Latif Kalin

Effect of geomorphologic resolution on modeling of runoff hydrograph and sedimentograph over small watersheds

Abstract In hydrologic models, Geographic Information Systems (GIS) interfaces are commonly used for extracting the channel network, and delineating the watershed. By overlaying soil and land use maps onto the extracted channel network, input files required by the model are prepared. However, the nature of the extracted channel network strongly depends on some pre-selected threshold values within the GIS framework, which in turn, determine the geomorphologic resolution. There are no accepted guidelines for selecting these threshold parameters making the extraction of channel networks a subjective process. In this study, we investigate the effect of geomorphologic resolution on runoff hydrographs and sedimentographs over two small USDA experimental watersheds. The KINEROS model with ArcView interface has been used for this purpose. An empirical relationship between optimal resolution, watershed characteristics and nature of the storm has been developed. Results reveal that geometric simplification of the watershed for rainfall-runoff-erosion studies may be acceptable under right combinations of rainfall events and watershed properties. Our results also indicate that the optimal geomorphologic resolution may not be the same for hydrographs and sedimentographs.

Determining Nutrient and Sediment Critical Source Areas with SWAT: Effect of Lumped Calibration

In many watershed modeling studies, due to limited data, model parameters for flow, sediment, and nutrients are calibrated and validated against observed data only at the watershed outlet. Model parameters are adjusted systematically for the entire watershed to obtain the closest match between the model-simulated and observed data at the watershed outlet (lumped calibration). It is hypothesized that the relative loadings of pollutants and/or sediments contributed by each computational unit are not affected by this calibration procedure. In other words, areas generating relatively higher pollutant loads with an uncalibrated model will still generate relatively higher loads after calibration. This study explored the effect of lumped calibration of the Soil and Water Assessment Tool (SWAT) on locations of sediment and nutrient critical source areas (CSAs). Two watersheds in Alabama with differing size, topography, hydrology, and land use/cover characteristics were used to study the variations in locations of sediment, total phosphorus (TP), and total nitrogen (TN) CSAs identified by calibrated and uncalibrated SWAT models. Identified CSAs for sediment, TP, and TN were mostly the same with and without the calibration of the model in both watersheds. This study thus concluded that lumped calibration of the SWAT model using data at the watershed outlet has little effect on the locations of CSAs. Based on the results from these two watersheds, it was further concluded that SWAT can be used without calibration for identification of CSAs in watersheds that lack sufficient data for model calibration, but not for all other modeling purposes. More studies are encouraged to support these findings.

Water Resources and Land Use and Cover in a Humid Region: The Southeastern United States

It is widely recognized that forest and water resources are intricately linked. Globally, changes in forest cover to accommodate agriculture and urban development introduce additional challenges for water management. The U.S. Southeast typifies this global trend as predictions of land-use change and population growth suggest increased pressure on water resources in coming years. Close attention has long been paid to interactions between people and water in arid regions; however, based on information from regions such as the Southeast, it is evident that much greater focus is required to sustain a high-quality water supply in humid areas as well. To that end, we review hydrological, physicochemical, biological, and human and environmental health responses to conversion of forests to agriculture and urban land uses in the Southeast. Commonly, forest removal leads to increased stream sediment and nutrients, more variable flow, altered habitat and stream and riparian communities, and increased risk of human health effects. Although indicators such as the percentage of impervious cover signify overall watershed alteration, the threshold to disturbance, or the point at which effects can been observed in stream and riparian parameters, can be quite low and often varies with physiographic conditions. In addition to current land use, historical practices can greatly influence current water quality. General inferences of this study may extend to many humid regions concerning climate, environmental thresholds, and the causes and nature of effects.

Uncertainty and sensitivity analysis of runoff and sediment yield in a small agricultural watershed with KINEROS2 / Analyse d'incertitude et de sensibilité des simulations d'écoulement et de transport solide de KINEROS2 dans un petit bassin versant agricole

Abstract Using the Monte Carlo (MC) method, this paper derives arithmetic and geometric means and associated variances of the net capillary drive parameter, G, that appears in the Parlange infiltration model, as a function of soil texture and antecedent soil moisture content. Approximate expressions for the arithmetic and geometric statistics of G are also obtained, which compare favourably with MC generated ones. This paper also applies the MC method to evaluate parameter sensitivity and predictive uncertainty of the distributed runoff and erosion model KINEROS2 in a small experimental watershed. The MC simulations of flow and sediment related variables show that those parameters which impart the greatest uncertainty to KINEROS2 model outputs are not necessarily the most sensitive ones. Soil hydraulic conductivity and wetting front net capillary drive, followed by initial effective relative saturation, dominated uncertainties of flow and sediment discharge model outputs at the watershed outlet. Model predictive uncertainty measured by the coefficient of variation decreased with rainfall intensity, thus implying improved model reliability for larger rainfall events. The antecedent relative saturation was the most sensitive parameter in all but the peak arrival times, followed by the overland plane roughness coefficient. Among the sediment related parameters, the median particle size and hydraulic erosion parameters dominated sediment model output uncertainty and sensitivity. Effect of rain splash erosion coefficient was negligible. Comparison of medians from MC simulations and simulations by direct substitution of average parameters with observed flow rates and sediment discharges indicates that KINEROS2 can be applied to ungauged watersheds and still produce runoff and sediment yield predictions within order of magnitude of accuracy.

Quantification of El Niño Southern Oscillation impact on precipitation and streamflows for improved management of water resources in Alabama

There is increased pressure on the water resources of the southeastern United States due to the rapidly growing population of the region. This pressure is further exacerbated by the severe seasonal to interannual climate variability this region experiences, most of which has been attributed to the El Niño Southern Oscillation (ENSO). Understanding the regional impacts of ENSO on precipitation and streamflow is a valuable tool for water resource managers in the region. This study was undertaken to develop a clear picture of the effect of ENSO on observed precipitation and streamflow anomalies in Alabama to help managers in the state with decision making. The effect of ENSO on precipitation in eight climate divisions of Alabama was assessed using 59 years (1950 to 2008) of monthly historical data. In addition, eight unimpaired streams (one in each climate division) were selected to study the relationship between ENSO and streamflow. Results indicate a significant relationship between ENSO and precipitation as well as between ENSO and streamflow. However, different parts of the state respond differently to ENSO. For precipitation, it was found that the relationship is significant during winter months with dry conditions being associated with La Niña in the southern climate divisions. A fairly strong relationship was also found during other months. Streamflows show high variability and positive correlation during winter months in the southern climate divisions. The results obtained can provide a basis for water resource managers in Alabama to incorporate climate variability caused by ENSO in their decision making related to soil and water conservation.

Performance comparison of Adoptive Neuro Fuzzy Inference System (ANFIS) with Loading Simulation Program C++ (LSPC) model for streamflow simulation in El Niño Southern Oscillation (ENSO)-affected watershed

ANFIS tends to equally simulate to a watershed model, LSPC.Since watershed was affected by ENSO, SST/SLP was helpful to improve simulation.ANFIS will be suitable for ENSO affected-watershed with limited rain gauge stations. Suitable selection of hydrological modeling tools and techniques for specific hydrological study is an essential step. Currently, hydrological simulation studies are relied on various physically based, conceptual and data driven models. Though data driven model such as Adoptive Neuro Fuzzy Inference System (ANFIS) has been successfully applied for hydrologic modeling ranging from small watershed scale to large river basin scale, its performance against physically based model has yet to be evaluated to ensure that ANFIS are as capable as any physically based model for simulation study. This study was conducted in Chickasaw Creek watershed, which is located in Mobile County of South Alabama. Since adequate rain gauge stations were not available near the watershed proximity, and also the study area was affected with the El Nino Southern Oscillation (ENSO), the sea surface temperature (SST) and sea level pressure (SLP) were additionally incorporated in the ANFIS model. The research concluded that ANFIS model performance was equally comparable to a physically based watershed model, Loading Simulation Program C++ (LSPC), especially when rain gauge stations were not adequate. Additionally, the research concludes that ANFIS model performance was equally comparable to that of LSPC no matter whether SST and SLP in ANFIS input vector was included or not.

Deriving Spatially Distributed Precipitation Data Using the Artificial Neural Network and Multilinear Regression Models

Precipitation is the primary driver for hydrologic modeling. Because hydrologic models often require long-term, spatially dis- tributed precipitation data sets for calibration and validation, a novel approach was developed to generate spatially distributed precipitation data using an artificial neural network (ANN) for the periods when Next-Generation Weather Radar (NEXRAD) data are either unavailable or the quality of the NEXRAD data is not good. The multilinear regression (MLR) technique was also evaluated for completeness. The studys focus was the Saugahatchee Creek watershed in southeast Alabama. In the study area, thewet seasons are dominated by frontal precipitations, whereas the dry seasons primarily contain patchy, convective thunderstorms. The basic approach was to train and validate the ANN and MLR models using recent NEXRAD and rain gauge precipitations, and then use the trained model with the rain gauge precipitation data to generate past, spatially distributed precipitation estimates at the NEXRAD grid locations. For the testing period, the ANN-simulated wet season precipitations in all the NEXRAD grids had a Nash-Sutcliffe efficiency greater than or equal to 0.72 and a mass balance error less than or equal to 14%. The same model performance parameters were 0.65 and 17%, respectively, for the dry season. The MLR model did not perform as well as the ANN model. For the MLR model, the wet season mass balance error ranged from 13-48%, whereas the dry season mass balance error ranged from 0.1-36% on the testing data set. An uncalibrated soil and water assessment tool model was used to assess the improvements in stream flow simulations with the ANN-simulated spatially distributed precipitation data. The stream flow simulations using ANN-generated, spatially distributed precipitations were closer to the observed stream flows relative to stream flows generated using the rain gauge precipitations. Overall, the results suggest that the method developed in this study can be used to generate past, spatially distributed precipitations at NEXRAD grid locations. DOI: 10.1061/(ASCE)HE.1943-5584.0000617.

An auxiliary method to reduce potential adverse impacts of projected land developments: subwatershed prioritization.

AbstractAn index based method is developed that ranks the subwatersheds of a watershed based on their relative impacts on watershed response to anticipated land developments, and then applied to an urbanizing watershed in Eastern Pennsylvania. Simulations with a semi-distributed hydrologic model show that computed low- and high-flow frequencies at the main outlet increase significantly with the projected landscape changes in the watershed. The developed index is utilized to prioritize areas in the urbanizing watershed based on their contributions to alterations in the magnitude of selected flow characteristics at two spatial resolutions. The low-flow measure, 7Q10, rankings are shown to mimic the spatial trend of groundwater recharge rates, whereas average annual maximum daily flow,

Validation of evaporation estimates from a modified surface energy balance algorithm for land (SEBAL) model in the south-eastern United States

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