Comparing the effects of climate and land use on surface water quality using future watershed scenarios

Unraveling spatial heterogeneity of soil legacy phosphorus in subtropical grasslands

Humans have profoundly altered phosphorus (P) cycling across scales. Agriculturally driven changes (e.g., excessive P-fertilization and manure addition), in particular, have resulted in pronounced P accumulations in soils, often known as "soil legacy P." These legacy P reserves serve as persistent and long-term nonpoint sources, inducing downstream eutrophication and ecosystem services degradation. While there is considerable scientific and policy interest in legacy P, its fine-scale spatial heterogeneity, underlying drivers, and scales of variance remain unclear. Here we present an extensive field sampling (150-m interval grid) and analysis of 1438 surface soils (0-15 cm) in 2020 for two typical subtropical grassland types managed for livestock production: Intensively managed (IM) and Semi-natural (SN) pastures. We ask the following questions: (1) What is the spatial variability, and are there hotspots of soil legacy P? (2) Does soil legacy P vary primarily within pastures, among pastures, or between pasture types? (3) How does soil legacy P relate to pasture management intensity, soil and geographic characteristics? and (4) What is the relationship between soil legacy P and aboveground plant tissue P concentration? Our results showed that three measurements of soil legacy P (total P, Mehlich-1, and Mehlich-3 extractable P representing labile P pools) varied substantially across the landscape. Spatial autoregressive models revealed that soil organic matter, pH, available Fe and Al, elevation, and pasture management intensity were crucial predictors for spatial patterns of soil P, although models were more reliable for predicting total P (68.9%) than labile P. Our analysis further demonstrated that total variance in soil legacy P was greater in IM than SN pastures, and intensified pasture management rescaled spatial patterns of soil legacy P. In particular, after controlling for sample size, soil P was extremely variable at small scales, with variance diminished as spatial scale increased. Our results suggest that broad pasture- or farm-level best management practices may be limited and less efficient, especially for more IM pastures. Rather, management to curtail soil legacy P and mitigate P loading and losses should be implemented at fine scales designed to target spatially distinct P hotspots across the landscape.

Exploring freshwater stream bacterial communities as indicators of land use intensity

BACKGROUND

Stream ecosystems comprise complex interactions among biological communities and their physicochemical surroundings, contributing to their overall ecological health. Despite this, many monitoring programs ignore changes in the bacterial communities that are the base of food webs in streams, often focusing on stream physicochemical assessments or macroinvertebrate community diversity instead. We used 16S rRNA gene sequencing to assess bacterial community compositions within 600 New Zealand stream biofilm samples from 204 sites within a 6-week period (February-March 2010). Sites were either dominated by indigenous forests, exotic plantation forests, horticulture, or pastoral grasslands in the upstream catchment. We sought to predict each site's catchment land use and environmental conditions based on the composition of the stream bacterial communities.

RESULTS

Random forest modelling allowed us to use bacterial community composition to predict upstream catchment land use with 65% accuracy; urban sites were correctly assigned 90% of the time. Despite the variation inherent when sampling across a ~ 1000-km distance, bacterial community data could correctly differentiate undisturbed sites, grouped by their dominant environmental properties, with 75% accuracy. The positive correlations between actual values and those predicted by the models built using the stream biofilm bacterial data ranged from weak (average log N concentration in the stream water, R2 = 0.02) to strong (annual mean air temperature, R2 = 0.69).

CONCLUSIONS

Freshwater bacterial community data provide useful insights into land use impacts on stream ecosystems; they may be used as an additional measure to screen stream catchment attributes.

Meeting the Growing Need for Land-Water System Modelling to Assess Land Management Actions

Elevated contaminant levels and hydrological alterations resulting from land use are degrading aquatic ecosystems on a global scale. A range of land management actions may be used to reduce or prevent this degradation. To select among alternative management actions, decision makers require predictions of their effectiveness, their economic impacts, estimated uncertainty in the predictions, and estimated time lags between management actions and environmental responses. There are multiple methods for generating these predictions, but the most rigorous and transparent methods involve quantitative modelling. The challenge for modellers is two-fold. First, they must employ models that represent complex land-water systems, including the causal chains linking land use to contaminant loss and water use, catchment processes that alter contaminant loads and flow regimes, and ecological responses in aquatic environments. Second, they must ensure that these models meet the needs of endusers in terms of reliability, usefulness, feasibility and transparency. Integrated modelling using coupled models to represent the land-water system can meet both challenges and has advantages over alternative approaches. The need for integrated land-water system modelling is growing as the extent and intensity of human land use increases, and regulatory agencies seek more effective land management actions to counter the adverse effects. Here we present recommendations for modelling teams, to help them improve current practices and meet the growing need for land-water system models. The recommendations address several aspects of integrated modelling: (1) assembling modelling teams; (2) problem framing and conceptual modelling; (3) developing spatial frameworks; (4) integrating economic and biophysical models; (5) selecting and coupling models.

Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality. To address these concerns this study presents a technique for augmenting limited ground-based observations of water quality variables with remote-sensed surface reflectance data by leveraging a machine learning model capable of accommodating the multidimensionality of water quality influences. Total Suspended Solids (TSS) can serve as a surrogate for chemical and biological pollutants of concern in surface water bodies. Historically, TSS data collection in the United States has been limited to the location of water treatment plants where state or federal agencies conduct regularly-scheduled water sampling. Mathematical models relating riverine TSS concentration to the explanatory factors have therefore been limited and the relationships between climate extremes and water contamination events have not been effectively diagnosed. This paper presents a method to identify these issues by utilizing a Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance data, which is calibrated to TSS data collected by the Ohio River Valley Water Sanitation Commission (ORSANCO). The methodology developed enables a thorough empirical analysis and data-driven algorithms able to account for spatial variability within the watershed and provide effective water quality prediction under uncertainty.

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework - which we call HydroGraphs - for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

A model based on a multivariate classification for assessing impacts on water quality in a DOCE river watershed after the Fundão tailings dam failure

The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.

Quantifying the Contribution of Agricultural and Urban Non-Point Source Pollutant Loads in Watershed with Urban Agglomeration

Urban agglomeration is a new characteristic of the Chinese urbanization process, and most of the urban agglomeration is located in the same watershed. Thus, urban non-point source (NPS) pollution, especially the characteristic pollutants in urban areas, aggravates NPS pollution at the watershed scale. Many agricultural studies have been performed at the watershed scale; however, few studies have provided a study framework for estimating the urban NPS pollution in an urban catchment. In this study, an integrated approach for estimating agricultural and urban NPS pollution in an urban agglomeration watershed was proposed by coupling the Soil and Water Assessment Tool (SWAT), the event mean concentration (EMC) method and the Storm Water Management Model (SWMM). The Hun-Taizi River watershed, which contains a typical urban agglomeration and is located in northeastern China, was chosen as the study case. The results indicated that the per unit areas of total nitrogen (TN) and total phosphorus (TP) in the built-up area simulated by the EMC method were 11.9% and 23 times higher than the values simulated by the SWAT. The SWAT greatly underestimated the nutrient yield in the built-up area. This integrated method could provide guidance for water environment management plans considering agricultural and urban NPS pollution in an urban catchment.

Simulation of streamflow and instream loads of total suspended solids and nitrate in a large transboundary river basin using Source model and geospatial analysis

The management of LULC changes in transboundary river basins continues to challenge water resources managers due to the differences in development and conservation priorities of the countries sharing the basin. While various watershed models (WMs) exist to support decision making, basin-wide sustainable application of the instituted WM depends on the management priorities, resources, data availability, and knowledge gaps at national and sub-basin levels. Building on the results of our prior comparative analysis of WMs for a large transboundary river basin, we applied the 'Source' model to the Lower Mekong Basin (LMB). The constructed LMB-Source model was evaluated based on its streamflow and instream total suspended solids (TSS) and nitrate loads simulative performances. A combination of predictive performance metrics (PPMs) and sophisticated hydrologic signatures were used to calibrate model parameters and diagnose the model performance. Calibration results indicated strong similarity between the simulated and observed time series data and were further confirmed by the validation results. The successful model calibration generated parameters that represent hydrologic response characteristics (HRCs) and overland TSS and nitrate generation and removal dynamics (GRDs) previously not available for the LMB. The HRCs and GRDs can be regionalised with physical attributes of the LMB in future studies which can be used to support the management of ungauged sub-basins. This study confirms Source's capability as a decision support tool for the management of transboundary river basins, and provides basin-specific values of HRCs and GRDs that can be used for a better evaluation of the potential effects of LULC changes.

Appraising drinking water quality in Ikem rural area (Nigeria) based on chemometrics and multiple indexical methods

The continuous deterioration of drinking water quality supplies by several anthropogenic activities is a serious global challenge in recent times. In this current study, the drinking water quality of Ikem rural agricultural area (southeastern Nigeria) was assessed using chemometrics and multiple indexical methods. Twenty-five groundwater samples were collected from hand-dug wells and analyzed for physicochemical parameters such as pH, major ions, and heavy metals. The pH of the samples (which ranged between 5.2 and 6.7) indicated that waters were slightly acidic. Cations and anions (except for phosphate) were within their respective standard limits. Except for Mn, heavy metals were also found to be below their maximum allowable limits. Factor analysis identified both geogenic processes and anthropogenic inputs as possible origins of the analyzed physicochemical parameters. Modified heavy metal index, geoaccumulation index, and overall index of pollution revealed that all the hand-dug wells were in excellent condition, and hence safe for drinking purposes. However, pollution load index, water quality index (WQI), and entropy-weighted water quality index (EWQI) revealed that some wells (about 8-12%) were slightly contaminated, and hence are placed in good water category. A hierarchical cluster analysis (HCA) was performed based on the integration of the WQI and EWQI results. The HCA revealed two major quality categories of the samples. While the first cluster comprises of samples classified as excellent drinking water by both WQI and EWQI models, the second cluster comprises of about 12% samples which were identified as good water by either the WQI or EWQI.