A comparison of similarity indices for catchment classification using a cross-regional dataset

134

Abstract. The absence of a generic modeling framework in hydrology has long been recognized. With our current practice of developing more and more complex models for specific individual situations, there is an increasing emphasis and urgency on this issue. There have been some attempts to provide guidelines for a catchment classification framework, but research in this area is still in a state of infancy. To move forward on this classification framework, identification of an appropriate basis and development of a suitable methodology for its representation are vital. The present study argues that hydrologic system complexity is an appropriate basis for this classification framework and nonlinear dynamic concepts constitute a suitable methodology. The study employs a popular nonlinear dynamic method for identification of the level of complexity of streamflow and for its classification. The correlation dimension method, which has its base on data reconstruction and nearest neighbor concepts, is applied to monthly streamflow time series from a large network of 117 gaging stations across 11 states in the western United States (US). The dimensionality of the time series forms the basis for identification of system complexity and, accordingly, streamflows are classified into four major categories: low-dimensional, medium-dimensional, highdimensional, and unidentifiable. The dimension estimates show some "homogeneity" in flow complexity within certain regions of the western US, but there are also strong exceptions.

Section: Classification In Hydrology: a Brief History And Scopementioning

confidence: 99%

“…Merz and Blöschl, 2004;Wardrop et al, 2005), similarity indices (e.g. Olden and Poff, 2003;Ali et al, 2012), eco-hydrologic factors (e.g. Harris et al, 2000;Olden et al, 2011), geostatistical properties (e.g.…”

Section: Classification In Hydrology: a Brief History And Scopementioning

confidence: 99%

Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework

Sivakumar

Singh

2012

134

“…While transport process studies provided crucial insights in small-scale dynamics, black-box model inter-comparison studies have shed light on the physical controls of the long-term average TTDs on the catchment scale (e.g. Laudon et al, 2007;Broxton et al, 2009;Hrachowitz et al, 2009a;Tetzlaff et al, 2009a;Katsuyama et al, 2009Katsuyama et al, , 2010Lyon et al, 2010a;Speed et al, 2010;Ali et al, 2012;Asano and Uchida, 2012;Capell et al, 2012a;McGrane et al, 2012).…”

Section: Hrachowitz Et Al: What Can Flux Tracking Teach Us Aboutmentioning

confidence: 99%

What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

Hrachowitz

Savenije

Bogaard

et al. 2013

272

443

Abstract. The complex interactions of runoff generation processes underlying the hydrological response of streams remain not entirely understood at the catchment scale. Extensive research has demonstrated the utility of tracers for both inferring flow path distributions and constraining model parameterizations. While useful, the common use of linearity assumptions, i.e. time invariance and complete mixing, in these studies provides only partial understanding of actual process dynamics. Here we use long-term (< 20 yr) precipitation, flow and tracer (chloride) data of three contrasting upland catchments in the Scottish Highlands to inform integrated conceptual models investigating different mixing assumptions. Using the models as diagnostic tools in a functional comparison, water and tracer fluxes were then tracked with the objective of exploring the differences between different water age distributions, such as flux and resident water age distributions, and characterizing the contrasting water age pattern of the dominant hydrological processes in the three study catchments to establish an improved understanding of the wetness-dependent temporal dynamics of these distributions.The results highlight the potential importance of partial mixing processes which can be dependent on the hydrological functioning of a catchment. Further, tracking tracer fluxes showed that the various components of a model can be characterized by fundamentally different water age distributions which may be highly sensitive to catchment wetness history, available storage, mixing mechanisms, flow path connectivity and the relative importance of the different hydrological processes involved. Flux tracking also revealed that, although negligible for simulating the runoff response, the omission of processes such as interception evaporation can result in considerably biased water age distributions. Finally, the modeling indicated that water age distributions in the three study catchments do have long, power-law tails, which are generated by the interplay of flow path connectivity, the relative importance of different flow paths as well as by the mixing mechanisms involved. In general this study highlights the potential of customized integrated conceptual models, based on multiple mixing assumptions, to infer system internal transport dynamics and their sensitivity to catchment wetness states.

“…Spatial proximity is considered the primary explanatory variable for hydrologic similarity (Sawicz et al, 2011) because of the first-order effects of climatic and topographic controls on hydrologic response. Close proximity, however, does not always result in hydrologic similarity (Vandewiele and Elias, 1995;Smakhtin, 2001;Ali et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Parameter regionalization of a monthly water balance model for the conterminous United States

Bock

Hay

McCabe

et al. 2016

Abstract. A parameter regionalization scheme to transfer parameter values from gaged to ungaged areas for a monthly water balance model (MWBM) was developed and tested for the conterminous United States (CONUS). The Fourier Amplitude Sensitivity Test, a global-sensitivity algorithm, was implemented on a MWBM to generate parameter sensitivities on a set of 109 951 hydrologic response units (HRUs) across the CONUS. The HRUs were grouped into 110 calibration regions based on similar parameter sensitivities. Subsequently, measured runoff from 1575 streamgages within the calibration regions were used to calibrate the MWBM parameters to produce parameter sets for each calibration region. Measured and simulated runoff at the 1575 streamgages showed good correspondence for the majority of the CONUS, with a median computed Nash-Sutcliffe efficiency coefficient of 0.76 over all streamgages. These methods maximize the use of available runoff information, resulting in a calibrated CONUS-wide application of the MWBM suitable for providing estimates of water availability at the HRU resolution for both gaged and ungaged areas of the CONUS.