FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015

CA-discharge: Geo-Located Discharge Time Series for Mountainous Rivers in Central Asia

We present a collection of 295 gauge locations in mountainous Central Asia with norm discharge as well as time series of river discharge from 135 of these locations collected from hydrological yearbooks in Central Asia. Time series have monthly, 10-day and daily temporal resolution and are available for different duration. A collection of third-party data allows basin characterization for all gauges. The time series data is validated using standard quality checks. Norm discharge is validated against literature values and by using a water balance approach. The novelty of the data consists in the combination of discharge time series and gauge locations for mountainous rivers in Central Asia which is not available anywhere else. The geo-located discharge time series can be used for water balance modelling and training of forecast models for river runoff in mountainous Central Asia.

Fate identification and management strategies of non-recyclable plastic waste through the integration of material flow analysis and leakage hotspot modeling

Low priority on waste management has impacted the complex environmental issue of plastic waste pollution, as evident by results of this study where it was found that 24.3% of waste generation in Jakarta and Bandung is emitted into the waterway due to the high intensity of human activity in the urban area. In this study, we investigated the viable integration between material flow analysis and leakage hotspot modeling to improve management strategies for plastic pollution in water systems and open environments. Using a multi-criteria assessment of plastic leakage from current waste management, a material flow analysis was developed on a city-wide scale defining the fate of plastic waste. Geospatial analysis was assigned to develop a calculation for identification and hydrological analysis while identifying the potential amount of plastic leakage to the river system. The results show that 2603 tons of plastic accumulated along the mainstream of the Ciliwung River on an annual basis, and a high-density population like that in Bandung discarded 1547 tons in a one-year period to the Cikapundung River. The methods and results of this study are applicable towards improving the control mechanisms of river rejuvenation from plastic leakage by addressing proper management in concentrated locations.

AquaDesign: A tool to assist aquaculture production design based on abiotic requirements of animal species

Farming new species and promoting polyculture can enhance aquaculture sustainability. This implies to define the rearing conditions that meet the ecological requirements of a target species and/or to assess if different species can live in the same farming environment. However, there is a large number of rearing conditions and/or taxon combinations that can be considered. In order to minimise cumbersome and expensive empirical trials to explore all possibilities, we introduce a tool, AquaDesign. It is based on a R-script and package which help to determine farming conditions that are most likely suitable for species through in silico assessment. We estimate farming conditions potentially suitable for an aquatic organism by considering the species niche. We define the species n-dimensional niche hypervolume using a correlative approach in which the species niche is estimated by relating distribution data to environmental conditions. Required input datasets are mined from several public databases. The assistant tool allows users to highlight (i) abiotic conditions that are most likely suitable for species and (ii) combinations of species potentially able to live in the same abiotic environment. Moreover, it offers the possibility to assess if a particular set of abiotic conditions or a given farming location is potentially suitable for the monoculture or the polyculture of species of interest. Our tool provides useful pieces of information to develop freshwater aquacultures. Using the large amount of biogeographic and abiotic information available in public databases allows us to propose a pragmatic and operational tool even for species for which abiotic requirements are poorly or not available in literature such as currently non-produced species. Overall, we argue that the assistant tool can act as a stepping stone to promote new aquatic productions which are required to enhance aquaculture sustainability.

Description of Myxobolus hoabinhensis n. sp. (Myxosporea: Myxobolidae), infecting the trunk muscles of goldfish Carassius auratus (Linnaeus, 1758) (Cypriniformes: Cyprinidae) in northern Vietnam

During a survey of myxosporean parasites of freshwater fishes in northern Vietnam, myxospores resembling those of the genus Myxobolus (Myxosporea: Myxobolidae) were found in the trunk muscle of 6 out of 35 specimens (17.14%) of wild goldfish Carassius auratus (Cypriniformes: Cyprinidae). The mature spores were 12.0 ± 0.4 (11.4 - 12.6) µm long, 8.5 ± 0.2 (7.9 - 9.0) µm wide and 6.1 ± 0.2 (5.8 - 6.3) µm thick, containing two pyriform-shaped polar capsules unequal in size. The larger polar capsule was 7.6 ± 0.3 (7.1 - 8.4) µm long and 3.5 ± 0.1 (3.3 - 3.8) µm wide, and the smaller polar capsule was 6.2 ± 0.3 (5.5 - 6.7) µm long and 2.9 ± 0.2 (2.6 - 3.4) µm wide. Each polar capsule contained a polar filament with 3-5 coils. A phylogenetic analysis based on the small subunit rDNA (SSU rDNA) sequence revealed that this Myxobolus species forms a distinct branch in the phylogenetic tree sister to Myxobolus artus and Myxobolus cyprini, with DNA sequence similarity at 97.6% to M. artus and 97.5% to M. cyprini. A combination of the morphological characteristics and molecular data suggest that this is an undescribed species, and we propose the name Myxobolus hoabinhensis n. sp.

Exploring deep learning capabilities for surge predictions in coastal areas

To improve coastal adaptation and management, it is critical to better understand and predict the characteristics of sea levels. Here, we explore the capabilities of artificial intelligence, from four deep learning methods to predict the surge component of sea-level variability based on local atmospheric conditions. We use an Artificial Neural Networks, Convolutional Neural Network, Long Short-Term Memory layer (LSTM) and a combination of the latter two (ConvLSTM), to construct ensembles of Neural Network (NN) models at 736 tide stations globally. The NN models show similar patterns of performance, with much higher skill in the mid-latitudes. Using our global model settings, the LSTM generally outperforms the other NN models. Furthermore, for 15 stations we assess the influence of adding complexity more predictor variables. This generally improves model performance but leads to substantial increases in computation time. The improvement in performance remains insufficient to fully capture observed dynamics in some regions. For example, in the tropics only modelling surges is insufficient to capture intra-annual sea level variability. While we focus on minimising mean absolute error for the full time series, the NN models presented here could be adapted for use in forecasting extreme sea levels or emergency response.

Multi-Scenario Model of Plastic Waste Accumulation Potential in Indonesia Using Integrated Remote Sensing, Statistic and Socio-Demographic Data

As a significant contributor of plastic waste to the marine environment, Indonesia is striving to construct a national strategy for reducing plastic debris. Hence, the primary aim of this study is to create a model for plastic waste quantity originating from the mainland, accumulated in estuaries. This was achieved by compiling baseline data of marine plastic disposal from the mainland via comprehensive contextualisation of data generated by remote sensing technology and spatial analysis. The parameters used in this study cover plastic waste generation, land cover, population distribution, and human activity identification. These parameters were then used to generate the plastic waste disposal index; that is, the distribution of waste from the mainland, flowing through the river, and ultimately accumulating in the estuary. The plastic waste distribution is calculated based on the weighting method and overlap analysis between land and coastal areas. The results indicate that 0.6% of Indonesia, including metropolitan cities, account for the highest generation of plastic waste. Indicating of plastic releases to the ocean applied by of developing three different scenarios with the highest estimation 11.94 tonnes on a daily basis in an urban area, intended as the baseline study for setting priority zone for plastic waste management.

Threats of global warming to the world's freshwater fishes

Climate change poses a significant threat to global biodiversity, but freshwater fishes have been largely ignored in climate change assessments. Here, we assess threats of future flow and water temperature extremes to ~11,500 riverine fish species. In a 3.2 °C warmer world (no further emission cuts after current governments’ pledges for 2030), 36% of the species have over half of their present-day geographic range exposed to climatic extremes beyond current levels. Threats are largest in tropical and sub-arid regions and increases in maximum water temperature are more threatening than changes in flow extremes. In comparison, 9% of the species are projected to have more than half of their present-day geographic range threatened in a 2 °C warmer world, which further reduces to 4% of the species if warming is limited to 1.5 °C. Our results highlight the need to intensify (inter)national commitments to limit global warming if freshwater biodiversity is to be safeguarded.

Climate change is a threat to global biodiversity, but the potential effects on freshwater fishes have not been well studied. Here the authors model future flow and water temperature extremes and predict that increases in water temperature in particular will pose serious threats to freshwater fishes

Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks

Gridded datasets provide spatially and temporally consistent runoff estimates that serve as reliable sources for assessing water resources from regional to global scales. This study presents LSTM-REG, a regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks. LSTM-REG covers the period from 1980 to 2016 at a 0.5° spatial and daily temporal resolution. LSTM-REG has been extensively validated and benchmarked against GR4J-REG, a gridded runoff dataset based on a parsimonious regionalization scheme and the GR4J hydrological model. While both datasets provide runoff estimates with reliable prediction efficiency, LSTM-REG outperforms GR4J-REG for most basins in the independent evaluation set. Thus, the results demonstrate a higher generalization capacity of LSTM-REG than GR4J-REG, which can be attributed to the higher efficiency of the proposed LSTM-based regionalization scheme. The developed datasets are freely available in open repositories to foster further regional hydrology research in northwest Russia.

On the importance of predictor choice, modelling technique, and number of pseudo-absences for bioclimatic envelope model performance

Bioclimatic envelope models are commonly used to assess the influence of climate change on species' distributions and biodiversity patterns. Understanding how methodological choices influence these models is critical for a comprehensive evaluation of the estimated impacts. Here we systematically assess the performance of bioclimatic envelope models in relation to the selection of predictors, modeling technique, and pseudo-absences. We considered (a) five different predictor sets, (b) seven commonly used modeling techniques and an ensemble model, and (c) three sets of pseudo-absences (1,000 pseudo-absences, 10,000 pseudo-absences, and the same as the number of presences). For each combination of predictor set, modeling technique, and pseudo-absence set, we fitted bioclimatic envelope models for 300 species of mammals, amphibians, and freshwater fish, and evaluated the predictive performance of the models using the true skill statistic (TSS), based on a spatially independent test set as well as cross-validation. On average across the species, model performance was mostly influenced by the choice of predictor set, followed by the choice of modeling technique. The number of the pseudo-absences did not have a strong effect on the model performance. Based on spatially independent testing, ensemble models based on species-specific nonredundant predictor sets revealed the highest predictive performance. In contrast, the Random Forest technique yielded the highest model performance in cross-validation but had the largest decrease in model performance when transferred to a different spatial context, thus highlighting the need for spatially independent model evaluation. We recommend building bioclimatic envelope models according to an ensemble modeling approach based on a nonredundant set of bioclimatic predictors, preferably selected for each modeled species.

A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.

From topography to hydrology-The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors-a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90-m digital-elevation model by using the GRASS-GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km2. We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land-use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1-93.2 and 0.61-0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.

Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide

Freshwater fish are highly threatened by dams that disrupt the longitudinal connectivity of rivers and may consequently impede fish movements to feeding and spawning grounds. In a comprehensive global analysis covering ∼10,000 freshwater fish species and ∼40,000 existing large dams we identified the most disconnected geographical ranges for species in the United States, Europe, South Africa, India, and China. The completion of near-future plans for ∼3,700 large hydropower dams will greatly increase habitat fragmentation in (sub)tropical river basins, where many livelihoods depend on inland fisheries. Our assessment can support infrastructure planning on multiple scales and assist in setting conservation priorities for species and basins at risk.

Dams contribute to water security, energy supply, and flood protection but also fragment habitats of freshwater species. Yet, a global species-level assessment of dam-induced fragmentation is lacking. Here, we assessed the degree of fragmentation of the occurrence ranges of ∼10,000 lotic fish species worldwide due to ∼40,000 existing large dams and ∼3,700 additional future large hydropower dams. Per river basin, we quantified a connectivity index (CI) for each fish species by combining its occurrence range with a high-resolution hydrography and the locations of the dams. Ranges of nondiadromous fish species were more fragmented (less connected) (CI = 73 ± 28%; mean ± SD) than ranges of diadromous species (CI = 86 ± 19%). Current levels of fragmentation were highest in the United States, Europe, South Africa, India, and China. Increases in fragmentation due to future dams were especially high in the tropics, with declines in CI of ∼20 to 40 percentage points on average across the species in the Amazon, Niger, Congo, Salween, and Mekong basins. Our assessment can guide river management at multiple scales and in various domains, including strategic hydropower planning, identification of species and basins at risk, and prioritization of restoration measures, such as dam removal and construction of fish bypasses.

The Role of Open Access Data in Geospatial Electrification Planning and the Achievement of SDG7. An OnSSET-Based Case Study for Malawi

Achieving universal access to electricity is a development challenge many countries are currently battling with. The advancement of information technology has, among others, vastly improved the availability of geographic data and information. That, in turn, has had a considerable impact on tracking progress as well as better informing decision making in the field of electrification. This paper provides an overview of open access geospatial data and GIS based electrification models aiming to support SDG7, while discussing their role in answering difficult policy questions. Upon those, an updated version of the Open Source Spatial Electrification Toolkit (OnSSET-2018) is introduced and tested against the case study of Malawi. At a cost of $1.83 billion the baseline scenario indicates that off-grid PV is the least cost electrification option for 67.4% Malawians, while grid extension can connect about 32.6% of population in 2030. Sensitivity analysis however, indicates that the electricity demand projection determines significantly both the least cost technology mix and the investment required, with the latter ranging between $1.65–7.78 billion.

A high-resolution streamflow and hydrological metrics dataset for ecological modeling using a regression model

Hydrological variables are among the most influential when analyzing or modeling stream ecosystems. However, available hydrological data are often limited in their spatiotemporal scale and resolution for use in ecological applications such as predictive modeling of species distributions. To overcome this limitation, a regression model was applied to a 1 km gridded stream network of Germany to obtain estimated daily stream flow data (m³ s^-1) spanning 64 years (1950-2013). The data are used as input to calculate hydrological indices characterizing stream flow regimes. Both temporal and spatial validations were performed. In addition, GLMs using both the calculated and observed hydrological indices were compared, suggesting that the predicted flow data are adequate for use in predictive ecological models. Accordingly, we provide estimated stream flow as well as a set of 53 hydrological metrics at 1 km grid for the stream network of Germany. In addition, we provide an R script where the presented methodology is implemented, that uses globally available data and can be directly applied to any other geographical region.

A High-Resolution Spatial Model to Predict Exposure to Pharmaceuticals in European Surface Waters: ePiE

Environmental risk assessment of pharmaceuticals requires the determination of their environmental exposure concentrations. Existing exposure modeling approaches are often computationally demanding, require extensive data collection and processing efforts, have a limited spatial resolution, and have undergone limited evaluation against monitoring data. Here, we present ePiE (exposure to Pharmaceuticals in the Environment), a spatially explicit model calculating concentrations of active pharmaceutical ingredients (APIs) in surface waters across Europe at ∼1 km resolution. ePiE strikes a balance between generating data on exposure at high spatial resolution while having limited computational and data requirements. Comparison of model predictions with measured concentrations of a diverse set of 35 APIs in the river Ouse (UK) and Rhine basins (North West Europe), showed around 95% were within an order of magnitude. Improved predictions were obtained for the river Ouse basin (95% within a factor of 6; 55% within a factor of 2), where reliable consumption data were available and the monitoring study design was coherent with the model outputs. Application of ePiE in a prioritisation exercise for the Ouse basin identified metformin, gabapentin, and acetaminophen as priority when based on predicted exposure concentrations. After incorporation of toxic potency, this changed to desvenlafaxine, loratadine, and hydrocodone.