About

James W. Jawitz is a professor in the Department of Soil, Water, and Ecosystem Sciences at the University of Florida, within the Institute of Food and Agricultural Sciences. His research emphasizes minimizing human impacts on natural hydrologic ecosystems, including watersheds, wetlands, and aquifers. He develops and applies hydro-ecological models to natural and constructed wetlands and works on techniques for the characterization and remediation of contaminated soil and groundwater. His work integrates water quality and watershed management, hydrology, and critical zone science, with a focus on understanding and improving water resource sustainability and ecosystem health.

Research topics

• Ecology
• Environmental science
• Geology
• Biology
• Geography
• Computer Science
• Geotechnical engineering
• Statistics
• Soil science
• Cartography
• Environmental chemistry
• Chemistry
• Econometrics
• Atmospheric sciences
• Mathematics
• Oceanography

Selected publications

• Drivers of spatiotemporal variability of river water quality

  Environmental Research Letters · 2026-05-06

  articleOpen access

  Abstract We investigated the drivers of spatial and temporal variability of river water quality at different scales using a stochastic modelling approach applied to synthetic river networks comprising hundreds of subcatchments. We simulated daily discharge and solute concentration time series throughout the network, systematically varying hydro-climatic regimes and the spatial configuration of mean solute source concentrations both within and between subcatchments. Locally mobilized discharge and solute loads were hydraulically routed through the network, subject to depth-dependent in-stream processing, to generate water quantity and quality time series for every node in the river network. We show that spatial variability of solute concentrations is predominantly determined by landscape source configuration, which reflect global drivers that remain autocorrelated over long time scales. In contrast, temporal variability was predominantly determined by local drivers that remain correlated to each other only over short (spatial) length scales. We further show that high landscape heterogeneity leads inexorably to persistence of spatial patterns through time, whereas high temporal variability does not necessarily result in synchronized temporal patterns between monitoring locations. Consequently, spatial patterns can be assessed with comparably low effort by conducting temporally sparse, spatially distributed sampling campaigns, especially in landscapes with substantial heterogeneity. These results offer insights on allocating limited resources between spatial and temporal sampling to maximize the information value of water quality monitoring.

  PublisherDOI

• Hydrologically-informed toxicity risks across river networks for mixtures of micropollutants discharged from wastewater treatment plants

  2026-03-14

  articleOpen accessCorresponding

  Chemicals in the aquatic environment can be harmful to biota and may cause toxic risks to the aquatic ecosystems. A high number of these chemicals originate from households, manufacturing and industries and are released to the aquatic environment as point source when connected to wastewater treatment plants (WWTP´s). A subset of the substances is permanently released and the load is proportional to the number of people connected to WWTPs, while the concentration of these substances shows higher variability. Especially at low discharges of the receiving waters the toxic risk may increase due to reduced dilution.With a hydrologically informed approach that combines river network hierarchy, river discharge, wastewater loads and spatial allocation of point sources we developed a parsimonious model to calculate the total toxicity risk at each location of wastewater treatment plant (WWTP) discharges. The total toxicity risk was calculated as the sum of individual risks for 42 substances selected from a reference mixture of chemicals being considered as representative for European wastewater treatment plant effluents for a river network in Central Germany with about 300 WWTP´s of various sizes.The results showed consistent patterns of substance specific cumulative toxicity and allowed an assessment of toxicity risks locally and at catchment scale. Different scenarios were analyzed to evaluate the consequences of different strategies to minimize toxic risks either by (1) source control, (2) relocation of WWTPs or their effluents or (3) end-of-pipe solutions like the 4th treatment level depending on local conditions. With these capabilities the approach and model may support the implementation of the revised European Urban Wastewater Treatment Directive.

  PublisherDOI

• ChemLotUS: A Benchmark Data Set of Lotic Chemistry Across US River Networks

  Water Resources Research · 2025-05-01 · 5 citations

  articleOpen accessSenior authorCorresponding

  Abstract We present a curated water chemistry data set for lotic systems across the contiguous US containing 35,000,000 records from 290,000 locations. These records are spatially joined to high‐resolution national hydrography data sets, providing information on watershed area, network position, and other hydrographic information. Our curation process follows best practices applied to raw query results from the Water Quality Portal, followed by assigning network context (position and watershed attributes) to each site from the high‐resolution National Hydrography Data set. The ChemLotUS data set currently includes 11 analytes selected to represent geogenic, biogenic, and anthropogenic processes: calcium, conductivity, pH, total suspended solids, turbidity, dissolved oxygen, total organic carbon, chlorophyll a, nitrate, soluble reactive phosphorus, and total phosphorus. All records from the raw query were modified during curation, most notably by removing duplicated observations, converting units, and aggregating strongly correlated chemical forms. Following curation, 65% of the original records were preserved, with significant reductions from raw to curated data in the means of nine constituents and, more notably, in the standard deviations of all constituents. 95% of monitored river reaches were linked to three or fewer monitoring sites, with sample patterns revealing a strong measurement bias to high order streams. We demonstrate the functionality of ChemLotUS by identifying spatiotemporal patterns in water quality at the CONUS‐scale, including diurnal variations of dissolved oxygen, pH in headwaters compared to their corresponding river mouths, and total suspended solids as a function of stream order. ChemLotUS enables new opportunities for investigations of continental scale variation in and controls on water quality.

  PublisherOA PDFDOI

• ChemLotUS: A Benchmark Dataset of Lotic Chemistry across US River Networks

  HydroShare Resources · 2025-03-20

  datasetOpen accessSenior author
  PublisherDOI

• Spatial and Temporal Variability of River Water Quality

  Hydrological Processes · 2025-05-01 · 6 citations

  articleOpen access

  ABSTRACT The deterioration of stream water quality threatens ecosystems and human water security worldwide. Effective risk assessment and mitigation requires spatial and temporal data from water quality monitoring networks (WQMNs). However, it remains challenging to quantify how well current WQMNs capture the spatiotemporal variability of stream water quality, making their evaluation and optimisation an important task for water management. Here, we investigate the spatial and temporal variability of concentrations of three constituents, representing different input pathways: anthropogenic (NO 3 − ), geogenic (Ca 2+ ) and biogenic (total organic carbon, TOC) at 1215 stations in three major river basins in Germany. We present a typology to classify each constituent on the basis of magnitude, range and dominance of spatial versus temporal variability. We found that mean measures of spatial variability dominated over those for temporal variability for NO 3 − and Ca 2+ , while for TOC they were approximately equal. The observed spatiotemporal patterns were robustly explained by a combination of local landscape composition and network‐scale landscape heterogeneity, as well as the degree of spatial auto‐correlation of water quality. Our analysis suggests that river network position systematically influences the inference of spatial variability more than temporal variability. By employing a space–time variance framework, this study provides a step towards optimising WQMNs to create water quality data sets that are balanced in time and space, ultimately improving the efficiency of resource allocation and maximising the value of the information obtained.

  PublisherOA PDFDOI

• ChemLotUS: A Benchmark Dataset of Lotic Chemistry across US River Networks

  HydroShare Resources · 2025-05-12 · 1 citations

  datasetOpen accessSenior author
  PublisherDOI

• Controls of space-time variance of water chemistry in river networks

  2025-03-14

  preprintOpen accessCorresponding

  River water quality is essential for ecosystem function and human well-being, yet anthropogenic impacts, such as pollutant input from agricultural activities or waste water, threaten water resources. An effective design of water quality monitoring networks is crucial to understanding and mitigating these impacts. However, optimizing monitoring is challenging because of the spatial and temporal variability of water quality, i.e. solute concentrations, driven by landscape and hydroclimatic heterogeneity.This study uses a stochastic modeling approach applied to artificial river networks to explore how landscape and hydroclimatic heterogeneity at different spatial scales shape the space-time variance of water chemistry. Building on a previously developed headwater-scale stochastic water quality model, we simulated daily discharge and solute concentration time series for equal area subcatchments within these networks. We systematically varied the spatial configuration of subcatchment solute source concentration across the network, the source zone distribution within subcatchments, and imposed different hydroclimatic regimes. Simulated discharge and solute loads were routed through the network, incorporating in-stream processing, to generate water quantity and quality time series for each network node. A global sensitivity analysis using the Morris method was performed to assess the influence of key parameters on the space-time variance of solute concentration.The results of the sensitivity analysis revealed that the macro-scale landscape configuration of source concentrations controls the spatial variability of solute concentrations in rivers and spatial stability, i.e. the persistence of spatial patterns through time. The relative influence of structured and random landscape heterogeneity on spatial variability was scale dependent, with distinct patterns observed across different stream orders. In contrast, subcatchment-scale processes, such as the source zone distribution, and the hydroclimatic forcing regulate temporal variability of water quality and synchrony between subcatchments. We conclude that optimal water quality monitoring network design should thus quantify spatial and temporal variability across scales, leveraging concepts like spatial stability and synchrony to maximize information gained and explicitly accounting for multiscale landscape heterogeneity.

  PublisherDOI

• A Reduced‐Complexity Model to Predict Seasonal Variation in Estuarine Salinity

  Geophysical Research Letters · 2025-11-14

  articleOpen accessSenior author

  Abstract Accurate prediction of seasonal variations in salinity is essential for assessing the health of estuarine environments. Traditional estuarine salinity models face challenges such as high computational demands and extensive data requirements. Here, we introduce a novel, reduced‐complexity model that computes seasonal variations in estuarine salinity based on three key inputs: river discharge, tidal water levels, and marine salinity. The model predicts the seasonal (monthly moving average) salinity at a given location using a single dimensionless variable that represents the ratio of freshwater discharge to tidally driven discharge. The model is validated using data from 11 estuaries globally, showing strong predictive performance for seasonal salinity time series in each estuary, with mean absolute errors (MAEs) of 2.5 ± 1.3 psu across all estuaries. Moreover, we also show that our reduced‐complexity model predicts seasonal estuarine salinity with comparable accuracy as a fully three‐dimensional Delft3D simulation in one estuary.

  PublisherDOI

• ChemLotUS: A Benchmark Dataset of Lotic Chemistry across US River Networks

  HydroShare Resources · 2025-05-12

  datasetOpen accessSenior author
  PublisherDOI

• A simple framework for attribution of changes in concentration-load-discharge relationships and estimating the longevity of biosolids legacy phosphorus

  Journal of Environmental Management · 2025-08-22

  articleOpen accessSenior authorCorresponding

  Legacy phosphorus (P) is a key driver of freshwater eutrophication, additionally limiting water quality improvements after implementing best managing practices. Recycled wastewater biosolids used as fertilizers are often significant contributors of legacy P in agricultural landscapes, leading to an urgent need to understand the timeframe of P releases from impacted soils. Therefore, this study aims to estimate the longevity of the effects of biosolids-derived P on water quality. We employed a parsimonious data-driven approach (two parameters estimated through linear least squares regression) to evaluate the drivers of P exports and their change over time using more than 20 years of discharge (Q) and P concentration (C) data from 9 catchments of the St. Johns River Basin, FL. We further developed novel elasticity coefficients to evaluate changes in C and exported loads (L) in response to changes in Q and the two regression parameters. To forecast the depletion of biosolids P, we implemented zero- and first-order mass balance models. We found the ratio of the variances of ln C and ln Q to be an effective metric to estimate solute exports drivers, and highlighted limitations with the commonly used metric of coefficients of variation, providing evidence to question whether commonplace statistics are the most appropriate. The novel elasticity coefficients identified the expansion of solute sources as the dominant driver of increased P exports, followed by changes in source spatial distribution, and negligible effects from changes in discharge. The longevity of biosolids P pools was estimated in the order of a hundred to a thousand years, providing valuable information for managers and policy makers to estimate times of recovery and to establish intervention priorities.

  PublisherDOI

Recent grants

• EAR-Climate: Continental-Scale Determinants of Stream Solute Spatial and Temporal Patterns

  NSF · $428k · 2023–2026

Frequent coauthors

• Michael D. Annable

  76 shared

• P. Suresh C. Rao

  Purdue University West Lafayette

  55 shared

• Harald Klammler

  Universidade Federal da Bahia

  39 shared

• Dietrich Borchardt

  Helmholtz Centre for Environmental Research

  30 shared

• Matthew J. Cohen

  University of Florida

  26 shared

• Kirk Hatfield

  University of Florida

  24 shared

• N. B. Basu

  16 shared

• J. Padowski

  Washington State University

  15 shared

Education

• PhD, Environmental Engineering

  University of Florida

  1999