Exploring the potential of artificial intelligence techniques in prediction of the removal efficiency of vortex tube silt ejector

A vortex tube silt ejector is a curative hydraulic structure used to remove sediment deposits from canals and is recognized as one of the most efficient substitutes for physically removing canal sediment. The spatially varied flow in the channel and the rotational flow behavior in the tube make the silt removal process complex. It is even harder to accurately predict the silt removal efficiency by traditional models accurately. However, artificial intelligence(AI) and machine learning approaches...     

Groundwater level simulation using gene expression programming and M5 model tree combined with wavelet transform

ABSTRACT

In order to understand and adequately manage hydrological stress, it is necessary to simulate groundwater levels accurately. In this research, gene expression programming (GEP) and M5 model tree (M5) are used to simulate monthly groundwater levels. The models are combined with wavelet transform to produce two hybrid models: wavelet gene expression programming (WGEP) and wavelet M5 model tree (WM5). For the simulation, groundwater level, temperature and precipitation values from three observation wells and one meteorological station, located in Iran, are used. The results indicate that the hybrid models, WGEP and WM5, lead to a better performance than the simple models, GEP and M5. The performance of the two hybrid models is similar. It is also observed that selecting a suitable time lag for inputs plays an important role in the accuracy of the simple models. The selection of a suitable decomposition level strongly affects the accuracy of hybrid models.     

Comparison of five snow water equivalent estimation methods across categories

Snow water equivalent (SWE) is an important indicator used in hydrology, water resources, and climate change impact. There are various methods of estimating SWE (falling in 3 categories: indirect sensors, empirical models, and process‐based models), but few studies that provide comparison across these different categories to help users make decisions on monitoring site design or method selection. Five SWE estimation methods were compared against manual snow course data collected over 2 years (2015–2016) from the Dorset Environmental Science Centre, including the gamma‐radiation‐based CS725 sensor, 3 empirical estimation models (Sexstone snow density model, McCreight & Small snow density model, and a meteorology‐based model), and the University of British Columbia Watershed Model snow energy‐balance model. Snow depth, density, and SWE were measured at the Dorset Environmental Science Centre weather station in south‐central Ontario, on a daily basis over 6 winters from 2011 to 2016. The 2 snow density‐based models, requiring daily snow depth as input, gave the best performance (R² of .92 and .92 for McCreight & Small and Sexstone models, respectively). The CS725 sensor that receives radiation coming from soil penetrating the snowpack provided the same performance (R² = .92), proving that the sensor is an applicable method, although it is expensive. The meteorology‐based empirical model, requiring daily climate data including temperature, precipitation and solar radiation, gave the poorest performance (R² = .77). The energy‐balance‐based University of British Columbia Watershed Model snow module, only requiring climate data, worked better than the empirical meteorology‐based model (R² = .9) but performed worse than the density models or CS725 sensor. Given differences in application objectives, site conditions, and budget, this comparison across SWE estimation methods may help users choose a suitable method. For ongoing and new monitoring sites, installation of a CS725 sensor coupled with intermittent manual snow course measurements (e.g., weekly) is recommended for further SWE method estimation testing and development of a snow density model.     

Application of Bayesian model and discriminant function analysis to the estimation of sediment source contributions

Bayesian and discriminant function analysis (DFA) models have recently been used as tools to estimate sediment source contributions. Unlike existing multivariate mixing models, the accuracy of these two models remains unclear. In the current study, four well-distinguished source samples were used to create artificial mixtures to test the performance of Bayesian and DFA models. These models were tested against the Walling-Collins model, a credible model used in estimation of sediment source contributions estimation, as a reference. The artificial mixtures were divided into five groups, with each group consisting of five samples with known source percentages. The relative contributions of the sediment sources to the individual and grouped samples were calculated using each of the models. The mean absolute error (MAE) and standard error of (SE) MAE were used to test the accuracy of each model and the robustness of the optimized solutions. For the individual sediment samples, the calculated source contributions obtained with the Bayesian (MAE = 7.4%, SE = 0.6%) and Walling-Collins (MAE = 7.5%, SE = 0.7%) models produced results which were closest to the actual percentages of the source contributions to the sediment mixtures. The DFA model produced the worst estimates (MAE = 18.4%, SE = 1.4%). For the grouped sediment samples, the Walling-Collins model (MAE = 5.4%) was the best predictor, closely followed by the Bayesian model (MAE = 5.9%). The results obtained with the DFA model were similar to the values for the individual sediment samples, with the accuracy of the source contribution value being the poorest obtained with any of the models (MAE = 18.5%). An increase in sample size improved the accuracies of the Walling-Collins and Bayesian models, but the DFA model produced similarly inaccurate results for both the individual and grouped sediment samples. Generally, the accuracy of the Walling-Collins and Bayesian models was similar (p > 0.01), while there were significant differences (p < 0.01) between the DFA model and the other models. This study demonstrated that the Bayesian model could provide a credible estimation of sediment source contributions and has great practical potential, while the accuracy of the DFA model still requires considerable improvement.     

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na⁺ and Ca²⁺ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.     

Anthropogenic influences on groundwater in the vicinity of a long‐lived radioactive waste repository

The groundwater flow system in the Culebra Dolomite Member (Culebra) of the Permian Rustler Formation is a potential radionuclide release pathway from the Waste Isolation Pilot Plant (WIPP), the only deep geological repository for transuranic waste in the United States. In early conceptual models of the Culebra, groundwater levels were not expected to fluctuate markedly, except in response to long‐term climatic changes, with response times on the order of hundreds to thousands of years. Recent groundwater pressures measured in monitoring wells record more than 25 m of drawdown. The fluctuations are attributed to pumping activities at a privately owned well that may be associated with the demand of the Permian Basin hydrocarbon industry for water. The unprecedented magnitude of drawdown provides an opportunity to quantitatively assess the influence of unplanned anthropogenic forcings near the WIPP. Spatially variable realizations of Culebra saturated hydraulic conductivity and specific storage were used to develop groundwater flow models to estimate a pumping rate for the private well and investigate its effect on advective transport. Simulated drawdown shows reasonable agreement with observations (average Model Efficiency coefficient = 0.7). Steepened hydraulic gradients associated with the pumping reduce estimates of conservative particle travel times across the domain by one half and shift the intersection of the average particle track with the compliance boundary by more than 2 km. The value of the transient simulations conducted for this study lies in their ability to (a) improve understanding of the Culebra groundwater flow system and (b) challenge the notion of time‐invariant land use in the vicinity of the WIPP.     

Hydrologic functioning of the deep critical zone and contributions to streamflow in a high‐elevation catchment: Testing of multiple conceptual models

High‐elevation mountain catchments are often subject to large climatic and topographic gradients. Therefore, high‐density hydrogeochemical observations are needed to understand water sources to streamflow and the temporal and spatial behaviour of flow paths. These sources and flow paths vary seasonally, which dictates short‐term storage and the flux of water in the critical zone (CZ) and affect long‐term CZ evolution. This study utilizes multiyear observations of chemical compositions and water residence times from the Santa Catalina Mountains Critical Zone Observatory, Tucson, Arizona to develop and evaluate competing conceptual models of seasonal streamflow generation. These models were tested using endmember mixing analysis, baseflow recession analysis, and tritium model “ages” of various catchment water sources. A conceptual model involving four endmembers (precipitation, soil water, shallow, and deep groundwater) provided the best match to observations. On average, precipitation contributes 39–69% (55 ± 16%), soil water contributes 25–56% (41 ± 16%), shallow groundwater contributes 1–5% (3 ± 2%), and deep groundwater contributes ~0–3% (1 ± 1%) towards annual streamflow. The mixing space comprised two principal planes formed by (a) precipitation‐soil water‐deep groundwater (dry and summer monsoon season samples) and (b) precipitation‐soil water‐shallow groundwater (winter season samples). Groundwater contribution was most important during the wet winter season. During periods of high dynamic groundwater storage and increased hydrologic connectivity (i.e., spring snowmelt), stream water was more geochemically heterogeneous, that is, geochemical heterogeneity of stream water is storage‐dependent. Endmember mixing analysis and ³H model age results indicate that only 1.4 ± 0.3% of the long‐term annual precipitation becomes deep CZ groundwater flux that influences long‐term deep CZ development through both intercatchment and intracatchment deep groundwater flows.     

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

Groundwater modelling has emerged as a powerful tool to develop a sustainable management plan for efficient groundwater utilization and protection of this vital resource. This study deals with the development of five hybrid artificial neural network (ANN) models and their critical assessment for simulating spatio‐temporal fluctuations of groundwater in an alluvial aquifer system. Unlike past studies, in this study, all the relevant input variables having significant influence on groundwater have been considered, and the hybrid ANN technique [ANN‐cum‐Genetic Algorithm (GA)] has been used to simulate groundwater levels at 17 sites over the study area. The parameters of the ANN models were optimized using a GA optimization technique. The predictive ability of the five hybrid ANN models developed for each of the 17 sites was evaluated using six goodness‐of‐fit criteria and graphical indicators, together with adequate uncertainty analyses. The analysis of the results of this study revealed that the multilayer perceptron Levenberg–Marquardt model is the most efficient in predicting monthly groundwater levels at almost all of the 17 sites, while the radial basis function model is the least efficient. The GA technique was found to be superior to the commonly used trial‐and‐error method for determining optimal ANN architecture and internal parameters. Of the goodness‐of‐fit statistics used in this study, only root‐mean‐squared error, r² and Nash–Sutcliffe efficiency were found to be more powerful and useful in assessing the performance of the ANN models. It can be concluded that the hybrid ANN modelling approach can be effectively used for predicting spatio‐temporal fluctuations of groundwater at basin or subbasin scales. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimation of groundwater vulnerability to pollution based on DRASTIC in the Niipele sub-basin of the Cuvelai Etosha Basin,Namibia

Surface water is a scarce resource in Namibia with about sixty percent of Namibia's population dependent on groundwater for drinking purposes. With increasing population, the country faces water challenges and thus groundwater resources need to be managed properly. One important aspect of Integrated Water Resources Management is the protection of water resources, including protection of groundwater from contamination and over-exploitation. This study explores vulnerability mapping as a basic tool for protecting groundwater resources from pollution. It estimates groundwater vulnerability to pollution in the upper Niipele sub-basin of the Cuvelai-Etosha in Northern Namibia using the DRASTIC index. The DRASTIC index uses GIS to estimate groundwater vulnerability by overlaying different spatially referenced hydrogeological parameters that affect groundwater contamination. The study assesses the discontinuous perched aquifer (KDP) and the Ohangwena multi-layered aquifer 1 (KOH-1). For perched aquifers, point data was regionalized by a hydrotope approach whereas for KOH-1 aquifer, inverse distance weighting was used. The hydrotope approach categorized different parts of the hydrogeological system with similar properties into five hydrotopes. The result suggests that the discontinuous perched aquifers are more vulnerable than Ohangwena multi-layered aquifer 1. This implies that vulnerability increases with decreasing depth to water table because contaminants have short travel time to reach the aquifer when they are introduced on land surface. The nitrate concentration ranges between 2 and 288 mg/l in perched aquifers while in Ohangwena multi-layered aquifer 1, it ranges between 1 and 133 mg/l. It was observed that perched aquifers have high nitrate concentrations than Ohangwena 1 aquifer, which correlates well with the vulnerability results.     

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer,Iran

Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze \( {\text{NO}}_{ 3}^{ - } \) concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the \( {\text{NO}}_{3}^{ - } \) data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using \( {\text{NO}}_{3}^{ - } \) concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and \( {\text{NO}}_{3}^{ - } \) concentration values. After model training, the AI models are verified by the second \( {\text{NO}}_{3}^{ - } \) concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.     

Assessing groundwater availability of the Maldives under future climate conditions

Groundwater resources of the Republic of the Maldives are threatened by a variety of factors including variable future rainfall patterns, continued population growth and associated pumping demands, rising sea level, and contamination from the land surface. This study assesses changes in groundwater availability due to variable rainfall patterns and sea level rise (SLR) in the coming decades, a key component of water resources management for the country. Using a suite of two‐dimensional density‐dependent groundwater flow models, time‐dependent thickness of the freshwater lens is simulated for a range of island sizes (200 to 1,100 m) during the time period of 2011 to 2050, with recharge to the freshwater lens calculated using rainfall patterns provided by general circulation models for the three distinct geographic regions of the Maldives. The effect of SLR on the freshwater lens is quantified using estimates of shoreline recession and associated decreases in island width. If rainfall is solely considered, groundwater availability is projected to increase, as lens thickness during the 2031–2050 time periods is slightly greater (1–5%) than during the 2011–2030 time period. However, including the impact of SLR indicates an overall decrease in lens thickness, with drastic decreases (60% to 100%) projected for small islands (200 m) and moderate decreases (12% to 14%) expected for 400 m islands, which accommodate one third of the national population. Similar methodologies can be used for other atoll island nations, such as the Republic of Marshall Islands, Federated States of Micronesia, and the Republic of Kiribati. For the Maldives, results from this study can be used in conjunction with population growth estimates to determine the feasibility of including groundwater in water resources planning and management for the country.     

Groundwater Depth Forecasting Using Configurational Entropy Spectral Analyses with the Optimal Input

Accurate groundwater depth forecasting is particularly important for human life and sustainable groundwater management in arid and semi-arid areas. To improve the groundwater forecasting accuracy, in this paper, a hybrid groundwater depth forecasting model using configurational entropy spectral analyses (CESA) with the optimal input is constructed. An original groundwater depth series is decomposed into subseries of different frequencies using the variational mode decomposition (VMD) method. Cross-correlation analysis and Shannon entropy methods are applied to select the optimal input series for the model. The ultimate forecasted values of the groundwater depth can be obtained from the various forecasted values of the selected series with the CESA model. The applicability of the hybrid model is verified using the groundwater depth data from four monitoring wells in the Xi'an of Northwest China. The forecasting accuracy of the models was evaluated based on the average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe coefficient (NSE). The results indicated that comparing with the CESA and autoregressive model, the hybrid model has higher prediction performance.     

Water balance estimation under the challenge of data scarcity in a hyperarid to Mediterranean region

Water budget analyses are important for the evaluation of the water resources in semiarid and arid regions. The lack of observed data is the major obstacle for hydrological modelling in arid regions. The aim of this study is the analysis and calculation of the natural water resources of the Western Dead Sea subsurface catchment, one which is highly sensitive to rainfall resulting in highly variable temporal and spatial groundwater recharge. We focus on the subsurface catchment and subsequently apply the findings to a large‐scale groundwater flow model to estimate the groundwater discharge to the Dead Sea. We apply a semidistributed hydrological model (J2000g), originally developed for the Mediterranean, to the hyperarid region of the Western Dead Sea catchment, where runoff data and meteorological records are sparsely available. The challenge is to simulate the water budget, where the localized nature of extreme rainstorms together with sparse runoff data results in few observed runoff and recharge events. To overcome the scarcity of climate input data, we enhance the database with mean monthly rainfall data. The rainfall data of 2 satellites are shown to be unsuitable to fill the missing rainfall data due to underrepresentation of the steep hydrological gradient and temporal resolution. Hydrological models need to be calibrated against measured values; hence, the absence of adequate data can be problematic. Therefore, our calibration approach is based on a nested strategy of diverse observations. We calculate a direct surface runoff of the Western Dead Sea surface area (1,801 km²) of 3.4 mm/a and an average recharge (36.7 mm/a) for the 3,816 km² subsurface drainage basin of the Cretaceous aquifer system.     

Comparison of groundwater age models for assessing nitrate loading,transport pathways,and management options in a complex aquifer system

In an aquifer system with complex hydrogeology, mixing of groundwater with different ages could occur associated with various flow pathways. In this study, we applied different groundwater age‐estimation techniques (lumped parameter model and numerical model) to characterize groundwater age distributions and the major pathways of nitrate contamination in the Gosan agricultural field, Jeju Island. According to the lumped parameter model, groundwater age in the study area could be explained by the binary mixing of the young groundwater (4–33 years) and the old water component (>60 years). The complex hydrogeologic regimes and local heterogeneity observed in the study area (multilayered aquifer, well leakage hydraulics) were particularly well reflected in the numerical model. The numerical model predicted that the regional aquifer of Gosan responded to the fertilizer applications more rapidly (mean age: 9.7–22.3 years) than as estimated by other models. Our study results demonstrated that application and comparison of multiple age‐estimation methods can be useful to understand better the flow regimes and the mixing characteristics of groundwater with different ages (pathways), and accordingly, to reduce the risk of improper groundwater management plans arising from the aquifer heterogeneity.     

Assessment of the impacts of land-use change and slope position on soil loss by magnetic susceptibility-based models

Soil loss is a global environmental problem resulting from the erosion process caused by many factors,including land use and slope position. Estimation of total soil loss from agricultural fields is useful for understanding the consequences of historical and current erosion. The main purposes of the current study are to explore the application of magnetic measurements in the mapping and measuring soil redistribution in cultivated(MZ13) and forested(MZ17) transects in a Moroccan subcatchment, to ...     

Comparative performance of soil water balance models in computing semi-arid aquifer recharge

Abstract

Estimating groundwater recharge is essential to ensure the sustainable use of groundwater resources, particularly in arid and semi-arid regions. Soil water balances have been frequently advocated as valuable tools to estimate groundwater recharge. This article compares the performance of three soil water balance models (Hydrobal, Visual Balan v2.0 and Thornthwaite) in the Ventós-Castellar aquifer, Spain. The models were used to simulate wet and dry years. Recharge estimates were transformed into water table fluctuations by means of a lumped groundwater model. These, in turn, were calibrated against piezometric data. Overall, the Hydrobal model shows the best fit between observed and calculated levels (r² = 0.84), highlighting the role of soil moisture and vegetation in recharge processes.

Editor D. Koutsoyiannis; Associate editor X. Chen

Citation Touhami, I., et al., 2014. Comparative performance of soil water balance models in computing semi-arid aquifer recharge. Hydrological Sciences Journal, 59 (1), 193–203.     

Aeromagnetic and Landsat TM structural interpretation for identifying regional groundwater exploration targets, south-central Zimbabwe Craton

Aeromagnetic (AM) and Landsat Thematic Mapper (TM) data from the south-central Zimbabwe Craton have been processed for the purpose of regional structural mapping and thereby to develop strategic models for groundwater exploration in hard-rock areas. The lineament density is greater on TM than on AM images, partly due to the resolution of the different datasets, and also because not all TM lineaments have a magnetic signature. The derived maps reveal several previously undetected lineaments corresponding to dykes, faults, shear zones and/or tectonically-related joints, striking predominantly NNE, NNW and WNW. We suggest the possible hydrogeological significance of some of these patterns as follows: the aeromagnetic data can be used to map faults and fractures of considerable depth which are likely to be open groundwater conduits at depth (typically under tension), while TM lineaments, although not necessarily open (mostly under compression), represent recharge areas.The interpreted persistent lineation and well developed fracture patterns are correlated with existing boreholes and indicate a spatial relationship between regional structures and high borehole yields (> 3 m³/h). This relationship is combined with other lithological and hydrogeological information to identify potential regional groundwater sites for detailed ground investigations. These are defined as dyke margins, faults, fractures/joints or intersections of any combination of these structures. Priority should be given to coincident AM/TM lineaments (e.g., NNW and NNE fractures) and continuous structures with large catchment areas (e.g., NNE and WNW faults). The late Archaean (2.6 Ga) granites are considered the most favourable unit because of their associated long and deep brittle fractures between numerous bornhardts (inselbergs) and kopjes. Several small-scale TM lineaments also form important local sources of groundwater for hand-dug wells. Based on measured rock susceptibilities from the area, we present a model of the typical magnetic responses from the possible groundwater exploration targets. The developed magnetic model could be applicable to similar terrains in other Archaean Cratons.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

Aquifer recharge from flash floods in the arid environment: A mass balance approach at the catchment scale

Estimation of the infiltration/natural recharge to groundwater from rainfall is an important issue in hydrology, particularly in arid regions. This paper proposes the application of The Natural Resources Conservation Service (NRCS) mass balance model to develop infiltration (F)–rainfall (P) relationship from flash flood events. Moreover, the NRCS method is compared with the rational and the Ф-index methods to investigate the discrepancies between these methods. The methods have been applied to five gauged basins and their 19 sub-basins (representative basins with detailed measurements) in the southwestern part of Saudi Arabia with 161 storms recorded in 4 years. The F–P relationships developed in this study based on NRCS method are: F = 39% P with R² = 0.932 for the initial abstraction factor, λ = 0.2. However, F = 77% P with R² = 0.986 for λ = 0.01. The model at λ = 0.01 is the best to fit the data, therefore, it is recommended to use the formula at λ = 0.01. The results show that the NRCS model is appropriate for the estimation of the F–P relationships in arid regions when compared with the rational and the Ф index methods. The latter overestimates the infiltration because they do not take λ into account. There is no significant difference between F–P relationships at different time scales. This helps the prediction of infiltration rates for aquifer recharge at ungauged basins from monthly and annual rainfall data with a single formula.     

Predicting dissolved oxygen in the Chesapeake Bay: applications and implications

Eutrophic depletion of dissolved oxygen (DO) and its consequences for ecosystem dynamics have been a central theme of research, assessment and management policies for several decades in the Chesapeake Bay. Ongoing forecast efforts predict the extent of the summer hypoxic/anoxic area due to nutrient loads from the watershed. However, these models neither predict DO levels nor address the intricate interactions among various ecological processes. The prediction of spatially explicit DO levels in the Chesapeake Bay can eventually lead to a reliable depiction of the comprehensive ecological structure and functioning, and can also allow the quantification of the role of nutrient reduction strategies in water quality management. In this paper, we describe a three dimensional empirical model to predict DO levels in the Chesapeake Bay as a function of water temperature, salinity and dissolved nutrient concentrations (TDN and TDP). The residual analysis shows that predicted DO values compare well with observations. Nash–Sutcliffe efficiency (NSE) and root mean square error-observations standard deviation ratio (RSR) are used to evaluate the performance of the empirical model; the scores demonstrate the usability of model predictions (NSE, surface layer = 0.82–0.86; middle layer = 0.65–0.82; bottom layer = 0.70–0.82; RSR surface layer = 0.37–0.44; middle layer = 0.43–0.58 and bottom layer = 0.43–0.54). The predicted DO values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models, can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the Chesapeake Bay.