James Phillip King

Numerical Comparison of Multi-Criteria Decision-Making Techniques: A Simulation of Flood Management Multi-Criteria Systems

Decision-making processes in water resources projects are often multi-criteria, and numerous techniques have been developed for evaluating these projects. The main concern in utilizing multi-criteria decision-making (MCDM) techniques is that different techniques may result different outputs, therefore, selecting an appropriate technique is crucially important. Most decision makers prefer simple and transparent decision-making approaches which simultaneously show the trade-offs among the different decisions. This study utilizes multiple comparisons of MCDM techniques to interpret the similarities and dissimilarities of those methods and their consequences in the same project, which is multi-criteria management of stochastic floods in the Sunland Park area (Diez Lagos) in southern New Mexico. The objectives of the Diez Lagos flood control system are flood damage reduction (FDR), increasing usable water supply (WS) from stochastic floods, E. coli remediation (ER) from storm water, riparian habitat restoration (RHR), and human health and safety (HHS) in the study area. For all techniques, we simulated the same decision in the form of a decision matrix with m alternatives (flood control rules) against n criteria (FDR, WS, ER, RHR, HHS, and related Costs). We investigate six techniques: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (in Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje), SAW (Simple Additive Weights), AHP (Analytic Hierarchy Process), ELECTRE (Elimination et Choice Translating Reality), and Compromise Programming (CP). The evaluation of the numerical results from this study can lead to the selection of the best decision-making technique, which can be extended to other projects.

Effect of elevation resolution on evapotranspiration simulations using MODFLOW.

Surface elevations represented in MODFLOW head-dependent packages are usually derived from digital elevation models (DEMs) that are available at much high resolution. Conventional grid refinement techniques to simulate the model at DEM resolution increases computational time, input file size, and in many cases are not feasible for regional applications. This research aims at utilizing the increasingly available high resolution DEMs for effective simulation of evapotranspiration (ET) in MODFLOW as an alternative to grid refinement techniques. The source code of the evapotranspiration package is modified by considering for a fixed MODFLOW grid resolution and for different DEM resolutions, the effect of variability in elevation data on ET estimates. Piezometric head at each DEM cell location is corrected by considering the gradient along row and column directions. Applicability of the research is tested for the lower Rio Grande (LRG) Basin in southern New Mexico. The DEM at 10 m resolution is aggregated to resampled DEM grid resolutions which are integer multiples of MODFLOW grid resolution. Cumulative outflows and ET rates are compared at different coarse resolution grids. Results of the analysis conclude that variability in depth-to-groundwater within the MODFLOW cell is a major contributing parameter to ET outflows in shallow groundwater regions. DEM aggregation methods for the LRG Basin have resulted in decreased volumetric outflow due to the formation of a smoothing error, which lowered the position of water table to a level below the extinction depth.

Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs

Extracting and using of time-dependent indices improved prediction accuracy.Pre-processing of data improved prediction accuracy.Intelligent selection of predictors via sensitivity analysis and data mining.Successful integration of a novel forecast expert system in an operation system. Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predictors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.

Grid-Size Dependency of Evapotranspiration Simulations in Shallow Aquifers: An Optimal Approach

AbstractThis research aims at improving the performance of regional groundwater models by incorporating high-resolution elevation data into the head-dependent packages of MODFLOW. Model code specific to the evapotranspiration package (EVT) of MODFLOW was modified to account for the variability in elevation data and to effectively delineate the evapotranspiration (ET) simulated region at user-specified digital elevation model (DEM) resolution. The regional groundwater model of the Rincon Valley–Mesilla Basins (NMOSE-2007 flow model) was improved and considered to evaluate and validate the developed code. The base DEM of the study area is smoothened and aggregated to various resampled resolutions that are integer divisors of NMOSE-2007 flow model resolution for use with ET simulation. A gradual decrease in ET outflow is observed when the variability in elevation is eliminated across the grid cell. Also, changes in cumulative ET outflow (as a fraction of total outflow) at different resampled grids followed a...

Integration of time series forecasting in a dynamic decision support system for multiple reservoir management to conserve water sources

ABSTRACT In most of arid and semi-arid regions, there are limited sources of available fresh water for different domestic and environmental demands. Strategic and parsimonious fresh water-use in water-scarce areas such as Southern New Mexico is crucially important. Elephant Butte and Caballo reservoirs are two integrated reservoirs in this region that provide water supply for many water users in downstream areas. Since Elephant Butte Reservoir is in a semi-arid region, it would be rational to utilize other energy sources such as wind energy to produce electricity and use the water supply to other critical demands in terms of time and availability. This study develops a strategy of optimal management of two integrated reservoirs to quantify the savable volume of water sources through optimal operation management. To optimize operations for the Elephant Butte and Caballo reservoirs as an integrated reservoir operation in New Mexico, the authors in this case study utilized two autoregressive integrated moving average models, one non-seasonal (daily, ARIMA model) and one seasonal (monthly, SARIMA model), to predict daily and monthly inflows to the Elephant Butte Reservoir. The coefficient of determination between predicted and observed daily values and the normalized mean of absolute error (NMAE) were 0.97 and 0.09, respectively, indicating that the daily ARIMA prediction model was significantly reliable and accurate for a univariate based streamflow forecast model. The developed time series prediction models were incorporated in a decision support system, which utilizes the predicted values for a day and a month ahead and leads to save significant amount of water volume by providing the optimal release schedule from Elephant Butte into the Caballo Reservoir. The predicted daily and monthly values from the developed ARIMA prediction models were integrated successfully with the dynamic operation model, which provides the optimal operation plans. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. The saved volume of the water would be considered as a significant water supply for environmental conservation actions in downstream of the Caballo Reservoir. Providing an integrated optimal management plan for two reservoirs led to save significant water sources in a region that water shortage has led to significant environmental consequences. Finally, since the models are univariate, they demonstrate an approach for reliable inflow prediction when information is limited to only streamflow values. We find that hydroelectric power generation forces the region to lose significant amount of water to evaporation and therefore hinder the optimal use of freshwater. Based on these findings, we conclude that a water scarce region like Southern New Mexico should gain independence from hydroelectric power and save the freshwater for supporting ecosystem services and environmental purposes.