Urban hydrogeology: Transport routes and mixing of water and solutes in a groundwater influenced urban lowland catchment.

Urban areas in coastal lowlands host a significant part of the world's population. In these areas, cities have often expanded to unfavorable locations that have to be drained or where excess rain water and groundwater need to be pumped away in order to maintain dry feet for its citizens. As a result, groundwater seepage influences surface water quality in many of such urban lowland catchments. This study aims at identifying the flow routes and mixing processes that control surface water quality in the groundwater-influenced urban catchment Polder Geuzenveld, which is part of the city of Amsterdam. Geuzenveld is a highly paved urban area with a subsurface rain water collection system, a groundwater drainage system, and a main surface water system that receive runoff from pavement and roofs, shallow groundwater and direct groundwater seepage, respectively. We conducted a field survey and systematic monitoring to identify the spatial and temporal variations in water quality in runoff, ditch water, drain water, and shallow and deep groundwater. We found that Geuzenveld receives a substantial inflow of deep, O2-depleted groundwater, which is enriched in ammonium and phosphorus due to the subsurface mineralization of organic matter under sulfate-reducing conditions. This groundwater is mixed in the ditches during wet periods with O2-rich runoff, and iron- and phosphate-rich drain water. Unlike natural catchments, the newly created, separated urban flow routes lead to mixing of water in the main surface water itself, shortcutting much of the soil and shallow subsurface. This leads to low O2 and high ammonia concentrations in dry periods, which might be mitigated by water level management or artificially increasing O2 levels by water inlet or artificially aeration of the main water canals. Further research is necessary how to optimize artificial urban systems to deliver a better ecological and chemical status of the surface water.

Sensors in the Stream: The High-Frequency Wave of the Present.

New scientific understanding is catalyzed by novel technologies that enhance measurement precision, resolution or type, and that provide new tools to test and develop theory. Over the last 50 years, technology has transformed the hydrologic sciences by enabling direct measurements of watershed fluxes (evapotranspiration, streamflow) at time scales and spatial extents aligned with variation in physical drivers. High frequency water quality measurements, increasingly obtained by in situ water quality sensors, are extending that transformation. Widely available sensors for some physical (temperature) and chemical (conductivity, dissolved oxygen) attributes have become integral to aquatic science, and emerging sensors for nutrients, dissolved CO2, turbidity, algal pigments, and dissolved organic matter are now enabling observations of watersheds and streams at time scales commensurate with their fundamental hydrological, energetic, elemental, and biological drivers. Here we synthesize insights from emerging technologies across a suite of applications, and envision future advances, enabled by sensors, in our ability to understand, predict, and restore watershed and stream systems.

Improving load estimates for NO3 and P in surface waters by characterizing the concentration response to rainfall events.

For the evaluation of action programs to reduce surface water pollution, water authorities invest heavily in water quality monitoring. However, sampling frequencies are generally insufficient to capture the dynamical behavior of solute concentrations. For this study, we used on-site equipment that performed semicontinuous (15 min interval) NO(3) and P concentration measurements from June 2007 to July 2008. We recorded the concentration responses to rainfall events with a wide range in antecedent conditions and rainfall durations and intensities. Through sequential linear multiple regression analysis, we successfully related the NO(3) and P event responses to high-frequency records of precipitation, discharge, and groundwater levels. We applied the regression models to reconstruct concentration patterns between low-frequency water quality measurements. This new approach significantly improved load estimates from a 20% to a 1% bias for NO(3) and from a 63% to a 5% bias for P. These results demonstrate the value of commonly available precipitation, discharge, and groundwater level data for the interpretation of water quality measurements. Improving load estimates from low-frequency concentration data just requires a period of high-frequency concentration measurements and a conceptual, statistical, or physical model for relating the rainfall event response of solute concentrations to quantitative hydrological changes.