scholarly journals Analysis of water level variations in Brazilian basins using GRACE

2012 ◽  
Vol 2 (2) ◽  
pp. 76-87 ◽  
Author(s):  
A. Matos ◽  
D. Blitzkow ◽  
F. Almeida ◽  
S. Costa ◽  
I. Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Water Level ◽  
Height Anomaly ◽  
Coefficient Of Determination ◽  
North East ◽  
Water Level Variations ◽  
Gravity Recovery ◽  
Water Height ◽  
Pampas Region

Analysis of water level variations in Brazilian basins using GRACEA comparison between daily in-situ water level time series measured at ground-based hydrometric stations (HS - 1,899 stations located in twelve Brazilian basins) of the Agência Nacional de Águas (ANA) with vertically-integrated water height anomaly deduced from the Gravity Recovery and Climate Experiment (GRACE) geoid is carried out in Brazil. The equivalent water height (EWH) of 10-day intervals of GRACE models were computed by GRGS/CNES. It is a 6-year analysis (July-2002 to May-2008). The coefficient of determination is computed between the ANA water level and GRACE EWH. Values higher than 0.6 were detected in the following basins: Amazon, north of Paraguay, Tocantins-Araguaia, Western North-East Atlantic and north of the Parnaíba. In the Uruguay (Pampas region) and the west of São Francisco basins, the coefficient of determination is around 0.5 and 0.6. These results were adjusted with a linear transfer function and two second degree polynomials (flood and ebb period) between GRACE EWH and ANA water level. The behavior of these two polynomials is related to the phase difference of the two time series and yielded four different types of responses. This paper shows seven ANA stations that represent these responses and relates them with their hydro-geological domain.

scholarly journals Satellite altimetry over small reservoirs in the Brazilian semiarid region

RBRH ◽  
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.

scholarly journals Estimación de la evapotranspiración real en zonas de llanura mediante productos de humedad de suelo de la misión SMAP

2018 ◽  
pp. 17
Author(s):  
E. Walker ◽  
G. A. García ◽  
V. Venturini
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Water Cycle ◽  
Coefficient Of Determination ◽  
Southern Great Plains ◽  
Passive Sensors ◽  
New Equation ◽  
Humid Period ◽  
Pampas Region

<p>Evapotranspiration (ET) is an important process in the water cycle and in the land-surface energy balance. Over the last decades, remote sensing has provided valuable information to quantify ET. However, methodologies that use data from microwave passive sensors, such as “Soil Moisture Active Passive” (SMAP) mission, have been recently developed. In this work, a formulation to derive the relative evapotranspiration and ET from <em>in situ</em> and microwave data, is presented. The methodology is based on a modification of the original Komatsu (2003) equation by introducing a calibration parameter to represent the wind speed and vegetation effects and estimate the relative evapotranspiration. This new equation was used on the Bouchet’s complementary relationship with the Priestley-Taylor’s equation, to estimate ET at regional scales. The results were compared with observed data in the Southern Great Plains – USA (SGP) area, indicating that the new model estimated ET with a root mean square error (RMSE) of 0.88 mmd<sup>–1</sup> and a coefficient of determination (R<sup>2</sup> ) greater than 0.8. The calibrated model was applied in an extremely humid period in Argentinean Pampas region with results near to potential rates.</p>

scholarly journals Time-variations of equivalent water heights'from Grace Mission and in-situ river stages in the Amazon basin

2012 ◽  
Vol 42 (1) ◽  
pp. 125-134 ◽  
Author(s):  
Flavio Guilherme Vaz de Almeida ◽  
Stephane Calmant ◽  
Frédérique Seyler ◽  
Guillaume Ramillien ◽  
Denizar Blitzkow ◽  
...  
Keyword(s):  
Time Series ◽  
Amazon Basin ◽  
Temporal Variations ◽  
Regression Coefficients ◽  
Linear Relationships ◽  
Time Variations ◽  
Gravity Recovery ◽  
Grace Mission

Gravity Recovery and Climate Experiment (GRACE) mission is dedicated to measuring temporal variations of the Earth's gravity field. In this study, the Stokes coefficients made available by Groupe de Recherche en Géodésie Spatiale (GRGS) at a 10-day interval were converted into equivalent water height (EWH) for a ~4-year period in the Amazon basin (from July-2002 to May-2006). The seasonal amplitudes of EWH signal are the largest on the surface of Earth and reach ~ 1250mm at that basin's center. Error budget represents ~130 mm of EWH, including formal errors on Stokes coefficient, leakage errors (12 ~ 21 mm) and spectrum truncation (10 ~ 15 mm). Comparison between in situ river level time series measured at 233 ground-based hydrometric stations (HS) in the Amazon basin and vertically-integrated EWH derived from GRACE is carried out in this paper. Although EWH and HS measure different water bodies, in most of the cases a high correlation (up to ~80%) is detected between the HS series and EWH series at the same site. This correlation allows adjusting linear relationships between in situ and GRACE-based series for the major tributaries of the Amazon river. The regression coefficients decrease from up to down stream along the rivers reaching the theoretical value 1 at the Amazon's mouth in the Atlantic Ocean. The variation of the regression coefficients versus the distance from estuary is analysed for the largest rivers in the basin. In a second step, a classification of the proportionality between in situ and GRACE time-series is proposed.

scholarly journals Comparative Analysis of GEDI’s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies

2022 ◽  
Vol 14 (2) ◽  
pp. 340
Author(s):  
Ibrahim Fayad ◽  
Nicolas Baghdadi ◽  
Frédéric Frappart
Keyword(s):  
Water Level ◽  
Water Surface ◽  
Strong Dependence ◽  
Ecosystem Dynamics ◽  
Estimation Accuracy ◽  
In Situ Data ◽  
Gauge Station ◽  
Water Level Variations ◽  
Lower Variability

Spaceborne LiDAR altimetry has been demonstrated to be an essential source of data for the estimation and monitoring of inland water level variations. In this study, water level estimates from the Global Ecosystem Dynamics Investigation (GEDI) were validated against in situ gauge station records over Lake Geneva for the period between April 2019 and September 2020. The performances of the first and second releases (V1 and V2, respectively) of the GEDI data products were compared, and the effects on the accuracy of the instrumental and environmental factors were analyzed in order to discern the most accurate GEDI acquisitions. The respective influences of five parameters were analyzed in this study: (1) the signal-over-noise ratio (SNR); (2) the width of the water surface peak within the waveform (gwidth); (3) the amplitude of the water surface peak within the waveform (A); (4) the viewing angle of GEDI (VA); and (5) the acquiring beam. Results indicated that all these factors, except the acquiring beam, had an effect on the accuracy of GEDI elevations. Nonetheless, using VA as a filtering criterion was demonstrated to be the best compromise between retained shot count and water level estimation accuracy. Indeed, by choosing the shots with a VA ≤ 3.5°, 74.6% of the shots (after an initial filter) were retained with accuracies similar to choosing A > 400 (46.2% retained shots), SNR > 15 dB (63.3% retained shots), or gwidth < 10 bins (46.5% of retained shots). Finally, the comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant, but higher, bias and fewer deviations to the in situ data than V1. Indeed, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r = 0.66) and 42.8 cm with V1 (r = 0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA ≤ 3.5°). Regarding the bias, a higher value was observed with V2, but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number and strong dependence on GEDI dates.

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

2015 ◽  
Vol 19 (10) ◽  
pp. 4345-4364 ◽  
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.

Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

2020 ◽  
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Sensing Data

&lt;p&gt;Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.&lt;/p&gt;&lt;p&gt;In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM&amp;#8217;s &amp;#8220;Database of Hydrological Time series of Inland Waters&amp;#8221; (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.&lt;/p&gt;&lt;p&gt;Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.&lt;/p&gt;

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

2015 ◽  
Vol 12 (5) ◽  
pp. 4813-4855 ◽  
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.

scholarly journals Monitoring Water Level Change and Seasonal Vegetation Change in the Coastal Wetlands of Louisiana Using L-Band Time-Series

2020 ◽  
Vol 12 (15) ◽  
pp. 2351
Author(s):  
Tien-Hao Liao ◽  
Marc Simard ◽  
Michael Denbina ◽  
Michael P. Lamb
Keyword(s):  
Time Series ◽  
Water Level ◽  
Vegetation Cover ◽  
Coastal Wetlands ◽  
Large Scale ◽  
Landscape Scale ◽  
Water Level Change ◽  
Level Change ◽  
L Band

Coastal wetlands are productive ecosystems driven by highly dynamic hydrological processes such as tides and river discharge, which operate at daily to seasonal timescales, respectively. The scientific community has been calling for landscape-scale measurements of hydrological variables that could help understand the flow of water and transport of sediment across coastal wetlands. While in situ water level gauge data have enabled significant advances, they are limited in coverage and largely unavailable in many parts of the world. In preparation for the NISAR mission, we investigate the use of spaceborne Interferometric Synthetic Aperture Radar (InSAR) observations of phase and coherence at L-band for landscape-scale monitoring of water level change and vegetation cover in coastal wetlands across seasons. We use L-band SAR images acquired by ALOS/PALSAR from 2007 to 2011 to study the impact of seasonal changes in vegetation cover on InSAR sensitivity to water level change in the wetlands of the Atchafalaya basin located in coastal Louisiana, USA. Seasonal variations are observed in the interferometric coherence ( γ ) time-series over wetlands, with higher coherence during the winter and lower coherence during the summer. We show with InSAR time-series that coherence is inversely correlated with Normalized Difference Vegetation Index (NDVI). Our analysis of polarimetric scattering mechanisms demonstrates that double-bounce is the dominant mechanism in swamps while its weakness in marshes hinders estimation of water level changes. In swamps, water level change maps derived from InSAR are highly correlated (r2 = 0.83) with in situ data from the Coastwide Reference Monitoring System (CRMS). From October to December, we observed that the water level may be below wetland elevation and thus not inundating wetlands significantly. Our analysis shows that water level can only be retrieved when both images used for InSAR are acquired when wetlands are inundated. The L-band derived-maps of water level change show large scale gradients originating from the Gulf Intracoastal Waterway rather than the main delta trunk channel, confirming its significant role as a source of hydrologic connectivity across these coastal wetlands. These results indicate that NISAR, with its InSAR observations every 12 days, will provide the measurements necessary to reveal large scale hydrodynamic processes that occur in swamps across seasons.

scholarly journals Investigating the effect of hydroclimatological variables on Urmia Lake water level using wavelet coherence measure

2018 ◽  
Vol 10 (1) ◽  
pp. 13-29 ◽  
Author(s):  
Vahid Nourani ◽  
Mahsa Ghasemzade ◽  
Ali Danande Mehr ◽  
Elnaz Sharghi
Keyword(s):  
Time Series ◽  
Water Level ◽  
Lake Water ◽  
Geographical Location ◽  
Urmia Lake ◽  
Wavelet Coherence ◽  
Water Level Fluctuations ◽  
Lake Water Level ◽  
North West ◽  
North East

Abstract In this paper, wavelet transform coherence is implemented to examine the impacts of hydroclimatological variables on water level fluctuations in two large saline lakes in the Middle East with a similar geographical location, namely, Urmia Lake in north-west Iran, which has an extremely simple ecological pyramid where water level decrease produces a very sensitive ecosystem, and Van Lake in north-east Turkey. The present study investigates trends in higher order moments of hydrological time series. The aim of this paper is to investigate the complexity of Urmia Lake water level time series which could lead to decrease fluctuations of time series. To this end, the strength and relationships between five hydroclimatological variables, including rainfall, runoff, temperature, relative humidity, as well as evaporation and water level fluctuations in the lakes were determined and discussed in terms of high common power region, phase relationships, and local multi-scale correlations. The results showed that among the hydroclimatological variables, runoff has the most coherencies (0.9–1) with water level fluctuations in the lakes. Although both lakes are located in a similar climatic region, for the recent 15 years, adverse trend in water level fluctuations of Urmia Lake indicates a critical condition for this lake.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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