Patterns of streamflow regimes along the river network: The case of the Thur river

Doulatyari, Behnam; Betterle, Andrea; Radny, Dirk; Celegon, Elisa Alessi; Fanton, Pietro; Schirmer, Mario; Botter, Gianluca

doi:10.1016/j.envsoft.2017.03.002

Cited by 21 publications

(27 citation statements)

References 53 publications

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“…The comparison between the forward and inverse estimation methods shows a clear underestimation of k n for most of the catchments, which was already discussed by Dralle et al (2015) and which is in line with previous work that tried to improve the results of the model in forward estimation mode, for the linear and the nonlinear formulation (Ceola et al, 2010;Basso et al, 2015). There is clearly a need to further improve the methods to estimate the recession parameters.…”

Section: Discussionsupporting

confidence: 87%

Analytical flow duration curves for summer streamflow in Switzerland

Santos

Portela

Rinaldo

et al. 2018

Hydrol. Earth Syst. Sci.

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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.

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Section: Discussionsupporting

confidence: 87%

Analytical flow duration curves for summer streamflow in Switzerland

Santos

Portela

Rinaldo

et al. 2018

Hydrol. Earth Syst. Sci.

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“…This model has been shown to give good results across a wide range of hydro‐climatic regimes (Basso et al, ; Botter et al, ; ; Botter et al, ; Ceola et al, ; Doulatyari et al, ; Santos, Portela, Rinaldo, & Schaefli, ), including extensions for winter in snow‐dominated catchments (Schaefli, Rinaldo, & Botter, ) and for dry climates (Müller et al, ). The recession parameters have generally been estimated with the master recession approach, with binning (Ceola et al, ), or mostly without binning (Ceola et al, ; Santos et al, ; Schaefli et al, ), or with an event‐scale recession analysis with a linear regression of data (Basso et al, ; Botter, Peratoner, Porporato, Rodriguez‐Iturbe, & Rinaldo, ; Botter et al, ; Botter et al, ; Botter, Zanardo, Porporato, Rodriguez‐Iturbe, & Rinaldo, ; Müller et al, ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of streamflow recession parameters: New insights from an analytic streamflow distribution model

Santos

Portela

Rinaldo

et al. 2019

Hydrological Processes

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Streamflow recession analysis characterizes the storage-outflow relationship in catchments. This relationship, which typically follows a power law, summarizes all catchment-scale subsurface hydrological processes and has long been known to be a key descriptor of the hydrologic response. In this paper, we tested a range of common recession analysis methods (RAMs) andpropose the use of an analytic streamflow distribution model as an alternative method for recession parameter estimation and to objectively compare different RAMs. The used analytical model assumes power law recessions, in combination with a stochastic process for streamflow triggering rainfall events. This streamflow distribution model is used in the present framework to establish reference values for the recession parameters via maximum likelihood estimation.The model-based method has two main advantages: (a) joint estimation of both power law recession parameters (coefficient and exponent), which are known to be strongly correlated, and (b) parameter estimation based on all available streamflow data (no recession selection).The approach is applied to five rainfall-dominated catchments in Switzerland with 40 years of continuous streamflow observations. The results show that the estimated recession parameters are highly dependent on methodological choices and that some RAMs lead to biased estimates.The recession selection method is shown to be of prime importance for a reliable description of catchment-scale recession behaviour, in particular in presence of short streamflow records. The newly proposed model-based RAM yields robust results, which supports the further development of this method for comparative hydrology and opens new perspectives for understanding the recession behaviour of catchments.

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“…This study takes advantage of a parsimonious probabilistic description of joint streamflow dynamics at arbitrary pairs of catchment outlets. The method, which is designed to statistically characterize the spatial variability of river flows, relies on a stochastic representation of specific (i.e., mm/day) streamflow dynamics (Botter, Porporato, Rodriguez‐Iturbe, & Rinaldo, ; Doulatyari et al, ). The Pearson correlation between synchronous daily streamflow time series at two river sites is expressed as a function of a limited number of hydroclimatic parameters (Betterle, Radny, et al, , Betterle, Schirmer, et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Analogously, α i t ,

α_{i}^{12}

, and α i are the mean rainfall depths of total, joint, and disjoint rainfall events, respectively, and r α is the Pearson correlation coefficient between the joint rainfall intensities. In this study, daily rainfall intensities are calculated as the exceedance of a threshold (1mm) representing canopy interception (Doulatyari et al, ; Lai & Katul, ; Laio et al, ).…”

Section: Estimation Of Model Parametersmentioning

confidence: 99%

“…Physically‐based classification frameworks based on similarity of hydrological functions are a key means to assess the dominant controls of water movement across the landscape (e.g., Doulatyari et al, ; McDonnell & Woods, ). In particular, classification can help understanding catchment hydrological functioning by linking similarities of catchment responses to specific geomorphoclimatic attributes (Berghuijs, Sivapalan, Woods, & Savenije, ; Botter, Basso, Rodriguez‐Iturbe, & Rinaldo, ).…”

Section: Introductionmentioning

confidence: 99%

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Flow dynamics at the continental scale: Streamflow correlation and hydrological similarity

Betterle

Schirmer

Botter

2019

Hydrological Processes

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Streamflow variability in space and time critically affects anthropic water uses and ecosystem services. Unfortunately, spatiotemporal patterns of flow regimes are often unknown, as discharge measurements are usually recorded at a limited number of hydrometric stations unevenly distributed along river networks. Advances in understanding the physical processes that control the spatial patterns of river flows are therefore necessary to predict water availability at ungauged locations or to extrapolate pointwise streamflow observations. This work explores the use of the spatial correlation of river flows as a metric to quantify the similarity between hydrological responses of two catchments. Following a stochastic framework, 340,000 cross-correlations between pairs of daily streamflows time series are predicted at a seasonal timescale across the contiguous United States using 413 catchments of the MOPEX dataset.Model predictions of streamflow correlation obtained in absence of run-off information are successfully used to identify catchment outlets sharing similar discharge dynamics and flow regimes across a broad range of geomorphoclimatic conditions, without relying on calibration. The selection of reference streamgauges based on predicted streamflow correlation generally outperforms the selection based on spatial proximity, especially as the density of available gauged sections decreases. Interestingly, correlated outlets share a broad spectrum of hydrological signatures (mean discharge, flow variability, and recession properties), suggesting that catchments forced by analogous frequency and intensity of effective rainfall events might exhibit common geomorphoecological traits leading to similar hydrological responses. The proposed framework provides a physical basis to assist the regionalization of flow dynamics and to interpret the spatial variability of flow regimes along stream networks.

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Patterns of streamflow regimes along the river network: The case of the Thur river

Cited by 21 publications

References 53 publications

Analytical flow duration curves for summer streamflow in Switzerland

Analytical flow duration curves for summer streamflow in Switzerland

Estimation of streamflow recession parameters: New insights from an analytic streamflow distribution model

Flow dynamics at the continental scale: Streamflow correlation and hydrological similarity

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