A systematic approach helps to illuminate the complex forest-water nexus

Potential evapotranspiration (PET) is crucial for water resources assessment. In this regard, the FAO (Food and Agriculture Organization)–Penman–Monteith method (PM) is commonly recognized as a standard method for PET estimation. However, due to requirement of detailed meteorological data, the application of PM is often constrained in many regions. Under such circumstances, an alternative method with similar efficiency to that of PM needs to be identified. In this study, three radiation-based methods, Makkink (Mak), Abtew (Abt), and Priestley–Taylor (PT), and five temperature-based methods, Hargreaves–Samani (HS), Thornthwaite (Tho), Hamon (Ham), Linacre (Lin), and Blaney–Criddle (BC), were compared with PM at yearly and seasonal scale, using long-term (50 years) data from 90 meteorology stations in southwest China. Indicators, viz. (videlicet) Nash–Sutcliffe efficiency (NSE), relative error (Re), normalized root mean squared error (NRMSE), and coefficient of determination (R²) were used to evaluate the performance of PET estimations by the above-mentioned eight methods. The results showed that the performance of the methods in PET estimation varied among regions; HS, PT, and Abt overestimated PET, while others underestimated. In Sichuan basin, Mak, Abt and HS yielded similar estimations to that of PM, while, in Yun-Gui plateau, Abt, Mak, HS, and PT showed better performances. Mak performed the best in the east Tibetan Plateau at yearly and seasonal scale, while HS showed a good performance in summer and autumn. In the arid river valley, HS, Mak, and Abt performed better than the others. On the other hand, Tho, Ham, Lin, and BC could not be used to estimate PET in some regions. In general, radiation-based methods for PET estimation performed better than temperature-based methods among the selected methods in the study area. Among the radiation-based methods, Mak performed the best, while HS showed the best performance among the temperature-based methods.

Extensive studies on hydrological responses to forest change have been published for centuries, yet partitioning the hydrological effects of forest change, climate variability and other factors in a large watershed remains a challenge. In this study, we developed a single watershed approach combining the modified double mass curve (MDMC) and the time series multivariate autoregressive integrated moving average model (ARIMAX) to separate the impact of forest change, climate variability and other factors on dry season runoff variation in two large watersheds in China. The Zagunao watershed was examined for the deforestation effect, while the Meijiang watershed was examined to study the hydrological impact of reforestation. The key findings are: (1) both deforestation and reforestation led to significant reductions in dry season runoff, while climate variability yielded positive effects in the studied watersheds; (2) the hydrological response to forest change varied over time due to changes in soil infiltration and evapotranspiration after vegetation regeneration; (3) changes of subalpine natural forests produced greater impact on dry season runoff than alteration of planted forests. These findings are beneficial to water resource and forest management under climate change and highlight a better planning of forest operations and management incorporated trade-off between carbon and water in different forests.