On runoff generation and the distribution of storage deficits

2013 ◽  
Vol 44 (4) ◽  
pp. 673-689
Author(s):  
A. Wood ◽  
K. J. Beven
Keyword(s):  
Distribution Function ◽  
Beta Function ◽  
Water Levels ◽  
Distribution Model ◽  
Slow Pathway ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Small Advantage ◽  
Better Than

A number of hydrological models use a distribution function to develop the non-linear rainfall–runoff catchment response. In this study the beta function is applied to represent a distribution of soil moisture storages in conjunction with a fast and slow pathway routing. The BETA3 and BETA4 modules, presented in this paper, have a distribution of discrete storage elements that have variable and redistributed water levels at each timestep. The PDM-BETA5 is an analytical solution with a similar structure to the commonly used probability distribution model (PDM). Model testing was performed on three catchments in the Northern Pennine region in England. The performances of the BETA models were compared with a commonly used formulation of the PDM. The BETA models performed marginally better than the PDM in calibration and parameter estimation was better with the BETA models than for the PDM. The BETA models had a small advantage in validation on the hydrologically fast responding test catchments.

scholarly journals A modified Xinanjiang model and its application in northern China

2005 ◽  
Vol 36 (2) ◽  
pp. 175-192 ◽  
Author(s):  
Caihong Hu ◽  
Shenglian Guo ◽  
Lihua Xiong ◽  
Dingzhi Peng
Keyword(s):  
Arid Regions ◽  
Southern China ◽  
Northern China ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Runoff Simulation ◽  
Xinanjiang Model ◽  
Infiltration Excess ◽  
Semi Arid ◽  
Better Than

The Xinanjiang model has been widely used in the humid regions in southern China as a basic tool for rainfall–runoff simulation, flood forecasting and water resources planning and management. However, its performance in the arid and semi-arid regions of northern China is usually not so good as in the humid regions. A modified Xinanjiang model, in which runoff generation in the watershed is based on both infiltration excess and saturation excess runoff mechanisms, is presented and discussed. Three different watersheds are selected for assessing and comparing the performance of the Xinanjiang model, the modified Xinanjiang model, the VIC model and the TOPMODEL in rainfall–runoff simulation. It is found that the modified Xinanjiang model performs better than the Xinanjiang model, and the models considering the Horton and Dunne runoff generation mechanisms are slightly better than those models considering the single runoff generation mechanism in semi-arid areas. It is suggested that the infiltration excess runoff mechanism should be included in rainfall–runoff models in arid and semi-arid regions.

scholarly journals How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

2017 ◽  
Author(s):  
Manuel Antonetti ◽  
Massimiliano Zappa
Keyword(s):  
Spatial Distribution ◽  
Expert Knowledge ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Top Down ◽  
Bottom Up ◽  
Process Maps ◽  
Uncertainty Sources ◽  
Simulation Results ◽  
Better Than

Abstract. Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter. Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test to which extent expert knowledge can improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the Inverse Distance Weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down setup relying on parameter and process constraints, and an experimentalists' setup based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed. The simulation results showed that: (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up setup performed better than the top-down one when simulating short-duration events, but similarly to the top-down setup when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up setup can help identify uncertainty sources, but is prone to overconfidence problems, whereas the top-down setup seems to accommodate uncertainties in the input data best. Overall, modellers' and experimentalists' concept of "model realism" differ. This means that the level of detail a model should have to accurately reproduce the DRPs expected must be agreed in advance.

scholarly journals Production decline analysis of shale gas based on a probability density distribution function

2020 ◽  
Vol 17 (2) ◽  
pp. 365-376 ◽  
Author(s):  
Yingzhong Yuan ◽  
Zhilin Qi ◽  
Zhangxing Chen ◽  
Wende Yan ◽  
Zhiheng Zhao
Keyword(s):  
Distribution Function ◽  
Density Distribution ◽  
Probability Density ◽  
Shale Gas ◽  
Distribution Model ◽  
Probability Density Distribution ◽  
Density Distribution Function ◽  
Gas Reservoir ◽  
Shale Gas Reservoir ◽  
Better Than

Abstract Production decline analysis is a simple and efficient method to forecast production dynamics of shale gas. The traditional Arps decline model is also widely used in the production decline analysis of shale gas, but an obvious error is often generated. Based on the Weibull and χ2 probability density distribution function, the monotonic decreasing production prediction equations of shale gas are established. It is deduced that recently, the widely used Duong model is essentially a Weibull probability density distribution model. Decline analysis results of production data from actual shale gas well and numerical simulations indicate that the fitting results of the Weibull (Duong) model and χ2 distribution model are better than the Arps model whose deviation of early data is large. For a shale gas reservoir with very low permeability, pressure conformance area is small and it is obviously influenced by fractures. Early shale gas production rate mainly contributed to by fractures declines quickly and the later production rate mainly contributed to by the matrix declines slowly over time. The production decline curve has obvious long-tail distribution characteristics and it is a better fit to the data with a χ2 distribution model. As for the increase of permeability, the fitting accuracy of the Weibull (Duong) model gradually becomes better than the χ2 distribution model. Research results provide theoretical guidance for choosing a reasonable production decline model of a shale gas reservoir with a different permeability.

scholarly journals Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

2018 ◽  
Author(s):  
Anqi Wang ◽  
Dimitri P. Solomatine
Keyword(s):  
Sensitivity Analysis ◽  
Comparative Analysis ◽  
Practical Experience ◽  
Ease Of Use ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Sensitivity Indices ◽  
Kolmogorov Smirnov ◽  
Better Than

Abstract. Sensitivity Analysis (SA) and Uncertainty Analysis (UA) are important steps for better understanding and evaluation of hydrological models. The aim of this paper is to briefly review main classes of SA methods, and to presents the results of the practical comparative analysis of applying them. Six different global SA methods: Sobol, eFAST, Morris, LH-OAT, RSA and PAWN are tested on three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod and HBV) applied to the case study of Bagmati basin (Nepal), and also initially tested on the case of Dapoling-Wangjiaba catchment in China. The methods are compared with respect to effectiveness, efficiency and convergence. A practical framework of selecting and using the SA methods is presented. The result shows that, first of all, all the six SA methods are effective. Morris and LH-OAT methods are the most efficient methods in computing SI and ranking. eFAST performs better than Sobol, thus can be seen as its viable alternative for Sobol. PAWN and RSA methods have issues of instability which we think are due to the ways CDFs are built, and using Kolmogorov-Smirnov statistics to compute Sensitivity Indices. All the methods require sufficient number of runs to reach convergence. Difference in efficiency of different methods is an inevitable consequence of the differences in the underlying principles. For SA of hydrological models, it is recommended to apply the presented practical framework assuming the use of several methods, and to explicitly take into account the constraints of effectiveness, efficiency (including convergence), ease of use, as well as availability of software.

scholarly journals Testing the realism of a topography driven model (FLEX-Topo) in the nested catchments of the Upper Heihe, China

2013 ◽  
Vol 10 (10) ◽  
pp. 12663-12716 ◽  
Author(s):  
H. Gao ◽  
M. Hrachowitz ◽  
F. Fenicia ◽  
S. Gharari ◽  
H. H. G. Savenije
Keyword(s):  
Bare Soil ◽  
Realistic Model ◽  
Distributed Model ◽  
Model Structure ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Landscape Elements ◽  
Elevation Data ◽  
Soil Climate ◽  
Better Than

Abstract. Although elevation data is globally available, and used in many existing hydrological models, its information content is still poorly understood and under-exploited. Topography is closely related to geology, soil, climate and land cover. As a result, it may reflect the dominant hydrological processes in a catchment. In this study, we evaluated this hypothesis through three progressively more complex conceptual rainfall-runoff models. The first model (FLEXL) is lumped, and it does not make use of elevation data. The second model (FLEXD) is semi-distributed. It also does not make use of elevation data, but it accounts for input spatial variability. The third model (FLEXT), also semi-distributed, makes explicit use of topography information. The structure of FLEXT consists of four parallel components representing the distinct hydrological function of different landscape elements. These elements were determined based on a topography based landscape classification approach. All models were calibrated and validated at the catchment outlet. Additionally, the models were evaluated at two nested sub-catchments. FLEXT, performs better than the other models in the nested sub-catchment validation and it is therefore better transferable. This supports the following hypotheses: (1) topography can be used as an integrated indicator to distinguish landscape elements with different hydrological function; (2) the model structure of FLEXT is much better equipped to represent hydrological signatures than a lumped or semi-distributed model, and hence has a more realistic model structure and parameterization; (3) the wetland/terrace and grassland hillslope landscape elements of the Upper Heihe contribute the main part of the fast runoff while the bare soil/rock landscape provides the main contribution to the groundwater. Most of the precipitation on the forested hillslopes is evaporated, thus generating relatively little runoff.

scholarly journals Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Ambrose Mubialiwo ◽  
Adane Abebe ◽  
Charles Onyutha
Keyword(s):  
Goodness Of Fit ◽  
Model Performance ◽  
Relevant Information ◽  
Automatic Calibration ◽  
Hydrological Models ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Low Flows ◽  
Hydrological Extremes ◽  
Better Than

AbstractDaily River Malaba flows recorded from 1999 to 2016 were modelled using seven lumped conceptual rainfall–runoff models including AWBM, SACRAMENTO, TANK, IHACRES, SIMHYD, SMAR and HMSV. Optimal parameters of each model were obtained using an automatic calibration strategy. Mismatches between observed and modelled flows were assessed using a total of nine “goodness-of-fit” metrics. Capacity of the models to reproduce historical hydrological extremes was assessed through comparison of amplitude–duration–frequency (ADF) relationships or curves constructed based on observed and modelled flow quantiles. Generally, most of the hydrological models performed better for high than low flows. ADF curves of both high and low flows for various return periods from 5 to 100 years were well reproduced by AWBM, SAC, TANK and HMSV. ADF curves for high and low flows were poorly reproduced by SIMHYD and SMAR, respectively. Overall, AWBM performed slightly better than other models if both high and low flows are to be considered simultaneously. The deviations of these models were larger for high than low return periods. It was found that the choice of a “goodness-of-fit” metric affects how model performance can be judged. Results from this study also show that when focusing on hydrological extremes, uncertainty due to the choice of a particular model should be taken into consideration. Insights from this study provide relevant information for planning of risk-based water resources applications.

Informational Measures of the Shape of the Amoroso Distributions for the Research of Dispersed Materials

2021 ◽  
Vol 1031 ◽  
pp. 58-66
Author(s):  
Vitaly Polosin
Keyword(s):  
Particle Size ◽  
Distribution Function ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Applied Research ◽  
Particle Distribution ◽  
Size Distribution Function ◽  
Distribution Model ◽  
Information Uncertainty ◽  
Particle Size Distribution Function

For the particle size distribution function various forms of exponential models are used to construct models of the properties of dispersed substance. The most difficult stage of applied research is to determine the shape of the particle distribution model. For the particle size distribution function various forms of exponential models are used to construct models of the properties of dispersed substance. The most difficult stage of applied research is to determine the shape of the particle distribution model. The article proposes a uniform model for setting the interval of information uncertainty of non-symmetric particle size distributions. Based on the analysis of statistical and information uncertainty intervals, new shape coefficients of distribution models are constructed, these are the entropy coefficients for shifted and non shifted distributions of the Amoroso family. Graphics of dependence of entropy coefficients of non-symmetrical distributions show that distributions well-known are distinguish at small of the shapes parameters. Also it is illustrated for parameters of the form more than 2 that it is preferable to use the entropy coefficients for the unshifted distributions.The material contains also information measures for the well-known logarithmic normal distribution which is a limiting case of distribution Amorozo.

scholarly journals Modelling monthly runoff generation processes following land use changes: groundwater–surface runoff interactions

2004 ◽  
Vol 8 (5) ◽  
pp. 903-922 ◽  
Author(s):  
M. Bari ◽  
K. R. J. Smettem
Keyword(s):  
Land Use ◽  
Water Balance ◽  
Surface Runoff ◽  
Land Use Changes ◽  
Annual Rainfall ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Mean Annual Rainfall ◽  
Monthly Runoff ◽  
Stream Bed

Abstract. A conceptual water balance model is presented to represent changes in monthly water balance following land use changes. Monthly rainfall–runoff, groundwater and soil moisture data from four experimental catchments in Western Australia have been analysed. Two of these catchments, "Ernies" (control, fully forested) and "Lemon" (54% cleared) are in a zone of mean annual rainfall of 725 mm, while "Salmon" (control, fully forested) and "Wights" (100% cleared) are in a zone with mean annual rainfall of 1125 mm. At the Salmon forested control catchment, streamflow comprises surface runoff, base flow and interflow components. In the Wights catchment, cleared of native forest for pasture development, all three components increased, groundwater levels rose significantly and stream zone saturated area increased from 1% to 15% of the catchment area. It took seven years after clearing for the rainfall–runoff generation process to stabilise in 1984. At the Ernies forested control catchment, the permanent groundwater system is 20 m below the stream bed and so does not contribute to streamflow. Following partial clearing of forest in the Lemon catchment, groundwater rose steadily and reached the stream bed by 1987. The streamflow increased in two phases: (i) immediately after clearing due to reduced evapotranspiration, and (ii) through an increase in the groundwater-induced stream zone saturated area after 1987. After analysing all the data available, a conceptual monthly model was created, comprising four inter-connecting stores: (i) an upper zone unsaturated store, (ii) a transient stream zone store, (ii) a lower zone unsaturated store and (iv) a saturated groundwater store. Data such as rooting depth, Leaf Area Index, soil porosity, profile thickness, depth to groundwater, stream length and surface slope were incorporated into the model as a priori defined attributes. The catchment average values for different stores were determined through matching observed and predicted monthly hydrographs. The observed and predicted monthly runoff for all catchments matched well with coefficients of determination (R2) ranging from 0.68 to 0.87. Predictions were relatively poor for: (i) the Ernies catchment (lowest rainfall, forested), and (ii) months with very high flows. Overall, the predicted mean annual streamflow was within ±8% of the observed values. Keywords: monthly streamflow, land use change, conceptual model, data-based approach, groundwater

scholarly journals How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps

2018 ◽  
Vol 22 (8) ◽  
pp. 4425-4447 ◽  
Author(s):  
Manuel Antonetti ◽  
Massimiliano Zappa
Keyword(s):  
Spatial Distribution ◽  
Expert Knowledge ◽  
Hydrological Models ◽  
Top Down ◽  
Bottom Up ◽  
Process Maps ◽  
Uncertainty Sources ◽  
Simulation Results ◽  
Set Up ◽  
Better Than

Abstract. Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter.Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test for the extent to which expert knowledge can improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the inverse distance weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down set-up relying on parameter and process constraints and an experimentalists' set-up based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed.The simulation results showed that (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up set-up performed better than the top-down one when simulating short-duration events, but similarly to the top-down set-up when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up set-up can help identify uncertainty sources, but is prone to overconfidence problems, whereas the top-down set-up seems to accommodate uncertainties in the input data best. Overall, modellers' and experimentalists' concept of model realism differ. This means that the level of detail a model should have to accurately reproduce the DRPs expected must be agreed in advance.

Sign in / Sign up

Export Citation Format

Share Document