Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

Abstract. Hydrometric monitoring of small water bodies (1–10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies, but the influence of their limited surface areas, vegetation growth, and rapid flood dynamics on long-term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimize the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these small water bodies. Extensive hydrometric field data (1999–2014) for seven small reservoirs within the Merguellil catchment in central Tunisia and SPOT imagery are used to calibrate the method and explore its limits. The Modified Normalised Difference Water Index (MNDWI) is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15 %. Applied to 546 Landsat 5, 7, and 8 images over 1999–2014, the method reproduces surface water extent variations across small lakes with high skill (R2=0.9) and a mean root mean square error (RMSE) of 9300 m2. Comparison with published global water datasets reveals a mean RMSE of 21 800 m2 (+134 %) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3 ha with Landsat (Normalised RMSE = 27 %). Interferences from clouds and scan line corrector failure on ETM+ after 2003 also decrease the number of operational images by 51 %, reducing performance on lakes with rapid flood declines. Combining Landsat observations with 10 m pansharpened Sentinel-2 imagery further reduces RMSE to 5200 m2, displaying the increased opportunities for surface water monitoring in small water bodies after 2015.

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Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

10.5194/hess-2018-19 ◽

2018 ◽

Cited By ~ 1

Author(s):

Andrew Ogilvie ◽

Gilles Belaud ◽

Sylvain Massuel ◽

Mark Mulligan ◽

Patrick Le Goulven ◽

...

Keyword(s):

Time Series ◽

Surface Water ◽

Landsat Imagery ◽

Water Monitoring ◽

Water Bodies ◽

Small Lakes ◽

Small Water ◽

Water Detection ◽

Automated Method ◽

Small Water Bodies

Abstract. Hydrometric monitoring of small water bodies (1–10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies but the influence of their limited surface areas, vegetation growth and rapid flood dynamics on long term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimise the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these smallest water bodies. Extensive hydrometric field data (1999–2014) for 7 small reservoirs within the Merguellil catchment in Central Tunisia are used to calibrate the method and explore its limits. MNDWI is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15 %. Applied to 546 Landsat 5, 7 and 8 images over 1999–2014, the method reproduces surface water extent variations across small lakes with high skill (R2 = 0.9) and mean RMSE of 9 300 m2. Comparison with published global water data sets reveals a mean RMSE of 21 800 m2 (+134 %) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3 ha with Landsat (NRMSE = 27 %). Interferences from clouds & scan line corrector failure on ETM+ after 2003 also decrease the number of operational images by 51 %, reducing performance on lakes with rapid flood declines.

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Remote Sensing of Surface Water Dynamics Between 2015 and 2020 in the Prairie Pothole Region

10.5194/egusphere-egu21-8737 ◽

2021 ◽

Author(s):

Stefan Schlaffer ◽

Marco Chini ◽

Wouter Dorigo

Keyword(s):

Surface Water ◽

Prairie Pothole Region ◽

Water Area ◽

Water Bodies ◽

Water Dynamics ◽

Drought Indices ◽

Prairie Pothole ◽

Small Water ◽

Wide Range ◽

Small Water Bodies

<p>The North American Prairie Pothole Region (PPR) consists of millions of wetlands and holds great importance for biodiversity, water storage and flood management. The wetlands cover a wide range of sizes from a few square metres to several square kilometres. Prairie hydrology is greatly influenced by the threshold behaviour of potholes leading to spilling as well as merging of adjacent wetlands. The knowledge of seasonal and inter-annual surface water dynamics in the PPR is critical for understanding this behaviour of connected and isolated wetlands. Synthetic aperture radar (SAR) sensors, e.g. used by the Copernicus Sentinel-1 mission, have great potential to provide high-accuracy wetland extent maps even when cloud cover is present. We derived water extent during the ice-free months May to October from 2015 to 2020 by fusing dual-polarised Sentinel-1 backscatter data with topographical information. The approach was applied to a prairie catchment in North Dakota. Total water area, number of water bodies and median area per water body were computed from the time series of water extent maps. Surface water dynamics showed strong seasonal dynamics especially in the case of small water bodies (<&#160;1&#160;ha) with a decrease in water area and number of small water bodies from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. Inter-annual dynamics were strongly related to drought indices based on climate data, such as the Palmer Drought Severity Index. During the extremely wet period of late 2019 to 2020, the dynamics of both small and large water bodies changed markedly. While a larger number of small water bodies was encountered, which remained stable throughout the wet period, also the area of larger water bodies increased, partly due to merging of smaller adjacent water bodies. The results demonstrate the potential of Sentinel-1 data for long-term monitoring of prairie wetlands while limitations exist due to the rather low temporal resolution of 12 days over the PPR.</p>

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Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets

Remote Sensing ◽

10.3390/rs13245176 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5176

Author(s):

Vinicius Perin ◽

Samapriya Roy ◽

Joe Kington ◽

Thomas Harris ◽

Mirela G. Tulbure ◽

...

Keyword(s):

Time Series ◽

Surface Area ◽

Water Bodies ◽

Validation Dataset ◽

Surface Hydrology ◽

Small Water ◽

Resolution Analysis ◽

Freshwater Management ◽

Area Classification ◽

Small Water Bodies

Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r2 ≥ 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 ≥ 0.55, and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs.

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Monitoring Surface Water Dynamics in the Prairie Pothole Region Using Dual-Polarised Sentinel-1 SAR Time Series

10.5194/hess-2021-330 ◽

2021 ◽

Author(s):

Stefan Schlaffer ◽

Marco Chini ◽

Wouter Dorigo ◽

Simon Plank

Keyword(s):

Surface Water ◽

High Resolution ◽

Prairie Pothole Region ◽

Open Water ◽

Water Area ◽

Water Bodies ◽

Water Dynamics ◽

Prairie Pothole ◽

Small Water ◽

Small Water Bodies

Abstract. The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors have been widely used to calibrate and validate hydrological models of wetland dynamics. Yet, they are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors, such as the ones on board the Copernicus Sentinel-1 mission, can potentially overcome such limitations. However, water extent retrieval from SAR data is often affected by environmental factors, such as wind on water surfaces. Hence, for reliably monitoring water extent over longer time periods robust retrieval methods are required. The aim of this study was to develop a robust approach for classifying open water extent dynamics in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the light of ancillary data. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of water bodies and the median area per water body were computed. The validation of the retrieved water maps yielded producer’s accuracies between 84 % and 95 % for calm days and between 74 % and 88 % on windy days. User’s accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information. Surface water dynamics showed strong intra-annual dynamics especially in the case of small water bodies (< 1 ha). Water area and number of small water bodies decreased from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. During the extremely wet period between the autumn of 2019 and mid-2020, however, the dynamics of both small and large water bodies changed markedly. While a larger number of small water bodies was encountered, which remained stable throughout the wet period, also the area of larger water bodies increased, partly due to merging of smaller adjacent water bodies. However, the area covered by small water bodies was more stable than the area covered by large water bodies. This suggests that large potholes released water faster via the drainage network, while small potholes released water mainly to the atmosphere via evaporation. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations exist related to wind inhibiting correct water extent retrieval and due to the rather low temporal resolution of 12 days over the PPR.

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The Role of External Factors in the Variability of the Structure of the Zooplankton Community of Small Lakes (South-East Kazakhstan)

Water ◽

10.3390/w13070962 ◽

2021 ◽

Vol 13 (7) ◽

pp. 962

Author(s):

Moldir Aubakirova ◽

Еlena Krupa ◽

Zhanara Mazhibayeva ◽

Kuanysh Isbekov ◽

Saule Assylbekova

Keyword(s):

External Factors ◽

Water Bodies ◽

Average Weight ◽

Ecological State ◽

Small Lakes ◽

Small Water ◽

Shannon Diversity ◽

East Kazakhstan ◽

Small Water Bodies

The variability of hydrochemical parameters, the heterogeneity of the habitat, and a low level of anthropogenic impact, create the premises for conserving the high biodiversity of aquatic communities of small water bodies. The study of small water bodies contributes to understanding aquatic organisms’ adaptation to sharp fluctuations in external factors. Studies of biological communities’ response to fluctuations in external factors can be used for bioindication of the ecological state of small water bodies. In this regard, the purpose of the research is to study the structure of zooplankton of small lakes in South-East Kazakhstan in connection with various physicochemical parameters to understand the role of biological variables in assessing the ecological state of aquatic ecosystems. According to hydrochemical data in summer 2019, the nutrient content was relatively high in all studied lakes. A total of 74 species were recorded in phytoplankton. The phytoplankton abundance varied significantly, from 8.5 × 107 to 2.71667 × 109cells/m3, with a biomass from 0.4 to 15.81 g/m3. Shannon diversity index of phytoplankton in the lakes at high altitude varied from 1.33 to 2.39 and from 0.46 to 3.65 in the lakes at lower altitudes. The average weight of the cells of algae species varied from 0.2079 to 1.5076 × 10−6 mg in the lakes at lower altitudes, the average weight of the cells of algae species changed from 0.6682 to 1.2963 × 10−6 mg in the lakes at higher altitudes. Zooplankton was represented by 58 taxa. The total abundance of zooplankton varied from 0.05 to 169.00 thousand ind./m3 with biomass of 0.51-349.01 mg/m3. Shannon diversity of zooplankton in the lakes at lower altitude fluctuated from 0.42 to 2.32 and it was 0.66–1.77 in the lakes at higher altitudes. The average individual mass of specimens in zooplankton in mountain lakes ranged from 0.021 to 0.037 mg and varied from 0.002 to 0.007 mg in other lakes. The main factors in the development of the structure of zooplankton communities in small lakes were temperature, TDS, the content of nitrates, phosphates, and the composition and biomass of planktonic algae. The hydrochemical and biological data of the investigated lakes indicated their organic pollution. Our results once again confirmed the applicability of structural variables of zooplankton in assessing water quality.

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Using the novel priority index in prioritizing the selection of inland water bodies for site-based fish species conservation

10.1101/2020.11.12.380618 ◽

2020 ◽

Author(s):

Anthony Basooma ◽

Herbert Nakiyende ◽

Mark Olokotum ◽

Winnie Nkalubo ◽

Laban Musinguzi ◽

...

Keyword(s):

Species Richness ◽

Surface Area ◽

Fish Species ◽

Water Bodies ◽

Small Lakes ◽

Large Lakes ◽

Priority Index ◽

Small Water ◽

Selection Of ◽

Small Water Bodies

AbstractFreshwater ecosystems occupy <1% of the Earth’s total surface area but provide an array of ecosystem services. However, these ecosystems are threatened by multiple stressors, including overexploitation, infrastructure developments, habitat alteration, and alien species introductions. The magnitude of these threats varies in different water bodies, requiring site-based conservation actions. In this paper, we aimed at developing a priority index (CPIw) that can be used to inform conservation managers in prioritizing the selection of a waterbody for site-based fish conservation purposes. We used data on distribution, diversity, and conservation status of fishes of Uganda, which were retrieved from the Global Biodiversity Information Facility (GBIF) and International Union Conservation for Nature (IUCN) databases. In the index, we incorporated the species richness, surface area of a waterbody, species rarity, and species IUCN status. A total of 288 fish species were recorded in 81 waterbodies (7 large lakes, 37 small lakes, and 37 rivers). Of these species, 110 were only found in large lakes, followed by rivers (19) and small lakes (6). Despite the higher species richness in large lakes relative to small lakes, the latter recorded significantly higher CPIw compared with the former (t = −2.8, df = 30, p-value = 0.008, d=0.7). This observation is consistent with the expectation, given the low ecological substitutability for the species and higher levels of exposure to human-induced threats in small water bodies compared with large systems. Therefore, we suggest that in situations where resources are limiting, small water bodies need to be given much attention, although we do not suggest ignoring water bodies with low CPIw values.

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