Apportioning non-linearity in conceptual rainfall–runoff models: examples from upland UK catchments

2013 ◽  
Vol 44 (6) ◽  
pp. 965-981 ◽  
Author(s):  
Neil McIntyre
Keyword(s):  
Objective Function ◽  
Time Resolution ◽  
Response Times ◽  
Generation Process ◽  
Rainfall Runoff ◽  
Flow Response ◽  
Flow Generation ◽  
Non Linear ◽  
Model Equations ◽  
Linear Loss

Rainfall–runoff modellers distinguish between flow generation and flow routing processes, and many models treat the two types of process independently. These models commonly assume that the dominant non-linearity in the rainfall–flow response resides in the flow generation process. This paper revisits three upland UK catchments where such an assumption has previously been made and explores the apportioning of non-linearity, its identifiability and how it is affected by catchment type, season, data time-resolution, objective function and model equations. The catchments showed stronger routing non-linearity than expected and comparatively little non-linearity in flow generation both in wet winter periods and in mixed wet-dry summer periods, although in one catchment this result was sensitive to a modification of the model equations. Aggregating data to time resolutions approaching the response times of the catchments makes the flow generation appear more non-linear that it actually is, less so if performance is assessed using log-transformed flows. In cases, using conceptually distinct models achieved similar Nash–Sutcliffe efficiency (NSE) performances; however, using a single non-linear routing function with a linear or near-linear loss model was considered the most efficient overall. Using this model, NSE values of up to 0.99 were obtained.

scholarly journals Impacts of Antecedent Soil moisture on the Rainfall–Runoff Transformation Process Based on High-Resolution Observations in Soil Tank Experiments

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 296 ◽  
Author(s):  
Shuang Song ◽  
Wen Wang
Keyword(s):  
Soil Moisture ◽  
Overland Flow ◽  
Response Times ◽  
Hysteretic Behavior ◽  
Rainfall Runoff ◽  
Moisture Index ◽  
Antecedent Soil Moisture ◽  
Non Linear ◽  
Subsurface Runoff ◽  
The Impact

An experimental soil tank (12 m long × 1.5 m wide × 1.5m deep) equipped with a spatially distributed instrument network was designed to conduct the artificial rainfall–runoff experiments. Soil moisture (SM), precipitation, surface runoff (SR) and subsurface runoff (SSR) were continuously monitored. A total of 32 rainfall–runoff events were analyzed to investigate the non-linear patterns of rainfall–runoff response and estimate the impact of antecedent soil moisture (ASM) on runoff formation. Results suggested that ASM had a significant impact on runoff at this plot scale, and a moisture threshold-like value which was close to field capacity existed in the relationship between soil water content and event-based runoff coefficient (φe), SSR and SSR/SR. A non-linear relationship between antecedent soil moisture index (ASI) that represented the initial storage capacity of the soil tank and total runoff was also observed. Response times of SR and SM to rainfall showed a marked variability under different conditions. Under wet conditions, SM at 10 cm started to increase prior to SR on average, whereas it responds slower than SR under dry conditions due to the effect of water repellency. The predominant contributor to SR generation for all events is the Hortonian overland flow (HOF). There is a hysteretic behavior between subsurface runoff flow and soil moisture with a switch in the hysteretic loop direction based on the wetness conditions prior to the event.

High Resolution Measurements of Pollution Wash-Off from an Asphalt Surface

1992 ◽  
Vol 23 (4) ◽  
pp. 245-256 ◽  
Author(s):  
Å. Spångberg ◽  
J. Niemczynowicz
Keyword(s):  
Water Quality ◽  
High Resolution ◽  
Time Resolution ◽  
Quality Data ◽  
Rainfall Runoff ◽  
Water Quality Data ◽  
Urban Catchment ◽  
Complex Process ◽  
Surface Measurements ◽  
Fine Time

The paper describes a measurement project aiming at delivering water quality data with the very fine time resolution necessary to discover deterministic elements of the complex process of pollution wash-off from an urban surface. Measurements of rainfall, runoff, turbidity, pH, conductivity and temperature with 10 sec time resolution were performed on a simple urban catchment, i.e. a single impermeable 270 m2 surface drained by one inlet. The paper presents data collection and some preliminary results.

scholarly journals Rainfall-runoff relationships at event scale in western Mediterranean ephemeral streams

2021 ◽  
Author(s):  
Roberto Serrano-Notivoli ◽  
Alberto Martínez-Salvador ◽  
Rafael García-Lorenzo ◽  
David Espín-Sánchez ◽  
Carmelo Conesa-García
Keyword(s):  
Western Mediterranean ◽  
Additive Models ◽  
Future Climate Change ◽  
Ephemeral Streams ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Event Scale ◽  
Rainfall Events ◽  
Flow Generation ◽  
Terrain Characteristics

Abstract. Ephemeral streams are highly dependent on rainfall and terrain characteristics and, therefore, very sensitive to minor changes in these environments. Western Mediterranean area exhibits a highly irregular precipitation regime with a great variety of rainfall events driving the flow generation on intermittent watercourses, and future climate change scenarios depict a lower magnitude and higher intensity of precipitation in this area, potentially leading to severe changes in flows. We explored the rainfall-runoff relationships in two semiarid watersheds in southern Spain (Algeciras and Upper Mula) to model the different types of rainfall events required to generate new flow in both intermittent streams. We used a nonlinear approach through Generalized Additive Models at event scale in terms of magnitude, duration, and intensity, contextualizing resulting thresholds in a long-term perspective through the calculation of return periods. Results showed that the average ~ 1.2-day and <1.5 mm event was not enough to create new flows. At least a 4-day event ranging from 4 to 20 mm, depending on the watershed was needed to ensure new flow at a high probability (95 %). While these thresholds represented low return periods (from 4 to 10 years), the great irregularity of annual precipitation and rainfall characteristics, makes prediction highly uncertain. Almost a third part of the rainfall events resulted in similar or lower flow than previous day, emphasizing the importance of lithological and terrain characteristics that lead to differences in flow generation between the watersheds.

scholarly journals Neural network emulation of a rainfall-runoff model

2007 ◽  
Vol 4 (1) ◽  
pp. 287-326 ◽  
Author(s):  
R. J. Abrahart ◽  
L. M. See
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Network Models ◽  
Exact Equation ◽  
Rainfall Runoff ◽  
Neural Network Models ◽  
Linear Relationships ◽  
Non Linear ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The modeling process was based on a set of uniform random distributions. The cloning operation facilitated a direct comparison with the exact equation-based relationship. It also provided broader information about the power of a neural network to emulate existing equations and model non-linear relationships. Several comparisons with least squares multiple linear regression were performed. The first experiment involved a direct emulation of the Xinanjiang Rainfall-Runoff Model. The next two experiments were designed to assess the competencies of two neural solutions that were developed on a reduced number of inputs. This involved the omission and conflation of previous inputs. The final experiment used derived variables to model intrinsic but otherwise concealed internal relationships that are of hydrological interest. Two recent studies have suggested that neural solutions offer no worthwhile improvements in comparison to traditional weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. Yet such fundamental properties are intrinsic aspects of catchment processes that cannot be excluded or ignored. The results from the four experiments that are reported in this paper are used to challenge the interpretations from these two earlier studies and thus further the debate with regards to the appropriateness of neural networks for hydrological modelling.

Hybrid Evolutionary Optimization Algorithms

2015 ◽  
pp. 1072-1107
Author(s):  
Pandian Vasant
Keyword(s):  
Objective Function ◽  
Production Planning ◽  
Membership Function ◽  
Industrial Production ◽  
Manufacturing Sector ◽  
Evolutionary Optimization ◽  
Industrial Manufacturing ◽  
Non Linear ◽  
Evolutionary Optimization Algorithms ◽  
S Curve

The novel industrial manufacturing sector inevitably faces problems of uncertainty in various aspects such as raw material availability, human resource availability, processing capability and constraints and limitations imposed by the marketing department. These problems have to be solved by a methodology which takes care of such unexpected information. As the analyst faces this man made chaotic and due to natural disaster problems, the decision maker and the implementer have to work collaboratively with the analyst for taking up a decision on an innovative strategy for implementation. Such complex problems of vagueness and uncertainty can be handled by the hybrid evolutionary intelligence algorithms. In this chapter, a new hybrid evolutionary optimization based methodology using a specific non-linear membership function, named as modified S-curve membership function, is proposed. The modified S-curve membership function is first formulated and its flexibility in taking up vagueness in parameters is established by an analytical approach. This membership function is applied for its useful performance through industrial production problems by employing hybrid evolutionary optimization algorithms. The novelty and the originality of this non-linear S-curve membership function are further established using a real life industrial production planning of an industrial manufacturing sector. The unit produces 8 products using 8 raw materials, mixed in various proportions by 9 different processes under 29 constraints. This complex problem has a cubic non-linear objective function. Comprehensive solutions to a non-linear real world objective function are achieved thus establishing the usefulness of the realistic membership function for decision making in industrial production planning.

scholarly journals A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2324
Author(s):  
Peng Lin ◽  
Pengfei Shi ◽  
Tao Yang ◽  
Chong-Yu Xu ◽  
Zhenya Li ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Mixed Model ◽  
Soil Surface ◽  
Extreme Rainfall ◽  
Distributed Model ◽  
Generation Process ◽  
Efficiency Coefficient ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Runoff Model

Hydrological models for regions characterized by complex runoff generation process been suffer from a great weakness. A delicate hydrological balance triggered by prolonged wet or dry underlying condition and variable extreme rainfall makes the rainfall-runoff process difficult to simulate with traditional models. To this end, this study develops a novel vertically mixed model for complex runoff estimation that considers both the runoff generation in excess of infiltration at soil surface and that on excess of storage capacity at subsurface. Different from traditional models, the model is first coupled through a statistical approach proposed in this study, which considers the spatial heterogeneity of water transport and runoff generation. The model has the advantage of distributed model to describe spatial heterogeneity and the merits of lumped conceptual model to conveniently and accurately forecast flood. The model is tested through comparison with other four models in three catchments in China. The Nash–Sutcliffe efficiency coefficient and the ratio of qualified results increase obviously. Results show that the model performs well in simulating various floods, providing a beneficial means to simulate floods in regions with complex runoff generation process.

scholarly journals A Bayesian approach to flow record infilling and extension for reservoir design

1999 ◽  
Vol 3 (4) ◽  
pp. 491-503 ◽  
Author(s):  
D. A. Jones ◽  
K. J. Sene
Keyword(s):  
Bayesian Approach ◽  
Sampling Procedure ◽  
Rainfall Data ◽  
Generation Process ◽  
Additional Information ◽  
Regional Flow ◽  
Flow Generation ◽  
Reservoir Design ◽  
New Procedures

Abstract. A Bayesian approach is described for dealing with the problem of infilling and generating stochastic flow sequences using rainfall data to guide the flow generation process, and including bounded (censored) observed flow and rainfall data to provide additional information. Solutions are obtained using a Gibbs sampling procedure. Particular problems discussed include developing new procedures for fitting transformations when bounded values are available, coping with additional information in the form of values, or bounds, for totals of flows across several sites, and developing relationships between annual flow and rainfall data. Examples are shown of both infilled values of unknown past river flows, with assessment of uncertainty, and realisations of flows representative of what might occur in the future. Several procedures for validating the model output are described and the central estimates of flows, taken as a surrogate for historical observed flows, are compared with long term regional flow and rainfall data.

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