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Although water availability depends both on qualitative and quantitative aspects, most studies focus only on one of these. Therefore, the goal here is to relate water quality and quantity with the construction of Load Duration Curves (LDC) and to estimate E. coli load patterns in different flow conditions, seasons, and positions of two sub-basins of the Doce watershed (Brazil): Piracicaba and Piranga. A novel methodology is proposed in which the Burr XII distribution is adjusted to the LDC to compare all observed loads to their respective Total Maximum Daily Load (TMDL), allowing the estimation of the relative difference (RD) between these. Higher values of RD were observed for low flows for the Piracicaba basin, more urbanized, where point sources of pollution are the primary concern, reaching up to 99% of needed load reduction. In the Piranga basin, more agricultural, there was a broader RD variation, from 9% to 97% load reduction needed, which is an evidence of point sources of pollution combined with non-point sources. The new methodology can be used to estimate the load reduction of any pollutant and can be used by environmental agencies to identify effective practices to minimize and control pollution in different locations of the basins.

Potholes are features with no evident natural outlet, formed in hydric landscapes, such as the Prairie Pothole Region (PPR). Potholes are commonly under cropland management, which is not consistent with their hydrological patterns since periodic flooding during the growing season is frequent. Although there are studies investigating undisturbed and/or restored potholes, there is limited information about the hydrology of features that are farmed and artificially drained, a common situation in the Des Moines Lobe, the Iowan part of the PPR. The estimation of pothole hydroperiod and water balance variations would allow their hydrological classification and estimation of their potential environmental impacts. To estimate pothole hydrology, Annualized Agricultural Non-Point Source model (AnnAGNPS), was used in this project to model two potholes located in Story County, IA, for which we had two years of periodic measurements of inundation depth. For a better understanding of the features, a high-resolution DEM was used to study their potential volume storage, before overflowing. A conserved scenario, in which the potholes were consider to be retired from cropland production and from artificial tile drainage was also simulated to estimate potential hydrological impacts of pothole conservation. After model calibration, AnnAGNPS was used to estimate pothole water volume and depth variations in the features under both current and conserved conditions, for 23 years of historical weather data. It was proved that AnnAGNPS can provide reliable representations of the observed data, particularly for water depth variations. Results include pothole hydroperiod, consecutive days of inundation, average water depth during ponding events, and frequency of overflow. In the current condition, the potholes water regimen suggests that these potholes are classified as semipermanent. Most ponding occurred in early stages of the growing season, and mostly lasted from one to two days, barely overwhelming their storage capacity. Nevertheless, crop failure is common within their extent, which indicates that their management does not agree with their hydrological patterns. In the conserved condition, potholes flooded more often, held water for longer periods, and exceed their maximum storage capacity more frequently than in the current scenario. Further research includes the assessment of potholes under different management conditions, improvement of AnnAGNPS tools to address wetland features, and investigation of the reliability of the results of pothole conservation. Worrell 0.52 Very Good 0.62 Fair Volume Walnut 0.52 Good 1.13 Unsatisfactory Worrell 0.57 Good 0.56 Good R 2 Pothole-year R 2-GS Performance R 2-VS Performance Depth Walnut 0.54 Good 0.54 Good Worrell 0.73 Good 0.67 Good Volume Walnut 0.10 Poor 0.0 Unsatisfactory Worrell 0.67 Good 0.51 Good In the RSE, the root mean square error of the simulated data is divided by the standard deviation. The lower the root-mean square of the data, or the squared difference between predicted

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