Search

Global demand for freshwater is increasing as human population grows, climate changes and water resources are being overexploited. Consequently, many freshwater ecosystems, particularly in water-stressed regions, are severely degraded. Here we present a unique case of an Interbasin Water Transfer (IWT) project aiming at ecosystem rehabilitation and recreation enhancement of an intermittent transboundary stream (Ayun, Israel). For the past century, water diversion at the Lebanese side had led to flow secession in the Israeli Ayun Nature Reserve during the dry season (May–November). To restore flow continuum, a sum of 0.5 million cubic meters of high quality freshwaters have been allocated annually during the dry months. The aim of this study is to evaluate the IWT project by: (1) examining the correlation between water flow in the Ayun and recreational visitation, and (2) performing a cost-benefit analysis of the IWT scheme, including non-market benefits. A time-series regression (Adj. R 2 = 0.688, n = 125) shows that a 10% increase in water flow corresponds to a 2.1% increase in monthly visitors. An estimated 18.8% of the visitation rate between 2009 and 2015 can be attributed to the water reallocation project. Through a single-site travel cost model, we estimate the visitor's willingness to pay in US$ 37.8 per person per trip. When non-market benefits for recreation are included, the total benefits of the IWT project substantially exceed its costs. Our results suggest that IWT can be applied to restore water flow and enhance ecosystem services also in water-stressed regions. [ABSTRACT FROM AUTHOR]

Geolocated social media data counts are increasingly used as proxy for number of visits in natural areas, including their spatial and temporal distribution. This paper synthesizes the empirical evidence concerning the correlation of social media data counts and visits through multi-level meta-analytical models. Analysis of 355 correlations from 41 studies reveals a strong correlation for annual number of visits over multiple sites (pooled Pearson's r = 0.73) and for monthly visits in a single site (pooled Pearson's r = 0.84). Using data from multiple social media sources improves the correlation. Mixed results are obtained with regard to the effect of social media penetration rate and designation as national park on the correlation. Future studies should focus on broadening the scope of investigation to middle and low-income countries, developing a systematic approach toward the use of covariates, and comparing the results from social media data to those from other emerging monitoring techniques. • 355 correlations between social media data counts and visits are analyzed. • Strong correlation for multi-site annual visits and monthly visits is found. • Using data from multiple social media sources improves correlation. • Covariates may increase correlation but a systematic approach is missing. • A broader empirical basis is needed to fully appreciate the technique's potential. [ABSTRACT FROM AUTHOR]

Social media data are increasingly utilised as a low-cost alternative to visitor surveys in characterising nature-based recreation. However, the information available on individual users is limited and typically does not include provenance, restricting the potential applications and impact of the data. Here we investigate a methodology to estimate social media visitors' home locations at various spatial scales and apply it to the entire network of national parks in Germany. We compare predicted visitor provenance to representative onsite survey data and explore group-specific spatial and temporal patterns of recreation as characterised by users' geotagged photographs. Results show that photograph metadata can be used to assign home locations with accuracies between 62 and 89% depending on spatial scale implemented. Said social media-based predictions are reasonably well representative of the surveyed visitor structure in German national parks with Flickr visitor-days composed of 19% local, 62% non-local German and 19% international visits. • Photographs metadata can be used to assign home locations of Flickr users. • Home locations are predicted with accuracies between 62 and 89% depending on scale. • Flickr visits are 19% local, 62% non-local German and 19% international. • Flickr photographs can characterize seasonal, weekly and daily visitation patterns. • International visitors take more photographs per visit than local visitors. [ABSTRACT FROM AUTHOR]