Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series

In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balance...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Pavel P Fil, Alla Yu Yurova, Alexey Dobrokhotov, Daniil Kozlov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	closed depressions temporary water bodies remote sensing infiltration Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/92ba014057724a7fbb8dac3bee3b64bd
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:92ba014057724a7fbb8dac3bee3b64bd
record_format	dspace
spelling	oai:doaj.org-article:92ba014057724a7fbb8dac3bee3b64bd2021-11-11T19:19:32ZEstimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series10.3390/s212174031424-8220https://doaj.org/article/92ba014057724a7fbb8dac3bee3b64bd2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7403https://doaj.org/toc/1424-8220In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations.Pavel P FilAlla Yu YurovaAlexey DobrokhotovDaniil KozlovMDPI AGarticleclosed depressionstemporary water bodiesremote sensinginfiltrationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7403, p 7403 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	closed depressions temporary water bodies remote sensing infiltration Chemical technology TP1-1185
spellingShingle	closed depressions temporary water bodies remote sensing infiltration Chemical technology TP1-1185 Pavel P Fil Alla Yu Yurova Alexey Dobrokhotov Daniil Kozlov Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series
description	In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations.
format	article
author	Pavel P Fil Alla Yu Yurova Alexey Dobrokhotov Daniil Kozlov
author_facet	Pavel P Fil Alla Yu Yurova Alexey Dobrokhotov Daniil Kozlov
author_sort	Pavel P Fil
title	Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series
title_short	Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series
title_full	Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series
title_fullStr	Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series
title_full_unstemmed	Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series
title_sort	estimation of infiltration volumes and rates in seasonally water-filled topographic depressions based on remote-sensing time series
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/92ba014057724a7fbb8dac3bee3b64bd
work_keys_str_mv	AT pavelpfil estimationofinfiltrationvolumesandratesinseasonallywaterfilledtopographicdepressionsbasedonremotesensingtimeseries AT allayuyurova estimationofinfiltrationvolumesandratesinseasonallywaterfilledtopographicdepressionsbasedonremotesensingtimeseries AT alexeydobrokhotov estimationofinfiltrationvolumesandratesinseasonallywaterfilledtopographicdepressionsbasedonremotesensingtimeseries AT daniilkozlov estimationofinfiltrationvolumesandratesinseasonallywaterfilledtopographicdepressionsbasedonremotesensingtimeseries
_version_	1718431540097056768

Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series

Ejemplares similares