Understanding the relative impacts of natural processes and human activities on the hydrology of the Central Rift Valley lakes,East Africa

Significant changes have been observed in the hydrology of Central Rift Valley (CRV) lakes in Ethiopia, East Africa as a result of both natural processes and human activities during the past three decades. This study applied an integrated approach (remote sensing, hydrologic modelling, and statistical analysis) to understand the relative effects of natural processes and human activities over a sparsely gauged CRV basin. Lake storage estimates were calculated from a hydrologic model constructed without inputs from human impacts such as water abstraction and compared with satellite‐based (observed) lake storage measurements to characterize the magnitude of human‐induced impacts. A non‐parametric Mann–Kendall test was used to detect the presence of climatic trends (e.g. a decreasing or increasing trends in precipitation), while the Standard Precipitation Index (SPI) analysis was used to assess the long‐term, inter‐annual climate variability within the basin. Results indicate human activities (e.g. abstraction) significantly contributed to the changes in the hydrology of the lakes, while no statistically significant climatic trend was seen in the basin, however inter‐annual natural climate variability, extreme dryness, and prolonged drought has negatively affected the lakes. The relative contributions of natural and human‐induced impacts on the lakes were quantified and evaluated by comparing hydrographs of the CRV lakes. Lake Abiyata has lost ~6.5 m in total lake height between 1985 and 2006, 70% (~4.5 m) of the loss has been attributed to human‐induced causes, whereas the remaining 30% is related to natural climate variability. The relative impact analysis utilized in this study could potentially be used to better plan and create effective water‐management practices in the basin and demonstrates the utility of this integrated methodology for similar studies assessing the relative natural and human‐induced impacts on lakes in data sparse areas. Copyright © 2015 John Wiley & Sons, Ltd.     

Zooplankton community structure and ecology of the tropical-highland Lake Hayq, Ethiopia

Lake Hayq, a highland lake in Ethiopia, was stocked with Tilapia fish (Oreochromis niloticus) in late 1970s, offering an opportunity to study the effect of fish predation in a natural lake. Since 1930s, some limnological surveys have been done sporadically documenting a change in zooplankton composition including the disappearance of cladocerans, hypothesizing the stocked planktivorous fish could be a cause. Nevertheless, no detailed research was conducted to identify potential effects of fish stocking predominantly due to its remote location. The article presents data about zooplankton composition, abundance and biomass done between October 2007 and January 2009 on short-time intervals including the underlying limnological variables. The zooplankton community was depauperate comprising two copepods, three cladocerans, and six rotifers taxa, as typical for tropical lakes. Total mean standing biomass of all crustacean zooplankton was 237 mg dry mass m⁻³, which gave Lake Hayq an intermediate position when compared with other tropical lakes. Of copepods, Thermocyclops ethiopiensis was almost an exclusive species, and its temporal variation was influenced by food supply and water temperature. We refute the hypothesis that Tilapia was the cause for the seasonal disappearance of cladocerans, and attribute it to the adverse effect of episodic mixing. Nevertheless, the planktivorous fish probably plays a key role in structuring the cladocerans in particular the large-sized Daphnia magna. In January 2008, we observed a massive planktivorous fish mortality that triggered high algal biomass, which was later grazed by large-sized D. magna demonstrating the trophic cascade hypothesis in a natural ecosystem.     

Suitability of satellite-based hydro-climate variables and machine learning for streamflow modeling at various scale watersheds

ABSTRACT

Streamflow modeling is essential to investigate processes in the hydrologic cycle and important for water resource management application. However, in-situ hydrologic data paucity, because of various factors such as economic, political, instrument malfunctioning, and poor spatial distribution, makes the modeling process challenging. To overcome this limitation, we introduced a satellite remote sensing-based machine learning approach – boosted regression tree (BRT) – that integrates spatial land surface and climate variables that describe the sub-units, and applied it in three variable size watersheds in the Upper Mississippi River Basin (UMRB), USA. The model simulation results were tested using an independent dataset and showed Nash–Sutcliffe efficiency values of 0.80, 0.76, and 0.69 for the UMRB, Illinois River Watershed, and Raccoon River Watershed, respectively. In addition, we compared the performance of the machine learning models with existing process-based modeling results. Overall performance is comparable with the process-based approaches, but with significantly less modeling effort and resources.     

Hydraulic relationships between buried valley sediments of the glacial drift and adjacent bedrock formations in northeastern Ohio,USA

Buried valleys are ancient river or stream valleys that predate the recent glaciation and since have been filled with glacial till and/or outwash. Outwash deposits are known to store and transmit large amounts of groundwater. In addition to their intrinsic hydraulic properties, their productivity depends on their hydraulic relationships with the adjacent bedrock formations. These relationships are examined using a steady-state three-dimensional groundwater flow model through a section of a buried valley in northeastern Ohio, USA. The flow domain was divided into five hydrostratigraphic units: low-conductivity (K) till, high-K outwash, and three bedrock units (Pottsville Formation, Cuyahoga Group and Berea Sandstone). The model input was prepared using the data from well logs and drilling reports of residential water wells. The model was calibrated using observed heads with mean residual head error of 0.3 m. The calibrated model was used to quantify flux between the buried valley and bedrock formations. Mass balance was calculated to within an error of 2–3 %. Mass balance of the buried valley layer indicates that it receives 1.6 Mm³/year (≈40 % of the total inflow) from the adjacent bedrock aquifers: Pottsville Formation contributes 0.96 Mm³/year (60 %) while the Berea Sandstone 0.64 Mm³/year (40 %).