have a prevailing influence on baseflow. Results suggest that areas which have the strongest impact on baseflow through rainfall and snowmelt are not necessarily the areas which experience the highest annual rates of snowmelt or rainfall; snowmelt occurring in the 3000–3700 m elevation range was found to be the most important driver of baseflow.

Recession constants are non-stationary: Impacts of multi-annual drought on catchment recession behaviour and storage dynamics

Recession analysis is commonly adopted to characterise catchment behaviour, particularly in relation to the role of catchment storage in streamflow production. Recession techniques are data hungry and characterise average behaviour over long periods of recorded data, making them generally unsuitable in transient conditions. Nevertheless, in the context of multi-annual drought, sufficient data may be available to characterise temporal change by examining different subperiods. In this work, we assessed changes in recession behaviour of catchments affected by multi-annual drought in south-eastern Australia, focusing on how changes in recession patterns over time correlate to drought-induced shifts in annual rainfall-runoff relationships. We applied two methods, drawn from the vast methodological literature on recession analysis. They consider different information and entail different assumptions; one is based on average daily recession rates and the other on hourly recession slope plots. Despite the differences, their results are consistent: catchments that exhibited significant changes in rainfall-runoff relationship during the drought also display significantly steeper recessions during and after the drought. Subsequent investigation suggests that only a portion of the increases in recession speed are attributable to increased evaporative demand. Rather, they are likely caused by decreased connectivity between subsurface storage and catchment surface water and increased transmission losses through the streambed. With this work, we demonstrate the usefulness of recession analysis to investigate sources and effects of non-stationary behaviour, providing insights into the impacts of long-term drought on streamflow production and catchment storage dynamics.

Inferring heavy tails of flood distributions through hydrograph recession analysis

Abstract. Floods are often disastrous due to underestimation of the magnitude of rare events. Underestimation commonly happens when the magnitudes of floods follow a heavy-tailed distribution, but this behavior is not recognized and thus neglected for flood hazard assessment. In fact, identifying heavy-tailed flood behavior is challenging because of limited data records and the lack of physical support for currently used indices. We address these issues by deriving a new index of heavy-tailed flood behavior from a physically based description of streamflow dynamics. The proposed index, which is embodied by the hydrograph recession exponent, enables inferring heavy-tailed flood behavior from daily flow records, even of short length. We test the index in a large set of case studies across Germany encompassing a variety of climatic and physiographic settings. Our findings demonstrate that the new index enables reliable identification of cases with either heavy- or non-heavy-tailed flood behavior from daily flow records. Additionally, the index suitably estimates the severity of tail heaviness and ranks it across cases, achieving robust results even with short data records. The new index addresses the main limitations of currently used metrics, which lack physical support and require long data records to correctly identify tail behaviors, and provides valuable information on the tail behavior of flood distributions and the related flood hazard in river basins using commonly available discharge data.

Event‐Based Recession Analysis for Estimation of Basin‐Wide Characteristic Drainage Timescale and Groundwater Storage Trends

AbstractIn the absence of precipitation or any other recharge source, baseflow sustains natural surface water bodies like streams and rivers. It also indicates the quality and quantity of storage in the aquifers underlying the basin. Thus, identifying and extracting baseflow is critical for most hydrological studies, for example, estimating water budget, watershed characteristics, and groundwater surface‐water interactions. Most approaches for baseflow separation are based on recession hydrographs obtained solely from streamflow observations. A few studies have used the absence of precipitation as a filter for extracting baseflows for recession analysis, but not for estimating changes in groundwater storage. We demonstrate that an accurate estimation of drainage characteristic recession timescale 𝐾 for a linear storage‐discharge relationship should be based on carefully selected individual baseflow events extracted from streamflow observations complemented with precipitation observations. The drainage/recession timescale varies with time, even for a single basin, and hence, should not be generalized to a specific range for a wide range of basins. Estimations of groundwater storage trends and aquifer characteristics from streamflow and lumped analysis can be misleading. Event‐based recession analysis yields large variations in characteristic drainage timescales for a given catchment, which appears a more realistic representation of basin‐wide aquifer characteristics that are known to vary with antecedent climate. In the Rock River basin, we find contrasting trends in the estimated groundwater storages in two selected periods, which are then attributed to the contrasting trends in annual rainfall and evapotranspiration. The approach presented here would be beneficial in investigating aquifer characteristics and storage trends.

US Recession Analysis Based on Ordinary Least Squares Regression

The United States is the first developed economy that rebounded from the 2008 global financial. Theoretically, US economic cycle is influenced by several factors, this paper mainly focuses on the linkage of the US economic growth and The correlation between the unemployment rate and the U.S. federal funds rate. Between times with rising economic activity, known as expansions. Whereas, the periods of diminishing economic activity is known as recessions. The economic climate constantly shifts throughout time. Firstly, the rate of employment and the rate of federal funding are cointegrated. The findings of a regression analysis demonstrate that there is a negative concurrent correlation between the federal funds rate and unemployment rate. Secondly, in order to verify the linkage, through an in-depth review of the data, several interlinked and external factors behind the pattern of such correlation related to the variation on economic growth also need to be elaborated.

Baseflow: a MATLAB and GNU Octave package for baseflow recession analysis

baseflow: a MATLAB and GNU Octave package for baseflow recession analysis

A proposed composite Boussinesq equation for estimating baseflow recessions and storage-outflow relationship

Baseflow recession analysis characterizes the interactions between ground and surface water, and many studies have focused on estimating the mathematical form of the relationship and its parameters. The Boussinesq equations can estimate baseflow recession and storage-outflow relationship. However, these equations assume either linear or nonlinear behavior, depending on the streamflow conditions. The linear equation cannot represent the nonlinear behavior of the baseflow, and the nonlinear equation cannot fit the lowest discharge values. In addition, the nonlinear equation is oversimplified and does not include baseflow from groundwater storage below the stream’s water level. Therefore, we propose a composite Boussinesq equation that includes the linear portion of baseflow below the stream’s water level, includes a nonlinear storage, and fits low discharge values. The performance of the linear, nonlinear, and composite methods are assessed using 1561 USGS stream gaging stations across the contiguous US (CONUS). The regression constants, which represent the aquifer characteristics, are estimated for all gages using the three methods. Results show that the composite equation results in smaller errors for estimating baseflow recession events and the storage-outflow relationship. Finally, four different variables, precipitation, evapotranspiration, average discharge, and drainage area, were analyzed to find the relationship between the watershed conditions and baseflow analysis in terms of the regression constants and the relative errors of the best fit curves. Results show that the nonlinearity increases in drier regions, and the relative error for all three methods increases in wet climate regions with high precipitation and evapotranspiration. The composite equation is a well-performing alternative to linear or nonlinear equations across all basin characteristics, allowing for estimating the full range of baseflow behavior.

A Nonlinear Recession Model for Horizontal Aquifers

AbstractRecession analysis is a powerful tool for determining the hydraulic characteristics of a riparian aquifer. The basis of the method relies on two analytical solutions of the nonlinear Boussinesq equation: one applies to a uniform water table profile in a semi‐infinite aquifer while the other pertains to a hypothetical initial profile in a finite aquifer. Both solutions assume that the water depth in the adjoining stream is negligible. In the present work, a nonlinear solution of the one‐dimensional Boussinesq equation is derived using the traveling wave approximation. The solution better represents the field conditions as it pertains to a uniform water table profile in a finite aquifer that is adjoining a stream with a non‐zero water level. It allows the derivation of simple algebraic expressions for the recession of the water table, the flow rate, and the associated drainage volume. Approximations relating the discharge rate to the outflow volume were also obtained for practical implementation in the estimation of the hydraulic parameters of the aquifer. A comparison of the proposed methodology of parameter estimation with the standard method of recession analysis showed that the present approach is superior as it avoids the pitfalls associated with the classical method, especially when daily discharge values with inherent observational errors are used. Application of the recession model to real‐world cases demonstrates its effectiveness in the estimation of the catchment‐scale hydraulic conductivity and drainable porosity.

Evaluation of the storage and evapotranspiration terms of the water budget for an agricultural watershed using local and remote-sensing measurements

The understanding of the water budget components, especially evapotranspiration and the amount of water stored in the watershed, is key to water resources management and hydrological modeling in general. In this work, we present the analysis of 13 hydrological years’ measurements of discharge, precipitation, evapotranspiration, and soil moisture over two agricultural watersheds in Eastern Nebraska, United States. These data were used to investigate several facets of the hydrological water budget: water- and energy-balance closures, water budget residues (here, the water budget residue is the remaining imbalance after all water budget terms have been considered; see Eq. (13)), storage in the unsaturated and saturated soil layers, and the evapotranspiration derived from the water budget. Remote-sensing data were also used to assess the spatial variability of net radiation and soil moisture over the basins. Adjusting the eddy-covariance measured evapotranspiration to close the energy budget agreed, in the long term, with the water budget evapotranspiration estimates. The evapotranspiration estimated with storage calculated with soil moisture measurements and recession analysis presented the best agreement with the (adjusted) measured evapotranspiration, but considering only either the vadose zone or the saturated zone for storage estimates still produced reasonable results. We found considerable variability in soil moisture over the basins in the final period of the growing season, coinciding with the time of the year when water budget residues were largest.

Revisiting the Origins of the Power‐Law Analysis for the Assessment of Concentration‐Discharge Relationships

AbstractConcentration‐discharge (C‐Q) relationships are frequently used to understand the controls on material export from watersheds. These analyses often use a log‐log power‐law function (C = aQb) to determine the relationship between C and Q. Use of the power‐law in C‐Q analyses dates to two seminal papers by Francis Hall (1970, https://doi.org/10.1029/WR006i003p00845) and Francis Hall (1971, https://doi.org/10.1029/WR007i003p00591), where he compared six increasingly complex hydrological models, concluding the power‐law had the greatest explanatory power. Hall's analyses and conclusions, however, were based on a limited data set, with assumptions regarding water volume and storage, and used simple model selection criteria. While the power‐law is applied widely, it has not been rigorously tested and evaluated in over 50 years. We reexamined Hall's original models across time scales using 8 years of high‐frequency and weekly specific conductance data and evaluated model performance using more sophisticated model selection criteria. While we found the power‐law analysis remains one of the best performing models, other models performed equally as well including the log‐linear functional form. Model performance was similar at the sub‐daily to weekly scale but varied with sampling method. More complex models performed poorly relative to simpler models and tended to underpredict concentration at flow extremes due to constraints in fitting model parameters to the observed data. While we conclude, based on the data analyzed here, that the power‐law remains a suitable model for C‐Q analyses, opportunities exist to refine and differentiate among C‐Q models based on underlying assumptions of data distribution, recession analyses, and for applying models to reactive solutes.

Estimation of specific yield in bedrock near-surface zone of hilly watersheds by examining the relationship between base runoff, storage and groundwater level

A catchment-scale method for estimation of specific yield (Sy) in the zone of groundwater level fluctuation is proposed. It is applicable to hilly watersheds, where deep groundwater discharge – not drained by local streams as baseflow – is small and can be neglected. Therefore, it is mostly employable for bedrock flow systems, dominated by shallow unconfined fractured rock aquifers. Method provides an estimate of specific yield (Sy) by combined analysis of streamflow recession, storage/runoff relationship and groundwater level fluctuation (Q-S-H). For groundwater storage (S) values evaluation, river discharge (Q) master recession curves are constructed and interpreted. The method produces as more reliable results as number of groundwater level observation sites increases. As example, it is demonstrated at the Levočský potok Brook watershed (Western Carpathians, Slovakia), built by fracture porosity dominated Paleogene sediments. Estimated characteristic Sy value is from the interval 0.001–0.002 and 0.002–0.005 for low and medium storage/runoff conditions – or bottom and middle part of GWL fluctuation zone – respectively.