A framework for dry period low flow forecasting in Mediterranean streams

K. Risva, D. Nikolopoulos, A. Efstratiadis, and I. Nalbantis, A framework for dry period low flow forecasting in Mediterranean streams, Water Resources Management, 32 (15), 4911–1432, doi:10.1007/s11269-018-2060-z, 2018.



The objective of this article is to provide a simple and effective tool for low flow forecasting up to six months ahead, with minimal data requirements, i.e. flow observations retrieved at the end of wet period (first half of April, for the Mediterranean region). The core of the methodological framework is the exponential decay function, while the typical split-sample approach for model calibration, which is known to suffer from the dependence on the selection of the calibration data set, is enhanced by introducing the so-called Randomly Selected Multiple Subsets (RSMS) calibration procedure. Moreover, we introduce and employ a modified efficiency metric, since in this modelling context the classical Nash-Sutcliffe efficiency yields unrealistically high performance. The proposed framework is evaluated at 25 Mediterranean rivers of different scales and flow dynamics, including streams with intermittent regime. Initially, signal processing and data smoothing techniques are applied to the raw hydrograph, in order to cut-off high flows that are due to flood events occurring in dry periods, and allow for keeping the decaying form of the baseflow component. We then employ the linear reservoir model to extract the annually varying recession coefficient, and, then, attempt to explain its median value (over a number of years) on the basis of typical hydrological indices and the catchment area. Next, we run the model in forecasting mode, by considering that the recession coefficient of each dry period ahead is a linear function of the observed flow at the end of the wet period. In most of the examined catchments, the model exhibits very satisfactory predictive capacity and is also robust, as indicated by the limited variability of the optimized model parameters across randomly selected calibration sets.

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Our works referenced by this work:

1. A. Efstratiadis, and D. Koutsoyiannis, One decade of multiobjective calibration approaches in hydrological modelling: a review, Hydrological Sciences Journal, 55 (1), 58–78, doi:10.1080/02626660903526292, 2010.
2. K. Risva, D. Nikolopoulos, A. Efstratiadis, and I. Nalbantis, A simple model for low flow forecasting in Mediterranean streams, European Water, 57, 337–343, 2017.

Our works that reference this work:

1. G.-K. Sakki, I. Tsoukalas, and A. Efstratiadis, A reverse engineering approach across small hydropower plants: a hidden treasure of hydrological data?, Hydrological Sciences Journal, 67 (1), 94–106, doi:10.1080/02626667.2021.2000992, 2022.

Other works that reference this work (this list might be obsolete):

1. Tsihrintzis, V. A., and H. Vangelis, Water resources and environment, Water Resources Management, 32(15), 4813-4817, doi:10.1007/s11269-018-2164-5, 2018.
2. Kapetas, L., N. Kazakis, K. Voudouris, and D. McNicholl, Water allocation and governance in multi-stakeholder environments: Insight from Axios Delta, Greece, Science of The Total Environment, 695, 133831, doi:10.1016/j.scitotenv.2019.133831, 2019.
3. Azarnivand, A., M. Camporese, S. Alaghmand, and E. Dal, Simulated response of an intermittent stream to rainfall frequency patterns, Hydrological Processes, 34(3), 615-632, doi:10.1002/hyp.13610, 2020.
4. Lee, D., H. Kim, I. Jung, and J. Yoon, Monthly reservoir inflow forecasting for dry period using teleconnection indices: A statistical ensemble approach, Applied Sciences, 10(10), 3470, doi:10.3390/app10103470, 2020.
5. Nicolle, P., F. Besson, O. Delaigue, P. Etchevers, D. François, M. Le Lay, C. Perrin, F. Rousset, D. Thiéry, F. Tilmant, C. Magand, T. Leurent, and É. Jacob, PREMHYCE: An operational tool for low-flow forecasting, Proceedings of the International Association of Hydrological Sciences, 383, 381-389, doi:10.5194/piahs-383-381-2020, 2020.
6. Tilmant, F., P. Nicolle, F. Bourgin, F. Besson, O. Delaigue, P. Etchevers, D. François, M. Le Lay, C. Perrin, F. Rousset, D. Thiéry, C. Magand, T. Leurent, et É. Jacob, PREMHYCE : un outil opérationnel pour la prévision des étiages, La Houille Blanche, 5, 37-44, doi:10.1051/lhb/2020043, 2020.
7. Singh, S. K., and G. A. Griffiths, Prediction of streamflow recession curves in gauged and ungauged basins, Water Resources Research, 57(11), e2021WR030618, doi:10.1029/2021WR030618, 2021.
8. Orta, S., and H. Aksoy, Development of low flow duration-frequency curves by hybrid frequency analysis, Water Resources Management, 36, 1521-1534, doi:10.1007/s11269-022-03095-3, 2022.
9. Kadu, A., and B. Biswal, A model combination approach for improving streamflow prediction, Water Resources Management, doi:10.1007/s11269-022-03336-5, 2022.

Tagged under: Hydrological models, Hydrological processes, Optimization, Students' works