Spatial and temporal variability of event runoff characteristics in a small agricultural catchment

Chen, Xiaofei; Parajka, Juraj; Széles, Borbála; Strauß, Peter; Blöschl, Günter

doi:10.1080/02626667.2020.1798451

Cited by 12 publications

(18 citation statements)

References 32 publications

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“…The Gail catchment with 51% area generated runoff in Karst has the highest median Tc (40 h) in January/February and the lowest (6 h) in July/August. Such difference is likely connected to the seasonal catchment storage variation as discussed by Chen et al and Patnaik et al [5,37]. The largest event peaks are observed in all catchments in the summer months, and the smallest peaks tend to occur in winter.…”

Section: Resultsmentioning

confidence: 84%

“…If no such point in the predefined time window is found, a higher limit for minimum direct runoff is allowed (stepwise increased from 1% to 40%). The end of an event is found in an analog way by searching forwards [5].…”

Section: Estimation Of Event Runoff Characteristicsmentioning

confidence: 99%

“…Runoff event peaks are identified, if the ratio of direct runoff and baseflow at time t is larger than a threshold value of parameter qdrat and there was no larger flow in the previous and following imax (h) time steps. The parameters qdrat and imax are chosen in this study to be 2 and 12 h, respectively, based on sensitivity analyses [1,5]. Storage parameter k 2 (h) used to determine runoff recession is chosen to be 5 h for Dornbirnerach and 7 h for Wimitzbach, Perschling, and Gail.…”

Section: Estimation Of Event Runoff Characteristicsmentioning

confidence: 99%

“…We assume a constant ratio of liquid to solid precipitation for each day from which liquid hourly precipitation is estimated. For the temporal pattern of snowmelt during a day, a truncated cosine statistical distribution is applied, where snowmelt starts at 09:00, the maximum occurs at 15:00 and snowmelt ceases at 21:00 [5]. Finally, hourly precipitation in different catchments is calculated by removing snow and adding melt to rainfall in each time step.…”

Section: Estimation Of Event Runoff Characteristicsmentioning

confidence: 99%

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Impact of Climate and Geology on Event Runoff Characteristics at the Regional Scale

Chen

Parajka

Széles

et al. 2020

Water

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The dynamics of flood event characteristics, such as the runoff coefficient and the recession time constant, differ in time and space, due to differences in climate, geology, and runoff generation mechanisms. This study examines the variability of event runoff characteristics and relates them to climatic and hydro-geological characteristics available at the regional scale. The main focus is to examine the role of rainfall patterns (i.e., event precipitation volume, precipitation intensity, and antecedent precipitation) and runoff regime (i.e., initial flow before runoff event and event duration) characteristics on the seasonal dynamics of runoff response. The analysis is performed in four small Austrian catchments representing different hydro-geological settings obtained by field mapping. The results are based on an analysis of 982 runoff events identified from hourly measurements of streamflow and precipitation in the period 2002 to 2013. The results show that larger event runoff coefficients and flow peaks are estimated in catchments with high mean annual precipitation than in drier catchments. In contrast to some previous studies, the results show only poor relation between antecedent precipitation (as an index of catchment wetness) and event runoff response. The initial flow is found to be the main factor influencing the magnitude of runoff coefficient and event peaks in all analyzed catchments and geological settings. The recession time constant tends to be inversely related to the maximum event precipitation intensity, with an exception for one catchment (Wimitzbach), which is characterized by the largest proportion of deep interflow contribution to runoff. The analysis of the runoff response by different event types indicates that runoff coefficients and recession time constants are the largest for snowmelt runoff events.

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Section: Resultsmentioning

confidence: 84%

Section: Estimation Of Event Runoff Characteristicsmentioning

confidence: 99%

Section: Estimation Of Event Runoff Characteristicsmentioning

confidence: 99%

Section: Estimation Of Event Runoff Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

Impact of Climate and Geology on Event Runoff Characteristics at the Regional Scale

Chen

Parajka

Széles

et al. 2020

Water

Self Cite

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“…However, the relationship of rainfall with atmospheric variables responsible for rainfall is often highly intriguing and cannot be represented using generally used statistical methods. Machine learning (ML) algorithms are often used for developing complex regression analysis [23][24][25]. erefore, the development of ML-based physical-empirical forecasting models has grown very fast in recent years.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Jabbar

Zahra

et al. 2021

Complexity

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Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.

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Research on coupled socio‐ecohydrological systems—implementing a highly integrative and interdisciplinary research agenda in the Doctoral School “Human River Systems in the 21st Century (HR21)”

Hein,

Auhser,

Byekwaso

et al. 2023

River Research & Apps

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The access to an adequate quantity and quality of water is vital to sustain healthy ecosystems and human socioeconomic development. However, the shift from an agrarian, solar energy based to an industrialized, fossil fuel‐based socio‐metabolic regime has put natural water resources under stress and led to dramatic transformations of riverine landscapes in the Anthropocene. Thus, we need interdisciplinary research approaches that integrate the knowledge and methods from ecology, humanities, and engineering. The Doctoral School “Human River Systems in the 21st Century” (HR21) studies riverine landscapes as coupled socio‐ecohydrological systems (SEHS) within four research clusters, that is, connectivity, metabolism, vulnerability, and governance. HR21 analyzes the transformation processes and coevolution of nature and society in rivers and their response to future environmental, social, cultural, and economic drivers of change. HR21 aims to improve the understanding of the coupling of the socioeconomic, ecological, and hydrological systems within industrialized riverine landscapes to support their urgent, more sustainable transformation. To discuss how this agenda can be implemented, five different HR21 PhD projects are presented, clearly routed in one scientific domain, applying a combination of the research clusters and addressing various topics: (i) hydropower effects in high alpine river ecosystems, (ii) wastewater impacts on carbon storage and greenhouse gas fluxes in tropical wetlands, (iii) the nutrient retention and ecosystem service potential of floodplains in large river catchments, and the influence of hydrometeorological variables on (iv) the runoff response and on (v) the transit time distribution of runoff, both impacting water resource management at the basin level. These examples highlight how disciplinary PhD research can be framed within an interdisciplinary research agenda to coupled SEHS.

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Spatial and temporal variability of event runoff characteristics in a small agricultural catchment

Cited by 12 publications

References 32 publications

Impact of Climate and Geology on Event Runoff Characteristics at the Regional Scale

Impact of Climate and Geology on Event Runoff Characteristics at the Regional Scale

Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Research on coupled socio‐ecohydrological systems—implementing a highly integrative and interdisciplinary research agenda in the Doctoral School “Human River Systems in the 21st Century (HR21)”

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