Yonas Demissie | Search USGS

USGS2014

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 km 3 ) than the reported amount (84 km 3 ). 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.

Water Resources Research↗

USGS2014

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.

Groundwater↗