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Stochastic Time-Variable Rainfall-Runoff Modeling
... The need for standardized application of proven methods was highlighted in hydrology by Ladson et al. (2013) when reviewing the widely used Lyne and Hollick (1979) baseflow index. The same observation can be made in karst hydrogeology for the application of signal processing methods, the use of a probability plot to interpret breakpoints in the cumulative distribution function of karst spring flow, or to analyze recession and baseflow dynamics in karst spring hydrographs. ...
... A lot of baseflow separation methods exist in the literature to compute the BFI. Recently, Ladson et al. (2013) proposed a standard approach using the Lyne and Hollick (1979) digital filter. The approach used a reflection of the time series of 30 days to address "warm up" issues, and up to 9 passes of the digital filter with a filter coefficient ranging from 0.9 to 0.98. ...
Karst aquifers are complex hydrogeological systems that require numerous in-situ measurements of hydrological and physico-chemical parameters to characterize transfer processes from the recharge area to the karst spring. Numerous graphical, statistical or signal processing methods have been developed for decades to interpret these measurements, but there is no simple and standardized tool that can be used for this purpose, which is necessary for a rigorous comparison of results between case studies. This Technical Note presents XLKarst, which has been developed to provide a simple and easy-to-use tool to process a selection of proven methods that characterize the functioning of karst systems. This tool allows (i) time series analysis based on correlation and spectral analysis and, for flow measurements, the use of other statistics and base flow separation, (ii) calculation of the cumulative distribution function to build a spring flow probability plot, and (iii) analysis of spring flow recession and expression of the results in a karst system classification scheme. These methods are first described by providing the key elements of their use and interpretation in the scientific literature. Then, an application to the Fontaine de Nîmes karst system (southern France) is used to highlight the complementarity of the methods proposed by XLKarst to describe the hydrodynamic behavior of a karst system based on daily data of rainfall and discharge over 22 years.
... Direct runoff, also known as quick flow and storm runoff, is separated from the baseflow using a "one-parameter recession digital filter" in BFI+ software (Chapman and Maxwell, 1996;Lyne and Hollick, 1979) and illustrated in Fig. 2. ...
The hydrological cycle comprises several components, including precipitation, infiltration, evaporation, transpiration, and runoff, with the latter being a vital part of the cycle that connects precipitation to bodies of water. modelling is necessary due to limitations in standard hydrological measurement techniques, providing a better understanding of hydrological systems that were previously inaccessible, and making it essential for water resource management. Event-based rainfall-runoff modelling is a prevalent method due to its simplicity, but conventional models are limited in accounting for antecedent conditions. In order to tackle this problem, pre-event baseflow is proposed as an indicator for assessing catchment conditions for flood forecasting. This study employs a novel event-based rainfall-runoff equation that incorporates rainfall characteristics, improving hydrological modelling accuracy, and outperforms conventional models in predicting direct flow in most case studies. Overall, the study demonstrates the potential for the new equation to improve hydrological modelling accuracy and aid in a wide range of applications such as flood prediction, water resources management, and planning.
... Baseflow separation is required to investigate whether any difference exists in the baseflow/quick flow between the rainy season and non-rainy season during the years 1960-2017. A recursive baseflow separation technique using a one parameter signal processing filter (Lyne and Hollick 1979) was applied to separate daily streamflow data. To conduct this analysis, the R package "EcoHydRology" was utilized for its straightforward implementation (Fuka et al. 2018). ...
This study examines potential changes in the summer rainy season of a watershed, using a cumulative rainy deficit curve to define the onset and demise of the rainy season. Historical daily rainfall data from 41 gauge stations within the Xiang River Basin in 1960–2017 were analyzed. Results indicate that the climatology of the onset date is Julian day 72 and the climatology of the demise date is Julian day 236. There is, however, a difference between the two segments, e.g., 1960–1989 and 1990–2017. The median rainy season duration in 1990–2017 is 138 days. This is larger than that of 1960–1989, which is 120 days. There is a statistically significant correlation between the onset date of the rainy season and the duration of the rainy season. Daily soil moisture estimates were derived from satellite remote sensing data. The averaged soil moisture during the rainy season is much higher than that of the non-rainy season. To examine the difference in quick flow between rainy and non-rainy season, baseflow separation was applied to daily streamflow time series. Result reveals the median of weekly quick flow in rainy season is 1316 cms, which is 5.7 times that of the weekly quick flow in non-rainy season. Different mechanisms that drive spring and summer rainfall, as well as potential use of the results in water management, are discussed.
... s − 1 ), the crossing of which signals the occurrence of a flood (Grangeon et al., 2017). The method of Lyne and Hollick (1979) was used to extract runoff from the total flow. This procedure, validated by visual inspection of the hydrographs, resulted in the same number of floods being individualized for each scenario (887). ...
Runoff and soil erosion are very pronounced in the Western European Loess Belt. In this study, the distributed physically-based model CLiDE is calibrated, validated, and applied to a catchment of this area (Dun, NW, France) to assess the hydro-sedimentary impacts of climate change scenarios. Despite considerable progress over the last decade in the study of runoff and soil erosion in the context of climate change, the effects of changes in the temporal variability of precipitation remain poorly understood, especially at the scale of a river basin. To examine these relationships more closely, we developed a stochastic weather generator to individually adjust the components that structure the temporal variability of rainfall. The climate scenarios considered represent projections to the year 2100 of the temporal variability of rainfall over NW Europe. The scenarios are based on historical daily rainfall records (1990-2012) and 4 exploratory assumptions: a 50 % decrease in the interannual rainfall regime (scenario 6yD), a 100 % increase in the interannual rainfall regime (scenario 6yI), a 50 % increase in the seasonal rainfall regime (scenario 1yI) and a 50 % increase in the synoptic rainfall regime (scenario 3dI). Simulated daily water and sediment discharges and erosion/deposition maps for each scenario are compared to those simulated for the situation without changes in rainfall. The time series were aggregated over different time intervals to allow for a multi-scale analysis of the differences. The results indicate that the model provides a satisfactory prediction of the catchment's water and sediment discharges, especially over the calibration period. Increased climate variability, whether on a synoptic (3dI), seasonal (1yI) or interannual (6yI) scale, leads to increased runoff and erosion. Increasing the synoptic rainfall variability (3dI) leads to the largest increase in mean annual runoff and erosion. Only the reduction of the interannual rainfall variability (6yD) provokes the decrease of these values.
Artificial intelligence (AI) tools, such as ChatGPT, have already changed the way that many industries operate. Much of the recent discussion surrounding AI tools has focused on their ability to generate text and the associated ethical concerns. Recent developments with the ChatGPT Code Interpreter, now called the Advanced Data Analysis plugin, demonstrate that AI's growing abilities in the generation, translation and adaptation of computer scripts also offer significant potential to change the way that data analyses are conducted. We demonstrate the new capabilities of ChatGPT, providing four worked examples of prompt‐based generation of Python scripts to analyse hydrological datasets. Our examples include (1) converting R functions to Python to perform baseflow separation, (2) converting MATLAB functions to Python to ‘web scrape’ temperature data, (3) creating a correlation matrix of major ion groundwater data, and (4) presenting boxplots of river flow data. We discuss the significant opportunities, current limitations and concerns relating to the use of ChatGPT in hydrological data analyses.
Baseflow is a critical component of streamflow, as it maintains flow during meteorological drought. However, our understanding of baseflow response to meteorological droughts is limited. In this study, we presented a flexible approach for detecting precipitation and baseflow droughts and their corresponding recovery. Using this framework, we analyzed data from 358 anthropogenically unaffected catchments to characterize the droughts and recovery properties of baseflow across the United States. Results showed that baseflow droughts were more severe than the precipitation droughts, with duration ranging between 9-104 months. There were delayed responses of baseflow to precipitation droughts, showing longer-lasting effects up to 41 months after the end of precipitation droughts. Our analysis also revealed that baseflow drought is controlled by climate and hydrologic responses of a catchment, whereas baseflow recovery primarily depends on post-drought climate conditions. Furthermore, the aridity index can differentiate the spatial patterns of baseflow responses to precipitation droughts. Decadal changes in baseflow droughts revealed that baseflow droughts have become more severe and prolonged due in part to the rise in temperature highlighting the impacts of climate change on baseflow in the mild temperate zone. Overall, this study provides comprehensive insights into baseflow drought detection and its response to precipitation droughts and underscores the importance of these processes for sustainable water resource management.
Runoff and baseflow are two hydrological elements most closely involved in water-resource management. Defining the response of runoff/baseflow to meteorological drought (MD) is helpful for designing precise drought resisting measures. Thus, Pearson correlation coefficients and mutual information scores between runoff/baseflow and MD in five sub-basins of the Weihe River Basin (WRB) were estimated on a weekly scale, and the best response times of runoff/baseflow to MD on annual and calendar months were determined according to the maximum degree of response. Furthermore, the spatial and seasonal differences in response characteristics in the WRB were discussed and the baseflow index (BFI) was introduced to further explain the propagation process of MD to runoff/baseflow. The results showed that (1) in addition to the response time, the transition sequences of MD propagating to runoff and baseflow varied across basins due to the specific basin properties; (2) Response time of runoff to MD was related to BFI value and showed significant seasonality and hydrological periodicity. In summer and autumn (wet season), the response was faster and stronger, whereas the opposite occurred in winter and spring (normal/dry season); (3) BFI values indicated the main path of drought propagation, explaining the variation in response time between basins and seasons; hence, it can be used to simply and effectively determine the propagation speed of MD to runoff. This study clarified the response characteristics of the runoff process to MD and enhanced our understanding of the drought propagation process, which is crucial for mitigating and managing drought-related hazards.
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