Eduardo Varas

Estimation of mean monthly solar global radiation as a function of temperature

Solar radiation is the primary energy source for all physical and biochemical processes that take place on earth. Energy balances are a key feature of processes such as temperature changes, snow melt, carbon fixation through photosynthesis in plants, evaporation, wind intensity and other biophysical processes. Solar radiation level is sometimes recorded, but generally it needs to be estimated by empirical models based on frequently available meteorological records such as hours of sunshine or temperature. This paper evaluates the behavior of two empirical models based on the difference between maximum and minimum temperatures and compares results with a model based on sunshine hours. This work concludes that empirical models based on temperature have a larger coefficient of determination than the model based on cloud cover for the normal conditions of Chile. These models are easy to use in any location if the parameters are correctly adjusted. In addition, probability distribution functions and confidence intervals for solar radiation estimates using stochastic modeling of temperature differences were calculated. ©2000 Published by Elsevier Science B.V. All rights reserved.

Artificial neural networks for streamflow prediction

This paper presents monthly streamflow prediction using artificial neural networks (ANN) on mountain watersheds. The procedure addresses the selection of input variables, the definition of model architecture and the strategy of the learning process. Results show that spring and summer monthly streamrlows can be adequately represented, improving the results of calculations obtained using other methods. Better streamflow prediction methods should have significant benefits for the optimal use of water resources for irrigation and hydroelectric energy generation.

Decision support model for operation of multi-purpose water resources systems

Models to search for optimal operation rules of complex water resources systems generally represent the physical system in a fixed static form, being difficult to incorporate changes in water offer, water demand and system structure. This paper presents a decision support procedure that integrates continuous simulation, artificial neural networks, and optimization to produce decision rules in watershed management for multiple purpose complex water resources systems. The system uses physical indexes to evaluate the compliance of targets for the different purposes of the system, such as occurrence of failure (frequency), resilience (duration and capacity of recovery of a state of failure) and vulnerability (severity or magnitude of the failure). It also introduces a global indicator of the behavior of the system, which combines, with user selected weights, the previous indexes in a measure of global effectiveness. The methodology was applied to the San Juan River Basin, Argentina, and results show conclusively the usefulness of simulation in the study of alternatives of water resources systems with multiple uses and the feasibility of using neural networks to encapsulate the behavior of simulation models. The encapsulated model and parametric operation rules can be included in a dynamic optimization process to search for optimal operation policies.

Use of mesoscale model MM5 forecasts as proxies for surface meteorological and agroclimatic variables

D.O. Silva, F.J. Meza, and E. Varas. 2009. Use of mesoscale model MM5 forecasts as proxies for surface meteorological and agroclimatic variables. Cien. Inv. Agr. 36(3):369-380. There is increasing interest in meteorological information and its application to strategic planning at the farm as well as regional level. Although we have recently seen signifi cant improvements to strengthen and enlarge networks of weather observations, their density is still insuffi cient to cover large extents at the desired spatial and temporal resolution. Climate scientists have developed and used mesoscale models to understand and predict future atmospheric conditions. These models represent a major contribution to objective weather forecasts throughout numerical simulations. They use global circulation outputs as boundary conditions and can be run in a nested manner so as to increase their spatial resolution. Because of this, we can obtain information about weather variables in grid cells spaced 15 km apart covering important areas and providing information in places where analog or automatic stations are not available. The objective of this work is to evaluate the use of raw data from the MM5 mesoscale model as well as MOS-corrected information (a statistical post-processing of MM5 outputs) as a proxy for surface meteorological data. Temperature, wind speed, relative humidity, and daily solar radiation forecasts were evaluated for eleven stations in the Maipo river basin. In all cases, the MOS forecast produced better results than the raw MM5 data. Determination coeffi cients reached values near 0.9, and the RMSE was usually smaller for MOS-corrected data. The small variability of the MOS parameters allows their use as regional values to estimate meteorological data for the whole region, particularly at a weekly time step.