Forecasting water quality using seasonal ARIMA model by integrating <i>in-situ</i> measurements and remote sensing techniques in Krishnagiri reservoir, India

Wahid, A. Abdul; Arunbabu, Elangovan

doi:10.2166/wpt.2022.046

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…In cases with high data latency of the forecast variable (e.g., microscope counts of phytoplankton requiring laboratory analysis), data fusion approaches that assimilate multiple data sources may improve forecast skill (e.g., Baracchini, Wüest, et al, 2020; Chen et al, 2021). For example, some studies have assimilated both in situ measurements and remote sensing data to forecast reservoir water quality variables, including chlorophyll a and conductivity (Abdul Wahid & Arunbabu, 2022; Chen et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Wander,

Thomas,

Moore

et al. 2024

Ecosphere

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Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data.

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Section: Discussionmentioning

confidence: 99%

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Wander,

Thomas,

Moore

et al. 2024

Ecosphere

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“…For example, lake freshwater quality forecast models may need to account for watershed inputs that are integrated into lake water quality, particularly over seasonal or annual time scales. However, recent innovations in freshwater quality forecasting methodology, including embedding freshwater‐relevant physical processes into machine learning model architectures (Daw et al, 2020; Read et al, 2019) and data assimilation of multiple freshwater quality data streams with different attributes (Abdul Wahid & Arunbabu, 2022; Chen et al, 2021; Cho et al, 2020; Cobo et al, 2022), illustrate the benefits of adopting practices from other disciplines for water quality forecasting.…”

Section: Discussion and Synthesis: Opportunities To Advance Near‐term...mentioning

confidence: 99%

Progress and opportunities in advancing near‐term forecasting of freshwater quality

Lofton

Howard

Thomas

et al. 2023

Global Change Biology

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Near‐term freshwater forecasts, defined as sub‐daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near‐term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water‐related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near‐term forecasts of water quantity and that near‐term water quality forecasts are fewer in number and in the early stages of development (i.e., non‐operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near‐term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end‐user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near‐term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

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“…In cases with high data latency of the forecast variable (e.g., microscope counts of phytoplankton requiring laboratory analysis), data fusion approaches that assimilate multiple data sources may improve forecast skill (e.g., Baracchini et al, 2020b, Chen et al, 2021. For example, some studies have assimilated both in-situ measurements and remote sensing data to forecast reservoir water quality variables, including chlorophyll a and conductivity (Abdul Wahid andArunbabu, 2022, Chen et al, 2021).…”

Section: Recommendations For Setting Up Da For Other Forecasting Systemsmentioning

confidence: 99%

Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir

Wander¹,

Thomas²,

Moore³

et al. 2023

Preprint

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Ecosystems around the globe are experiencing increased variability due to land use and climate change. In response, ecologists are increasingly using near-term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near-term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost-prohibitive or impossible for forecasting ecological variables that lack high-frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in-situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using FLARE (Forecasting Lake And Reservoir Ecosystems), an open-source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1 to 35-day-ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1-day-ahead forecast root mean square error (RMSE) of 0.94°C, mean 7-day RMSE of 1.33°C, and mean 35-day RMSE of 2.15°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1-7-day-ahead horizons, weekly data assimilation resulted in the most skillful forecasts at 8-35-day-ahead horizons. Within a year, daily to fortnightly data assimilation substantially outperformed monthly data assimilation in the stratified summer period, whereas all data assimilation frequencies resulted in skillful forecasts across depths in the mixed spring/autumn periods for shorter forecast horizons. Our results suggest that lower-frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high-frequency sensor data.

show abstract

Forecasting water quality using seasonal ARIMA model by integrating in-situ measurements and remote sensing techniques in Krishnagiri reservoir, India

Cited by 12 publications

References 37 publications

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Progress and opportunities in advancing near‐term forecasting of freshwater quality

Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir

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