hydrographr: An R package for scalable hydrographic data processing

1. Freshwater ecosystems are considered biodiversity hotspots, but assessing the spatial distribution of species remains challenging. One major obstacle lies in the complex geospatial processing of large amounts of data, such as stream network, sub-catchment and basin data, that are necessary for addressing the longitudinal connectivity among water bodies. Workflows thus need to be scalable, especially when working across large spatial extents and at high spatial resolution. This in turn requires advanced command-line GIS skills and programming language integration, which often poses a challenge for freshwater researchers. 2. To address this challenge, we developed the package hydrographr that provides scalable hydrographic data processing in R. The package contains functions for downloading data of the high-resolution Hydrography90m dataset, processing, reading and extracting information, as well as assessing network distances and connectivity. While the functions are, by default, tailored toward the Hydrography90m data, they can also be generalised toward other data and purposes, such as efficient cropping and merging of raster and vector data, point-raster extraction, raster reclassification and data aggregation. The package depends on the open-source software GDAL/OGR, GRASS-GIS and the AWK programming language in the Linux environment, allowing a seamless language integration. Since the data is processed outside R, hydrographr allows creating scalable geo-processing workflows. 3. We illustrate the hydrographr functions using two workflows that focus on (i) a freshwater species distribution modelling approach, and (ii) assessing stream connectivity given the fragmentation by dams. We also provide a detailed guide for the initial installation of the required software. Windows users need to first enable the Windows Subsystem for Linux (WSL) feature, and can then follow the same software installation as Linux users. hydrographr is maintained on GitHub at https://github.com/glowabio/hydrographr. 4. hydrographr provides a set of key functions for processing freshwater geospatial data. We expect that the package will support the freshwater-related research communities given the easy-to-use wrapper functions that allow capitalizing on powerful open-source command-line software, which may otherwise require a steep learning curve. Users can thus perform large-scale freshwater-specific longitudinal connectivity and network analyses across large geographic extents while staying within the R environment

hydrographr: An R package for scalable hydrographic data processing

Abstract Freshwater ecosystems are considered biodiversity hotspots, but assessing the spatial distribution of species remains challenging. One major obstacle lies in the complex geospatial processing of large amounts of data, such as stream network, sub‐catchment and basin data, that are necessary for addressing the longitudinal connectivity among water bodies. Workflows thus need to be scalable, especially when working across large spatial extents and at high spatial resolution. This in turn requires advanced command‐line GIS skills and programming language integration, which often poses a challenge for freshwater researchers. To address this challenge, we developed the package hydrographr that provides scalable hydrographic data processing in R. The package contains functions for downloading data of the high‐resolution Hydrography90m dataset, processing, reading and extracting information, as well as assessing network distances and connectivity. While the functions are, by default, tailored toward the Hydrography90m data, they can also be generalised toward other data and purposes, such as efficient cropping and merging of raster and vector data, point‐raster extraction, raster reclassification and data aggregation. The package depends on the open‐source software GDAL/OGR, GRASS‐GIS and the AWK programming language in the Linux environment, allowing a seamless language integration. Since the data is processed outside R, hydrographr allows creating scalable geo‐processing workflows. We illustrate the hydrographr functions using two workflows that focus on (i) a freshwater species distribution modelling approach, and (ii) assessing stream connectivity given the fragmentation by dams. We also provide a detailed guide for the initial installation of the required software. Windows users need to first enable the Windows Subsystem for Linux (WSL) feature, and can then follow the same software installation as Linux users. hydrographr is maintained on GitHub at https://github.com/glowabio/hydrographr. hydrographr provides a set of key functions for processing freshwater geospatial data. We expect that the package will support the freshwater‐related research communities given the easy‐to‐use wrapper functions that allow capitalizing on powerful open‐source command‐line software, which may otherwise require a steep learning curve. Users can thus perform large‐scale freshwater‐specific longitudinal connectivity and network analyses across large geographic extents while staying within the R environment

N2O Emissions from Two Austrian Agricultural Catchments Simulated with an N2O Submodule Developed for the SWAT Model

Nitrous oxide (N2O) is a potent greenhouse gas stemming mainly from nitrogen (N)-fertilizer application. It is challenging to quantify N2O emissions from agroecosystems because of the dearth of measured data and high spatial variability of the emissions. The eco-hydrological model SWAT (Soil and Water Assessment Tool) simulates hydrological processes and N fluxes in a catchment. However, the routine for simulating N2O emissions is still missing in the SWAT model. A submodule was developed based on the outputs of the SWAT model to partition N2O from the simulated nitrification by applying a coefficient (K2) and also to isolate N2O from the simulated denitrification (N2O + N2) with a modified semi-empirical equation. The submodule was applied to quantify N2O emissions and N2O emission factors from selected crops in two agricultural catchments by using NH4NO3 fertilizer and the combination of organic N and NO3− fertilizer as N input data. The setup with the combination of organic N and NO3− fertilizer simulated lower N2O emissions than the setup with NH4NO3 fertilizer. When the water balance was simulated well (absolute percentage error <11%), the impact of N fertilizer application on the simulated N2O emissions was captured. More research to test the submodule with measured data is needed

N2O Emissions from Two Austrian Agricultural Catchments Simulated with an N2O Submodule Developed for the SWAT Model

Nitrous oxide (N2O) is a potent greenhouse gas stemming mainly from nitrogen (N)‐fertilizer application. It is challenging to quantify N2O emissions from agroecosystems because of the dearth of measured data and high spatial variability of the emissions. The eco‐hydrological model SWAT (Soil and Water Assessment Tool) simulates hydrological processes and N fluxes in a catchment. However, the routine for simulating N2O emissions is still missing in the SWAT model. A submodule was developed based on the outputs of the SWAT model to partition N2O from the simulated nitrification by applying a coefficient (K2) and also to isolate N2O from the simulated denitrification (N2O + N2) with a modified semi‐empirical equation. The submodule was applied to quantify N2O emissions and N2O emission factors from selected crops in two agricultural catchments by using NH4NO3 fertilizer and the combination of organic N and NO3− fertilizer as N input data. The setup with the combination of organic N and NO3− fertilizer simulated lower N2O emissions than the setup with NH4NO3 fertilizer. When the water balance was simulated well (absolute percentage error <11%), the impact of N fertilizer application on the simulated N2O emissions was captured. More research to test the submodule with measured data is needed.1282

N₂O Emissions from Two Austrian Agricultural Catchments Simulated with an N₂O Submodule Developed for the SWAT Model

Nitrous oxide (N2O) is a potent greenhouse gas stemming mainly from nitrogen (N)-fertilizer application. It is challenging to quantify N2O emissions from agroecosystems because of the dearth of measured data and high spatial variability of the emissions. The eco-hydrological model SWAT (Soil and Water Assessment Tool) simulates hydrological processes and N fluxes in a catchment. However, the routine for simulating N2O emissions is still missing in the SWAT model. A submodule was developed based on the outputs of the SWAT model to partition N2O from the simulated nitrification by applying a coefficient (K2) and also to isolate N2O from the simulated denitrification (N2O + N2) with a modified semi-empirical equation. The submodule was applied to quantify N2O emissions and N2O emission factors from selected crops in two agricultural catchments by using NH4NO3 fertilizer and the combination of organic N and NO3− fertilizer as N input data. The setup with the combination of organic N and NO3− fertilizer simulated lower N2O emissions than the setup with NH4NO3 fertilizer. When the water balance was simulated well (absolute percentage error 2O emissions was captured. More research to test the submodule with measured data is needed

Can local nutrient-circularity and erosion control increase yields of resource-constraint smallholder farmers? A case study in Kenya and Uganda

As many regions in sub-Saharan Africa, the border region of Kenya (KE) and Uganda (UG) has faced a declining soil fertility for decades, resulting from soil erosion, intensely managed agricultural soils due to population pressure and small inputs of mineral and organic fertilizers. With limited financial means, farmers need measures and/or technologies that effectively reduce nutrient losses or increase inputs at a low cost. In this study, four such measures are in focus, namely erosion reduction practices, vermicomposting of animal manure, collection of human urine in jerry cans and, collection of human excreta in urine-diverting dry toilets. Current soil nutrient balances in five districts in the Sio-Malaba-Malakisi River Basin and the potential of these measures to reduce the soil nutrient deficit are studied using the method of material flow analysis and the software STAN. Furthermore, crop-nutrient-response functions are used to determine their potential impact on maize harvests. Overall, results reveal that there exists a non-negligible and exploitable potential of local resources to reduce the soil nutrient deficit, improve harvests and in turn food security of the smallholder farmers in the region. Soil nutrient deficits could be reduced by 20–30%, 23–42% and 9–15% for nitrogen (N), phosphorus (P) and potassium (K), respectively. Subsequently, maize harvests could be increased by 8–40%, depending on the applied technology and area. This research provides useful insights for agricultural extension workers, politicians and researchers alike, highlighting that simple and easily available technologies can harness similar amounts of nutrients as more complex and expensive ones if all specific technology-constraints are adequately incorporated in the analyses.1171

A comprehensive calibration and validation of SWAT-T using local datasets, evapotranspiration and streamflow in a tropical montane cloud forest area with permeable substrate in central Veracruz, Mexico

Tropical montane cloud forests (TMCF) are threatened ecosystems despite their capacity to maintain high dry-season baseflow. A number of conservation policies, including payments for hydrological services, have been implemented to protect these forests. However, since most of the modeling tools used to assess the impacts of these policies were developed for temperate zones, more work is needed to understand and improve the applicability of popular models in tropical contexts. This study uses local evapotranspiration and streamflow datasets to calibrate and validate an improved version of the Soil and Water Assessment Tool model for the Tropics (SWAT-T). Vegetation growth and canopy water storage capacity were calibrated using field data. Three methods provided by SWAT-T to calculate potential evapotranspiration (PET) were compared: Penman-Monteith (SWAT-T-PM), Hargreaves (SWAT-T-HA), and Priestly-Taylor (SWAT-T-PT). Sensitivity analysis and calibration of daily streamflow were conducted at the catchment scale (34 km2). Furthermore, the calibrated models were validated at three sites with evapotranspiration data, and at four distinct micro-catchments (0.137–0.446 km2) with gauged streamflow data. Overall, SWAT-T satisfactorily simulated streamflow during the calibration period producing acceptable goodness of fit indices. However, the model incorrectly predicted the dominance of lateral flow instead of the deep groundwater flow observed from isotope-based studies. SWAT-T-HA performed better than SWAT-T-PM and SWAT-T-PT, but all models underestimated the influence of rainfall interception losses since evaporation is limited by daily PET in forests. Finally, SWAT-T largely over- and underestimated mean annual daily low flow in pastures and forests, respectively. Taken together, these results indicate that improvements in the parametrization of rainfall interception and deep subsurface flow dynamics in SWAT-T are required to improve applicability of this modeling tool in tropical montane areas underlain by permeable substrates