Dynamic Hydrological Modeling in Drylands with TRMM Based Rainfall

Tarnavsky, Elena; Mulligan, Mark; Ouessar, Mohamed; Faye, A.; Black, Emily

doi:10.3390/rs5126691

Cited by 19 publications

(12 citation statements)

References 28 publications

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“…However, the SM spatial variations do not show the same clear NW-SE gradient found for the rainfall. The main reason is that, despite the weak orography, the SM distribution in Ferlo is also constrained by the watershed hydrology, as shown by Tarnavsky et al [9]. At the scale of the VSZs, SM temporal variations are quite similar to those of rainfall ( Figure 5), but with smoother variations.…”

Section: Rainfall Parametersmentioning

confidence: 55%

“…However, the local rain and SM variations in the SE of TSvS-F2 are stronger than those expected from the mean rainfall gradient. The proximity of the Ferlo riverbed, with a confluence of three rivers, is possibly the cause: as shown by Tarnavsky et al [9], SM and runoff are locally maximal, and Soti et al [10] showed the sensitivity of ponds in this area to rain perturbations.…”

Section: Mean Patterns Of the Rainfall And Vegetation Phenologymentioning

confidence: 99%

“…Nevertheless, satellites provide the only available measurements with the coverage to allow us to analyze sub-regional variations in rainfall. As an example, Tarnavsky et al (2013) [9] demonstrated how satellite products can be used to constrain a hydrological model in the Ferlo watershed, and similarly, Soti et al (2010) [10] successfully assessed the spatial and temporal dynamics of pond water levels and water areas in the Ferlo, using remote sensing rainfall and land-cover products.…”

Section: Introductionmentioning

confidence: 99%

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Rainfall Intra-Seasonal Variability and Vegetation Growth in the Ferlo Basin (Senegal)

et al. 2016

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During the monsoon season, the spatiotemporal variability of rainfall impacts the growth of vegetation in the Sahel. This study evaluates this effect for the Ferlo basin in central northern Senegal. Relationships between rainfall, soil moisture (SM), and vegetation are assessed using remote sensing data (TRMM3B42 and RFE 2.0 for rainfall, ESA-CCI.SM for soil moisture and MODIS Leaf Area Index (LAI)). The principal objective was to analyze the response of vegetation growth to water availability during the rainy season using statistical criteria at the scale of homogeneous vegetation-soil zones. The study covers the period from June to September for the years 2000 to 2010. The surface SM is well correlated with both rainfall products. On ferruginous soils, better correlation of intra-seasonal variations and stronger sensitivity of the vegetation to rainfall are found compared to lithosols soils. LAI responds, on average, two to three weeks after a rainfall anomaly. Moreover, dry spells (negative anomalies) of seven days' length (three days for SM anomaly) significantly affect vegetation growth (maximum LAI within the season). A strong and significant link is also found between total precipitation and the number of dry spells. These datasets proved to be sufficiently reliable to assess the impacts of rainfall variability on vegetation dynamics.

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Section: Rainfall Parametersmentioning

confidence: 55%

Section: Mean Patterns Of the Rainfall And Vegetation Phenologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rainfall Intra-Seasonal Variability and Vegetation Growth in the Ferlo Basin (Senegal)

et al. 2016

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“…In recent decades, because of the population growth and migration and changes in land use practices, new burdens have been placed on the world's dryland ecosystems [ UNCCD , ; Nyamadzawo et al ., ; Hoover et al ., ]. As a result of surface and soil water deficits, land degradation is becoming a serious threat to drylands [ Nahal , ; Qi et al ., ; Tarnavsky et al ., ]. Dryland climate change is also increasing the challenges related to food security and water supply in these regions [ Kumar and Parikh , ; Asseng and Pannell , ; Zinyengere et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Dryland climate change: Recent progress and challenges

Huang

et al. 2017

Reviews of Geophysics

638

331

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Drylands are home to more than 38% of the world's population and are one of the most sensitive areas to climate change and human activities. This review describes recent progress in dryland climate change research. Recent findings indicate that the long‐term trend of the aridity index (AI) is mainly attributable to increased greenhouse gas emissions, while anthropogenic aerosols exert small effects but alter its attributions. Atmosphere‐land interactions determine the intensity of regional response. The largest warming during the last 100 years was observed over drylands and accounted for more than half of the continental warming. The global pattern and interdecadal variability of aridity changes are modulated by oceanic oscillations. The different phases of those oceanic oscillations induce significant changes in land‐sea and north‐south thermal contrasts, which affect the intensity of the westerlies and planetary waves and the blocking frequency, thereby altering global changes in temperature and precipitation. During 1948–2008, the drylands in the Americas became wetter due to enhanced westerlies, whereas the drylands in the Eastern Hemisphere became drier because of the weakened East Asian summer monsoon. Drylands as defined by the AI have expanded over the last 60 years and are projected to expand in the 21st century. The largest expansion of drylands has occurred in semiarid regions since the early 1960s. Dryland expansion will lead to reduced carbon sequestration and enhanced regional warming. The increasing aridity, enhanced warming, and rapidly growing population will exacerbate the risk of land degradation and desertification in the near future in developing countries.

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“…A series of satellite-derived global precipitation datasets have been developed, such as the Climate Precipitation Center morphing method [4], Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [5], the Global Satellite Mapping of Precipitation Project [6], and TRMM Multi-Satellite Precipitation Analysis (TMPA) [7] products. Among these datasets, TMPA products, which have a relatively high level of accuracy, have been used widely [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of IMERG and TRMM 3B43 Monthly Precipitation Products over Mainland China

2016

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Abstract:As the successor of the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission significantly improves the spatial resolution of precipitation estimates from 0.25˝to 0.1˝. The present study analyzed the error structures of Integrated Multisatellite Retrievals for GPM (IMERG) monthly precipitation products over Mainland China from March 2014 to February 2015 using gauge measurements at multiple spatiotemporal scales. Moreover, IMERG products were also compared with TRMM 3B43 products. The results show that: (1) overall, IMERG can capture the spatial patterns of precipitation over China well. It performs a little better than TRMM 3B43 at seasonal and monthly scales; (2) the performance of IMERG varies greatly spatially and temporally. IMERG performs better at low latitudes than at middle latitudes, and shows worse performance in winter than at other times; (3) compared with TRMM 3B43, IMERG significantly improves the estimation accuracy of precipitation over the Xinjiang region and the Qinghai-Tibetan Plateau, especially over the former where IMERG increases Pearson correlation coefficient by 0.18 and decreases root-mean-square error by 54.47 mm for annual precipitation estimates. However, most IMERG products over these areas are unreliable; and (4) IMERG shows poor performance in winter as TRMM 3B43 even if GPM improved its ability to sense frozen precipitation. Most of them over North China are unreliable during this period.

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Dynamic Hydrological Modeling in Drylands with TRMM Based Rainfall

Cited by 19 publications

References 28 publications

Rainfall Intra-Seasonal Variability and Vegetation Growth in the Ferlo Basin (Senegal)

Rainfall Intra-Seasonal Variability and Vegetation Growth in the Ferlo Basin (Senegal)

Dryland climate change: Recent progress and challenges

Evaluation of IMERG and TRMM 3B43 Monthly Precipitation Products over Mainland China

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