scholarly journals A New Framework for Exploring Process Controls of Flow Duration Curves

2020 ◽  
Vol 56 (1) ◽  
Author(s):  
Saba Ghotbi ◽  
Dingbao Wang ◽  
Arvind Singh ◽  
Günter Blöschl ◽  
Murugesu Sivapalan
2012 ◽  
Vol 9 (6) ◽  
pp. 7035-7084 ◽  
Author(s):  
S. Ye ◽  
M. A. Yaeger ◽  
E. Coopersmith ◽  
L. Cheng ◽  
M. Sivapalan

Abstract. The goal of this paper is to explore the process controls underpinning regional patterns of variations of runoff regime behavior, i.e., the mean seasonal variation of runoff within the year, across the continental United States. The ultimate motivation is to use the resulting process understanding to generate insights into the physical controls of Flow Duration Curves, in view of the close connection between these two alternative signatures of runoff variability. To achieve these aims a top-down modeling approach is adopted; we start with a simple two-stage bucket model, which is systematically enhanced through addition of new processes on the basis of model performance assessment in relation to observations, using rainfall-runoff data from 197 United States catchments belonging to the MOPEX dataset. Exploration of dominant processes and the determination of required model complexity are carried out through model-based sensitivity analyses, guided by a performance metric. Results indicated systematic regional trends in dominant processes: snowmelt was a key process control in cold mountainous catchments in the north and north-west, whereas snowmelt and vegetation cover dynamics were key controls in the north-east; seasonal vegetation cover dynamics (phenology and interception) were important along the Appalachian mountain range in the east. A simple two-bucket model (with no other additions) was found to be adequate in warm humid catchments along the west coast and in the south-east, with both regions exhibiting strong seasonality, whereas much more complex models are needed in the dry south and south-west. Agricultural catchments in the mid-west were found to be difficult to predict with the use of simple lumped models, due to the strong influence of human activities. Overall, these process controls arose from general east-west (seasonality) and north-south (aridity, temperature) trends in climate (with some exceptions), compounded by complex dynamics of vegetation cover and to a less extent by landscape factors (soils, geology and topography).


2007 ◽  
Vol 30 (4) ◽  
pp. 937-953 ◽  
Author(s):  
Attilio Castellarin ◽  
Giorgio Camorani ◽  
Armando Brath

2012 ◽  
Vol 16 (11) ◽  
pp. 4483-4498 ◽  
Author(s):  
M. Yaeger ◽  
E. Coopersmith ◽  
S. Ye ◽  
L. Cheng ◽  
A. Viglione ◽  
...  

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.


2014 ◽  
Vol 519 ◽  
pp. 258-270 ◽  
Author(s):  
D. Pumo ◽  
F. Viola ◽  
G. La Loggia ◽  
L.V. Noto

2012 ◽  
Vol 16 (11) ◽  
pp. 4435-4446 ◽  
Author(s):  
L. Cheng ◽  
M. Yaeger ◽  
A. Viglione ◽  
E. Coopersmith ◽  
S. Ye ◽  
...  

Abstract. The flow duration curve (FDC) is a classical method used to graphically represent the relationship between the frequency and magnitude of streamflow. In this sense it represents a compact signature of temporal runoff variability that can also be used to diagnose catchment rainfall-runoff responses, including similarity and differences between catchments. This paper is aimed at extracting regional patterns of the FDCs from observed daily flow data and elucidating the physical controls underlying these patterns, as a way to aid towards their regionalization and predictions in ungauged basins. The FDCs of total runoff (TFDC) using multi-decadal streamflow records for 197 catchments across the continental United States are separated into the FDCs of two runoff components, i.e., fast flow (FFDC) and slow flow (SFDC). In order to compactly display these regional patterns, the 3-parameter mixed gamma distribution is employed to characterize the shapes of the normalized FDCs (i.e., TFDC, FFDC and SFDC) over the entire data record. This is repeated to also characterize the between-year variability of "annual" FDCs for 8 representative catchments chosen across a climate gradient. Results show that the mixed gamma distribution can adequately capture the shapes of the FDCs and their variation between catchments and also between years. Comparison between the between-catchment and between-year variability of the FDCs revealed significant space-time symmetry. Possible relationships between the parameters of the fitted mixed gamma distribution and catchment climatic and physiographic characteristics are explored in order to decipher and point to the underlying physical controls. The baseflow index (a surrogate for the collective impact of geology, soils, topography and vegetation, as well as climate) is found to be the dominant control on the shapes of the normalized TFDC and SFDC, whereas the product of maximum daily precipitation and the fraction of non-rainy days was found to control the shape of the FFDC. These relationships, arising from the separation of total runoff into its two components, provide a potential physical basis for regionalization of FDCs, as well as providing a conceptual framework for developing deeper process-based understanding of the FDCs.


2019 ◽  
Vol 7 (3) ◽  
pp. 198-206 ◽  
Author(s):  
Raimunda da Silva e Silva ◽  
Claudio José Cavalcante Blanco ◽  
Francisco Carlos Lira Pessoa

RBRH ◽  
2016 ◽  
Vol 21 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Daniel Detzel ◽  
Cristóvão Fernandes ◽  
Miriam Mine

2019 ◽  
Vol 23 (11) ◽  
pp. 4471-4489 ◽  
Author(s):  
Manuela I. Brunner ◽  
Daniel Farinotti ◽  
Harry Zekollari ◽  
Matthias Huss ◽  
Massimiliano Zappa

Abstract. Extreme low and high flows can have negative economic, social, and ecological effects and are expected to become more severe in many regions due to climate change. Besides low and high flows, the whole flow regime, i.e., annual hydrograph comprised of monthly mean flows, is subject to changes. Knowledge on future changes in flow regimes is important since regimes contain information on both extremes and conditions prior to the dry and wet seasons. Changes in individual low- and high-flow characteristics as well as flow regimes under mean conditions have been thoroughly studied. In contrast, little is known about changes in extreme flow regimes. We here propose two methods for the estimation of extreme flow regimes and apply them to simulated discharge time series for future climate conditions in Switzerland. The first method relies on frequency analysis performed on annual flow duration curves. The second approach performs frequency analysis of the discharge sums of a large set of stochastically generated annual hydrographs. Both approaches were found to produce similar 100-year regime estimates when applied to a data set of 19 hydrological regions in Switzerland. Our results show that changes in both extreme low- and high-flow regimes for rainfall-dominated regions are distinct from those in melt-dominated regions. In rainfall-dominated regions, the minimum discharge of low-flow regimes decreases by up to 50 %, whilst the reduction is 25 % for high-flow regimes. In contrast, the maximum discharge of low- and high-flow regimes increases by up to 50 %. In melt-dominated regions, the changes point in the other direction than those in rainfall-dominated regions. The minimum and maximum discharges of extreme regimes increase by up to 100 % and decrease by less than 50 %, respectively. Our findings provide guidance in water resource planning and management and the extreme regime estimates are a valuable basis for climate impact studies. Highlights Estimation of 100-year low- and high-flow regimes using annual flow duration curves and stochastically simulated discharge time series Both mean and extreme regimes will change under future climate conditions. The minimum discharge of extreme regimes will decrease in rainfall-dominated regions but increase in melt-dominated regions. The maximum discharge of extreme regimes will increase and decrease in rainfall-dominated and melt-dominated regions, respectively.


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