Shalamu Abudu

Forecasting Monthly Streamflow of Spring-Summer Runoff Season in Rio Grande Headwaters Basin Using Stochastic Hybrid Modeling Approach

Monthly streamflow forecasting during spring-summer runoff season using snow telemetry (SNOTEL) precipitation and snow water equivalent (SWE) as predictors in the Rio Grande Headwaters Basin in Colorado was investigated. The transfer-function noise (TFN) models with SNOTEL precipitation input were built for monthly streamflow. Then, one-month-ahead forecasts of TFN models for the spring-summer runoff season were modified with SWE using an artificial neural networks (ANN) technique denoted in this study as hybrid TFN+ANN. The results indicated that the hybrid TFN+ANN approach not only demonstrated better generalization capability but also improved one-month-ahead forecast accuracy significantly when compared with single TFN and ANN models. The normalized root mean squared errors (NRMSE) of one-month-ahead forecasts of TFN, ANN, and TFN+ANN approaches for spring-summer runoff season were 0.38, 0.30, and 0.25. These findings accentuate that the presented stochastic hybrid modeling approach is an advantageous...

Optimization of adaptive fuzzy logic controller using novel combined evolutionary algorithms, and its application in Diez Lagos flood controlling system, Southern New Mexico

Using evolutionary algorithms in a novel search engine led to superior performance.Using the Takagi-Sugeno method led to near-optimum initial values for the MFPs.Developing a feedback monitoring system reliably led to reliable operating rules.Successful application of an optimized FLC in monitoring a simulated dynamic model. In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimizes the deviation (error term) between the decisions of the fuzzy logic systems and the decisions of experts. A range of approaches - such as genetic algorithms (GA), particle swarm optimization (PSO), artificial neural networks (ANN), and adaptive network based fuzzy inference systems (ANFIS) - can be used to pursue optimal performance for FLCs by refining the membership function parameters (MFPs) that control performance. Multiple studies have been conducted to refine MFPs and improve the performance of fuzzy logic systems through the application of a single optimization approach, but since different optimization approaches yield different error terms under different scenarios, the use of a single optimization approach does not necessarily produce truly optimal results. Therefore, this study employed several optimization approaches - ANFIS, GA, and PSO - within a defined search engine unit that compared the error values from the different approaches under different scenarios and, in each scenario, selected the results that had the minimum error value. Additionally, appropriate initial variables for the optimization process were introduced through the Takagi-Sugeno method. This system was applied to a case study of the Diez Lagos (DL) flood controlling system in southern New Mexico, and we found that it had lower average error terms than a single optimization approach in monitoring a flood control gate and pump across a range of scenarios. Overall, using evolutionary algorithms in a novel search engine led to superior performance, using the Takagi-Sugeno method led to near-optimum initial values for the MFPs, and developing a feedback monitoring system consistently led to reliable operating rules. Therefore, we recommend the use of different methods in the search engine unit for finding the optimal MFPs, and selecting the MFPs from the method which has the lowest error value among them.

Infilling Missing Daily Evapotranspiration Data Using Neural Networks

This study used artificial neural networks (ANNs) computing technique for infilling missing daily saltcedar evapotranspiration (ET) as measured by the eddy-covariance method. The study site was at Bosque del Apache National Wildlife Refuge in the Middle Rio Grande Valley, New Mexico. Data was collected from 2001 to 2003. Several ANN models were evaluated for infilling of different combinations of missing data percentages and different gap sizes. The ANN model using daily maximum and minimum temperature, daily solar radiation, day of the year, and the calendar year as inputs showed the best estimation performance. Results showed coefficient of determination ( R2 ) of 0.96, root-mean-square error (RMSE) of 0.4 mm/day for 10% missing data and a maximum of half-month gap size data set. Missing data greater than 30% and maximum data gap size greater than 3 months resulted in R2 less than 0.90 and RMSE greater than 0.6 mm/day. The results from this study suggest that infilling of daily saltcedar ET using ANN an...

Application of Partial Least-Squares Regression in Seasonal Streamflow Forecasting

The application of partial least-squares regression (PLSR) in seasonal streamflow forecasting was investigated using snow water equivalent, precipitation, temperature from automatic Snow Telemetry sites, and previous flow conditions as input variables. The forecast performance of PLSR models was compared to principal components regression (PCR) models as well as to the Natural Resources Conservation Service (NRCS) official forecasts in three Rio Grande watersheds including the Rio Grande Headwater Basin, Conejos River Basin in Colorado, and Rio Grande Basin above Elephant Butte Reservoir, New Mexico. The results indicated that using a correlation-weighted precipitation index is a relatively effective method in both improving forecast accuracy and developing relatively parsimonious regression models. In comparison of PLSR and PCR, similar forecast accuracies were obtained for both methods in jackknife cross validation and the test period (2003–2007) although PLSR has higher calibration coefficient of deter...

Coupled GSI-SVAT Model with Groundwater-Surface Water Interaction in the Riparian Zone of Tarim River

AbstractThe Tarim River is located in the arid areas of northwestern China, where groundwater (GW) and surface water (SW) in different landscape units have undergone regular and duplicate transformation processes, greatly improving the utilization of water resources. Investigation of the interaction between groundwater and surface water is critical to determine proper water resources planning and management in the Tarim River region. A new approach of coupling the soil-vegetation-atmosphere transport model (SVAT) with the groundwater-surface water interaction model (GSI) is presented in this paper. Usually, the surface water recharges to groundwater and groundwater-soil water exchange are not considered in the SVAT model. However, in reality, the soil water content profiles and soil heat profiles are intensely affected by shallow groundwater table, especially in arid riparian zones where groundwater levels fluctuate substantially. A new method linking the SVAT model with the GSI model is proposed in this ...

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.

Special Section on Interconnection of Atmospheric Water, Surface Water, and Groundwater

As the climate changes, conjunctive use and management of water resources, or integrated water resources management, has become critically important for water resource planners and managers. Climate variability has resulted in irregular precipitation and temperature patterns, and, in turn, extreme storm events causing floods and frequent droughts. Such extreme events have raised stakeholders’ concerns for water availability. Conjunctive uses of multiple water resources are best management practices/ strategies to address this change in water availability. To implement such strategies, an important aspect gaining a better understanding of the hydrological interconnections among atmospheric water, surface water, and groundwater (three waters) as well as their trends or patterns with climate variations. The Earth’s hydrologic cycle is defined as “the pathway of water as it moves in its various phase through the atmosphere, to the Earth, over and through the land, to the ocean, and back to the atmosphere” (National Research Council 1991). Water in this hydrologic cycle (Chow et al. 1988) may be divided into atmospheric water, surface water, and groundwater (subsurface water). Surface water and atmospheric water transfer to each other through water surface evaporation, evapotranspiration by aquatic plants, and precipitation; groundwater and atmospheric water transfer to each other through near-surface evaporation, evapotranspiration through plants, and deep percolation of precipitation. Surface water and groundwater transfer to each other through seepage when they are hydraulically connected or by infiltration when they are hydraulically disconnected. This three-water transfer has been recognized since the latter part of the seventeenth century (Todd and Mays 2005). The three-water interactions mainly occur on the water’s surface, the land’s surface, and in the vadose zone above the groundwater table, as well as in the hyporheic zone between surface water and groundwater. Along with this water transfer, bio-chemical, physical, and kinetic interactions occur between atmospheric water, surface water, and groundwater. The threewater interactions involve multiple disciplines such as meteorology, surface hydrology, subsurface hydrology, geology, agronomy, pedology, and bio-ecology, to name just a few. Significant advances in understanding three-water interactions have been made over the last several decades. For example, advances in interactions between groundwater and surface water have been overviewed (Winter 1995, 1999; Sophocleous 2002; Diiwu 2003) since the growth in research related to surface-subsurface exchange processes mushroomed during the 1990s (Stanley and Jones 2000). Winter (1995) reviewed advances in understanding the interaction of groundwater and surface water in different landscapes: mountain, riverine, coastal, hummocky, and karst terrains. Winter (1999) proposed three general theoretical considerations regarding the interaction of groundwater with surface water. This interaction is affected by (1) different-scale groundwater flow systems, (2) local soil and geologic controls on seepage distribution, and (3) the magnitude of transpiration directly from groundwater around a surface-water perimeter since this transpiration intercepts potential groundwater inflows or draws water from surface-water bodies. Sophocleous (2002) synthesized and exemplified the interactions between groundwater and surface water in relation to climate, landform, geology, and biotic factors. Diiwu (2003) reviewed fundamental concepts of the ecohydrology of the interaction between groundwater and surface water, and discussed the relevance of this interaction to the sustainable management of water resources in semi-arid regions. A task committee (see subsequent information) was established within the Groundwater Council to promote scientific exchange and share experiences by inviting scientists and researchers to prepare articles and presentations focusing on the state of science relative to the interaction of atmospheric water, surface water, and groundwater, and on the impacts of climate change on water resources, as well as their conjunctive management and uses. This special section includes a collection of invited and peer-reviewed papers including field investigations, numerical simulations, and practical case studies on the following topics: physical/hydrological characterization of interactions; analytical and numerical models for simulating interactions; conjunctive uses and management of the three waters; climate change impacts on interaction of the three waters, including extreme events such as floods and drought; and water quality issues related to such interactions. In this special section, authors present their recent research findings on the interactions of the three waters and their associated processes. It is anticipated that this collection will promote further scientific exchange and further advances of our knowledge in this research area. We are very appreciative of the authors’ contribution and efforts and of the constructive comments and timely reports by reviewers and editors.