Assefa M. Melesse

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.

EVALUATION OF THE SWAT MODEL'S SNOWMELT HYDROLOGY IN A NORTHWESTERN MINNESOTA WATERSHED

Snowmelt hydrology is a very important component for applying SWAT (Soil and Water Assessment Tool) in watersheds where the stream flows in spring are predominantly generated from melting snow. However, there is a lack of information about the performance of this component because most published studies were conducted in rainfall-runoff dominant watersheds. The objective of this study was to evaluate the performance of the SWAT model’s snowmelt hydrology by simulating stream flows for the Wild Rice River watershed, located in northwestern Minnesota. Along with the three snowmelt-related parameters determined to be sensitive for the simulation (snowmelt temperature, maximum snowmelt factor, and snowpack temperature lag factor), eight additional parameters (surface runoff lag coefficient, Muskingum translation coefficients for normal and low flows, SCS curve number, threshold depth of water in the shallow aquifer required for return flow to occur, groundwater “revap” coefficient, threshold depth of water in the shallow aquifer for “revap” or percolation to the deep aquifer to occur, and soil evaporation compensation factor) were adjusted using the PEST (Parameter ESTimation) software. Subsequently, the PEST-determined values for these parameters were manually adjusted to further refine the model. In addition to two commonly used statistics (Nash-Sutcliffe coefficient, and coefficient of determination), a measure designated “performance virtue” was developed and used to evaluate the model. This evaluation indicated that for the study watershed, the SWAT model had a good performance on simulating the monthly, seasonal, and annual mean discharges and a satisfactory performance on predicting the daily discharges. When analyzed alone, the daily stream flows in spring, which were predominantly generated from melting snow, could be predicted with an acceptable accuracy, and the corresponding monthly and seasonal mean discharges could be simulated very well. Further, the model had an overall better performance for evaluation years with a larger snowpack than for those with a smaller snowpack, and tended to perform relatively better for one of the stations tested than for the other.

Impact of climate change on the hydroclimatology of Lake Tana Basin, Ethiopia

[1] Climate change has the potential to reduce water resource availability in the Nile Basin countries in the forthcoming decades. We investigated the sensitivity of water resources to climate change in the Lake Tana Basin, Ethiopia, using outputs from global climate models (GCMs). First, we compiled projected changes in monthly precipitation and temperature in the basin from 15 GCMs. Although the GCMs uniformly suggest increases in temperature, the rainfall projections are not consistent. Second, we investigated how changes in daily temperature and precipitation might translate into changes in streamflow and other hydrological components. For this, we generated daily climate projections by modifying the historical data sets to represent the changes in the GCM climatologies and calculated hydrological changes using the Soil and Water Assessment Tool (SWAT). The SWAT model itself was calibrated and validated using the flows from four tributaries of Lake Tana. For the Special Report on Emissions Scenarios A2 scenario, four of the nine GCMs investigated showed statistically significant declines in annual streamflow for the 2080–2100 period. We interpret our results to mean that anthropogenic climate changes may indeed alter the water balance in the Lake Tana Basin during the next century but that the direction of change cannot be determined with confidence using the current generation of GCMs.

Influences of potential evapotranspiration estimation methods on SWAT's hydrologic simulation in a northwestern Minnesota watershed

The Soil and Water Assessment Tool (SWAT), a widely used watershed hydrology and water quality model, provides three different methods (Hargreaves, Priestley-Taylor, and Penman-Monteith) for estimating potential evapotranspiration (PET) and the corresponding actual evapotranspiration (AET). Although these methods have been extensively tested, the effects of using them within SWATs framework are largely unknown. The objective of this study was to test the three PET methods within SWATs framework using data collected in the Wild Rice River watershed, located in northwestern Minnesota. The performance of the SWAT models was measured using three statistics: the Nash-Sutcliffe coefficient (Ej2), coefficient of determination (R2), and performance virtue (PVk). The three models were independently calibrated and validated using the observed daily stream flows at two USGS gauging stations. The simulated stream discharges were compared with the corresponding observed values and the estimated evapotranspiration examined in accordance with the wet-environment areal evapotranspiration (ETW) derived from the evaporation data for Williams Lake, located about 100 km southeast of the study watershed. The use of the three PET methods resulted in different values for two calibration parameters, namely the soil evaporation compensation factor and SCS curve number. At the lower station, which is near the watershed outlet, the observed annual mean discharge (8.33 m3/s) during the model validation period was predicted to be 10.25, 10.87, and 9.69 m3/s by SWAT-Penman, SWAT-Priestley, and SWAT-Hargreaves, respectively. The annual mean discharge (10.83 m3/s) was more accurately predicted during the model calibration period, with an absolute error of less than 0.5 m3/s. The prediction errors for the upper station were comparable with those for the lower station. In addition, all three models exhibited good performance when simulating the monthly, seasonal, and annual mean discharges (Ej2 >0.75 and PVk >0.80) and satisfactory performance when predicting the daily stream flows (Ej2 >0.36 and PVk >0.70). In estimating evapotranspiration for the study watershed, SWAT-Hargreaves seemed to be slightly superior to the other two models, while SWAT-Priestley might be more appropriate for an ETW value greater than 8.0 mm/d. Nevertheless, the AET values estimated by the three models shared a concurrent spatial pattern and temporal trend, and were insignificantly different from each other at a 5% significance level (p-values > 0.05). The results indicated that after calibration, using the three ET methods within SWAT produced very similar hydrologic (AET and discharge) predictions for the study watershed.

Spatially distributed storm runoff depth estimation using Landsat images and GIS

The use of geographic information systems (GISs) and remote sensing to facilitate the estimation of runoff from watershed and agricultural fields has gained increasing attention in recent years. This is mainly due to the fact that rainfall-runoff models include both spatial and geomorphologic variations. The US Department of Agriculture, Natural Resources Conservation Service Curve Number (USDA-NRCS-CN) method was used in this study for determining the runoff depth. Runoff curve number was determined based on the factors of hydrologic soil group, land use, land treatment, and hydrologic conditions. GIS and remote sensing were used to provide quantitative measurements of drainage basin morphology for input into runoff models so as to estimate runoff response. The study was conducted on the S65A sub-basin of the Kissimmee River basin in south Florida. Land use from Landsat images for 1980, 1990 and 2000 were considered in the study. The process of determining spatially distributed runoff curve numbers from Landsat images is presented in this study using GIS and image processing software. Spatially distributed runoff curve numbers and runoff depth were determined for the watershed for different land use classes. Results of the study show that land use changes determined from Landsat images are useful in studying the runoff response of the basin. It is shown that the S-65A sub-basin has undergone land use and runoff response changes over the 20 years period of time. The area covered by water and wetlands in 2000 is higher than in 1980 and 1990. In 2000 areas having CN of greater than 90 accounted for 3% compared to 0.9 and 0.6% in 1980 and 1990 respectively. This was due to the increase in wetlands and water covered areas attributed to the Kissimmee River restoration work, which started in 1997 and aimed at restoring lost wetlands and floodplains. # 2002 Elsevier Science B.V. All rights reserved.

Simulated wetland conservation-restoration effects on water quantity and quality at watershed scale.

Wetlands are one of the most important watershed microtopographic features that affect hydrologic processes (e.g., routing) and the fate and transport of constituents (e.g., sediment and nutrients). Efforts to conserve existing wetlands and/or to restore lost wetlands require that watershed-level effects of wetlands on water quantity and water quality be quantified. Because monitoring approaches are usually cost or logistics prohibitive at watershed scale, distributed watershed models such as the Soil and Water Assessment Tool (SWAT), enhanced by the hydrologic equivalent wetland (HEW) concept developed by Wang [Wang, X., Yang, W., Melesse, A.M., 2008. Using hydrologic equivalent wetland concept within SWAT to estimate streamflow in watersheds with numerous wetlands. Trans. ASABE 51 (1), 55-72.], can be a best resort. However, there is a serious lack of information about simulated effects using this kind of integrated modeling approach. The objective of this study was to use the HEW concept in SWAT to assess effects of wetland restoration within the Broughtons Creek watershed located in southwestern Manitoba, and of wetland conservation within the upper portion of the Otter Tail River watershed located in northwestern Minnesota. The results indicated that the HEW concept allows the nonlinear functional relations between watershed processes and wetland characteristics (e.g., size and morphology) to be accurately represented in the models. The loss of the first 10-20% of the wetlands in the Minnesota study area would drastically increase the peak discharge and loadings of sediment, total phosphorus (TP), and total nitrogen (TN). On the other hand, the justifiable reductions of the peak discharge and loadings of sediment, TP, and TN in the Manitoba study area may require that 50-80% of the lost wetlands be restored. Further, the comparison between the predicted restoration and conservation effects revealed that wetland conservation seems to deserve a higher priority while both wetland conservation and restoration may be equally important.

Evaporation and Evapotranspiration

Meteorological Parameter Monitoring and Data Quality.- Evaporation and Evapotranspiration Measurement.- Energy Requirements of Dew Evaporation.- Vapor Pressure Calculation Methods.- Evaporation and Evapotranspiration Estimation Methods.- Wetland Evapotranspiration.- Lake Evaporation.- Reference and Crop Evapotranspiration.- Spatially Distributed Surface Energy Flux Modeling.- Crop Yield Estimation Using Remote Sensing and Surface Energy Flux Model.- Wetland Restoration Assessment using Remote Sensing and Surface Energy Budget Based Evapotranspiration.- Climate Change and Evapotranspiration.

Impact of Climate Change on the Hydrology of Upper Tiber River Basin Using Bias Corrected Regional Climate Model

The use of regional climate model (RCM) outputs has been getting due attention in most European River basins because of the availability of large number of the models and modelling institutes in the continent; and the relative robustness the models to represent local climate. This paper presents the hydrological responses to climate change in the Upper Tiber River basin (Central Italy) using bias corrected daily regional climate model outputs. The hydrological analysis include both control (1961–1990) and future (2071–2100) climate scenarios. Three RCMs (RegCM, RCAO, and PROMES) that were forced by the same lateral boundary condition under A2 and B2 emission scenarios were used in this study. The projected climate variables from bias corrected models have shown that the precipitation and temperature tends to decrease and increase in summer season, respectively. The impact of climate change on the hydrology of the river basin was predicted using physically based Soil and Water Assessment Tool (SWAT). The SWAT model was first calibrated and validated using observed datasets at the sub-basin outlet. A total of six simulations were performed under each scenario and RCM combinations. The simulated result indicated that there is a significant annual and seasonal change in the hydrological water balance components. The annual water balance of the study area showed a decrease in surface runoff, aquifer recharge and total basin water yield under A2 scenario for RegCM and RCAO RCMs and an increase in PROMES RCM under B2 scenario. The overall hydrological behaviour of the basin indicated that there will be a reduction of water yield in the basin due to projected changes in temperature and precipitation. The changes in all other hydrological components are in agreement with the change in projected precipitation and temperature.

A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.

Modeling Coastal Eutrophication at Florida Bay using Neural Networks

Abstract Nutrient loading and eutrophication in coastal waters are the causes of water quality degradation and loss of marine biota, which has led to ecological imbalance. Understanding and modeling the level of eutrophication as a function of environmental parameters can be beneficial to coastal ecosystem management. The limitation of deterministic and empirical models in accurately predicting the level of algal blooms, and the nonlinear relationship between the water quality and environmental parameters and that of the level of chlorophyll a necessitate a new approach using machine learning and data-driven modeling. A multilayer perceptron-back propagation (MLP-BP) algorithm of artificial neural network (ANN) was used to predict the level of eutrophication (chlorophyll a) from water quality parameters monitored at two Florida Bay water quality monitoring stations (FLAB03 and FLAB14). Based on the correlation of monthly nutrients (total phosphate, nitrite, ammonium) and other water data (temperature, turbidity, and dissolved oxygen) to the level of chlorophyll a, an input-output data structure was selected. Seven input data scenarios were studied, and model performance was compared using four indices. Monthly data from 1992 to 2004 were partitioned into training and testing subsets. Results show that chlorophyll a was predicted well with the selected inputs, with an average R2 and model efficiency (E) of 0.856 and 0.582, respectively. Prediction with antecedent chlorophyll a alone gave a stable result with smaller error and higher performance attributed to easier and more efficient training. It was also found that ANN performed better at FLA03 than at FLA14. It is shown that the MLP-BP technique is applicable to the monitoring and prediction of algal blooms and will be crucial to coastal watershed management.