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Closing the water balance of mountainous catchments may sometimes become tricky, even before applying a hydrological model. In this paper, we focus on mountainous, snow-affected catchments and try to understand the reasons for the “unrealistic” hydrological behaviours that they sometimes show: when annual runoff is greater than the annual areal precipitation estimate, something is obviously wrong, but finding the appropriate means to adjust the water balance is far from a trivial matter. This paper aims to improve our knowledge of the water balance of mountainous and snow-affected catchments in two different countries: Sweden and France. We use a simple non-dimensional framework to detect outliers and then propose an regionalization of precipitation and air temperature in order to better estimate inputs over high-altitude catchments. Since we are interested in catchment water balance, we evaluate our regionalized input estimates by comparing them to streamflow measurements. The results are mixed: in Sweden, our approach is successful because it can make most outlier catchments become regular ones; but in France it is disappointing because it does not solve most of the water balance problems identified. However, for both countries, the regionalization approach significantly improves the performance of a rainfall-runoff model at a daily time step. Citation Valery, A., Andreassian, V. & Perrin, C. (2010) Regionalization of precipitation and air temperature over high-altitude catchments - learning from outliers. Hydrol. Sci. J. 55(6), 928-940. [ABSTRACT FROM AUTHOR]

A significant decrease in mean river flow as well as shifts in flood regimes have been reported at several locations along the River Niger. These changes are the combined effect of persistent droughts, damming and increased consumption of water. Moreover, it is believed that climate change will impact on the hydrological regime of the river in the next decades and exacerbate existing problems. While decision makers and stakeholders are aware of these issues, it is hard for them to figure out what actions should be taken without a quantitative estimate of future changes. In this paper, a Soil and Water Assessment Tool (SWAT) model of the Niger River watershed at Koulikoro was successfully calibrated, then forced with the climate time series of variable length generated by nine regional climate models (RCMs) from the AMMA-ENSEMBLES experiment. The RCMs were run under the SRES A1B emissions scenario. A combination of quantile-quantile transformation and nearest-neighbour search was used to correct biases in the distributions of RCM outputs. Streamflow time series were generated for the 2026–2050 period (all nine RCMs), and for the 2051–2075 and 2076–2100 periods (three out of nine RCMs) based on the availability of RCM simulations. It was found that the quantile-quantile transformation improved the simulation of both precipitation extremes and ratio of monthly dry days/wet days. All RCMs predicted an increase in temperature and solar radiation, and a decrease in average annual relative humidity in all three future periods relative to the 1981–1989 period, but there was no consensus among them about the direction of change of annual average wind speed, precipitation and streamflow. When all model projections were averaged, mean annual precipitation was projected to decrease, while the total precipitation in the flood season (August, September, October) increased, driving the mean annual flow up by 6.9% (2026–2050), 0.9% (2051–2075) and 5.6% (2076–2100). At-test showed that changes in multi-model annual mean flow and annual maximum monthly flow between all four periods were not statistically significant at the 95% confidence level. [ABSTRACT FROM PUBLISHER]

This paper presents a calibration framework for a precipitation-runoff model for flood prediction in a mesoscale Alpine basin with discharges strongly influenced by hydraulic works. The developed methodology addresses two classical hydrological calibration challenges: computational limitations to run optimization algorithms for distributed hourly models and the absence of concomitant meteorological and natural discharge time series. The presented processes-oriented, multi-signal approach is based on hydrological data from a variety of sources and for different periods, corresponding to various spatial scales. The model parameters are calibrated by sequentially minimizing differences between observed and simulated values for different hydrological signals and signatures such as: (a) the phase of precipitations, (b) the time evolution of point-scale snow heights, (c) the mean inter-annual cycle of daily discharges, and (d) timing of snowmelt-induced spring runoff. We compare the model performance to a benchmark model obtained by simply using the globally optimal parameter values from the nearest gauged and non perturbed catchment. For prediction of flow seasonality and also extreme events, the calibration methodology outperforms the benchmark. Citation Hingray, B., Schaefli, B., Mezghani, A. & Hamdi, Y. (2010) Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments. Hydrol. Sci. J. 55(6), 1002-1016. [ABSTRACT FROM AUTHOR]

In earlier studies involving simulation of the Fergus River flows in Ireland, the conceptual Soil Moisture Accounting and Routing (SMAR) model had been found to consistently outperform a number of black-box models. Subsequently, in investigating any loss of flow through this catchment's subsurface karstic features, it was verified from the overall long-term water balance that such losses were substantial. This raised the awkward question of why the volume-conservative SMAR model had performed so well on this considerably non-conservative catchment. Further analyses revealed that, to compensate for the excess volume of total runoff generated by the model's conservative water balance component, the memory length of the surface runoff response function had been unrealistically curtailed in the optimization process, effectively truncating that function and thereby violating the conservation property of the routing process. This embarrassing revelation called for reconsideration of the model structure to account more sensibly for actual losses, while still achieving high model efficiency. This paper highlights not only the discovery of the karstic Fergus catchment as a “hydrological monster”, in the context of the SMAR model, but also why conservative models perform poorly in such cases. In an attempt to “tame the monster”, better simulation of the observed flows was achieved by conceptually adapting the SMAR model, in a pragmatic empirical manner, by simply modifying its water balance component. Citation Goswami, M. & O'Connor, K. M. (2010) A “monster” that made the SMAR conceptual model “right for the wrong reasons”. Hydrol. Sci. J. 55(6), 913-927. [ABSTRACT FROM AUTHOR]