Development of a methodology to assess future trends in low flows at the watershed scale using solely climate data

Abstract

1Low flow conditions are governed by short-to-medium term weather conditions or long term 2 climate conditions. This prompts the question: given climate scenarios, is it possible to 3 assess future extreme low flow conditions from climate data indices (CDIs)? Or should we 4 rely on the conventional approach of using outputs of climate models as inputs to a 5 hydrological model? Several CDIs were computed using 42 climate scenarios over the years 6 1961 to 2100 for two watersheds located in Québec, Canada. The relationship between the 7 CDIs and hydrological data indices (HDIs; 7-and 30-day low flows for two hydrological 8 seasons) were examined through correlation analysis to identify the indices governing low 9 flows. Results of the Mann-Kendall test, with a modification for autocorrelated data, clearly 10 identified trends. A partial correlation analysis allowed attributing the observed trends in HDIs 11 to trends in specific CDIs. Furthermore, results showed that, even during the spatial 12 validation process, the methodological framework was able to assess trends in low flow 13 series from: (i) trends in the effective drought index (EDI) computed from rainfall plus 14 snowmelt minus PET amounts over ten to twelve months of the hydrological snow cover 15 season or (ii) the cumulative difference between rainfall and potential evapotranspiration over 16 five months of the snow free season. For 80% of the climate scenarios, trends in HDIs were 17 successfully attributed to trends in CDIs. Overall, this paper introduces an efficient 18 methodological framework to assess future trends in low flows given climate scenarios. The 19 outcome may prove useful to municipalities concerned with source water management under 20 changing climate conditions. 21 22 Keywords: 23 effective drought index; 7-day low flow; 30-day low flow; HYDROTEL; trends; climate model 24 for the 2001-2100 period, (ii) uncertainty of the climate change signal was addressed through 78 the use of 42 climate simulations, and (iii) future flows were simulated using a distributed 79 hydrological model. 80 Materials and methods 81The organization and mapping of the Materials and methods and Results sections are 82 introduced in Figure 1. Throughout the paper, and in accordance with CEHQ (2013a); IPCC 83 (2013), "simulation" or "climate simulation" refers to the raw climate model outputs. 84 "Scenario" or "climate scenario" refers to a post-processed simulation, which is a simulation 85 for which a series of specific choices have been made (study region and period, spatial and 86 temporal resolutions, bias-correction method). White boxes present how the climate 87 scenarios were obtained from 42 different bias-corrected climate simulations. Grey boxes 88 introduce the methodological framework proposed in this paper. It required computing CDIs 89 from climate data extracted from the aforementioned climate scenarios and HDIs from 90 simulated streamflows using a calibrated hydrological model. Afterwards, the statistical 91 relationships between CDIs and HD...

Application of a High‐Resolution Distributed Hydrological Model on a U.S.‐Canada Transboundary Basin: Simulation of the Multiyear Mean AnnualHydrograph and 2011 Flood of theRichelieu River Basin

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Water Supply-Water Environmental Capacity Nexus in a Saltwater Intrusion Area under Nonstationary Conditions

“…The trend of the annual minimum daily flow time series will be tested using the Mann-Kendall (M-K) method and the Pettitt change point analysis will be employed to determine the change point [49][50][51]. The M-K test is a rank-based nonparametric trend detection method that is commonly used to assess the significance of monotonic trends in hydrological data time series [40,[52][53][54]. The null hypothesis for no-trend in the M-K test is that the low flow time series are randomly ordered.…”

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