scholarly journals Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 333
Author(s):  
Altemar L. Pedreira Junior ◽  
Marcelo S. Biudes ◽  
Nadja G. Machado ◽  
George L. Vourlitis ◽  
Hatim M. E. Geli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Agricultural Production ◽  
Daily Precipitation ◽  
Annual Precipitation ◽  
Monthly Precipitation ◽  
Mato Grosso ◽  
Significant Difference ◽  
Precipitation Estimates ◽  
Global Precipitation

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

scholarly journals Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

2021 ◽  
Vol 13 (2) ◽  
pp. 254 ◽  
Author(s):  
Jie Hsu ◽  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Xiuzhen Li
Keyword(s):  
Annual Cycle ◽  
Interannual Variation ◽  
Winter Precipitation ◽  
Monthly Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Variation ◽  
Global Precipitation ◽  
Multiple Timescale ◽  
Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

A Gauge-Based Analysis of Daily Precipitation over East Asia

2007 ◽  
Vol 8 (3) ◽  
pp. 607-626 ◽  
Author(s):  
Pingping Xie ◽  
Mingyue Chen ◽  
Song Yang ◽  
Akiyo Yatagai ◽  
Tadahiro Hayasaka ◽  
...  
Keyword(s):  
East Asia ◽  
Daily Precipitation ◽  
Interpolation Method ◽  
Optimal Interpolation ◽  
Network Density ◽  
Monthly Precipitation ◽  
Orographic Effects ◽  
Precipitation Estimates ◽  
The Mean ◽  
Validation Tests

Abstract A new gauge-based analysis of daily precipitation has been constructed on a 0.5° latitude–longitude grid over East Asia (5°–60°N, 65°–155°E) for a 26-yr period from 1978 to 2003 using gauge observations at over 2200 stations collected from several individual sources. First, analyzed fields of daily climatology are computed by interpolating station climatology defined as the summation of the first six harmonics of the 365-calendar-day time series of the mean daily values averaged over a 20-yr period from 1978 to 1997. These fields of daily climatology are then adjusted by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) monthly precipitation climatology to correct the bias caused by orographic effects. Gridded fields of the ratio of daily precipitation to the daily climatology are created by interpolating the corresponding station values using the optimal interpolation method. Analyses of total daily precipitation are finally calculated by multiplying the daily climatology by the daily ratio. Cross-validation tests indicated that this gauge-based analysis has high quantitative quality with a negligible bias and a correlation coefficient of ∼0.6 for comparisons between withdrawn station data and the analysis at a 0.05° latitude–longitude grid box. The quality of the analysis increases with the gauge network density. The mean distribution and annual cycle of this new gauge analysis present similar patterns but with more detailed structures and slightly larger magnitude compared to other published monthly gauge analyses over the region. The East Asia gauge analysis is applied to verify the performance of five satellite-based precipitation estimates. This examination reveals the regionally and seasonally dependent performance of the satellite products with the best statistics observed for relatively wet regions. Further improvements of the daily gauge analysis are underway to increase the gauge network density and to refine the algorithm to better deal with the orographic effects especially over South and Southeast Asia.

scholarly journals An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

2021 ◽  
Vol 13 (2) ◽  
pp. 234
Author(s):  
Na Zhao
Keyword(s):  
High Resolution ◽  
Model Performance ◽  
Absolute Error ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Residual Correction ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

scholarly journals Water resources and flooding risk in Kumamoto based on observed hydrologic data analysis

2020 ◽  
Author(s):  
Makoto Higashino ◽  
Heinz G. Stefan
Keyword(s):  
Water Resources ◽  
Rainy Season ◽  
Daily Precipitation ◽  
Southern Oscillation ◽  
Monsoon Season ◽  
Annual Precipitation ◽  
Daily Maximum ◽  
Annual Maximum ◽  
Hydrologic Data ◽  
Flooding Risk

Abstract Variability and change of precipitation were investigated in Kumamoto on Kyushu Island in southwestern Japan, to assess water resources and flooding risk. Annual precipitation, annual maximum daily precipitation, and annual maximum hourly precipitation have increased over the period from 1891 to 2018 (128 years). Trends are 26.2 mm per decade, 6.07 mm/day per decade, and 2.17 mm/h/decade, respectively. Precipitation in the rainy season (June and July) is on average 37% (ranging from 12 to 59%) of annual precipitation for the 128-year period. Maximum daily precipitation in a year occurred at Kumamoto in the rainy season in 92/128 (72%) of the years of observation from 1891 to 2018, in the typhoon (August to November) season in 23/128 (18%), and in the March to May season in 12/128 (10%). This indicates that the rainy monsoon season poses the largest daily flooding risk. A wavelet analysis revealed that from 1891 to 2018 annual precipitation and daily maximum precipitation fluctuate with 2 and 4 years periods, which may be related to the El Nino-Southern Oscillation (ENSO). It is likely that air temperature rises, ENSO and topographical characteristics contributed to an increase in precipitation in the period. The analysis also showed that typhoons hitting or approaching Kumamoto have significantly affected annual precipitation and annual maximum daily precipitation, while the interval between typhoons affecting Kumamoto has been getting longer since the 1970s.

scholarly journals AVALIAÇÃO DA TEMPERATURA DA SUPERFÍCIE NO PANTANAL MATO-GROSSENSE POR SENSORIAMENTO REMOTO

2019 ◽  
Vol 12 (5) ◽  
pp. 1784
Author(s):  
Otávio Rodrigues Mendes ◽  
Victor Hugo De Morais Danelichen ◽  
Osvaldo Alves Pereira
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Landsat 8 ◽  
Mato Grosso ◽  
Partial Analysis ◽  
Significant Difference ◽  
Ideal Tool ◽  
Indispensable Tool ◽  
Natural Monument ◽  
Thermal Band

Devido sua grande importância, o Pantanal é objeto de estudos de trabalhos que visam conhecer e proteger sua biodiversidade. Como instrumento indispensável, o Sensoriamento Remoto surge como uma ferramenta ideal para o mapeamento, identificação de queimadas, desmatamento e estudos climáticos. Dentre as variáveis estimadas pelos sensores orbitais, a temperatura da superfície é a menos explorada. Estudos que são conduzidos para validação da temperatura da superfície por meio de sensores orbitais e de estações meteorológicas no bioma Pantanal são escassos. Diante disso, este trabalho visou analisar a dinâmica da temperatura da superfície em uma unidade de conservação no norte do Pantanal a partir de imagens adquiridas do satélite Landsat 8. O estudo foi realizado em uma torre micrometeorológica, localizada entre os municípios de Cuiabá e Santo Antônio de Leverger - MT, situada na encosta de uma unidade de conservação, denominada Monumento Natural Morro de Santo Antônio. Para validação da temperatura da superfície, foram adquiridas imagens do satélite Landsat 8 fornecidos pela USGS. Resultados encontrados nesse trabalho demonstraram o uso das bandas termais do Landsat 8 no norte do Pantanal mato-grossense na discriminação de diferentes alvos na superfície. A análise parcial de cada banda do sensor TIRS demonstrou valores mais elevados na banda 10. A análise estatística dos dados indica que as maiores correlações da temperatura medida na torre meteorológica foram constatadas com a média das bandas termais do satélite Landsat 8. A análise de variância demonstrou que houve diferença significativa entre as médias das temperaturas medida e estimada.Palavras-chaves: banda termal, sensor orbital, Pantanal.  Surface temperature evaluation in the Pantanal Mato Grosso by Remote Sensing A B S T R A C TDue to its great importance, the Pantanal is the object of studies that aim to know and protect its biodiversity. As an indispensable tool, Remote Sensing emerges as an ideal tool for the mapping, identification of fires, deforestation and climatic studies. Among the variables estimated by the orbital sensors, the surface temperature is the least explored. Studies that are conducted for surface temperature validation by orbital sensors and meteorological stations in the Pantanal biome are scarce. The study was carried out in a micrometeorological tower, located between the municipalities of Cuiabá and Santo Antônio, located on the slope of a conservation unit, called Natural Monument Morro de Santo Antônio. To validate the surface temperature, images of the Landsat 8 satellite provided by the USGS were acquired. Results obtained in this work demonstrated the use of the Landsat 8 thermal bands in the northern Pantanal of Mato Grosso in the discrimination of different surface targets. The partial analysis of each TIRS sensor band showed higher values in the 10 band. Statistical analysis of the data indicates that the highest correlations of the temperature measured in the meteorological tower were verified with the average of the thermal bands of the satellite Landsat 8. The analysis of variance showed that there was a significant difference between the measured and estimated temperatures.Keywords: thermal band, orbital sensor, Pantanal.

scholarly journals A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates

2021 ◽  
Vol 13 (14) ◽  
pp. 2693
Author(s):  
Na Zhao ◽  
Yimeng Jiao
Keyword(s):  
Spatial Resolution ◽  
High Accuracy ◽  
Surface Modeling ◽  
Scale Transformation ◽  
Monthly Precipitation ◽  
High Quality ◽  
Explanatory Variables ◽  
Downscaling Method ◽  
Precipitation Estimates ◽  
Global Precipitation

Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01° by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R2 values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R2, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales.

Fluctuations of Annual Precipitation and Water Resources Quality in Ukraine

2016 ◽  
Vol 10 (4s) ◽  
pp. 621-629
Author(s):  
Valentina Pidlisnyuk ◽  
◽  
John Harrington JR ◽  
Yulia Melnyk ◽  
Yuliya Vystavna ◽  
...  
Keyword(s):  
Water Resources ◽  
Surface Waters ◽  
Precipitation Amount ◽  
Ion Concentration ◽  
Annual Precipitation ◽  
Resource Quality ◽  
Phosphate Ion ◽  
Regional Trends

The article focuses on examining the influence of fluctuations in annual precipitation amount on the quality of surface waters. Water quality was estimated with data on BOD, COD and phosphate–ion concentration within five selected regions of Ukraine. Analysis of the precipitation data (1991 – 2010) showed different regional trends. Using the statistics, determination of the interconnection between precipitation amount and water resources quality were done. The obtained regularities and associated uncertainties can be used for prediction of changes in water resource quality and as a guide for future adaptation to possible climate change.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2014
Author(s):  
Celina Aznarez ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Juan Pablo Pacheco ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ecosystem Services ◽  
Water Resources ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Erosion Control ◽  
Sensing Data ◽  
Extreme Precipitation Events ◽  
The Impact

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

scholarly journals Ozone Response of Leaf Physiological and Stomatal Characteristics in Brassica juncea L. at Supraoptimal Temperatures

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 357
Author(s):  
Jong Kyu Lee ◽  
Myeong Ja Kwak ◽  
Sang Hee Park ◽  
Han Dong Kim ◽  
Yea Ji Lim ◽  
...  
Keyword(s):  
Brassica Juncea ◽  
Agricultural Production ◽  
Stomatal Density ◽  
Anthropogenic Activities ◽  
Interactive Effects ◽  
Target Concentration ◽  
Stomatal Characteristics ◽  
Elevated O3 ◽  
Significant Difference ◽  
Supraoptimal Temperatures

Plants are affected by the features of their surrounding environment, such as climate change and air pollution caused by anthropogenic activities. In particular, agricultural production is highly sensitive to environmental characteristics. Since no environmental factor is independent, the interactive effects of these factors on plants are essential for agricultural production. In this context, the interactive effects of ozone (O3) and supraoptimal temperatures remain unclear. Here, we investigated the physiological and stomatal characteristics of leaf mustard (Brassica juncea L.) in the presence of charcoal-filtered (target concentration, 10 ppb) and elevated (target concentration, 120 ppb) O3 concentrations and/or optimal (22/20 °C day/night) and supraoptimal temperatures (27/25 °C). Regarding physiological characteristics, the maximum rate of electron transport and triose phosphate use significantly decreased in the presence of elevated O3 at a supraoptimal temperature (OT conditions) compared with those in the presence of elevated O3 at an optimal temperature (O conditions). Total chlorophyll content was also significantly affected by supraoptimal temperature and elevated O3. The chlorophyll a/b ratio significantly reduced under OT conditions compared to C condition at 7 days after the beginning of exposure (DAE). Regarding stomatal characteristics, there was no significant difference in stomatal pore area between O and OT conditions, but stomatal density under OT conditions was significantly increased compared with that under O conditions. At 14 DAE, the levels of superoxide (O2-), which is a reactive oxygen species, were significantly increased under OT conditions compared with those under O conditions. Furthermore, leaf weight was significantly reduced under OT conditions compared with that under O conditions. Collectively, these results indicate that temperature is a key driver of the O3 response of B. juncea via changes in leaf physiological and stomatal characteristics.

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