scholarly journals Analytical flow duration curves for summer streamflow in Switzerland

2018 ◽  
Vol 22 (4) ◽  
pp. 2377-2389 ◽  
Author(s):  
Ana Clara Santos ◽  
Maria Manuela Portela ◽  
Andrea Rinaldo ◽  
Bettina Schaefli
Keyword(s):  
Linear Model ◽  
Nonlinear Model ◽  
Low Flow ◽  
Model Parameters ◽  
Modeling Framework ◽  
Data Set ◽  
Flow Duration ◽  
Daily Precipitation Data ◽  
Wide Range ◽  
Flow Duration Curves

Abstract. This paper proposes a systematic assessment of the performance of an analytical modeling framework for streamflow probability distributions for a set of 25 Swiss catchments. These catchments show a wide range of hydroclimatic regimes, including namely snow-influenced streamflows. The model parameters are calculated from a spatially averaged gridded daily precipitation data set and from observed daily discharge time series, both in a forward estimation mode (direct parameter calculation from observed data) and in an inverse estimation mode (maximum likelihood estimation). The performance of the linear and the nonlinear model versions is assessed in terms of reproducing observed flow duration curves and their natural variability. Overall, the nonlinear model version outperforms the linear model for all regimes, but the linear model shows a notable performance increase with catchment elevation. More importantly, the obtained results demonstrate that the analytical model performs well for summer discharge for all analyzed streamflow regimes, ranging from rainfall-driven regimes with summer low flow to snow and glacier regimes with summer high flow. These results suggest that the model's encoding of discharge-generating events based on stochastic soil moisture dynamics is more flexible than previously thought. As shown in this paper, the presence of snowmelt or ice melt is accommodated by a relative increase in the discharge-generating frequency, a key parameter of the model. Explicit quantification of this frequency increase as a function of mean catchment meteorological conditions is left for future research.

scholarly journals Inference of analytical flow duration curves in Swiss alpine environments

2017 ◽  
Author(s):  
Ana Clara Santos ◽  
Maria Manuela Portela ◽  
Andrea Rinaldo ◽  
Bettina Schaefli
Keyword(s):  
Nonlinear Model ◽  
Model Performance ◽  
Model Parameters ◽  
Modeling Framework ◽  
Data Set ◽  
Flow Duration ◽  
Model Version ◽  
Daily Precipitation Data ◽  
Wide Range ◽  
Flow Duration Curves

Abstract. This paper assesses the performance of an analytical modeling framework for streamflow probability distributions for summer streamflow of 26 Swiss catchments characterized by negligible anthropic influence. These catchments show a wide range of hydroclimatic regimes, including snow- and icemelt influenced streamflows. The model parameters are estimated from a gridded daily precipitation data set and observed daily discharge time series. The performance of the linear and nonlinear model version is assessed in terms of reproducing observed flow duration curves and their natural variability. The results show that the model performs well for summer discharges under all analyzed regimes and that there is a clear model performance increase with mean catchment elevation (i.e with transition from rainfall-dominated to snow-influenced regimes). The nonlinear model version outperforms the linear model for all regimes but the performance difference decreases also with mean catchment elevation. Future work will focus on the extension of the modeling framework, addressing snowmelt and snowfall onset.

scholarly journals Exploring the physical controls of regional patterns of flow duration curves – Part 4: A synthesis of empirical analysis, process modeling and catchment classification

2012 ◽  
Vol 16 (11) ◽  
pp. 4483-4498 ◽  
Author(s):  
M. Yaeger ◽  
E. Coopersmith ◽  
S. Ye ◽  
L. Cheng ◽  
A. Viglione ◽  
...  
Keyword(s):  
Low Flow ◽  
Middle Portion ◽  
Qualitative Synthesis ◽  
Statistical Parameters ◽  
Regional Patterns ◽  
Flow Duration ◽  
Analysis Process ◽  
Wide Range ◽  
Physical Controls ◽  
Flow Duration Curves

Abstract. The paper reports on a four-pronged study of the physical controls on regional patterns of the flow duration curve (FDC). This involved a comparative analysis of long-term continuous data from nearly 200 catchments around the US, encompassing a wide range of climates, geology, and ecology. The analysis was done from three different perspectives – statistical analysis, process-based modeling, and data-based classification – followed by a synthesis, which is the focus of this paper. Streamflow data were separated into fast and slow flow responses, and associated signatures, and both total flow and its components were analyzed to generate patterns. Regional patterns emerged in all aspects of the study. The mixed gamma distribution described well the shape of the FDC; regression analysis indicated that certain climate and catchment properties were first-order controls on the shape of the FDC. In order to understand the spatial patterns revealed by the statistical study, and guided by the hypothesis that the middle portion of the FDC is a function of the regime curve (RC, mean within-year variation of flow), we set out to classify these catchments, both empirically and through process-based modeling, in terms of their regime behavior. The classification analysis showed that climate seasonality and aridity, either directly (empirical classes) or through phenology (vegetation processes), were the dominant controls on the RC. Quantitative synthesis of these results determined that these classes were indeed related to the FDC through its slope and related statistical parameters. Qualitative synthesis revealed much diversity in the shapes of the FDCs even within each climate-based homogeneous class, especially in the low-flow tails, suggesting that catchment properties may have become the dominant controls. Thus, while the middle portion of the FDC contains the average response of the catchment, and is mainly controlled by climate, the tails of the FDC, notably the low-flow tails, are mainly controlled by catchment properties such as geology and soils. The regime behavior explains only part of the FDC; to gain a deeper understanding of the physical controls on the FDC, these extremes must be analyzed as well. Thus, to completely separate the climate controls from the catchment controls, the roles of catchment properties such as soils, geology, topography etc. must be explored in detail.

scholarly journals Exploring the physical controls of regional patterns of flow duration curves – Part 4: A synthesis of empirical analysis, process modeling and catchment classification

2012 ◽  
Vol 9 (6) ◽  
pp. 7131-7180 ◽  
Author(s):  
M. Yaeger ◽  
E. Coopersmith ◽  
S. Ye ◽  
L. Cheng ◽  
A. Viglione ◽  
...  
Keyword(s):  
Low Flow ◽  
Middle Portion ◽  
Qualitative Synthesis ◽  
Statistical Parameters ◽  
Regional Patterns ◽  
Flow Duration ◽  
Analysis Process ◽  
Wide Range ◽  
Physical Controls ◽  
Flow Duration Curves

Abstract. The paper reports on a four-pronged study of the physical controls on regional patterns of the Flow Duration Curve (FDC). This involved a comparative analysis of long-term continuous data from nearly 200 catchments around the US, encompassing a wide range of climates, geology and ecology. The analysis was done from three different perspectives – statistical analysis, process-based modeling, and data-based classification, followed by a synthesis, which is the focus of this paper. Streamflow data was separated into fast and slow flow responses, and associated signatures, and both total flow and its components were analyzed to generate patterns. Regional patterns emerged in all aspects of the study. The mixed gamma distribution described well the shape of the FDC; regression analysis indicated that certain climate and catchment properties were first order controls on the shape of the FDC. In order to understand the spatial patterns revealed by the statistical study, and guided by the hypothesis that the middle portion of the FDC is a function of the regime curve (RC, mean within year variation of flow), we set out to classify these catchments, both empirically and through process-based modeling, in terms of their regime behavior. The classification analysis showed that climate seasonality and aridity, either directly (empirical classes) or through phenology (vegetation processes), were the dominant controls on the RC. Quantitative synthesis of these results determined that these classes were indeed related to the FDC through its slope and related statistical parameters. Qualitative synthesis revealed much diversity in the shapes of the FDCs even within each climate-based homogeneous class, especially in the low-flow tails, suggesting that catchment properties may have become the dominant controls. Thus, while the middle portion of the FDC contains the average response of the catchment, and is mainly controlled by climate, the tails of the FDC, notably the low-flow tails, are mainly controlled by catchment properties such as geology and soils. The regime behavior explains only part of the FDC; to gain a deeper understanding of the physical controls on the FDC, these extremes must be analyzed as well. Thus, to completely separate the climate controls from the catchment controls, the roles of catchment properties such as soils, geology, topography etc., must be explored in detail.

scholarly journals Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package Ipcr

Psych ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 360-385
Author(s):  
Manuel Arnold ◽  
Andreas M. Brandmaier ◽  
Manuel C. Voelkle
Keyword(s):  
False Negative ◽  
R Package ◽  
Parametric Models ◽  
Alternative Methods ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Modeling Framework ◽  
Data Set ◽  
Individual Parameter ◽  
Wide Range

Unmodeled differences between individuals or groups can bias parameter estimates and may lead to false-positive or false-negative findings. Such instances of heterogeneity can often be detected and predicted with additional covariates. However, predicting differences with covariates can be challenging or even infeasible, depending on the modeling framework and type of parameter. Here, we demonstrate how the individual parameter contribution (IPC) regression framework, as implemented in the R package ipcr, can be leveraged to predict differences in any parameter across a wide range of parametric models. First and foremost, IPC regression is an exploratory analysis technique to determine if and how the parameters of a fitted model vary as a linear function of covariates. After introducing the theoretical foundation of IPC regression, we use an empirical data set to demonstrate how parameter differences in a structural equation model can be predicted with the ipcr package. Then, we analyze the performance of IPC regression in comparison to alternative methods for modeling parameter heterogeneity in a Monte Carlo simulation.

scholarly journals Classifying low flow hydrological regimes at a regional scale

2011 ◽  
Vol 15 (12) ◽  
pp. 3741-3750 ◽  
Author(s):  
M. J. Kirkby ◽  
F. Gallart ◽  
T. R. Kjeldsen ◽  
B. J. Irvine ◽  
J. Froebrich ◽  
...  
Keyword(s):  
Regional Scale ◽  
Low Flow ◽  
Balance Model ◽  
Hydraulic Geometry ◽  
Climate Data ◽  
Cross Sectional ◽  
Flow Duration ◽  
River Bed ◽  
Climatic Scenarios ◽  
Flow Duration Curves

Abstract. The paper uses a simple water balance model that partitions the precipitation between actual evapotranspiration, quick flow and delayed flow, and has sufficient complexity to capture the essence of climate and vegetation controls on this partitioning. Using this model, monthly flow duration curves have been constructed from climate data across Europe to address the relative frequency of ecologically critical low flow stages in semi-arid rivers, when flow commonly persists only in disconnected pools in the river bed. The hydrological model is based on a dynamic partitioning of precipitation to estimate water available for evapotranspiration and plant growth and for residual runoff. The duration curve for monthly flows has then been analysed to give an estimate of bankfull flow based on recurrence interval. Arguing from observed ratios of cross-sectional areas at flood and low flows, hydraulic geometry suggests that disconnected flow under "pool" conditions is approximately 0.1% of bankfull flow. Flow duration curves define a measure of bankfull discharge on the basis of frequency. The corresponding frequency for pools is then read from the duration curve, using this (0.1%) ratio to estimate pool discharge from bank full discharge. The flow duration curve then provides an estimate of the frequency of poorly connected pool conditions, corresponding to this discharge, that constrain survival of river-dwelling arthropods and fish. The methodology has here been applied across Europe at 15 km resolution, and the potential is demonstrated for applying the methodology under alternative climatic scenarios.

scholarly journals Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

2015 ◽  
Vol 19 (1) ◽  
pp. 209-223 ◽  
Author(s):  
A. J. Newman ◽  
M. P. Clark ◽  
K. Sampson ◽  
A. Wood ◽  
L. E. Hay ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Cycle ◽  
Model Performance ◽  
The United States ◽  
Hydrologic Model ◽  
Low Flow ◽  
Reference Level ◽  
Hydrologic Response ◽  
Data Set ◽  
Wide Range

Abstract. We present a community data set of daily forcing and hydrologic response data for 671 small- to medium-sized basins across the contiguous United States (median basin size of 336 km2) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980–2010 was generated for three basin spatial configurations – basin mean, hydrologic response units (HRUs) and elevation bands – by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90% of the basins have Nash–Sutcliffe efficiency scores ≥0.55 for the calibration period and 34% ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases.

scholarly journals Process-based karst modelling to relate hydrodynamic and hydrochemical characteristics to system properties

2013 ◽  
Vol 10 (3) ◽  
pp. 2835-2878
Author(s):  
A. Hartmann ◽  
M. Weiler ◽  
T. Wagener ◽  
J. Lange ◽  
M. Kralik ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Model Parameters ◽  
Karst Water ◽  
Flow Duration ◽  
Karst Areas ◽  
Flow Duration Curves ◽  
System Properties ◽  
Karst Systems ◽  
Sensitive Parameters

Abstract. More than 30% of Europe's land surface is made up of karst exposures. In some countries, water from karst aquifers constitutes almost half of the drinking water supply. Hydrological simulation models can predict the large-scale impact of future environmental change on hydrological variables. However, the information needed to obtain model parameters is not available everywhere and regionalisation methods have to be applied. The responsive behaviour of hydrological systems can be quantified by individual metrics, so-called system signatures. This study explores their value for distinguishing the dominant processes and properties of five different karst systems in Europe and the Middle East with the overall aim of regionalising system signatures and model parameters to ungauged karst areas. By defining ten system signatures derived from hydrodynamic and hydrochemical observations, a process-based karst model is applied to the five karst systems. In a stepwise model evaluation strategy, optimum parameters and their sensitivity are identified using automatic calibration and global variance-based sensitivity analysis. System signatures and sensitive parameters serve as proxies for dominant processes and optimised parameters are used to determine system properties. To test the transferability of the signatures, they are compared with the optimised model parameters and simple climatic and topographic descriptors of the five karst systems. By sensitivity analysis, the set of system signatures was able to distinguish the karst systems from one another by providing separate information about dominant soil, epikarst, and fast and slow groundwater flow processes. Comparing sensitive parameters to the system signatures revealed that annual discharge can serve as a proxy for the recharge area, that the slopes of the high flow parts of the flow duration curves correlate with the fast flow storage constant, and that the dampening of the isotopic signal of the rain as well as the medium flow parts of the flow duration curves have a non-linear relation to the distribution of groundwater dynamics. Even though, only weak correlations between system signatures and climatic and topographic factors could be found, our approach enabled us to identify dominant processes of the different systems and to provide directions for future large-scale simulation of karst areas to predict the impact of future change on karst water resources.

scholarly journals Catchment classification by runoff behaviour with self-organizing maps (SOM)

2011 ◽  
Vol 8 (2) ◽  
pp. 3047-3083 ◽  
Author(s):  
R. Ley ◽  
M. C. Casper ◽  
H. Hellebrand ◽  
R. Merz
Keyword(s):  
Cluster Analysis ◽  
Ungauged Catchments ◽  
Self Organizing Maps ◽  
Response Behaviour ◽  
Flow Duration ◽  
Wide Range ◽  
Spatio Temporal ◽  
Flow Duration Curves ◽  
Climatic Characteristics ◽  
Self Organizing

Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

RESEARCH OF MULTI-TASK LEARNING BASED ON EXTREME LEARNING MACHINE

2013 ◽  
Vol 21 (supp02) ◽  
pp. 75-85 ◽  
Author(s):  
WENTAO MAO ◽  
JIUCHENG XU ◽  
SHENGJIE ZHAO ◽  
MEI TIAN
Keyword(s):  
Domain Knowledge ◽  
Real Life ◽  
Training Data ◽  
Learning Performance ◽  
Mixed Integer ◽  
Model Parameters ◽  
Minimization Method ◽  
Data Set ◽  
Task Learning ◽  
Wide Range

Recently, extreme learning machines (ELMs) have been a promising tool in solving a wide range of regression and classification applications. However, when modeling multiple related tasks in which only limited training data per task are available and the dimension is low, ELMs are generally hard to get impressive performance due to little help from the informative domain knowledge across tasks. To solve this problem, this paper extends ELM to the scenario of multi-task learning (MTL). First, based on the assumption that model parameters of related tasks are close to each other, a new regularization-based MTL algorithm for ELM is proposed to learn related tasks jointly via simple matrix inversion. For improving the learning performance, the algorithm proposed above is further formulated as a mixed integer programming in order to identify the grouping structure in which parameters are closer than others, and finally an alternating minimization method is presented to solve this optimization. Experiments conducted on a toy problem as well as real-life data set demonstrate the effectiveness of the proposed MTL algorithm compared to the classical ELM and the standard MTL algorithm.

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