Volume Variations of Small Inland Water Bodies from a Combination of Satellite Altimetry and Optical Imagery

In this study, a new approach for estimating volume variations of lakes and reservoirs using water levels from satellite altimetry and surface areas from optical imagery is presented. Both input data sets, namely water level time series and surface area time series, are provided by the Database of Hydrological Time Series of Inland Waters (DAHITI), developed and maintained by the Deutsches Geodätisches Forschungsinsitut der Technischen Universität München (DGFI-TUM). The approach is divided into three parts. In the first part, a hypsometry model based on the new modified Strahler approach is computed by combining water levels and surface areas. The hypsometry model describes the dependency between water levels and surface areas of lakes and reservoirs. In the second part, a bathymetry between minimum and maximum surface area is computed. For this purpose, DAHITI land-water masks are stacked using water levels derived from the hypsometry model. Finally, water levels and surface areas are intersected with the bathymetry to estimate a time series of volume variations in relation to the minimum observed surface area. The results are validated with volume time series derived from in-situ water levels in combination with bathymetric surveys. In this study, 28 lakes and reservoirs located in Texas are investigated. The absolute volumes of the investigated lakes and reservoirs vary between 0.062 km 3 and 6.041 km 3 . The correlation coefficients of the resulting volume variation time series with validation data vary between 0.80 and 0.99. Overall, the relative errors with respect to volume variations vary between 2.8% and 14.9% with an average of 8.3% for all 28 investigated lakes and reservoirs. When comparing the resulting RMSE with absolute volumes, the absolute errors vary between 1.5% and 6.4% with an average of 3.1%. This study shows that volume variations can be calculated with a high accuracy which depends essentially on the quality of the used water levels and surface areas. In addition, this study provides a hypsometry model, high-resolution bathymetry and water level time series derived from surface areas based on the hypsometry model. All data sets are publicly available on the Database of Hydrological Time Series of Inland Waters.

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DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4345-2015 ◽

2015 ◽

Vol 19 (10) ◽

pp. 4345-4364 ◽

Cited By ~ 106

Author(s):

C. Schwatke ◽

D. Dettmering ◽

W. Bosch ◽

F. Seitz

Keyword(s):

Time Series ◽

Water Level ◽

Satellite Altimetry ◽

Water Levels ◽

Altimeter Data ◽

Inland Waters ◽

New Approach ◽

Inland Water ◽

Rivers And Lakes

Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, for some years, this technology has also been used to retrieve water levels from reservoirs, wetlands and in general any inland water body, although the radar altimetry technique has been especially applied to rivers and lakes. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series of rivers and lakes available through the web service "Database for Hydrological Time Series over Inland Waters" (DAHITI). The new method is based on an extended outlier rejection and a Kalman filter approach incorporating cross-calibrated multi-mission altimeter data from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, including their uncertainties. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparisons with in situ gauge data and results from external inland altimeter databases. The new approach yields rms differences with respect to in situ data between 4 and 36 cm for lakes and 8 and 114 cm for rivers. For most study cases, more accurate height information than from other available altimeter databases can be achieved.

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Kalman filter approach for estimating water level time series over inland water using multi-mission satellite altimetry

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-4813-2015 ◽

2015 ◽

Vol 12 (5) ◽

pp. 4813-4855 ◽

Cited By ~ 8

Author(s):

C. Schwatke ◽

D. Dettmering ◽

W. Bosch ◽

F. Seitz

Keyword(s):

Time Series ◽

Kalman Filter ◽

Water Level ◽

Satellite Altimetry ◽

Water Levels ◽

Altimeter Data ◽

New Approach ◽

Inland Water ◽

Filter Approach

Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, since some years, this technology is also used for observing inland water levels of lakes and rivers. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series available through the web service "Database for Hydrological Time Series over Inland Water" (DAHITI). The method is based on a Kalman filter approach incorporating multi-mission altimeter observations and their uncertainties. As input data, cross-calibrated altimeter data from Envisat, ERS-2, Jason-1, Jason-2, Topex/Poseidon, and SARAL/AltiKa are used. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparison with in-situ gauge data and results from external inland altimeter databases. The new approach yields RMS differences with respect to in-situ data between 4 and 38 cm for lakes and 12 and 139 cm for rivers, respectively. For most study cases, more accurate height information than from available other altimeter data bases can be achieved.

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Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2

Remote Sensing ◽

10.3390/rs11091010 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1010 ◽

Cited By ~ 16

Author(s):

Christian Schwatke ◽

Daniel Scherer ◽

Denise Dettmering

Keyword(s):

Time Series ◽

Surface Area ◽

Time Variable ◽

New Approach ◽

Data Gaps ◽

Lakes And Reservoirs ◽

Variable Water ◽

Land Water ◽

Sentinel 2

In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km 2 are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI).

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Investigation of surface area of lakes and marshes from satellite images by using remote sensing and geographic information system integration in Iraq

MATEC Web of Conferences ◽

10.1051/matecconf/201816203016 ◽

2018 ◽

Vol 162 ◽

pp. 03016

Author(s):

Alaa Dawood ◽

Yousif Kalaf ◽

Nagham Abdulateef ◽

Mohammed Falih

Keyword(s):

Remote Sensing ◽

Surface Area ◽

System Integration ◽

Hydrological Regime ◽

Remotely Sensed ◽

Total Surface Area ◽

Water Levels ◽

Remotely Sensed Data ◽

Surface Areas ◽

Water Volumes

Water level and distribution is very essential in almost all life aspects. Natural and artificial lakes represent a large percentage of these water bodies in Iraq. In this research the changes in water levels are observed by calculating the areas of five different lakes in five different regions and two different marshes in two different regions of the country, in a period of 12 years (2001 - 2012), archived remotely sensed images were used to determine surface areas around lakes and marshes in Iraq for the chosen years . Level of the lakes corresponding to satellite determined surface areas were retrieved from remotely sensed data .These data were collected to give explanations on lake level and surface area fluctuations. It is important to determine these areas at different water levels to know areas which are being flooded in addition to the total area inundated .The behavior of hydrological regime of these lakes during the period was assessed using an integration of remote sensing and GIS techniques which found that the total surface area of the lakes had diminished and their water volumes reduced. The study further revealed that the levels of the lakes surfaces had lowered through these years.

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Validation of Sentinel-3A Based Lake Level over US and Canada

Remote Sensing ◽

10.3390/rs12172835 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2835

Author(s):

Karina Nielsen ◽

Ole Baltazar Andersen ◽

Heidi Ranndal

Keyword(s):

Water Level ◽

Satellite Altimetry ◽

Lake Level ◽

Mean Squared Error ◽

Pearson Correlation ◽

The United States ◽

Water Levels ◽

Winter Period ◽

Large Lakes ◽

In Situ Data

Satellite altimetry is an important contributor for measuring the water level of continental water bodies. The technique has been applied for almost three decades. In this period the data quality has increased and the applications have evolved from the study of a few large lakes and rivers, to near global applications at various scales. Products from current satellite altimetry missions should be validated to continuously improve the measurements. Sentinel-3A has been operating since 2016 and is the first mission operating in synthetic aperture radar (SAR) mode globally. Here we evaluate its performance in capturing lake level variations based on a physical and an empirical retracker provided in the official level 2 product. The validation is performed for more than 100 lakes in the United States and Canada where the altimetry based water levels are compared with in situ data. As validation measures we consider the root mean squared error, the Pearson correlation, and the percentage of outliers. For the US sites the median of the RMSE value is 25 cm and 19 cm and the median of the Pearson correlations are 0.86 and 0.93 for the physical and empirical retracker, respectively. The percentage of outliers (median) is 11% for both retrackers. The validations measures are slightly poorer for the Canadian sites; the median RMSE is approximately 5 cm larger, the Pearson correlation 0.1 lower, and the percentage of outliers 5% larger. The poorer performance for the Canadian sites is mainly related to the presence of lake ice in the winter period where the surface elevations are not able to map the surface correctly. The validation measures improve considerably when evaluated for summer data only. For both areas we show that the reconstruction of the water level variations based on the empirical retracker is significantly better compared to that of the physical retracker in terms of the RMSE and the Pearson correlation.

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Satellite altimetry over small reservoirs in the Brazilian semiarid region

RBRH ◽

10.1590/2318-0331.262120210038 ◽

2021 ◽

Vol 26 ◽

Author(s):

Alfredo Ribeiro Neto ◽

Sajedeh Behnia ◽

Mohammad J. Tourian ◽

Fábio Araújo da Costa ◽

Nico Sneeuw

Keyword(s):

Time Series ◽

Water Level ◽

Satellite Altimetry ◽

Water Storage ◽

Semiarid Region ◽

Northeast Brazil ◽

Drought Period ◽

Small Reservoirs ◽

Brazilian Semiarid

ABSTRACT Northeast Brazil is one of the most populated semiarid regions in the world. The region is highly dependent on reservoirs for human water supply, irrigation, industry, and livestock. The objective of this study was to validate water level time series from the satellites Envisat, SARAL, Sentinel-3A/-3B, Jason-2/-3 in small reservoirs in Northeast Brazil. In total, we evaluated the water level time series of 20 reservoirs. The Sentinel-3B outperforms the other altimeters with a maximum RMSE of 0.21 m. In seven reservoirs with updated depth-area-volume curves, the altimetric water level was used to calculate the corresponding volume. The obtained volume was then compared to the volume given by the same curve by using in situ stage. Our investigations showed that, in the case of small reservoirs, the precision of water level time series derived from satellite altimetry is mainly governed by the seasonal variability of the water storage especially at the end of the 2012-2017 drought period.

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Characterising hillslope–stream connectivity with a joint event analysis of stream and groundwater levels

Hydrology and Earth System Sciences ◽

10.5194/hess-24-5713-2020 ◽

2020 ◽

Vol 24 (12) ◽

pp. 5713-5744

Author(s):

Daniel Beiter ◽

Markus Weiler ◽

Theresa Blume

Keyword(s):

Time Series ◽

Water Level ◽

Groundwater Level ◽

Stream Water ◽

Water Levels ◽

Joint Analysis ◽

Strong Increase ◽

Groundwater Levels ◽

The Relationship ◽

Insight Into

Abstract. Hillslope–stream connectivity controls runoff generation, during events and during baseflow conditions. However, assessing subsurface connectivity is a challenging task, as it occurs in the hidden subsurface domain where water flow can not be easily observed. We therefore investigated if the results of a joint analysis of rainfall event responses of near-stream groundwater levels and stream water levels could serve as a viable proxy for hillslope–stream connectivity. The analysis focuses on the extent of response, correlations, lag times and synchronicity. As a first step, a new data analysis scheme was developed, separating the aspects of (a) response timing and (b) extent of water level change. This provides new perspectives on the relationship between groundwater and stream responses. In a second step we investigated if this analysis can give an indication of hillslope–stream connectivity at the catchment scale. Stream water levels and groundwater levels were measured at five different hillslopes over 5 to 6 years. Using a new detection algorithm, we extracted 706 rainfall response events for subsequent analysis. Carrying out this analysis in two different geological regions (schist and marls) allowed us to test the usefulness of the proxy under different hydrological settings while also providing insight into the geologically driven differences in response behaviour. For rainfall events with low initial groundwater level, groundwater level responses often lag behind the stream with respect to the start of rise and the time of peak. This lag disappears at high antecedent groundwater levels. At low groundwater levels the relationship between groundwater and stream water level responses to rainfall are highly variable, while at high groundwater levels, above a certain threshold, this relationship tends to become more uniform. The same threshold was able to predict increased likelihood for high runoff coefficients, indicating a strong increase in connectivity once the groundwater level threshold was surpassed. The joint analysis of shallow near-stream groundwater and stream water levels provided information on the presence or absence and to a certain extent also on the degree of subsurface hillslope–stream connectivity. The underlying threshold processes were interpreted as transmissivity feedback in the marls and fill-and-spill in the schist. The value of these measurements is high; however, time series of several years and a large number of events are necessary to produce representative results. We also find that locally measured thresholds in groundwater levels can provide insight into the connectivity and event response of the corresponding headwater catchments. If the location of the well is chosen wisely, a single time series of shallow groundwater can indicate if the catchment is in a state of high or low connectivity.

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A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

10.5194/hess-2018-21 ◽

2018 ◽

Cited By ~ 2

Author(s):

Tim Busker ◽

Ad de Roo ◽

Emiliano Gelati ◽

Christian Schwatke ◽

Marko Adamovic ◽

...

Keyword(s):

Surface Water ◽

Satellite Altimetry ◽

Google Earth ◽

Water Levels ◽

Water Volume ◽

Lake Area ◽

Reservoir Volume ◽

Long Time ◽

Lakes And Reservoirs ◽

Computationally Intensive

Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lakes and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 135 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh/dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with an R2 > 0.8 and an average R2 of 0.91. For these lakes and for lakes with a nearly constant lake area (coefficient of variation

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Historic maps as a data source for socio-hydrology: a case study of the Lake Balaton wetland system, Hungary

Hydrology and Earth System Sciences ◽

10.5194/hess-17-4589-2013 ◽

2013 ◽

Vol 17 (11) ◽

pp. 4589-4606 ◽

Cited By ~ 29

Author(s):

A. Zlinszky ◽

G. Timár

Keyword(s):

Time Series ◽

Water Level ◽

Social Systems ◽

Wetland Loss ◽

Water Level Fluctuations ◽

Data Sets ◽

Lake Balaton ◽

Hydrological Data ◽

Historic Maps

Abstract. Socio-hydrology is the science of human influence on hydrology and the influence of the water cycle on human social systems. This newly emerging discipline inherently involves a historic perspective, often focusing on timescales of several centuries. While data on human history is typically available for this time frame, gathering information on the hydrological situation during such a period can prove difficult: measured hydrological data for such long periods are rare, while models and secondary data sets from geomorphology, pedology or archaeology are typically not accurate enough over such a short time. In the first part of this study, the use of historic maps in hydrology is reviewed. Major breakthroughs were the acceptance of historic map content as valid data, the use of preserved features for investigating situations earlier than the map, and the onset of digital georeferencing and data integration. Historic maps can be primary quantitative sources of hydro-geomorphological information, they can provide a context for point-based measurements over larger areas, and they can deliver time series for a better understanding of change scenarios. In the second part, a case study is presented: water level fluctuations of Lake Balaton were reconstructed from maps, levelling logs and other documents. An 18th century map system of the whole 5700 km2 catchment was georeferenced, integrated with two 19th century map systems, and wetlands, forests and open water digitized. Changes in wetland area were compared with lake water level changes in a 220 yr time series. Historic maps show that the water level of the lake was closer to present-day levels than expected, and that wetland loss pre-dates drainage of the lake. The present and future role of historic maps is discussed. Historic hydrological data has to be treated with caution: while it is possible to learn form the past, the assumption that future changes will be like past changes does not always hold. Nevertheless, old maps are relatively accessible data sets and the knowledge base for using them is rapidly growing, and it can be expected that long-term time series will be established by integrating georeferenced map systems over large areas. In the Appendix, a step-by-step guide to using historic maps in hydrology is given, starting from finding a map, through georeferencing and processing the map to publication of the results.

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Discharge estimation and monitoring extreme events by satellite altimetry

10.5194/egusphere-egu2020-8020 ◽

2020 ◽

Author(s):

Rossella Belloni ◽

Stefania Camici ◽

Angelica Tarpanelli

Keyword(s):

Time Series ◽

Water Level ◽

River Discharge ◽

Satellite Altimetry ◽

Global Scale ◽

Discharge Time ◽

Discharge Data ◽

The World ◽

Daily Discharge ◽

Rating Curves

In view of recent dramatic floods and drought events, the detection of trends in the frequency and magnitude of long time series of flood data is of scientific interest and practical importance. It is essential in many fields, from climate change impact assessment to water resources management, from flood forecasting to drought monitoring, for the planning of future water resources and flood protection systems.&#160; To detect long-term changes in river discharge a dense, in space and time, network of monitoring stations is required. However, ground hydro-meteorological monitoring networks are often missing or inadequate in many parts of the world and the global supply of the available river discharge data is often restricted, preventing to identify trends over large areas. &#160; The most direct method of deriving such information on a global scale involves satellite earth observation. Over the last two decades, the growing availability of satellite sensors, and the results so far obtained in the estimation of river discharge from the monitoring of the water level through satellite radar altimetry has fostered the interest on this subject. &#160; Therefore, in the attempt to overcome the lack of long continuous observed time series, in this study satellite altimetry water level data are used to set-up a consistent, continuous and up-to-date daily discharge dataset for different sites across the world. Satellite-derived water levels provided by publicly available datasets (Podaac, Dahiti, River& Lake, Hydroweb and Theia) are used along with available ground observed river discharges to estimate rating curves. Once validated, the rating curves are used to fill and extrapolate discharge data over the whole period of altimetry water level observations. The advantage of using water level observations provided by the various datasets allowed to obtain discharge time series with improved spatio-temporal coverages and resolutions, enabling to extend the study on a global scale and to efficiently perform the analysis even for small to medium-sized basins. &#160; Long continuous discharge time series so obtained are used to perform a global trend analysis on extreme flood and drought events. Specifically, annual maximum discharge and peak-over threshold values are extracted from the simulated daily discharge time series, as proxy variables of independent flood events. For flood and drought events, a trend analysis is carried out to identify changes in the frequency and magnitude of extreme events through the Mann-Kendall (M-K) test and a linear regression model between time and the flood magnitude. &#160; The analysis has permitted to identify areas of the world prone to floods and drought, so that appropriate actions for disaster risk mitigation and continuous improvement in disaster preparedness, response, and recovery practices can be adopted.&#160;

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