Fog drip and related microclimatic factors were measured between January 1999 and December 2002 at a tropical seasonal rain forest in Xishuangbanna, South-West China. During the study period, the annual average fog drip was 89.4±13.5 mm (mean±1 SD). Fog drip contributes an estimated 5% of the annual rainfall, with 86% of the fog drip occurring in the dry season (November–April). Annual fog drip was negatively correlated with annual rainfall. Monthly variation in fog drip was also negatively correlated with monthly rainfall. Average daily fog drip was 0.38±0.27 mm d⁻¹ for all days on which fog drip occurred. Daily fog drip was negatively correlated with minimum air temperature and positively correlated with mean above-canopy wind speed. The results indicate that fog drip is an important additional input of water to this seasonal rain forest during the dry season.

In this paper we develop a method to estimate land-surface water content in amostly forest-dominated (humid) and topographically-varied region of eastern Canada. Theapproach is centered on a temperature-vegetation wetness index (TVWI) that uses standard 8-day MODIS-based image composites of land surface temperature (T_S) and surface reflectanceas primary input. In an attempt to improve estimates of TVWI in high elevation areas, terrain-induced variations in T_S are removed by applying grid, digital elevation model-basedcalculations of vertical atmospheric pressure to calculations of surface potential temperature(θS). Here, θ_S corrects T_S to the temperature value to what it would be at mean sea level (i.e.,~101.3 kPa) in a neutral atmosphere. The vegetation component of the TVWI uses 8-daycomposites of surface reflectance in the calculation of normalized difference vegetation index(NDVI) values. TVWI and corresponding wet and dry edges are based on an interpretation ofscatterplots generated by plotting θ_S as a function of NDVI. A comparison of spatially-averaged field measurements of volumetric soil water content (VSWC) and TVWI for the 2003-2005 period revealed that variation with time to both was similar in magnitudes. Growing season, point mean measurements of VSWC and TVWI were 31.0% and 28.8% for 2003, 28.6% and 29.4% for 2004, and 40.0% and 38.4% for 2005, respectively. An evaluation of the long-term spatial distribution of land-surface wetness generated with the new θ_S-NDVI function and a process-based model of soil water content showed a strong relationship (i.e., r² = 95.7%).

An in situ field test on nine commonly-used soil water sensors was carried out in a sandy loam soil located in the Potato Research Center, Fredericton, NB (Canada) using the gravimetric method as a reference. The results showed that among the tested sensors, regardless of installation depths and soil water regimes, CS615, Trase, and Troxler performed the best with the factory calibrations, with a relative root mean square error (RRMSE) of 15.78, 16.93, and 17.65%, and a r² of 0.75, 0.77, and 0.65, respectively. TRIME, Moisture Point (MP917), and Gopher performed slightly worse with the factory calibrations, with a RRMSE of 45.76, 26.57, and 20.41%, and a r² of 0.65, 0.72, and 0.78, respectively, while the Gypsum, WaterMark, and Netafim showed a frequent need for calibration in the application in this region.

Four methods of estimating daily reference evapotranspiration ( ETo ) were evaluated with the data collected from 2004 to 2006 in a Maritime weather station, the Potato Research Centre, Fredericton, N.B., Canada. We tested two models [i.e., the FAO-56 Penman–Monteith (PM) and the Priestley–Taylor (PT) equations] and two Class A pan methods (Cuenca and Snyder equations). In order to assess the Evaporation Pan methods, an automatic Class A Pan system was installed in a grassed field surrounded by potato fields and continuously measured from 2004 to 2006. The results from three growing seasons (years 2004–2006) indicated that both evaporation pan methods generated lower estimations of ETo compared to the PM and PT methods. The PT method produced the highest ETo estimation. The Snyder method showed a better agreement with the PM ( r2 >0.56) . However, the agreement varied from year to year with an r2 value range of 0.4–0.7. Kpan coefficients (a factor to convert pan observation to ETo ) varied from 0.78 to 0....