Variability Of Hydrological Processes Research Articles

Spatial variability of hydrological processes and model structure diagnostics in a 50 km2catchment

In this paper, we develop diagnostic methods to assess spatial variability in hydrological processes, particularly those relevant to catchment modelling. We target a range of catchment responses, including runoff volume, runoff timing, storage–discharge relationships, and threshold responses to rainfall and soil moisture. The diagnostics allow us to map the scales and patterns of process variability, to test whether climate or physical catchment characteristics can be used to predict patterns in processes, and to explore the implications for appropriate spatial variability in hydrological model structures or parameters. We apply the diagnostic tests to the mid-sized (50 km2) Mahurangi catchment in Northland, New Zealand, combining data from 28 flow gauges, 13 rain gauges, and 18 soil moisture measurement sites to build a comprehensive description of spatial variation in catchment response. The results show a complex picture: different diagnostics reveal different patterns of hydrological processes, and large variations in processes occur, even over the short length scales involved (~10 km). Catchment and climate characteristics almost all show the same pattern, i.e. that subcatchments in the far North and South of the Mahurangi are similar to each other (higher elevation, steep, forested), but contrast with central subcatchments (lower elevation, shallower slopes, pasture). Surprisingly, this pattern is not reflected in the patterns of diagnostic indices, demonstrating the difficulty of defining realistic a priori estimates of spatial variability in processes. We discuss how process variations correspond to the design of components of a simple lumped conceptual model. In the Mahurangi catchment, we find that spatial variations in multiple aspects of the hydrological response imply a need for spatial variation in both model structures and parameters. Copyright © 2013 John Wiley & Sons, Ltd.

El niño driven climate variability and drainage anomalies in Patagonian region Argentina

Forecasting of interannual and seasonal variability of hydrological processes is very important when planification of water resources is involved.The hydrological cycle and the climate system are intimately linked, and the knowledge of the atmospheric general circulation disturbances allows the mentioned forecast. It is increasingly clear that hydrological variability can be interpreted in terms of large-scale climatic anomalies-such as those associated with El Niño/Souther Oscillation (ENSO), and that there are strong relationships between hydrological anomalies in different parts of the world. Environmental and human characteristics of Patagonian region also contribute to its vulnerability to changes in water availability. Important characteristics are large demand for water supply, extensive development in floodplains, vulnerable groundwater supplies, water-quality problems, dependence on rain fed agriculture, and extensive dependence on hydroelectricity. Regions where water is already scarce during part or along the whole year are especially vulnerable to the disruption of supply caused by such climatic variability as prolonged or intense droughts. This paper describes the relationship between ENSO and river discharges of several Patagonian basins. The period with the highest discharge was selected and the accumulated anomalies of the river discharges were used as hydrological variable. The indexes considered were the sea surface temperature (SST) in the Pacific Ocean as a function of El Niño3 (90°W-180 ° W; 5°N-5°S) on the Tropical Pacific Ocean, and the Multivariate ENSO Index (MEI). The results were evaluated with a simple linear regression model. They showed a relationship between ENSO (as function of SST) and annual discharges, while MEI index has no significant results.

Real-world hydrologic assessment of a fully-distributed hydrological model in a parallel computing environment

► We quantify the performance of a new parallelization method for a distributed model. ► Parallel model performance is found to be dependent on the hydrologic variability. ► Load balancing methods improve parallel performance in particular for large domains. ► A wider range of distributed applications is now possible through parallelization. A major challenge in the use of fully-distributed hydrologic models has been the lack of computational capabilities for high-resolution, long-term simulations in large river basins. In this study, we present the parallel model implementation and real-world hydrologic assessment of the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS). Our parallelization approach is based on the decomposition of a complex watershed using the channel network as a directed graph. The resulting sub-basin partitioning divides effort among processors and handles hydrologic exchanges across boundaries. Through numerical experiments in a set of nested basins, we quantify parallel performance relative to serial runs for a range of processors, simulation complexities and lengths, and sub-basin partitioning methods, while accounting for inter-run variability on a parallel computing system. In contrast to serial simulations, the parallel model speed-up depends on the variability of hydrologic processes. Load balancing significantly improves parallel speed-up with proportionally faster runs as simulation complexity (domain resolution and channel network extent) increases. The best strategy for large river basins is to combine a balanced partitioning with an extended channel network, with potential savings through a lower TIN resolution. Based on these advances, a wider range of applications for fully-distributed hydrologic models are now possible. This is illustrated through a set of ensemble forecasts that account for precipitation uncertainty derived from a statistical downscaling model.

The impact of climate variability on flood risk in Poland.

This article examines the role of climatic and hydrological variability in assessing the cumulative risk of flood events in Poland over a T-year period. In a broad sense flood-risk estimation combines a frequency analysis of extreme hydrological phenomena with an evaluation of flood-induced damages. The damage from floods depends on the critical values of the river discharges. The probabilistic flood analysis usually includes an estimation of the expected annual probability of the critical discharge Qcr being exceeded and the equivalent long-term risk of it being exceeded over the next T years. If, however, the process is nonstationary, the T-year risk of flood damage may depend importantly on the variation of hydrological processes. As a possible explanation for the variations observed in snowmelt-induced floods in Polish rivers, this article investigates the possible impact of the North Atlantic Oscillation (NAO) on surface air temperature T and precipitation P. The spatial distribution of the correlation coefficients between NAO and T, as well as NAO and P, show very significant differences in the NAO impact on meteorological variables in various parts of Europe. To assess the implications of NAO variations on spring flood discharges, a simple model of Snow Cover Water Equivalent (SCWE) was applied to selected Polish river catchments. The conclusion of this analysis is that the yearly maximum of SCWE values significantly decreases with increasing NAO. This leads to a temporal redistribution of winter and spring runoff. The question of spring flood characteristics being stationary or nonstationary may therefore be linked with stochastic properties of the NAO index time series.