Effects of climatic variability on the hydrologic response of a freshwater watershed

Nikolaidis, Nikolaos P.; Hu, Haiming; Ecsedy, Christopher J.

doi:10.1007/bf00877206

Cited by 7 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To account for the limitation of projecting pSWA using data outside the bounds of the training data (e.g., higher maximum temperatures in the future than observed thus far), we used a Monte Carlo simulation to increase the generalization of the model by adding variance to the input projected data (Figure 2; Nishant et al., 2020). Monte Carlo simulations have been used to add stochasticity to climate scenarios (Nawaz & Adeloye, 2006; Nikolaidis et al., 1994; Prudhomme et al., 2003; Sankarasubramanian et al., 2001) and these varied parameters are then used in hydrological models to assess the impacts of climate change (Cameron et al., 2000). The variance we added was based on the deviation distribution of each projection data set from observed data.…”

Section: Methodsmentioning

confidence: 99%

Projecting Surface Water Area Under Different Climate and Development Scenarios

Gaines,

Tulbure,

Perin

et al. 2024

Earth's Future

View full text Add to dashboard Cite

Changes in climate and land‐use/land‐cover will impact surface water dynamics throughout the 21st century and influence global surface water availability. However, most projections of surface water dynamics focus on climate drivers using local‐scale hydrological models, with few studies accounting for climate and human drivers such as land‐use/land‐cover change. We used a data‐driven, machine learning model to project seasonal surface water areas (SWAs) in the southeastern U.S. from 2006 to 2099 that combined land‐cover and climate projections under eight different development and emissions scenarios. The model was fitted with historic Landsat imagery, land‐use/land‐cover, and climate observation data (mean squared error 0.14). We assessed the change in SWA for each scenario, and we compared the surface water projections from our data‐driven model and a process‐based model. We found that the scenario with the largest forest‐dominated land cover loss and most extreme climate change had watersheds with the greatest projected increases (in the South Atlantic Gulf) and decreases (in the Lower Mississippi) in SWA. When compared to the increase or decrease in surface water projected by the process‐based model, most of the watersheds across scenarios agreed on the direction of change. Our findings highlight the importance of forest‐dominated land cover in maintaining stable surface water availability throughout the 21st century, which can inform land‐use management policies for adaptation and water‐stress mitigation as well as strategies to prepare for future flood and drought events.

show abstract

Section: Methodsmentioning

confidence: 99%

Projecting Surface Water Area Under Different Climate and Development Scenarios

Gaines,

Tulbure,

Perin

et al. 2024

Earth's Future

View full text Add to dashboard Cite

show abstract

“…To account for variability, a normal distribution was applied to flow. An exponential distribution was applied to the amount of rainfall, since rainfall is known to follow this statistical trend (Nikolaidis et al, 1994). The mean and standard deviation of flow (10 analyzed storms) from the copper roof area (42 ± 29 m 3 ), the paved area (2,720 ± 1,900 m 3 ), the grass area (3,500 ± 1,550 m 3 ), the noncopper roof area (1,610 ± 1,200 m 3 ), and the baseflow (1,620 ± 750 m 3 ), all contributed to the 9,320 ± 6,560 m 3 of water observed at the outlet of the watershed during a rainfall event.…”

Section: Parameter Estimationmentioning

confidence: 99%

MODELING FRAMEWORK FOR MANAGING COPPER RUNOFF IN URBAN WATERSHEDS¹

Boulanger¹,

Nikolaidis²

2003

J American Water Resour Assoc

Self Cite

View full text Add to dashboard Cite

A modeling framework was developed for managing copper runoff in urban watersheds that incorporates water quality characterization, watershed land use areas, hydrologic data, a statistical simulator, a biotic ligand binding model to characterize acute toxicity, and a statistical method for setting a watershed specific copper loading. The modeling framework is driven by export coefficients derived from water quality parameters and hydrologic inputs measured in an urban watershed's storm water system. This framework was applied to a watershed containing a copper roof built in 1992. A series of simulations was run to predict the change in receiving stream water chemistry caused by roof aging and to determine the maximum copper loading (at the 99 percent confidence level) a watershed could accept without causing acute toxicity in the receiving stream. Forecasting the amount of copper flux responsible for exceeding the assimilation capacity of a watershed can be directly related to maximum copper loadings responsible for causing toxicity in the receiving streams. The framework developed in this study can be used to evaluate copper utilization in urban watersheds.

show abstract