A spatial model to aggregate point-source and nonpoint-source water-quality data for large areas

1992 ◽  
Vol 18 (8) ◽  
pp. 1055-1073 ◽  
Author(s):  
Dale A. White ◽  
Richard A. Smith ◽  
Curtis V. Price ◽  
Richard B. Alexander ◽  
Keith W. Robinson
Keyword(s):  
Water Quality ◽  
Point Source ◽  
Spatial Model ◽  
Nonpoint Source ◽  
Quality Data ◽  
Source Water ◽  
Water Quality Data ◽  
Source Water Quality

scholarly journals Applying Community Data Reporting Formats to Open-Source Water Quality Data

2021 ◽  
Author(s):  
Dylan O'Ryan ◽  
Robert Crystal-Ornelas ◽  
Deb Agarwal ◽  
Kristin Boye ◽  
Shreyas Cholia ◽  
...  
Keyword(s):  
Water Quality ◽  
Open Source ◽  
Quality Data ◽  
Source Water ◽  
Water Quality Data ◽  
Data Reporting ◽  
Community Data ◽  
Source Water Quality

Water quality monitoring: the basis for watershed management in the Oldman River Basin, Canada

2006 ◽  
Vol 53 (10) ◽  
pp. 153-161 ◽  
Author(s):  
C.W. Koning ◽  
K.A. Saffran ◽  
J.L. Little ◽  
L. Fent
Keyword(s):  
Water Quality ◽  
Point Source ◽  
River Basin ◽  
Watershed Management ◽  
Health Concern ◽  
Quality Data ◽  
Oil And Gas Development ◽  
Water Quality Data ◽  
Oldman River Basin ◽  
Oldman River

The Oldman River flows 440 km from its headwaters in south-western Alberta, through mountains, foothills and plains into the South Saskatchewan River. Peak flows occur in May and June. Three major reservoirs, together with more than a dozen other structures, supply water to nine irrigation districts and other water users in the Oldman basin. Human activity in the basin includes forestry, recreation, oil and gas development, and agriculture, including a large number of confined livestock feeding operations. Based on the perception of basin residents that water quality was declining and of human health concern, the Oldman River Basin Water Quality Initiative was formed in 1997 to address the concerns. There was limited factual information, and at the time there was a desire for finger pointing. Results (1998–2002) show that mainstem water quality remains good whereas tributary water quality is more of a challenge. Key variables of concern are nutrients, bacteria and pesticides. Point source discharges are better understood and better regulated, whereas non-point source runoff requires more attention. Recent data on Cryptosporidium and Giardia species are providing benefit for focusing watershed management activities. The water quality data collected is providing a foundation to implement community-supported urban and rural better management practices to improve water quality.

Effective Monitoring Strategies for Demonstrating Water Quality Changes from Nonpoint Source Controls on a Watershed Scale

1993 ◽  
Vol 28 (3-5) ◽  
pp. 143-148 ◽  
Author(s):  
Jean Spooner ◽  
Daniel E. Line
Keyword(s):  
Water Quality ◽  
Management Practice ◽  
Nonpoint Source ◽  
Lessons Learned ◽  
Quality Data ◽  
Experimental Program ◽  
Quality Changes ◽  
Water Quality Data ◽  
Treatment Implementation ◽  
Land Treatment

Demonstrating water quality improvements from nonpoint source (NPS) controls by monitoring both land treatment and water quality in at least a subset of watershed projects is necessary to provide feedback to project coordinators Feedback to regional and national policy makers is also essential to achieve political and economic support for NPS control programs. To meet this objective, two challenges must be met in designing the monitoring network and analyzing the data: (1) Detecting significant (or real) trends in both water quality and land treatment implementation and (2) Associating water quality trends with land treatment trends. Land treatment and water quality monitoring requirements for meeting these challenges are discussed based on lessons learned from the Rural Clean Water Program, a 10-15 year experimental program designed to control agricultural NPS pollution in rural watersheds. At minimum, monitoring of land treatment and water quality should be multi-year before and after best management practice (BMP) implementation. The paired watershed design is the best for documenting BMP effectiveness in the shortest number of years (a minimum of 3-5 years). The water quality and land treatment data bases should be temporally related. All significant sources of variability in the land treatment and water quality data should be taken into account to increase the chances of isolating true water quality changes due to BMPs.

scholarly journals Nontuberculous Mycobacterial Disease and Molybdenum in Colorado Watersheds

2020 ◽  
Vol 17 (11) ◽  
pp. 3854
Author(s):  
Ettie M. Lipner ◽  
Joshua French ◽  
Carleton R. Bern ◽  
Katherine Walton-Day ◽  
David Knox ◽  
...  
Keyword(s):  
Water Quality ◽  
Linear Models ◽  
Disease Risk ◽  
Mitigation Strategies ◽  
Quality Data ◽  
Source Water ◽  
Water Quality Data ◽  
Unit Increase ◽  
Discrete Responses ◽  
Environmental Bacteria

Nontuberculous mycobacteria (NTM) are environmental bacteria that may cause chronic lung disease. Environmental factors that favor NTM growth likely increase the risk of NTM exposure within specific environments. We aimed to identify water-quality constituents (Al, As, Cd, Ca, Cu, Fe, Pb, Mg, Mn, Mo, Ni, K, Se, Na, Zn, and pH) associated with NTM disease across Colorado watersheds. We conducted a geospatial, ecological study, associating data from patients with NTM disease treated at National Jewish Health and water-quality data from the Water Quality Portal. Water-quality constituents associated with disease risk were identified using generalized linear models with Poisson-distributed discrete responses. We observed a highly robust association between molybdenum (Mo) in the source water and disease risk. For every 1- unit increase in the log concentration of molybdenum in the source water, disease risk increased by 17.0%. We also observed a statistically significant association between calcium (Ca) in the source water and disease risk. The risk of NTM varied by watershed and was associated with watershed-specific water-quality constituents. These findings may inform mitigation strategies to decrease the overall risk of exposure.

NASA-Modified Precipitation Products to Improve USEPA Nonpoint Source Water Quality Modeling for the Chesapeake Bay

2010 ◽  
Vol 39 (4) ◽  
pp. 1388-1401 ◽  
Author(s):  
Nigro Joseph ◽  
Toll David ◽  
Partington Ed ◽  
Wenge Ni-Meister ◽  
Lee Shihyan ◽  
...  
Keyword(s):  
Water Quality ◽  
Chesapeake Bay ◽  
Nonpoint Source ◽  
Water Quality Modeling ◽  
Source Water ◽  
Quality Modeling ◽  
Source Water Quality

scholarly journals The Fluctuation of Water Quality of Ci Lutung flow areas in Majalengka and Sumedang.

2018 ◽  
Vol 73 ◽  
pp. 04013
Author(s):  
Deddy Caesar Agusto ◽  
Eko Kusratmoko
Keyword(s):  
Water Quality ◽  
Point Source ◽  
Agricultural Land ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Indicators ◽  
Quality Of Water ◽  
The Moment ◽  
The Impact

The river is the main source of water in Indonesia, which at the moment, this quality tends to get worse and is no longer worth consuming for various needs. The cause of the pollution is the entry of pollutants both point source (industrial waste) and non-point source (residential and agricultural land). Rainfall can be a non-point source pollutant agent from a watershed to a water body. The impact of rainfall on increasing concentrations of pollutants is very significant, especially the high intensity rainfall that falls after the long dry season. In this study, water quality data is obtained from river outlets located in Damkamun taken every 30 minutes during the rainfall event so that fluctuation in water quality can be seen. Water quality indicators studied in this research are TDS, DHLNitrate, Phosphate and Ph. The author, in analyzing, using rainfall Himawari 8 which is obtained every 10 minutes. The result shows that rainfall is directly related to the water flow and the fluctuation of the discharge affects the water quality. From the calculations, the chemical quality of water is also influenced by the use of land in the watershed. Nitrate value increases when the occurrence of rain occurs in land use while phosphate experiences a high value during the event.

Determining Drainage Pattern Using DEM Data for Nonpoint-Source Water Quality Modeling

1992 ◽  
Vol 26 (5-6) ◽  
pp. 1431-1438 ◽  
Author(s):  
J.-J. Kao
Keyword(s):  
Water Quality ◽  
Nonpoint Source ◽  
Water Quality Modeling ◽  
Uniform Grid ◽  
Source Water ◽  
Drainage Pattern ◽  
Drainage Patterns ◽  
Quality Modeling ◽  
Elevation Model ◽  
Source Water Quality

The drainage pattern of a watershed is an important parameter in nonpoint-source water quality modeling. Manual preparation of this pattern from topographic maps is time–consuming and sometimes subjective. Digital Elevation Model (DEM) data are fundamental cartographic data stored in a uniform grid system which can be easily processed by the computer. Using DEM data and a set of pre-defined rules, the drainage pattern can be automatically determined. Six grid-based methods have been developed, and three of them have been tested for a case study, an area enclosing a subwatershed of Chin-Mei River, Taipei County, Taiwan, R. O. C. The results are comparable to the manually prepared drainage pattern, although several complexities exist for areas such as depression and highly irregular areas. Several refined rules based on the drainage patterns in adjacent areas are utilized to modify the drainage patterns in these areas. Compared with the conventional manual method, the proposed automated methods are much more efficient for regional water quality studies.

Sign in / Sign up

Export Citation Format

Share Document