The potential for remote sensing and hydrologic modelling to assess the spatio-temporal dynamics of ponds in the Ferlo Region (Senegal)

Abstract. In the Ferlo Region in Senegal, livestock depend on temporary ponds for water but are exposed to the Rift Valley Fever (RVF), a disease transmitted to herds by mosquitoes which develop in these ponds. Mosquito abundance is related to the emptying and filling phases of the ponds, and in order to study the epidemiology of RVF, pond modelling is required. In the context of a data scarce region, a simple hydrologic model which makes use of remote sensing data was developed to simulate pond water dynamics from daily rainfall. Two sets of ponds were considered: those located in the main stream of the Ferlo Valley whose hydrological dynamics are essentially due to runoff, and the ponds located outside, which are smaller and whose filling mechanisms are mainly due to direct rainfall. Separate calibrations and validations were made for each set of ponds. Calibration was performed from daily field data (rainfall, water level) collected during the 2001 and 2002 rainy seasons and from three different sources of remote sensing data: 1) very high spatial resolution optical satellite images to access pond location and surface area at given dates, 2) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) data to estimate pond catchment area and 3) Tropical Rainfall Measuring Mission (TRMM) data for rainfall estimates. The model was applied to all ponds of the study area, the results were validated and a sensitivity analysis was performed. Water height simulations using gauge rainfall as input were compared to water level measurements from four ponds and Nash coefficients >0.7 were obtained. Comparison with simulations using TRMM rainfall data gave mixed results, with poor water height simulations for the year 2001 and good estimations for the year 2002. A pond map derived from a Quickbird satellite image was used to assess model accuracy for simulating pond water areas for all the ponds of the study area. The validation showed that modelled water areas were mostly underestimated but significantly correlated, particularly for the larger ponds. The results of the sensitivity analysis showed that parameters relative to pond shape and catchment area estimation have less effects on model simulation than parameters relative to soil properties (rainfall threshold causing runoff in dry soils and the coefficient expressing soil moisture decrease with time) or the water loss coefficient. Overall, our results demonstrate the possibility of using a simple hydrologic model with remote sensing data to track pond water heights and water areas in a homogeneous arid area.

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Sensitivity analysis on surface soil moisture to the time parameter of land surface variables: Application with remote sensing data

2011 19th International Conference on Geoinformatics ◽

10.1109/geoinformatics.2011.5980725 ◽

2011 ◽

Author(s):

Pei Leng ◽

Xiaoning Song ◽

Jianwei Ma ◽

Xinhui Li

Keyword(s):

Remote Sensing ◽

Sensitivity Analysis ◽

Soil Moisture ◽

Land Surface ◽

Surface Soil ◽

Remote Sensing Data ◽

Time Parameter ◽

Surface Soil Moisture ◽

Sensing Data

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Monitoring temporary ponds dynamics in arid areas with remote sensing and spatial modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-103-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 103-133

Author(s):

V. Soti ◽

C. Puech ◽

D. Lo Seen ◽

A. Bertran ◽

C. Vignolles ◽

...

Keyword(s):

Remote Sensing ◽

Thermal Emission ◽

Satellite Image ◽

Remote Sensing Data ◽

Pond Water ◽

Temporal Variations ◽

Hydrologic Model ◽

Meteorological Station ◽

Rainfall Data ◽

Temporary Ponds

Abstract. A hydrologic pond model was developed that simulates daily spatial and temporal variations (area, volume and height) of temporary ponds around Barkedji, a village located in the Ferlo Region in Senegal. The model was tested with rainfall input data from a meteorological station and from Tropical Rainfall Measuring Mission (TRMM) satellites. During calibration phase, we used climatic, hydrologic and topographic field data of Barkedji pond collected daily during the 2002 rainy season. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) and a QuickBird satellite image acquired in August 2005 (2.5 m pixel size) were used to apply the hydrologic model to all ponds (98 ponds) of the study area. With input rainfall data from the meteorological station, simulated water heights values for years 2001 and 2002 were significantly correlated with observed water heights for Furdu, Mous 2 and Mous 3 ponds, respectively with 0.81, 0.67 and 0.88 Nash coefficients. With rainfall data from TRMM satellite as model input, correlations were lower, particularly for year 2001. For year 2002, the results were acceptable with 0.61, 0.65 and 0.57 Nash coefficients for Barkedji, Furdu and Mous 3 ponds, respectively. To assess the accuracy of our model for simulating water areas, we used a pond map derived from Quickbird imagery (August 2007). The validation showed that modelled water areas were significantly correlated with observed pond surfaces (r2=0.90). Overall, our results demonstrate the possibility of using a simple hydrologic model with remote sensing data (Quickbird, ASTER DEM, TRMM) to assess pond water heights and water areas of a homogeneous arid area.

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Geoinformation Modeling of Flooded Areas in Settlements (in the Example of Lutsk)

10.20944/preprints201802.0042.v2 ◽

2018 ◽

Author(s):

Anna Shostak ◽

Volodymyr Voloshyn ◽

Oleksandr Melnyk ◽

Pavlo Manko

Keyword(s):

Remote Sensing ◽

Water Level ◽

Remote Sensing Data ◽

Water Levels ◽

Practical Significance ◽

Natural Phenomenon ◽

Short Term ◽

Sensing Data ◽

Geoinformation Modeling ◽

Flooded Areas

Object. Flooding in Ukraine is a common natural phenomenon that repeats periodically and in some cases it becomes disastrous. In an average year floods on the rivers of Volyn region take place from one to three times which extend beyond the limits of the floodplain. The floodplain of Styr river is located in the historical center of Lutsk city, that`s why issues of research and forecasting of floods are very important for a given city. Methodology. Using modern technologies of geodesy and remote sensing allows to quickly determine and predict the floodplain area of settlements. Based on the statistical data of the Volyn Regional Center for Hydrometeorology during the 7 year period 2011-2017 about water levels of the river Styr. We conducted mathematical modeling of fluctuations of water levels within the territory of Lutsk, based on creating a partial Fourier series for discrete values of middle-ten-day water levels values. The post hydrological measurements of Styr river water levels in the territory of Lutsk located on the Shevchenko Street comply with an altitude 172.87 meters. Based on the data of short-term flood forecasting in February and March, and relief data from the Department of Architecture and Urban Development of Volyn State Administration, we conducted visualization of the results using geographic information system QGIS. Results. The results of mathematical processing were the basis for geoinformation simulation of flooded areas using remote sensing data that are publicly available. Use of statistical and geospatial data in this article has great potential for further application in modeling the processes of natural and technogenic origin. Scientific novelty. The mathematical model of short-term forecasting of water levels during the flood period on the river Styr with implementation of geoinformation modeling of flooded areas using remote sensing data is proposed. Practical significance. The research results of water level changes on the Styr River and flood zones within the limits of Lutsk is proposed. The spring flood in February-March 2018, with the maximum water level 5.33 m, corresponds to an absolute mark of 178.20 m, which is forecasted in this article.

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Uncertainty reduction and parameters estimation of a~distributed hydrological model with ground and remote sensing data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-6215-2014 ◽

2014 ◽

Vol 11 (6) ◽

pp. 6215-6271

Author(s):

F. Silvestro ◽

S. Gabellani ◽

R. Rudari ◽

F. Delogu ◽

P. Laiolo ◽

...

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Hydrological Model ◽

Hydrological Cycle ◽

Remote Sensing Data ◽

Hydrologic Model ◽

Parameters Estimation ◽

State Variables ◽

Test Bed ◽

Sensing Data

Abstract. During the last decade the opportunity and usefulness of using remote sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite based products often provide the advantage of observing hydrologic variables in a distributed way while offering a different view that can help to understand and model the hydrological cycle. Moreover, remote sensing data are fundamental in scarce data environments. The use of satellite derived DTM, which are globally available (e.g. from SRTM as used in this work), have become standard practice in hydrologic model implementation, but other types of satellite derived data are still underutilized. In this work, Meteosat Second Generation Land Surface Temperature (LST) estimates and Surface Soil Moisture (SSM) available from EUMETSAT H-SAF are used to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. This work aims at proving that satellite observations dramatically reduce uncertainties in parameters calibration by reducing their equifinality. Two parameter estimation strategies are implemented and tested: a multi-objective approach that includes ground observations and one solely based on remotely sensed data. Two Italian catchments are used as the test bed to verify the model capability in reproducing long-term (multi-year) simulations.

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Local sensitivity analysis of Satellite Monitoring of Irrigation software (SAMIR) over Semi-Arid climate of Morocco

10.5194/egusphere-egu21-5383 ◽

2021 ◽

Author(s):

Aicha Moumni ◽

Alhousseine Diarra ◽

Abderrahman Lahrouni

Keyword(s):

Remote Sensing ◽

Sensitivity Analysis ◽

Water Resources ◽

Land Cover ◽

Remote Sensing Data ◽

Soil Parameters ◽

Sensing Data ◽

Annual Crops ◽

Irrigated Area ◽

Semi Arid

<p>Nowadays, the assessment of agricultural management is based mainly on the good management of water resources (i.e., to estimate the crops water consumption and provide their irrigation requirements). In this context, several agro-environmental models, (i.e., STICS, AQUACROP, TSEB, &#8230;) have been developed to assess the agricultural needs such as grain yield and/or irrigation demand prediction. These models are mainly based on the remote sensing data which contribute highly to the knowledge of some key-variables of crop models, in particular their time and space variations. The study area is the Haouz plain located in central Morocco. The climate of the plain is semi-arid continental type characterized by strong spatiotemporal irregular rains (mean annual precipitation up to 250 mm).The region relies mainly on the agricultural activities. Therefore, about 85% of available water is used for irrigated crops within the plain. The irrigated area is covered by 25% tree plantations and 75% annual crops. However, the annual crops extent depends strongly on the water availability during the season. Hence, for sustainable monitoring and optimal use of water resources (using physical modeling, satellite images and ground data), SAMIR software is developed in order to spatialize the irrigation water budget over Haouz plain. SAMIR (Simonneaux et al., 2009; Saadi et al., 2015; Tazekrit et al., 2018) is a tool for irrigation management based mainly on the use of remote sensing data. It estimates the crop evapotranspiration (ET) based on the FAO-56 model. This model requires three types of data: climatic variables for calculation of reference Evapotranspiration (ET0), land cover for computing crop coefficient Kc, and periodical phonological information for adjusting the Kc. SAMIR offers the possibility to calculate the ET of a large agricultural areas, with different land use/ land cover types, and subsequently deduce the necessary water irrigation for these areas. This model has been calibrated and validated over R3 perimeter (Diarra et al., 2017). In the present work, we studied the sensitivity (local sensibility analysis) of SAMIR software to the variations of each input parameter (i.e., ET0, precipitations, soil parameters, and irrigation configuration &#8220;real or automatic&#8221;). The simulations were made using the ground truth observations and irrigation dataset of the agricultural season of 2011/2012 over an irrigated area of Haouz plain. For the climatic variables, the obtained results showed that the effect of the ET0 is more significant compared to the effect of precipitations. It led to large shifts of the actual ET simulated by SAMIR compared to all tested parameters. For soil parameters, the sensitivity analysis illustrates that the effect is almost linear for all parameters. But the proportion of total available water, P, is the high sensitive parameter (Lenhart, et al., 2002). Finally, the comparison between the simulation of real evapotranspiration using automatic irrigation or real irrigation configuration offers an interesting result. The obtained ET values are similar for both configurations. Thus, this result offers the possibility of using only automatic irrigation configuration, in case of non-availability of the real irrigation.</p>

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