Abstract

Abstract. Quantifying the influence of human activities, such as reservoir building, water abstraction, and land use change, on hydrology is crucial for sustainable future water management, especially during drought. Model-based methods are very time-consuming to set up and require a good understanding of human processes and time series of water abstraction, land use change, and water infrastructure and management, which often are not available. Therefore, observation-based methods are being developed that give an indication of the direction and magnitude of the human influence on hydrological drought based on limited data. We suggest adding to those methods a “paired-catchment” approach, based on the classic hydrology approach that was developed in the 1920s for assessing the impact of land cover treatment on water quantity and quality. When applying the paired-catchment approach to long-term pre-existing human influences trying to detect an influence on extreme events such as droughts, a good catchment selection is crucial. The disturbed catchment needs to be paired with a catchment that is similar in all aspects except for the human activity under study, in that way isolating the effect of that specific activity. In this paper, we present a framework for selecting suitable paired catchments for the study of the human influence on hydrological drought. Essential elements in this framework are the availability of qualitative information on the human activity under study (type, timing, and magnitude), and the similarity of climate, geology, and other human influences between the catchments. We show the application of the framework on two contrasting case studies, one impacted by groundwater abstraction and one with a water transfer from another region. Applying the paired-catchment approach showed how the groundwater abstraction aggravated streamflow drought by more than 200 % for some metrics (total drought duration and total drought deficit) and the water transfer alleviated droughts with 25 % to 80 %, dependent on the metric. Benefits of the paired-catchment approach are that climate variability between pre- and post-disturbance periods does not have to be considered as the same time periods are used for analysis, and that it avoids assumptions considered when partly or fully relying on simulation modelling. Limitations of the approach are that finding a suitable catchment pair can be very challenging, often no pre-disturbance records are available to establish the natural difference between the catchments, and long time series of hydrological data are needed to robustly detect the effect of the human activities on hydrological drought. We suggest that the approach can be used for a first estimate of the human influence on hydrological drought, to steer campaigns to collect more data, and to complement and improve other existing methods (e.g. model-based or large-sample approaches).

Highlights

  • 1.1 BackgroundIn our human-modified era, the Anthropocene, human activities have direct and indirect effects on the hydrological system (UNESCO, 2009, 2012; Montanari et al, 2013; Destouni et al, 2013; McMillan et al, 2016)

  • In our human-dominated world we need to find ways to use our tools and methods to study the human influence on hydrology

  • We show the first application of the paired-catchment approach to quantify the human influence on hydrological droughts

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Summary

Introduction

1.1 BackgroundIn our human-modified era, the Anthropocene, human activities have direct and indirect effects on the hydrological system (UNESCO, 2009, 2012; Montanari et al, 2013; Destouni et al, 2013; McMillan et al, 2016). Wanders and Wada, 2015; Van Loon and Van Lanen, 2015; Veldkamp et al, 2015; Wada et al, 2017) In other fields, such as hydroecology, modelling is used to quantify hydrologic alteration, i.e. the deviation of flows between actual and baseline conditions The “observation-modelling” approach (e.g. Van Loon and Van Lanen, 2013) compares human-influenced observed droughts with naturalised simulated droughts. Downsides of this method are that the simulated hydrological data have uncertainties and a pre-disturbed period is needed for calibration to reduce those

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