Nitrate Pathways, Processes, and Timing in an Agricultural Karst System: Development and Application of a Numerical Model

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.Nitrogen (N) contamination within agricultural‐karst landscapes and aquifers is widely reported; however, the complex hydrological pathways of karst make N fate difficult to ascertain. We developed a hydrologic and N numerical model for agricultural‐karst, including simulation of soil, epikarst, phreatic, and quick flow pathways as well as biochemical processes such as nitrification, mineralization, and denitrification. We tested the model on four years of nitrate (NO3−) data collected from a phreatic conduit and an overlying surface channel in the Cane Run watershed, Kentucky, USA. Model results indicate that slow to moderate flow pathways (phreatic and epikarst) dominate the N load and account for nearly 90% of downstream NO3− delivery. Further, quick flow pathways dilute NO3− concentrations relative to background aquifer levels. Net denitrification distributed across soil, epikarst, and phreatic water removes approximately 36% of the N inputs to the system at rates comparable to nonkarst systems. Evidence is provided by numerical modeling that NO3− accumulation via evapotranspiration in the soil followed by leaching through the epikarst acts as a control on spring NO3− concentration and loading. Compared to a fluvial‐dominated immature karst system, mature‐karst systems behave as natural detention basins for NO3−, temporarily delaying NO3− delivery to downstream waters and maintaining elevated NO3− concentrations for days to weeks after hydrologic activity ends. This study shows the efficacy of numerical modeling to elucidate complex pathways, processes, and timing of N in karst systems

NeuralHydrology -- Interpreting LSTMs in Hydrology

Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations. LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system. On basis of two different catchments, one with snow influence and one without, we demonstrate how the trained model can be analyzed and interpreted. In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system.Comment: Pre-print of published book chapter. See journal reference and DOI for more inf

Comparative analysis of NOAA REFM and SNB 3 GEO tools for the forecast of the fluxes of high-energy electrons at GEO

Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field Bz observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast

How land use/land cover changes can affect water, flooding and sedimentation in a tropical watershed: a case study using distributed modeling in the Upper Citarum watershed, Indonesia

[EN] Human activity has produced severe LULC changes within the Upper Citarum watershed and these changes are predicted to continue in the future. With an increase in population parallel to a 141% increment in urban areas, a reduction of rice fields and the replacement of forests with cultivations have been found in the past. Accordingly, LCM model was used to forecast the LULC in 2029. A distributed model called TETIS was implemented in the Upper Citarum watershed to assess the impact of the different historical and future LULC scenarios on its water and sediment cycles. This model was calibrated and validated with different LULCs. For the implementation of the sediment sub-model, it was crucial to use the bathymetric information of the reservoir located at the catchment's outlet. Deforestation and urbanization have been shown to be the most influential factors affecting the alteration of the hydrological and sedimentological processes in the Upper Citarum watershed. The change of LULC decreases evapotranspiration and as a direct consequence, the water yield increased by 15% and 40% during the periods 1994-2014 and 2014-2029, respectively. These increments are caused by the rise of three components in the runoff: overland flow, interflow and base flow. Apart from that, these changes in LULC increased the area of non-tolerable erosion from 412 km(2) in 1994 to 499 km(2) in 2029. The mean sediment yield increased from 3.1 Mton -yr(-1) in the 1994 LULC scenario to 6.7 Mton-yr(-1) in the 2029 LULC scenario. An increment of this magnitude will be catastrophic for the operation of the Saguling Dam.This study was partially funded by the Spanish Ministry of Economy and Competitiveness through the research projects TETISMED (CGL2014-58,127-C3-3-R) and TETISCHANGE (RTI2018-093717-B-I00). The authors are also thankful to the Directorate General of Higher Education of Indonesia (DIKTI) for the Ph.D. funding of the first author.Siswanto, SY.; Francés, F. (2019). How land use/land cover changes can affect water, flooding and sedimentation in a tropical watershed: a case study using distributed modeling in the Upper Citarum watershed, Indonesia. Environmental Earth Sciences. 78(17):1-15. https://doi.org/10.1007/s12665-019-8561-0S115781

Attenuation Coefficients for Water Quality Trading

Water quality trading has been proposed as a cost-effective approach for reducing nutrient loads through credit generation from agricultural or point source reductions sold to buyers facing costly options. We present a systematic approach to determine attenuation coefficients and their uncertainty. Using a process-based model, we determine attenuation with safety margins at many watersheds for total nitrogen (TN) and total phosphorus (TP) loads as they transport from point of load reduction to the credit buyer. TN and TP in-stream attenuation generally increases with decreasing mean river flow; smaller rivers in the modeled region of the Ohio River Basin had TN attenuation factors per km, including safety margins, of 0.19-1.6%, medium rivers of 0.14-1.2%, large rivers of 0.13-1.1%, and very large rivers of 0.04-0.42%. Attenuation in ditches transporting nutrients from farms to receiving rivers is 0.4%/km for TN, while for TP attenuation in ditches can be up to 2%/km. A 95 percentile safety margin of 30-40% for TN and 6-10% for TP, applied to the attenuation per km factors, was determined from the in-stream sensitivity of load reductions to watershed model parameters. For perspective, over 50 km a 1% per km factor would result in 50% attenuation = 2:1 trading ratio