Abstract
Land use change is an important driver of trends in streamflow. However, the effects are often difficult to disentangle from climate effects. The aim of this paper is to demonstrate that trends in streamflow can be identified by analysing residuals of rainfall-runoff simulations using a Generalized Additive Mixed Model. This assumes that the rainfall-runoff model removes the average climate forcing from streamflow. The case study involves the Santa Lucía river (Uruguay), the GR4J rainfall-runoff model, three nested catchments ranging from 690 to 4900 km 2 and 35 years of observations (1981–2016). Two exogenous variables were considered to influence the streamflow. Using satellite data, growth in forest cover was identified, while the growth in water licenses was obtained from the water authority. Depending on the catchment, effects of land use change differ, with the largest catchment most impacted by afforestation, while the middle size catchment was more influenced by the growth in water licenses.
Highlights
Global water resources are limited and under pressure across the world [1]
Based on the Nash - Sutcliffe efficiency (NSE), R-squared (r2 ) and BIAS; the fit was better for the largest catchment Catchment 1 (C1), followed by C2 and Catchment 3 (C3) (Table 2)
Catchment C1 show the highest increase in transformed runoff residuals (TRR) in the early part of simulation (Figure 5a), preliminarily identified in the Figure by linear regressions on the early and late parts of the simulation period
Summary
The total amount of water that can be used for irrigation, water supply or power generation is defined by the transfer of moisture between climate and landscape [2]. The effect of climate on water resources is often concealed because the processes in the landscape are changing [3,4]. There have been a number of recent publications identifying the combined impact on water resources of land use change and climate change and variability (e.g., [5,6,7,8]). The identification of land-use trends is difficult due to the effect of the overlying climate trends and variability [5,8]. As a result, disentangling the different impacts on streamflow is not an easy task, since it is ver difficult to perform paired catchment studies, or before and after studies, at regional scales [5]
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