Browsing by Author "Garcia, Luis A., advisor"
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Item Open Access Characterizing hydroclimatic variability in tributaries of the Upper Colorado River Basin - WY 1911-2001(Colorado State University. Libraries, 2009) Matter, Margaret A., author; Garcia, Luis A., advisor; Fontane, Darrell G., advisorMountain snowpack is the main source of water in the semi-arid Colorado River Basin (CRB), and while the demands for water are increasing, competing and often conflicting, the supply is limited and has become increasingly variable over the 20th Century. Greater variability is believed to contribute to lower accuracy in water supply forecasts, plus greater variability violates the assumption of stationarity, a fundamental assumption of many methods used by water resources engineers in planning, design and management. Thus, it is essential to understand the underpinnings of hydroclimatic variability in order to effectively meet future water supply challenges. A new methodology was applied to characterize time series of temperature, precipitation, and streamflow (i.e., historic and reconstructed undepleted flows) according to the three climate regimes that occurred in CRB during the 20th Century. Results for two tributaries in the Upper CRB show that hydroclimatic variability is more deterministic than previously thought because it entails complementary temperature and precipitation patterns associated with wetter or drier conditions on climate regime and annual scales. Complementary temperature (T) and precipitation (P) patterns characterize climate regime type (e.g., cool/wet and warm/dry), and temperatures increase or decrease and precipitation changes magnitude and timing according to the type of climate regime Accompanying each climate regime type, are complementary T and P patterns on annual scales that are associated with upcoming precipitation and annual basin yield. Annual complementary T and P patterns: (a) establish by fall; (b) are detectable as early as September; (c) persist to early spring; (d) are related to the relative magnitude of upcoming precipitation and annual basin yield; (e) are unique to climate regime type; and (f) are specific to each river basin. Thus, while most of the water supply in the Upper CRB originates from winter snowpack, statistically significant indictors of relative magnitude of upcoming precipitation and snowmelt runoff are evident in the fall, well before appreciable snow accumulation. Since natural and anthropogenic external forcings, including solar variability, anthropogenic climate change, and modifications to land use, land cover and water use, influence the climate modes that shape climate regimes, the external forcings also influence the complementary temperature and precipitation patterns accompanying each climate regime. Consequently, although complementary temperature and precipitation patterns are similar for climate regimes of the same type (e.g., cool/wet climate regimes), they also differ and the differences may be associated with anticipated or observed effects of external forcings. In summary, this research shows that hydroclimatic variability during the 20th Century is more deterministic than previously thought, and includes: (a) a series of alternating patterns in temperature and precipitation corresponding with changes in climate regimes; and (b) effects of anthropogenic external forcings on the complementary temperature and precipitation patterns accompanying the climate regimes. Results of this research suggest alternative strategies to incorporate into existing water supply forecasting methods to improve forecast accuracy and increase lead time up to six months, from April 1 to October 1 of the previous year. Based on the relationships revealed by this research, the physical mechanisms behind the relationships may be determined and used to improve models for water supply forecasting and water management; develop long-range forecasts; and downscale climate models. In addition, the research results may also be used: (a) to improve application of or develop alternatives to engineering and hydrologic methods based on the assumption of stationarity; (b) in developing science-based adaptive management strategies for natural and cultural resource managers; and (c) in developing restoration, conservation and management plants for fish, wildlife, forest, and other natural resources.Item Open Access Stochastic analysis of flow and salt transport modeling in irrigation-drainage systems(Colorado State University. Libraries, 2012) Alzraiee, Ayman H., author; Garcia, Luis A., advisor; Gates, Timothy K., advisor; Bau, Domenico, committee member; Butters, Greg, committee memberSustainability of crop production in the Lower Arkansas River Basin in Colorado is seriously threatened by the continuous degradation of irrigated lands by the dual impact of soil salinization and waterlogging problems. Integration of improved irrigation practices, upgrades to the irrigation systems, and subsurface drainage are essential components of any plan to stop the deterioration of irrigated lands. Numerical simulations of irrigation and drainage systems are necessary to justify the consequent management actions. Despite the uncertainty of their predictions, numerical models are still indispensable decision support tools to investigate the feasibility of irrigation and drainage systems management plans. However, the uncertainties in input parameters to these models create a risk of misleading numerical results. That is beside the fact that the numerical models themselves are conceptual simplifications of the complex reality. The overarching objective of this dissertation is to investigate the impact of parameters uncertainty on the response of simulated irrigation-drainage systems. In the first part of the research, a Global Sensitivity Analysis (GSA) is conducted using a one-dimensional variably saturated problem to prioritize parameters according to their importance with respect to predefined performance indices. A number of GSA methods are employed for this purpose, and their comparative performances are investigated. Results show that only five parameters out of 18 parameters are responsible for around 73% of crop yield uncertainty. The second part introduces a method to reduce the computational requirements of Monte Carlo Simulations. Numerical simulation of variably saturated three-dimensional fields is typically a computationally intensive process, let alone Monte Carlo Simulations of such problems. In order to reduce the number of model evaluations while producing acceptable estimates of the output statistical properties, Cluster Analysis (CA) is used to group the input parameter realizations, e.g. hydraulic conductivity. The potentials of this approach are investigated using different: 1) clustering schemes; 2) clustering configurations, and 3) subsampling schemes. . Results show that response of 400 realizations ensemble can be efficiently approximated using selected 50 realizations. The third part of the research investigates the impact of input parameter uncertainty on the response of irrigation-drainage systems, particularly on crop yield and root zone hydrosalinity. The three-dimensional soil parameters, i.e. hydraulic conductivity, porosity, the pore size distribution (van Genuchten β) parameter, the inverse of the air entry pressure (van Genuchten α) parameter, the residual moisture content parameter, and dispersivity; are treated as spatial random processes. A sequential multivariate Monte Carlo simulation approach is implemented to produce correlated input parameter realizations. Other uncertain parameters that are considered in the study are irrigation application variability, irrigation water salinity, irrigation uniformity, preferential flow fraction, drain conductance coefficient, and crop yield model parameters. Results show that as the crop sensitivity to salinity increases, the crop yield standard deviation increases. The fourth part of the research investigates an approach for optimal sampling of multivariate spatial parameters in order to reduce their uncertainty. The Ensemble Kalman Filter is used as instrumentation to integrate the sampling of the hydraulic conductivity and the water level for a two-dimensional steady state problem. The possibility of combining designs for efficient prediction and for efficient geostatistical parameter estimation is also investigated. Moreover, the effect of relative prices of sampled parameters is also investigated. A multi-objective genetic algorithm is employed to solve the formulated integer optimization problem. Results reveal that the multi-objective genetic algorithm constitutes a convenient framework to integrate designs that are efficient for prediction and for geostatistical parameter estimation.Item Open Access Use of remote sensing to estimate soil salinity and evapotranspiration in agricultural fields(Colorado State University. Libraries, 2007) Elhaddad, Aymn, author; Garcia, Luis A., advisor; Loftis, Jim C., committee member; Albertson, Maurice L., committee memberIn recent years, methods for detecting soil salinity have improved greatly. This research describes methods to detect soil salinity levels in agricultural lands based on crop conditions and evapotranspiration (ET) using satellite imagery. Elevated levels of soil salinity affect the growth of most crops as well as their appearance. For this research, satellite images of the study area, the Arkansas River Basin in Colorado, are used to classify the condition of the crops being grown in fields according to their different reflectance values. Using spatially referenced ground data collected in the study area, each class in the satellite image is related to a level of soil salinity. These classes are then used to create a signature file to classify other areas within the same image having the same crop. For the purpose of detecting soil salinity in this study, two satellite scenes were used: a multi-spectral Ikonos image from July 27, 2001 and a Landsat 7 image from July 8, 2001. While the Ikonos image provides more details, the results of this study indicate that the Landsat imagery also performed remarkably well. Evapotranspiration (ET) is one of the processes that are affected by soil salinity. Reliable estimates of evapotranspiration from vegetation are needed for investigations of the relationship between soil salinity and ET. Satellite-derived information has been found useful for estimation of aerial ET. For this purpose, a surface energy balance-based model (RESET) was developed using remotely sensed data from satellite imagery. The RESET model takes into consideration the spatial variability in weather. Moreover, the model implements a spatiotemporal interpolation methodology in order to obtain ET information between satellite scenes. The RESET model was applied to estimate ET values in the study area. A geographic information system (GIS) was used to spatially relate the ET values to soil salinity data. The ET values were regressed against the spatially corresponding soil salinity values to develop a relationship between ET and soil salinity. The ET values were found to correlate well with the soil salinity levels in the study area, with correlation coefficients of up to 0.92.Item Open Access Using nonlinear geostatistical models for soil salinity and yeild management(Colorado State University. Libraries, 2013) Eldeiry, Ahmed, author; Garcia, Luis A., advisor; Reich, Robin M., advisor; Grigg, Neil S., committee memberCrop production losses associated with soil salinity on irrigated lands are significant. The genetic complexity of crops with regards to salt tolerance has limited the success of improving salt tolerance through conventional breeding programs. In the meantime, land reclamation and leaching can be expensive and sometimes impractical when fresh water sources are scarce or not readily available. This research introduces a geostatistical approach for the management of crop yield under current soil salinity conditions. It uses three nonlinear geostatistical models - disjunctive kriging (DK), indicator kriging (IK), and probability kriging (PK) - to manage soil salinity and crop yield. The nonlinear models were applied to selected irrigated fields in a study area located in the south eastern part of the Arkansas River Basin in Colorado where soil salinity is a problem in some areas. The overall objectives of this research are: 1) estimate soil salinity in irrigated fields using nonlinear gestatistical models; 2) develop conditional probability (CP) maps that divide each field into zones with different soil salinity levels; 3) estimate the expected yield potential (YP) for several crops at different zones in fields under multiple soil salinity thresholds; 4) evaluate the performance of the nonlinear geostatistical models in developing the interpolated and CP maps provide guidance to farmers and researchers by considering the output of this research as input for precision management of agriculture; and 5) provide guidance to farmers and decision makers in precision management of agriculture. The three nonlinear geostatistical models DK, IK, and PK were used to develop CP maps based on soil salinity thresholds for different crops. These CP maps were compared with actual yield data taken while conducting a soil salinity survey for two fields cultivated with alfalfa and corn. The CP maps divide each field of interest into zones with different probabilities to reach a specific YP for a given crop at a specific soil salinity threshold. Different crops were selected to represent the dominant crops grown in the study area: alfalfa, corn, sorghum, and wheat. Six fields were selected to represent the range of soil salinity levels in the area. Soil salinity data were collected in the fields using an EM-38 and the location of each soil salinity sample point was determined using a GPS unit. Datasets of soil salinity collected in irrigated fields were used to generate the CP maps and to evaluate different scenarios of the expected YP% of several crops at multiple soil salinity thresholds. These datasets were selected to represent a wide range of soil salinity conditions in order to be able to evaluate a wide variety of crops (larger set of crops than those grown in the study area) according to their soil salinity tolerances. Yield data were collected at the same fields to compare the actual data with that estimated by the models. The crops were used for evaluation were selected based on two criteria: dominant in the study area, and represent high, moderate, and low soil salinity tolerances. Different scenarios of crops and salinity levels were evaluated. Semivariograms were constructed for each scenario to represent the different classes of percent yield potential based on soil salinity thresholds of each crop. The results of this research show the nonlinear geostatistical models are efficient in assessing the impact of soil salinity on the spatial variability yield productivity. The comparison of the actual yield data with the estimated yield from the three models shows good agreement where most of the yield samples were located at the appropriate zones estimated with the models. The IK and PK models generated very similar estimates for each of the zones. However, the zones generated by both of these models are slightly different to the zones generated using the DK model. Wheat and sorghum show the highest expected yield potential based on the different soil salinity conditions that were evaluated. Expected net revenue for alfalfa and corn are the highest under the different soil salinity conditions that were evaluated. The CP maps generated using the DK technique give an accurate characterization and quantification of the different zones of the fields. Upon the knowledge of the YP% of different areas, a management decision action can be taken to manage the productivity of a field by selecting another crop or adjusting the inputs such as fertilizer, seeding rates and herbicides in low productivity areas. The information provided by the models about the variability and hotspots can be used for the precision management of agricultural resources. The IK model can be used to generate guidance maps that divide each field into areas of expected percent yield potential based on soil salinity thresholds for different crops. Zones of uncertainty can be quantified by IK and used for risk assessment of the percent yield potential.