Umesh Adhikari

Impacts of climate change on water resources in Malawi

AbstractThis study examines climate change impacts on water resources in the African country of Malawi. Downscaled outputs from six general circulation models, for the most extreme Representative Concentration Pathway (RCP 8.5), were used as inputs to the soil and water assessment tool to assess the impacts of climate change on evapotranspiration, surface runoff, water yield, and soil moisture content at the country, watershed, and subbasin levels by the 2050s. At the country level, the results showed a –5.4% to +24.6% change in annual rainfall, a −5.0% to +3.1% change in annual evapotranspiration, from –7.5% to over +50% change in annual surface runoff and water yield, and up to an 11.5% increase in annual soil moisture. At the watershed level, results showed an increase in annual rainfall and evapotranspiration in the north and a gradual decline towards the south. Subbasin-level analysis showed a large probability of increase in the annual precipitation, surface runoff, water yield, and soil moisture, e...

Modeling Escherichia coli removal in constructed wetlands under pulse loading

Manure-borne pathogens are a threat to water quality and have resulted in disease outbreaks globally. Land application of livestock manure to croplands may result in pathogen transport through surface runoff and tile drains, eventually entering water bodies such as rivers and wetlands. The goal of this study was to develop a robust model for estimating the pathogen removal in surface flow wetlands under pulse loading conditions. A new modeling approach was used to describe Escherichia coli removal in pulse-loaded constructed wetlands using adaptive neuro-fuzzy inference systems (ANFIS). Several ANFIS models were developed and validated using experimental data under pulse loading over two seasons (winter and summer). In addition to ANFIS, a mechanistic fecal coliform removal model was validated using the same sets of experimental data. The results showed that the ANFIS model significantly improved the ability to describe the dynamics of E. coli removal under pulse loading. The mechanistic model performed poorly as demonstrated by lower coefficient of determination and higher root mean squared error compared to the ANFIS models. The E. coli concentrations corresponding to the inflection points on the tracer study were keys to improving the predictability of the E. coli removal model.

Multiscale Assessment of the Impacts of Climate Change on Water Resources in Tanzania

AbstractIn the context of changing climate, this study assessed the effects of global warming on water resources in Tanzania for the mid-21st century. Climate projections from six global circulation models under the most extreme emission scenario (RCP 8.5) were used as inputs to the soil and water assessment tool (SWAT) to examine the effects. The results were analyzed both at spatial (country-level, watershed-level, and subbasin-level) and temporal (annual and seasonal) scales concerning potential and actual evapotranspiration, surface runoff, water yield, and soil moisture. At the country level, the results showed a 0.8–27.4% increase in annual precipitation, which led to a general increase in evapotranspiration (−2.2–7.3%), surface runoff (12.6–94.1%), water yield (7.5–73.4%), and soil moisture (2.9–20.7%). Watershed-level analysis showed 2.4–31.5%, −2.6–6.8%, 18.4–159.7%, and 3.2–22.8% changes in average precipitation, evapotranspiration, surface runoff, and soil moisture, respectively. While no disti...

Applicability of Benford’s Law to Compliance Assessment of Self-Reported Wastewater Treatment Plant Discharge Data

The United States (U.S.) environmental regulatory system relies heavily on self-reports to assess compliance among regulated facilities. However, the regulatory agencies have expressed concerns regarding the potential for fraud in self-reports and suggested that the likelihood of detection in the federal and state enforcement processes is low. In this paper, we apply Benford’s Law to three years of self-reported discharge parameters from wastewater treatment plant facilities in one U.S. state. We conclude that Benford’s Law alone may not be a reliable method for detecting potential data mishandling for individual facility–parameter combinations, but may provide information about the types of parameters most likely to be fraudulently reported and types of facilities most likely to do so. From a regulatory perspective, this information may help to prioritise potential fraud risks in self reporting and better direct limited resources.

Evaluation of neuro-fuzzy and Bayesian techniques in estimating suspended sediment loads

Sediment is considered the largest surface water pollutant by volume, which is crucial for surface water planning and management. Different management scenario evaluations require multiple in-stream suspended sediment forecasts and estimations. Physically-based models are considered to be good modeling techniques for suspended sediment estimation; nevertheless, they require a large number of parameters and intensive calculations. This study aims to enhance suspended sediment predicting techniques using efficient fusion modeling that can be used for evaluations by watershed managers and stakeholders. Adaptive neuro-fuzzy inference system (ANFIS) and Bayesian regression models were tested to find the best alternative to a calibrated and validated Soil and Water Assessment Tool (SWAT) model to predict suspended sediment loads in the Saginaw River watershed. For both methods, four different method-types were tested, namely General, Temporal, Spatial and Spatiotemporal. Results of the study showed that both methods can be used as good alternatives to the SWAT model at the global level for watershed estimations. The best suspended sediment replicating models, the Bayesian Spatiotemporal and ANFIS Spatial, produced results with Nash–Sutcliffe model efficiency values of 0.95 and 0.94, respectively. For the subbasin level, Bayesian and ANFIS techniques showed satisfactory results for 84 and 77 subbasins, respectively, out of 155 subbasins in the watershed. Box-Cox transformation of suspended sediment load values, made the use of the Bayesian model feasible and improved the prediction of the ANFIS models. However, suspended sediment data exhibited a bimodal distribution after transformation, making the modeling process challenging and complex.

Evaluation of the effectiveness of conservation practices under implementation site uncertainty

Agricultural nonpoint source pollution is the leading source of water quality degradation in United States, which has led to the development of programs that aim to mitigate this pollution. One common approach to mitigating nonpoint source pollution is the use of best management practices (BMPs). However, it can be challenging to evaluate the effectiveness of implemented BMPs due to polices that limit data sharing. In this study, the uncertainty introduced by data sharing limitations is quantified through the use of a watershed model. Results indicated that BMP implementation improved the overall water quality in the region (up to ∼15% pollution reduction) and that increasing the area of BMP implementation resulted in higher pollution reduction. However, the model outputs also indicated that uncertainty caused by data sharing limitations resulted in variabilities ranging from -160% to 140%. This shows the importance of data sharing among agencies to better guide current and future conservation programs.