Predicting streamflow distributions and flow duration curves from landscape and climate

Characterizing the probability distribution of streamflows in catchments lacking in discharge measurements represents an attractive prospect with consequences for practical and scientific applications, in particular water resources management. In this paper, a physically-based analytic model of streamflow dynamics is combined with a set of water balance models and a geomorphological recession flow model in order to estimate streamflow probability distributions based on catchment-scale climatic and morphologic features. The models used are described and the novel parameterization approach is elaborated on. Starting from rainfall data, potential evapotranspiration and digital terrain maps, the method proved capable of capturing the statistics of observed streamflows reasonably well in 11 test catchments distributed throughout the United States, east of the rocky mountains. The method developed offers a unique approach for estimating probability distribution of streamflows where only climatic and geomorphologic features are known

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

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) and propose 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

Assessing the Impact of Climate Change on Water Resources: The Challenge Posed by a Multitude of Options

Global warming due to anthropogenic emissions of greenhouse gases is already altering the Earth’s climate. A changing climate implies shifts in long-term temperature and precipitation patterns, which in turn will affect the spatiotemporal distribution of water resources. It is imperative to study the impact of climate change on water resources so that adequate adaptation actions can be planned well in advance. We, therefore, need reliable methods to estimate how the timing and magnitude of available fresh water in a region may change in response to a changing climate. This chapter summarizes the main approaches that are used to achieve this goal. We focus on the numerous choices that a modeler faces when attempting to quantify the impact of climate change on water resources of a region. We discuss the relative strengths and weaknesses of each approach. These choices vary from the choice of model structures representing global climate and local hydrology, possible trajectories of greenhouse gas emissions in the future, to methods for model evaluation. Wherever feasible, we provide recommendations that can help a modeler in choosing an appropriate course of action. We conclude the chapter with a discussion on recent techniques developed to deal with large uncertainties in projections of climate change and possible research directions that will benefit the impact assessment community