Diagnostic analysis of water balance variability: A comparative modeling study of catchments in Perth, Newcastle, and Darwin, Australia

Abstract. The goal of this paper is to explore the process controls underpinning regional patterns of variations of streamflow regime behavior, i.e., the mean seasonal variation of streamflow 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 another signature of streamflow variability, namely the flow duration curve (FDC). The construction of the FDC removes the time dependence of flows. Thus in order to better understand the physical controls in regions that exhibit strong seasonal dependence, the regime curve (RC), which is closely connected to the FDC, is studied in this paper and later linked back to the FDC. 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 southwest. 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).

Section: Regional Distribution Of Model Parametersmentioning

confidence: 99%

Exploring the physical controls of regional patterns of flow duration curves – Part 2: Role of seasonality, the regime curve, and associated process controls

Yaeger

Coopersmith

et al. 2012

“…Given the chosen laws in the model, it can be studied what result these laws in this model predict if a certain set of initial and boundary conditions were the case, or whether an emergent (statistical) relation exists between initial and boundary conditions and model outcomes, or what initial and boundary conditions are required to yield a certain result. For specific cases, the downward approach can be turned around to do a diagnostic analysis: by adding and calibrating a single process description, the hypothesis that this process explains a certain aspect of the data can be tested (Samuel et al, 2008).…”

Section: "Experimental" Approach To Explanatory Modellingmentioning

confidence: 99%

HESS Opinions On the use of laboratory experimentation: "Hydrologists, bring out shovels and garden hoses and hit the dirt"

Kleinhans

Bierkens

Perk

2010

Abstract.From an outsider's perspective, hydrology combines field work with modelling, but mostly ignores the potential for gaining understanding and conceiving new hypotheses from controlled laboratory experiments. Sivapalan (2009) pleaded for a question-and hypothesis-driven hydrology where data analysis and top-down modelling approaches lead to general explanations and understanding of general trends and patterns. We discuss why and how such understanding is gained very effectively from controlled experimentation in comparison to field work and modelling. We argue that many major issues in hydrology are open to experimental investigations. Though experiments may have scale problems, these are of similar gravity as the well-known problems of fieldwork and modelling and have not impeded spectacular progress through experimentation in other geosciences.

“…It is the purpose of this paper to present a general method of hydrologic analysis by means of a process-based model to develop a bottom-up catchment classification system that is compatible with and complementary to top-down classification methods developed elsewhere (Sawicz et al, 2011). In Sect.…”

Section: Introductionmentioning

confidence: 99%

“…Differences between the hydrologic responses of catchments can be quantified by means of specific signatures of catchment behavior, such as the runoff coefficient, the flow duration curve or the master recession curve. Gauged catchments can be clustered into separate groups with similar hydrologic signatures and this provides information about similarity of hydrologic responses (Sawicz et al, 2011). Such groups or classes can be regarded as a first step in catchment classification, which offer a catalogue of hydrologic behavior within G. Carrillo et al: Hydrological analysis of catchment behavior through process-based modeling a region.…”

Section: Introductionmentioning

confidence: 99%

“…Assuming an appropriate process-based model can be constructed for a wide range of catchments, we can use it to analyze the relationships between hydrologic response and catchment functioning (Samuel et al, 2008). A catchment can be considered as a filter that transforms the climate signal into a hydrologic response by partitioning, storing and releasing incoming energy and water (Black, 1997;Wagener et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient

Carrillo

Troch

Sivapalan

et al. 2011

120

Abstract. Catchment classification is an efficient method to synthesize our understanding of how climate variability and catchment characteristics interact to define hydrological response. One way to accomplish catchment classification is to empirically relate climate and catchment characteristics to hydrologic behavior and to quantify the skill of predicting hydrologic response based on the combination of climate and catchment characteristics. Here we present results using an alternative approach that uses our current level of hydrological understanding, expressed in the form of a process-based model, to interrogate how climate and catchment characteristics interact to produce observed hydrologic response. The model uses topographic, geomorphologic, soil and vegetation information at the catchment scale and conditions parameter values using readily available data on precipitation, temperature and streamflow. It is applicable to a wide range of catchments in different climate settings. We have developed a step-by-step procedure to analyze the observed hydrologic response and to assign parameter values related to specific components of the model. We applied this procedure to 12 catchments across a climate gradient east of the Rocky Mountains, USA. We show that the model is capable of reproducing the observed hydrologic behavior measured through hydrologic signatures chosen at different temporal scales. Next, we analyze the dominant time scales of catchment response and their dimensionless ratios with respect to climate and observable landscape features in an attempt to explain hydrologic partitioning. We find that only a limited Correspondence to: G. Carrillo (gustavoc@email.arizona.edu) number of model parameters can be related to observable landscape features. However, several climate-model time scales, and the associated dimensionless numbers, show scaling relationships with respect to the investigated hydrological signatures (runoff coefficient, baseflow index, and slope of the flow duration curve). Moreover, some dimensionless numbers vary systematically across the climate gradient, possibly as a result of systematic co-variation of climate, vegetation and soil related time scales. If such co-variation can be shown to be robust across many catchments along different climate gradients, it opens perspective for model parameterization in ungauged catchments as well as prediction of hydrologic response in a rapidly changing environment.