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Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use

Cite this dataset

Montefiore, Lise; Nelson, Natalie; Staudinger, Michelle; Terando, Adam (2023). Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use [Dataset]. Dryad.


Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (1) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios to altered nutrient loads, (2) sensitivity (i.e., how responsive estuaries are to altered nutrient loads), and (3) adaptive capacity (i.e., how the socio-ecological system can use existing resources to reduce the impacts associated with increased exposure). The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increase is expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries. A web-based application has been developed to visualize and download the data.


The vulnerability framework is composed of three key components: the  exposure (i.e., magnitude and extent of exposure to change-driven impacts), sensitivity (i.e.,  how a system is affected by exposure to hazards), and adaptive capacity (i.e., potential ability and opportunities to adapt or accommodate the combined effects of the exposure and sensitivity of the system). The dataset gives the score for the indicators used in the exposure, sensitivity, and adaptive capacity.

Exposure is defined as the degree to which an estuarine system is projected to be exposed to increased nutrient loading through terrestrial surface water runoff, altered by changes in precipitation and LULC. To compute the exposure,  average changes in TN and TP loads between the historical (1990-2020) and future (2035-2065) periods obtained from STEPLgrid (Montefiore and Nelson, 2022) were calculated annually (∆TN and ∆TP). ∆TN and ∆TP were re-scaled from 0 to 1 for each nutrient using a linear relationship, where 0 represented estuarine watersheds with negative change (i.e., decreased nutrient loading under future climate and LULC scenarios) and 1 equated to watersheds with a positive change in nutrient loads equal to or greater than 200%. Exposure scores were computed for 8 climate (i.e., RCPs 4.5. and 8.5) and land-use scenarios (i.e., A1B, A2, B1, B2) combinations for TN and TP. 

Sensitivity was evaluated using two indicators: the degree to which a system is (1) already eutrophic, and (2) physical susceptible to increased nutrient loading. The column "eutrophication" reports the eutrophic conditions of the estuaries.  The columns susceptibility_TN and susceptibility_TP represent the physical susceptibility of the estuaries to TN and TP loads, which were re-scaled from 0 to 1 (i.e., lowest to highest susceptibility of estuaries to nutrient loads). 

Adaptive capacity is defined as the magnitude of existing socio-ecological resources available to an estuarine system to cope with, adapt to, or mitigate changes in nutrient loads. The adaptive capacity was composed of the (1) human adaptive capacity and (2) natural adaptive capacity. The human and natural adaptive capacity categories were represented by scores ranging from 0 to 1, with 0 given to estuarine watersheds with low adaptive capacity (i.e., low ability to mitigate nutrient loading impact) and 1 to those with high adaptive capacity (i.e., high ability to mitigate nutrient loading impact).

  1. The human adaptive capacity category was composed of two indicators: access to scientific knowledge and legislative/governmental actions already undertaken.Access to scientific knowledge was composed of three sub-indicators: (1) the number of published peer-reviewed research articles related to climate change, nutrients, and eutrophication of the estuary, (2) the number of academic staff in R1 and R2 universities in the states where the watershed was located, normalized by the state population, and (3) the information provided by the long-term monitoring stations present within the estuary and its watershed. The legislative/governmental actions already undertaken was composed of three sub-indicators (1) the existence of a climate adaptation plan, (2) the amount of the annual state budget dedicated to environmental and natural resources departments normalized by the population of the respective state, and (3) the state adoption of numeric water quality parameter criteria. Each sub-indicator was re-scaled from 0 to 1 and an overall score of the human adaptive capacity category was calculated by averaging the scores of the two sub-indicators.
  2. The natural adaptive capacity indicator was calculated as the density of wetlands per estuarine watershed. Note that the human and natural adaptive capacity scores were not combined and should not be.

The vulnerability data can also be accessed and visualized through the web-based application.

Usage notes

The tabular files can be opened with Excel.

The shapefiles can be opened with any GIS software (e.g., QGIS, ArcGIS, R, Python). 


U.S. Geological Survey Southeast Climate Adaptation Science Center

National Climate Adaptation Science Center Science , Award: G18AC00336

National Academies of Sciences, Engineering, and Medicine

USDA National Institute of Food and Agriculture Hatch project , Award: 1016068