Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> Maintaining good surface water quality is crucial to protect ecosystem health and for safeguarding human water use activities. Yet, our quantitative understanding of surface water quality is mostly predicated upon observations at monitoring stations that are highly limited in space and fragmented across time. Physically-based models, based upon pollutant emissions and subsequent routing through the hydrological network, provide opportunities to overcome these shortcomings. To this end, we have developed the dynamical surface water quality model (DynQual) for simulating water temperature (Tw) and concentrations of total dissolved solids (TDS), biological oxygen demand (BOD) and fecal coliform (FC) with a daily timestep and at 5 arc-minute (~10 km) spatial resolution. Here, we describe the main components of this new global surface water quality model and evaluate model performance against in-situ water quality observations. Furthermore, we describe both the spatial patterns and temporal trends in TDS, BOD and FC concentrations for the period 1980–2019, also attributing the dominant contributing sectors. The model code is available open-source (<a href="https://github.com/UU-Hydro/DYNQUAL" target="_blank" rel="noopener">https://github.com/UU-Hydro/DYNQUAL</a>) and we provide global datasets of simulated hydrology, Tw, TDS, BOD and FC at 5 arc-minute resolution with a monthly timestep (<a href="https://doi.org/10.5281/zenodo.7139222" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7139222</a>). This data has potential to inform assessments in a broad range of fields, including ecological, human health and water scarcity studies.
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