Water Resource Management

2022 ◽  
pp. 197-218
Author(s):  
Satya Prakash ◽  
Pinakana Sai Deepak
Keyword(s):  
Big Data ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Satellite Images ◽  
Measurement Techniques ◽  
Satellite Image ◽  
Quality Parameters ◽  
In Situ Measurement

Water is an essential component for the survival of mankind and for balancing the ecosystem and livelihood. The world is experiencing a scarcity of water, both in terms of quality and quantity. Although there are several in-situ measurement techniques, they seem insufficient for large areas involving several parameters. Analysis of satellite images for estimating the quality and quantity of natural water has become an accepted tool for better spatial planning. With the increase in variety, volume, and velocity of satellite image, a tool for faster and accurate processing of the data is needed. Google Earth Engine (GEE) is one such cloud-based geo-big data platform. This chapter reviews the work of several researchers worldwide who have used and demonstrated the capability of satellite images with other geo-big data such as elevation, landcover, etc. for water resource management on the GEE platform. It can be concluded from the review work that GEE can help in estimating the water quality parameters with reasonable accuracy, comparable to the in-situ measurement, albeit quickly.

scholarly journals A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products

2020 ◽  
Vol 24 (5) ◽  
pp. 2687-2710 ◽  
Author(s):  
Christian Massari ◽  
Luca Brocca ◽  
Thierry Pellarin ◽  
Gab Abramowitz ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Short Latency ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

scholarly journals Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

2021 ◽  
Vol 13 (15) ◽  
pp. 2899
Author(s):  
Ghada Y.H. El Serafy ◽  
Blake A. Schaeffer ◽  
Merrie-Beth Neely ◽  
Anna Spinosa ◽  
Daniel Odermatt ◽  
...  
Keyword(s):  
Water Quality ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Management Policy ◽  
Quality Data ◽  
Water Quality Data ◽  
Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

scholarly journals A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products

2019 ◽  
Author(s):  
Christian Massari ◽  
Luca Brocca ◽  
Thierry Pellarin ◽  
Gab Abramowitz ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Short Latency ◽  
In Situ Data ◽  
Long Latency ◽  
Agricultural Applications ◽  
Unique Source

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative are satellite-based rainfall estimates, yet comparisons with in-situ data suggest they're often not optimal. In this study, we developed a short-latency (i.e., 2–3 days) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM early run (IMERG-ER) with multiple satellite soil moisture-based rainfall products derived from ASCAT, SMOS and SMAP L3 satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high quality ground-based rainfall datasets (India, Conterminous United States, Australia and Europe) and over data scarce regions in Africa and South America by using Triple Collocation analysis (TC). We found the integration of satellite SM observations with in-situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long latency datasets. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

Catchment surveillance for water resource management

Waterlines ◽  
1997 ◽  
Vol 16 (1) ◽  
pp. 23-25
Author(s):  
Barry Lloyd ◽  
Teresa Thorpe
Keyword(s):  
Resource Management ◽  
Water Resource ◽  
Water Resource Management
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