The successful application of hydrological models relies on careful calibration and uncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms, and each could be run with different objective functions. In this paper, we highlight the fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance; this makes the interpretation of dominant hydrological processes in a watershed highly uncertain. We used three different optimization algorithms (SUFI-2, GLUE, and PSO), and eight different objective functions (R², bR², NSE, MNS, RSR, SSQR, KGE, and PBIAS) in a SWAT model to calibrate the monthly discharges in two watersheds in Iran. The results show that all three algorithms, using the same objective function, produced acceptable calibration results; however, with significantly different parameter ranges. Similarly, an algorithm using different objective functions also produced acceptable calibration results, but with different parameter ranges. The different calibrated parameter ranges consequently resulted in significantly different water resource estimates. Hence, the parameters and the outputs that they produce in a calibrated model are “conditioned” on the choices of the optimization algorithm and objective function. This adds another level of non-negligible uncertainty to watershed models, calling for more attention and investigation in this area.

Remote sensing has shown an immense capability for large‐scale estimation of air temperature (T_air), one of the most important environmental state variables, using land surface temperature (LST) data. Following recent investigations on the T_air–LST relationship, in this article, we propose an advanced statistical approach to this realm. We tested the approach for estimation of T_air in eastern part of Iran using MODIS daytime and nighttime LST products and 11 auxiliary variables including Julian day, solar zenith angle, extraterrestrial solar radiation, latitude, altitude, reflectance at various visible and infrared bands and vegetation indices. Fourteen statistical models constructed through a stepwise regression analysis were evaluated along a 5‐year period (2000–2004) using MODIS and meteorological station data. Results of this study indicated that the statistical approach performed reasonably well, where our final proposed model could estimate average T_air with validation mean absolute error of 2.3 and 1.8 °C at daily and weekly scales, respectively. Nighttime LST, Julian day, altitude and solar zenith angle indicated to be the most effective variables capturing most variations of T_air in the study region. Variables influenced by land surface and land cover properties including reflectance at different bands and vegetation indices showed a negligible effect on the T_air‐LST relationship within the study area. It was indicated that the proposed models generally performed better for lower altitude regions.

Drought has significantly affected environmental and socio‐economic conditions in Iran. In this study three drought indices were used for monitoring drought intensity and duration in the Kashafrood basin (northeast Iran). The standardized precipitation Index (SPI), Precipitation Index Percent of Normal (PNPI) and Agricultural Rainfall Index (ARI) were calculated for the base period (1990–1961). All these indices were used to assess future drought events in the Kashafrood basin under climate change attributed to low and high greenhouse gas emission scenarios (SRES B2 and A2, respectively) for three periods (2010–2039, 2040–2069 and 2070–2100). Projected changes in precipitation, temperature and potential evaporation were simulated by statistical downscaling of HadCM3 outputs. The main results showed: (1) slight increase in precipitation means, around 2.2–5.4% under the A2 scenario and 6.8 and 8.6% under the B2 scenario: (2) slight increase in maximum temperature, around 4.6–5.6 °C for the A2 scenario and 4.25 and 4.6 °C under the B2 scenario; (3) slight increase in minimum temperature, around 1.6–1.9 °C under the A2 scenario and 1 and 2.23 °C under the B2 scenario, and, (4) higher drought frequency associated with global warming was demonstrated by all indices under both scenarios. Such an increase in drought frequency would have major implications for natural resource management, water security planning, water demand management strategies and drought relief payments. Copyright © 2012 Royal Meteorological Society

Estimation of riparian forest structural attributes, such as the Leaf Area Index (LAI), is an important step in identifying the amount of water use in riparian forest areas. In this study, small-footprint lidar data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the Upper San Pedro River Basin, Arizona, USA. Canopy height and maximum and mean laser heights were derived for the cottonwood trees from lidar data. Linear regression models were used to develop equations relating lidar height metrics with corresponding field-measured LAI for each age class of cottonwoods. Four metrics (tree height, height of median energy, ground return ratio, and canopy return ratio) were derived by synthetically constructing a large-footprint lidar waveform from small-footprint lidar data which were compared to ground-based high-resolution Intelligent Laser Ranging and Imaging System (ILRIS) scanner images. These four metrics were incorporated into a stepwise regression procedure to predict field-derived LAI for different age classes of cottonwoods. This research applied the Penman–Monteith model to estimate transpiration of the cottonwoods using lidar-derived canopy metrics. These transpiration estimates compared very well to ground-based sap flux transpiration estimates indicating lidar-derived LAI can be used to improve riparian cottonwood water-use estimates.

Airborne lidar (light detecting and ranging) is a useful tool for probing the structure of forest canopies. Such information is not readily available from other remote sensing methods and is essential for modern forest inventories. In this study, small-footprint lidar data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the San Pedro River basin near Benson, Arizona. The lidar data were acquired in June 2004, using Optech’s 1233 ALTM during flyovers conducted at an altitude of 600 m. Canopy height, crown diameter, stem dbh, canopy cover, and mean intensity of return laser pulses from the canopy surface were estimated for the cottonwood trees from the data. Linear regression models were used to develop equations relating lidar-derived tree characteristics with corresponding field acquired data for each age class of cottonwoods. The lidar estimates show a good degree of correlation with ground-based measurements. This study also shows that other parameters of young, mature, and old cottonwood trees such as height and canopy cover, when derived from lidar, are significantly different (P < 0.05). Additionally, mean crown diameters of mature and young trees are not statistically different at the study site (P = 0.31). The results illustrate the potential of airborne lidar data to differentiate different age classes of cottonwood trees for riparian areas quickly and quantitatively. West. J. Appl. For. 21(3):149–158.