Yonas Demissie

Assessing regional hydrology and water quality implications of large-scale biofuel feedstock production in the Upper Mississippi River Basin.

A recent U.S. Department of Energy study estimated that more than one billion tons of biofuel feedstock could be produced by 2030 in the United States from increased corn yield, and changes in agricultural and forest residue management and land uses. To understand the implications of such increased production on water resources and stream quality at regional and local scales, we have applied a watershed model for the Upper Mississippi River Basin, where most of the current and future crop/residue-based biofuel production is expected. The model simulates changes in water quality (soil erosion, nitrogen and phosphorus loadings in streams) and resources (soil-water content, evapotranspiration, and runoff) under projected biofuel production versus the 2006 baseline year and a business-as-usual scenario. The basin average results suggest that the projected feedstock production could change the rate of evapotranspiration in the UMRB by approximately +2%, soil-water content by about -2%, and discharge to streams by -5% from the baseline scenario. However, unlike the impacts on regional water availability, the projected feedstock production has a mixed effect on water quality, resulting in 12% and 45% increases in annual suspended sediment and total phosphorus loadings, respectively, but a 3% decrease in total nitrogen loading. These differences in water quantity and quality are statistically significant (p < 0.05). The basin responses are further analyzed at monthly time steps and finer spatial scales to evaluate underlying physical processes, which would be essential for future optimization of environmentally sustainable biofuel productions.

Vegetation regulation on streamflow intra-annual variability through adaption to climate variations

This study aims to provide a mechanistic explanation of the empirical patterns of streamflow intra-annual variability revealed by watershed-scale hydrological data across the contiguous United States. A mathematical extension of the Budyko formula with explicit account for the soil moisture storage change is used to show that, in catchments with a strong seasonal coupling between precipitation and potential evaporation, climate aridity has a dominant control on intra-annual streamflow variability. But in other catchments, additional factors related to soil water storage change also have important controls on how precipitation seasonality propagates to streamflow. More importantly, use of leaf area index as a direct and indirect indicator of the above ground biomass and plant root system, respectively, reveals the vital role of vegetation in regulating soil moisture storage and hence streamflow intra-annual variability under different climate conditions.

Understanding the hydrologic sources and sinks in the Nile Basin using multisource climate and remote sensing data sets

In this study, we integrated satellite-drived precipitation and modeled evapotranspiration data (2000–2012) to describe spatial variability of hydrologic sources and sinks in the Nile Basin. Over 2000–2012 period, 4 out of 11 countries (Ethiopia, Tanzania, Kenya, and Uganda) in the Nile Basin showed a positive water balance while three downstream countries (South Sudan, Sudan, and Egypt) showed a negative balance. Gravity Recovery and Climate Experiment (GRACE) mass deviation in storage data analysis showed that at annual timescales, the Nile Basin storage change is substantial while over longer time periods, it is minimal (<1% of basin precipitation). We also used long-term gridded runoff and river discharge data (1869–1984) to understand the discrepancy in the observed and expected flow along the Nile River. The top three countries that contribute most to the flow are Ethiopia, Tanzania, and Kenya. The study revealed that ∼85% of the runoff generated in the equatorial region is lost in an interstation basin that includes the Sudd wetlands in South Sudan; this proportion is higher than the literature reported loss of 50% at the Sudd wetlands alone. The loss in runoff and flow volume at different sections of the river tend to be more than what can be explained by evaporation losses, suggesting a potential recharge to deeper aquifers that are not connected to the Nile channel systems. On the other hand, we also found that the expected average annual Nile flow at Aswan is greater (97 km3) than the reported amount (84 km3). Due to the large variations of the reported Nile flow at different locations and time periods, the study results indicate the need for increased hydrometeorological instrumentation of the basin. The study also helped improve our understanding of the spatial dynamics of water sources and sinks in the Nile Basin and identified emerging hydrologic questions that require further attention.

Parameter Estimation for Groundwater Models under Uncertain Irrigation Data

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.

Attributing runoff changes to climate variability and human activities: uncertainty analysis using four monthly water balance models

Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging of four monthly water balance models was proposed. The method was applied to the Weihe River Basin, the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956–1990) and human-impacted period (1991–2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.

Hydrologic and water quality impacts of biofuel feedstock production in the Ohio River Basin

This study addresses the uncertainties related to potential changes in land use and management and associated impacts on hydrology and water quality resulting from increased production of biofuel from the conventional and cellulosic feedstock. The Soil Water Assessment Tool (SWAT) was used to assess the impacts on regional and field scale evapotranspiration, soil moisture content, stream flow, sediment, and nutrient loadings in the Ohio River Basin. The model incorporates spatially and temporally detailed hydrologic, climate and agricultural practice data that are pertinent to simulate biofuel feedstock production, watershed hydrology and water quality. Three future biofuel production scenarios in the region were considered, including a feedstock projection from the DOE Billion‐Ton (BT2) Study, a change in corn rotations to continuous corn, and harvest of 50% corn stover. The impacts were evaluated on the basis of relative changes in hydrology and water quality from historical baseline and future business‐as‐usual conditions of the basin. The overall impact on water quality is an order of magnitude higher than the impact on hydrology. For all the three future scenarios, the sub‐basin results indicated an overall increase in annual evapotranspiration of up to 6%, a decrease in runoff up to 10% and minimal change in soil moisture. The sediment and phosphorous loading at both regional and field levels increased considerably (up to 40–90%) for all the biofuel feedstock scenario considered, while the nitrogen loading increased up to 45% in some regions under the BT2 Study scenario, decreased up to 10% when corn are grown continuously instead of in rotations, and changed minimally when 50% of the stover are harvested. Field level analyses revealed significant variability in hydrology and water quality impacts that can further be used to identify suitable locations for the feedstock productions without causing major impacts on water quantity and quality.

Historical and future drought in Bangladesh using copula-based bivariate regional frequency analysis

The study aims at regional and probabilistic evaluation of bivariate drought characteristics to assess both the past and future drought duration and severity in Bangladesh. The procedures involve applying (1) standardized precipitation index to identify drought duration and severity, (2) regional frequency analysis to determine the appropriate marginal distributions for both duration and severity, (3) copula model to estimate the joint probability distribution of drought duration and severity, and (4) precipitation projections from multiple climate models to assess future drought trends. Since drought duration and severity in Bangladesh are often strongly correlated and do not follow same marginal distributions, the joint and conditional return periods of droughts are characterized using the copula-based joint distribution. The country is divided into three homogeneous regions using Fuzzy clustering and multivariate discordancy and homogeneity measures. For given severity and duration values, the joint return periods for a drought to exceed both values are on average 45% larger, while to exceed either value are 40% less than the return periods from the univariate frequency analysis, which treats drought duration and severity independently. These suggest that compared to the bivariate drought frequency analysis, the standard univariate frequency analysis under/overestimate the frequency and severity of droughts depending on how their duration and severity are related. Overall, more frequent and severe droughts are observed in the west side of the country. Future drought trend based on four climate models and two scenarios showed the possibility of less frequent drought in the future (2020–2100) than in the past (1961–2010).

Potential Drought Impacts on Electricity Generation in Texas

Many power plants in the Electric Reliability Council of Texas (ERCOT) region require a large amount of water for system cooling. To improve the understanding of potential risks of electricity generation curtailment due to drought, an assessment of water availability and its potential impacts on generation during drought was performed. For this impact analysis, we identified three drought scenarios based on historical drought records and projected climate data from the Geophysical Fluid Dynamics Laboratory global climate model, for greenhouse gas emission scenario A2 defined by the Intergovernmental Panel on Climate Change. The three drought scenarios are (1) 2011 drought conditions (the worst drought in history), with the current level of water use; (2) a single-year drought (2022) projected for the period of 2020–2030, with the assumed projected water use level for 2030; and (3) a multiple-year drought constructed with climate data for 1950–1957 and water demand projected for 2030. The projected drought scenario in 2022 and the historical droughts in 2011 and 1950–1957 represent two different precipitation patterns in the Texas-Gulf river basin.The hydrologic model constructed for the Texas-Gulf river basin covers most of the ERCOT region. The model incorporates climate and water use data that correspond to three drought scenarios, respectively, to estimate evapotranspiration, water yield from watersheds, stream flow and water storage in reservoirs. Using criteria based on observed (< 50% storage) and predicted (< 55% storage) reservoir data, we identified 15 low-storage reservoirs in 2011, 10 in 2022, and 20 in 1956 (the last year of the multiple-year drought). The power plants that are supported by these reservoirs would be potentially at risk of being derated for thermoelectric cooling because of a lack of water supply. These power plants are located mainly in watersheds near and between Houston and Austin, as well as surrounding Dallas.Copyright