Use of hydroclimatic forecasts for improved water management in central Texas

Accurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Hydrological persistence (streamflow and precipitation) and large-scale recurrent oceanic-atmospheric patterns such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific North American (PNA), and customized sea surface temperature (SST) indices were investigated for their potential to improve streamflow forecast accuracy and increase forecast lead-time in a river basin in central Texas. First, an ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distributions-oriented metrics, and implications for decision making are discussed. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. Secondly, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas. Lastly, a simplified two-stage stochastic economic-optimization model was proposed to investigate improvement in water use efficiency and the potential value of using seasonal forecasts, under the assumption of optimal decision making under uncertainty. Model results demonstrate that incorporating the probabilistic inflow forecasts into the optimization model can provide a significant improvement in seasonal water contract benefits over climatology, with lower average deficits (increased reliability) for a given average contract amount, or improved mean contract benefits for a given level of reliability compared to climatology. The results also illustrate the trade-off between the expected contract amount and reliability, i.e., larger contracts can be signed at greater risk.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

UNDERSTANDING FARMERS’ PERCEPTIONS AND THE EFFECTS OF SHEA TREE Vitellaria paradoxa DISTRIBUTION IN AGROFORESTRY PARKLANDS OF THE UPPER WEST REGION, GHANA

Agroforestry parklands represent a vast majority of the agricultural landscape under subsistent-oriented farming in semi-arid West Africa. Parklands are characterized by the growth of well- maintained trees (e.g., shea) on cultivated fields as a result of both environmental and human influences. Shea (Vitellaria paradoxa) provides a cultural and economic benefit to the local people of Ghana, especially women. Periods between traditional fallow rotation systems have reduced recently due to agricultural development and a demand for higher production. As a result, shea trees, which regenerate during fallow periods, has decreased over the landscape. The aim of this study was to determine beneficial spatial distributions of V. paradoxa to maintain high yields of staple crops, and how management of V. paradoxa will differ between male and female farmers as a result of farmer based needs and use of shea. Vegetation growth and grain yield of maize (Zea mays) associated with individual trees, clumped trees, and open fields were measured. Soil moisture and light availability were also measured to determine how V. paradoxa affected resource availability of maize in either clumped or scattered distributions of V. paradoxa. As expected, light availability increased as measurement locations moved farther away from all trees. However, soil moisture was actually greater under trees in clumps than under individual trees. Maize stalk height and cob length showed no difference between clumped and single trees at each measurement location. Grain yield per plot and per cob increased as measurement locations moved farther from single trees, but was actually greater near clumped trees that in the open field subplots. Cob length and maize stalk height increased with greater light availability, but grain yield per cob or per plot showed no relationship with light, but were not affected by soil moisture. Conversely, grain yield increased with increasing soil moisture, but had no relationship with light availability. Initial farming capital is the largest constraint to female farmers; therefore the collection of shea can help provide women with added income that could meet their specific farming needs. Our data indicate that overall effects of maintaining clumped distributions of V. paradoxa provided beneficial microclimates for staple crops when compared to single trees. It is recommended that male and female farmers allow shea to grow in clumped spatial distributions rather than maintaining scattered, individual trees.