Uncertainty of Hydrological Predictions

Self Cite

130

Abstract.Globally, many different kinds of water resources management issues call for policy-and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal-to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle -a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.

Section: Challenge 3: Uncertainty Predictability and Observations Ofmentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Self Cite

130

“…Examples of Monte Carlo simulations applied in statistical modeling can be found in much of the literature (Beersma and Buishand, 2007;Chu and Wang, 1998;Davidson and Hinkley, 1997;Onyutha and Willems, 2013). Other statistical approaches of uncertainty assessment can be found elaborated in Montanari (2011). To derive bounds of variability using 95 % CI, the nonparametric bootstrapping method (Davidson and Hinkley, 1997) was employed as follows:…”

Section: Test Of Significancementioning

confidence: 99%

Spatial and temporal variability of rainfall in the Nile Basin

Onyutha

Willems

2015

Abstract. Spatiotemporal variability in annual and seasonal rainfall totals were assessed at 37 locations of the Nile Basin in Africa using quantile perturbation method (QPM). To get insight into the spatial difference in rainfall statistics, the stations were grouped based on the pattern of the long-term mean (LTM) of monthly rainfall and that of temporal variability. To find the origin of the driving forces for the temporal variability in rainfall, correlation analyses were carried out using global monthly sea level pressure (SLP) and sea surface temperature

“…Any stream gauge may be far from the ungauged basin of interest and there may be uncertainties in the collected data (Montanari, 2007). Moreover predictive errors of methods arise from data uncertainties, model structure uncertainties and model parameter uncertainties (Montanari, 2011).…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

Viglione

Parajka

Rogger

et al. 2013

113

Abstract. This is the third of a three-part paper series through which we assess the performance of runoff predictions in ungauged basins in a comparative way. Whereas the two previous papers by Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation performance of hydrographs and hydrological extremes on the basis of a comprehensive literature review of thousands of case studies around the world, in this paper we jointly assess prediction performance of a range of runoff signatures for a consistent and rich dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall-runoff model and by Top-kriging, a geostatistical estimation method that accounts for the river network hierarchy. From the runoff timeseries, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrographs. The predictive performance is assessed in terms of the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation (blind testing) mode. Results of the comparative assessment show that, in Austria, the predictive performance increases with catchment area for both methods and for most signatures, it tends to increase with elevation for the regionalised rainfall-runoff model, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows in ungauged basins is the most difficult to estimate followed by the low flows. It also turns out that in this data-rich study in Austria, the geostatistical approach (Top-kriging) generally outperforms the regionalised rainfall-runoff model.