Search

We assessed how climate change may impact the canopy hydrology of individual Pinus eldarica, Pinus brutia, and Cupressus arizonica trees planted in arid and semi-arid climates in Iran. Precipitation that reaches the forest floor as throughfall (TF) represents a significant portion of individual precipitation events (Pg). We used a nondimensional relative sensitivity coefficient to examine the sensitivity of TF to changes in Pg. The Coupled Model Intercomparison Project phase 5 (CMIP5) HadGEM2-ES product was used under two emission scenarios (Representative Concentration Pathway (RCP) 2.6 and 8.5) to project yearly precipitation and Pg for the measurement sites from 2020–50. There was a strong linear relationship between TF and Pg at all sites [TF = 0.6 (Pg) − 0.2; R2 = 0.9]. The arid P. eldarica trees showed the highest sensitivity coefficient (5.3) compared with semi-arid C. arizonica trees (1.6). The sensitivity coefficient approximately doubled for individual P. eldarica and C. arizonica trees in the arid climate relative to P. brutia and C. arizonica trees in the semi-arid climate. This suggests that TF under trees in the arid climate were more affected by changing Pg than in the semi-arid climate. Pinus eldarica trees planted in the dry climate exhibited the largest sensitivity coefficient differences (3.4) between small and large Pg. Changes in precipitation and increased smaller storm frequency are expected for arid and semi-arid regions. To buffer expected shifts in storm size due to climate change, it may be suitable to incorporate TF sensitivity when selecting trees for landscaping and urban greening. [ABSTRACT FROM AUTHOR]

In this study, we corrected the bias in the Princeton forcing dataset, i.e., precipitation, maximum and minimum temperatures, and wind speed, by adjusting its long-term mean monthly climatology to match observations for the period 1988–2012 using the delta-ratio method. To this end, we collected meteorological data from 97 stations covering the domain of Iran. We divided Iran into three climatic zones based on the De Martonne classification, i.e., Arid, Humid, and Per-Humid zones, and then applied the delta-ratio method for each climatic zone separately to adjust the bias. After adjustment, the new datasets were compared to the observations in 1958–1987. Results based on four skill scores, including the Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), root-mean-square error (RMSE), and R2, indicate that the adjustment greatly improved the quality of the gridded dataset, specifically, precipitation, maximum temperature, and wind speed. For example, NSE for annual precipitation during the validation time period increased from −0.03 to 0.72, PBIAS reduced from 29.2% to 6.6%, RMSE decreased by 182.44 mm, and R2 increased from 0.06 to 0.75. Assessing the results in different climatic zones of Iran reveals that precipitation improved more significantly in the Per-Humid zone followed by the Humid zone, while maximum temperature improved better in the Arid areas. For wind speed, the values improved comparably in the three climate zones. However, the delta values for monthly minimum temperature calculated during the adjustment time period cannot be applied in the validation time period, due to the fact that the Princeton climate data cannot follow the behavior of minimum temperature during the validation phase. In short, we showed that a simple bias adjustment approach, along with minimum observed station data, can significantly improve the performance of global gridded datasets. [ABSTRACT FROM AUTHOR]

• Reformulated Gash Analytical models (RGAM) applied for seven reforestations. • Models need frequent numbers of rainfall to estimate rainfall interception loss. • RGAM is advisable for predicting interception with small numbers of rain storms. • Ecohydrological parameters dramatically differs between studied stands. Modeling rainfall interception (I) is fundamental for understanding the forests' role in ecohydrological processes in local and regional scales. This research examines the Reformulated Gash Analytical Model (RGAM) applicability for estimating I in seven dissimilar plantations (Acer velutinum , Quercus castaneifolia , Pinus brutia , Picea abies , and Cupressus sempervirens L. var horizontalis) in the four reforested areas across the Hyrcanian temperate forests, northern Iran. Field measurements were performed from May-2012 to March-2014 for 167 distinct throughfall and gross rainfall observations. According to our results, the effects of inter-species variation on I value was evident, as P. abies might be the most excellent among the five frequently used tree species for planting project in temperate forests if the primary goal of plantation is to decrease erosion and surface runoff as it intercepted and evaporated more rainfall than other species. Our findings showed that in terms of average-based calculation, the mean of free throughfall coefficient (p) value in evergreen needle-leaved reforestations (0.47) was lower than those calculated for deciduous broad-leaved reforestations (0.59), and vice versa for the canopy storage capacity (S ; 1.85 mm vs. 1.36 mm, respectively), and mean of the ratio of evaporation rate to the mean of rainfall intensity during saturated canopy conditions ( E ¯ / R ¯ ; 0.45 vs. 0.38, respectively). The RGAM satisfactorily estimated I for all species (except for A. velutinum stand), because the estimation error of I was lower than 15% and the Nash–Sutcliffe efficiency was equal to or higher than 0.87. In conclusion, using data from a short time study period to derive canopy ecohydrological parameters as well as modeling I by the RGAM is advisable in reforestation areas across the Hyrcanian temperate forests and regions with similar forest and meteorological attributes. [ABSTRACT FROM AUTHOR]