Numerical rivers: A synthetic streamflow generator for water resources vulnerability assessments

Abstract

The vulnerability of water supplies to shortage depends on the complex interplay between streamflow variability and the management and demands of the water system. Assessments of water supply vulnerability to potential changes in streamflow require methods capable of generating a wide range of possible streamflow sequences. This paper presents a method to generate synthetic monthly streamflow sequences that reproduce the statistics of the historical record and that can express climate-induced changes in user-specified streamflow characteristics. The streamflow sequences are numerically simulated through random sampling from a parametric or a nonparametric distribution fitted to the historical data while shuffling the values in the time series until a sequence matching a set of desired temporal properties is generated. The desired properties are specified in an objective function which is optimized using simulated annealing. The properties in the objective function can be manipulated to generate streamflow sequences that exhibit climate-induced changes in streamflow characteristics such as interannual variability or persistence. The method is applied to monthly streamflow data from the Thames River at Kingston (UK) to generate sequences that reproduce historical streamflow statistics at the monthly and annual time scales and to generate perturbed synthetic sequences expressing changes in short-term persistence and interannual variability.

Assessing water resource system vulnerability to unprecedented hydrological drought using copulas to characterize drought duration and deficit

“…The autocorrelation function (Figure c) of the simulated sequences is comparable with the autocorrelation of the observed sequences, although it shows a slight underestimation for lags lower than seven which may be due to the fact that our copula model is only fitted to the first lag (i.e., only consecutive month pairs were used to fit the copula). The mismatch for lags greater than eight is not concerning given that the monthly autocorrelation for the Thames streamflow data at Kingston is only significant up to lag‐8 [ Borgomeo et al ., ].…”

Exploring Cooperative Transboundary River Management Strategies for the Eastern Nile Basin

“…A reconstruction of naturalized hydrologic conditions for the eastern Nile Basin from 1900 to 2002 (van der Krogt & Ogink, ) was used to generate ensembles of 100 stochastic synthetic flow time series across 162 inflow locations using a simulated annealing algorithm (Borgomeo et al, ). The first set of 100 synthetic time series matches the statistical properties of the naturalized hydrology including the Hurst coefficient for hydrologic persistence (current conditions) and is considered to reflect the probable hydrologic conditions.…”

“…The ENRM simulation model was configured to study long-term management strategies after the GERD has completed the filling process. (Borgomeo et al, 2015). The first set of 100 synthetic time series matches the statistical properties of the naturalized hydrology including the Hurst coefficient for hydrologic persistence (current conditions) and is considered to reflect the probable hydrologic conditions.…”

Building Hydrologic Foundations for Applications of ELOHA: How Long A Record Should You Have?

“…As another step, investigators should consider synthetic hydrology, which can be used to generate longer records with the same statistical properties as the original (Rajagopalan et al, ; You et al, ; Borgomeo et al, ). This allows for better estimates of the probability of rare events, for example, of a drought of given length in occurring in 100 years.…”