Hydrology Corner Blog: Hawkes Bay Regional Council

Hydrology Corner Blog - flow measurements rising bubble method

How to Get Better Flow Measurements Using the Rising Bubble Method

One objective of the Hydrology Corner is to provide a forum where hydrometric problems can be discussed and clever solutions to those problems can be shared. The stream gaugers vs. beavers post is a good example of a discussion of a difficult problem. Not only have several people posted on the blog but the post also resulted in an email exchange with Jeff Watson from Horizons Regional Council who realized that New Zealand may have a solution to a North American problem.

Water News

Community Advice to Young Hydrologists, Part 1

This blog post is a fantastic insight into the minds of the most influential hydrologists in the world today. The question: “WHAT BOOK OR PAPER HAS BEEN MOST INFLUENTIAL TO YOUR CAREER AND WHY?” was posed to senior hydrologists all over the world and the answers run from the predictable (Groundwater by Freeze and Cherry), the unexpected (House at Pooh Corner), classic (The Method of Multiple Working Hypotheses), eclectic (Slowness) to difficult (Scale of Fluctuation of Rainfall Models). Out of this broad range of influential reading, it is the explanation of why a particular body of work made a difference in a career path that is most illuminating. I particularly like the quote provided by Gregory Pastenack: “Show me a person who has read a thousand books and I’ll show you my best friend; show me a person who has read but one and I will show you my worst enemy.” Happy reading.
– Stu Hamilton

Making Better Sense of the Mosaic of Environmental Measurement Networks: A System-of-Systems Approach and Quantitative Assessment

The intent of this paper is to develop a system of diverse observation systems for the purpose of ground-truthing satellite observation systems. The challenge is that while it is possible to develop methods to identify incorrect data, the residue is not necessarily all correct data. Confidence in the data has to come from attributes such as whether the data source is well documented, well understood, representative, updated, publicly available, and maintains rich metadata. If broadly adopted, the system-of-systems approach will have potential benefits in guiding users to the most appropriate set of observations for their needs and in highlighting to network owners and operators areas for potential improvement.
– Stu Hamilton

Murky Waters: Taking a Snapshot of Freshwater Sustainability in BC

This statement: “There’s a huge opportunity here to improve data collection, monitoring, and reporting. Reliable data would help governments, funders, and non-profits to track progress, make better decisions, and coordinate their efforts” is from Jack Wong, the CEO of the Real Estate Foundation of BC. The recommendations of this study include: “Regular public opinion surveys on freshwater attitudes (…) conducted by a cross-section of water partners to ensure long-term availability of the data”; “A multi-faceted solution (…) involving diverse groups that gather water data to increase the quantity and quality of data and improve data accessibility”; “This report shows a huge opportunity to convene relevant players and discuss solutions for freshwater sustainability data collection, monitoring, and reporting. If successfully implemented, communities across the province will be more informed and better stewards of BC’s most precious resource.”
– Stu Hamilton

Attributing Changing Rates of Temperature Record-Breaking to Anthropogenic Influences

Attribution of cause to effect in natural environments is a difficult problem. It is one thing to be able to use monitoring data to say what is happening. It is much more challenging to say why it is happening. While difficult, attribution is important. Without compelling attribution, there are deeply entrenched reasons to stay the course and not make the changes necessary to achieve better outcomes. I am interested to know if the relatively simple method used in this paper for attribution of cause to extreme temperatures could be applied for other types of data. For example, wouldn’t it be good to have compelling attribution of cause for harmful algal blooms?
– Stu Hamilton

U.S. Government Global Water Strategy 2017

This report makes a compelling argument about how looking after water is in the best strategic interests of the United States. It is better to anticipate predictable problems and take relatively inexpensive actions (e.g. wise use of data to influence proactive measures) to avoid or mitigate challenges to human health and economic development, both of which must be managed to ensure peace and security.
– Stu Hamilton

Designing the Climate Observing System of the Future

Print this article and put it on the desk of the senior administrators in your water monitoring agency. The arguments made here that climate observation networks offer a magnified return on investment all hold true for water monitoring as well. “Climate change is but one example of the need to make decisions under deep uncertainty. Developing new approaches to decision making that go beyond traditional point and probabilistic predictions is the focus of a new scientific undertaking. Developing adaptation pathways that will be robust under many possible futures will in part require observing systems that are designed with these needs in mind.” “The economic value of such a system at ~ $10 trillion dollars to the world economy in today’s value (known as “net present value” in economics using a 3% discount rate). In the simplest sense, this is the economic value of moving climate scientific understanding forward 15 to 20 years by using better observations, analysis, and modeling capabilities. The studies further estimated that if the world tripled its current economic investments in climate research (observations, analysis, modeling) to achieve such an advanced observing system, the return on investment would be ~ $50 for every $1 invested by society.”

– Stu Hamilton

WMO: 2017 Is Set to Be In Top Three Hottest Years, With Record-Breaking Extreme Weather

The average global temperature from January to September 2017 was approximately 1.1°C above the pre-industrial era. The years of 2013-2017 are set to be the warmest five-year period on record. The past three years have all been in the top three years in terms of temperature records. The WMO statement is based on five independently maintained global temperature data sets. The rate of increase in CO₂ from 2015 to 2016 was the highest on record, 3.3 parts per million/year, reaching 403.3 parts per million.
– Stu Hamilton

Citizen Science for Hydrological Risk Reduction and Resilience Building

This paper affirms the central theme of my whitepaper “Improving Outcomes for Freshwater Availability, Security and Sustainability: Water Data Asset Management as a Strategic Investment.” The key to good governance is well-informed stakeholders. Water monitoring best serves public interests when data is managed as a strategic asset.
– Stu Hamilton

Where Does the Water in Streams Come From When It Rains?

I like this blog post for how it explains catchment processes as a lead-in to explaining the value of isotope hydrology. I think this approach is a good one for anyone in monitoring to remember when explaining what we do and why we do it. Start with the why and end with the what.
– Stu Hamilton

A Multi-Scaled Geospatial and Temporal Database of Lake Ecological Context and Water Quality for Thousands of U.S. Lakes

An integrated database of data from 51,101 lakes in the northeast United States has been developed to assist with the problems arising from too many disperse and limited data sources. Three decades of data can now be discovered in its location and context (i.e. land use, geologic, climatic, and hydrologic settings). The database contains 150,000 measures of total phosphorus, 200,000 measures of chlorophyll, and 900,000 measures of Secchi depth.
– Stu Hamilton

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Hydrology Corner Blog: Hawkes Bay Regional Council

How to Get Better Flow Measurements Using the Rising Bubble Method