The U.S. Geological Survey (USGS) Water Resources Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand and an understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use. This data release contains Mann-Kendall monotonic trend analyses for 18 observed annual and monthly streamflow metrics at 6,347 U.S. Geological Survey streamgages located in the conterminous United States, Alaska, Hawaii, and Puerto Rico. Streamflow metrics include annual mean flow, maximum 1-day and 7-day flows, minimum 7-day and 30-day flows, and the date of the center of volume (the date on which 50% of the annual flow has passed by a gage), along with the mean flow for each month of the year. Annual streamflow metrics are computed from mean daily discharge records at U.S. Geological Survey streamgages that are publicly available from the National Water Information System (NWIS). Trend analyses are computed using annual streamflow metrics computed through climate year 2022 (April 2022- March 2023) for low-flow metrics and water year 2022 (October 2021 - September 2022) for all other metrics. Trends at each site are available for up to four different periods: (i) the longest possible period that meets completeness criteria at each site, (ii) 1980-2020, (iii) 1990-2020, (iv) 2000-2020. Annual metric time series analyzed for trends must have 80 percent complete records during fixed periods. In addition, each of these time series must have 80 percent complete records during their first and last decades. All longest possible period time series must be at least 10 years long and have annual metric values for at least 80% of the years running from 2013 to 2022. This data release provides the following five CSV output files along with a model archive: (1) streamflow_trend_results.csv - contains test results of all trend analyses with each row representing one unique combination of (i) NWIS streamgage identifiers, (ii) metric (computed using Oct 1 - Sep 30 water years except for low-flow metrics computed using climate years (Apr 1 - Mar 31), (iii) trend periods of interest (longest possible period through 2022, 1980-2020, 1990-2020, 2000-2020) and (iv) records containing either the full trend period or only a portion of the trend period following substantial increases in cumulative upstream reservoir storage capacity. This is an output from the final process step (#5) of the workflow. (2) streamflow_trend_trajectories_with_confidence_bands.csv - contains annual trend trajectories estimated using Theil-Sen regression, which estimates the median of the probability distribution of a metric for a given year, along with 90 percent confidence intervals (5th and 95h percentile values). This is an output from the final process step (#5) of the workflow. (3) streamflow_trend_screening_all_steps.csv - contains the screening results of all 7,873 streamgages initially considered as candidate sites for trend analysis and identifies the screens that prevented some sites from being included in the Mann-Kendall trend analysis. (4) all_site_year_metrics.csv - contains annual time series values of streamflow metrics computed from mean daily discharge data at 7,873 candidate sites. This is an output of Process Step 1 in the workflow. (5) all_site_year_filters.csv - contains information about the completeness and quality of daily mean discharge at each streamgage during each year (water year, climate year, and calendar year). This is also an output of Process Step 1 in the workflow and is combined with all_site_year_metrics.csv in Process Step 2. In addition, a .zip file contains a model archive for reproducing the trend results using R 4.4.1 statistical software. See the README file contained in the model archive for more information. Caution must be exercised when utilizing monotonic trend analyses conducted over periods of up to several decades (and in some places longer ones) due to the potential for confounding deterministic gradual trends with multi-decadal climatic fluctuations. In addition, trend results are available for post-reservoir construction periods within the four trend periods described above to avoid including abrupt changes arising from the construction of larger reservoirs in periods for which gradual monotonic trends are computed. Other abrupt changes, such as changes to water withdrawals and wastewater return flows, or episodic disturbances with multi-year recovery periods, such as wildfires, are not evaluated. Sites with pronounced abrupt changes or other non-monotonic trajectories of change may require more sophisticated trend analyses than those presented in this data release.

Trend Detection and Forecasting

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the second part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. Time series analysis is a powerful technique that can be used to understand the various temporal patterns in our data by decomposing data into different cyclic trends. Time series analysis can also be used to predict how levels of a variable will change in the future, taking into account what has happened in the past.

Learning Objectives

1. Choose appropriate time series analyses for trend detection and forecasting
2. Discuss the influence of seasonality on time series analysis
3. Interpret and communicate results of time series analyses

The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was further improved by incorporating additional datasets. Next, the Landsat compositing approach was improved to better capture the range of conditions from across each year and through time. These composites are based on Collection 2 Landsat data with improved geolocation accuracy and dynamic range. Finally, the Canadian portion of the sagebrush biome was included, which expanded the study area by 29,199 km2. Processing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded https://www.mrlc.gov/data.

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