Climatologia da Chuva em Maceió: Aspectos Climáticos e Ambientais

The objectives of the study are: i) to evaluate the climatology of rain in Maceió based on observed data, with emphasis on climatic and environmental aspects and ii) to validate the precipitation product for the municipality. Data from 1979 to 2013 of the precipitation product CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) were validated by rainfall data from the National Water Agency (NWA) from 1960 to 2016. Statistical indicators showed a high coefficient of determination and linear correlation between CHELSA and observed data (R2 = 0.80; r = 0.89) and the smallest errors (SEE = 6.58 mm and RMSE = 18.76 mm), therefore the CHELSA product can be applied in the region. The time series presented a period 1 (P1) - (1960 to 1989) with rainfall above the historical average and a period 2 (P2) - (1990 to 2016) with a significant reduction in rainfall. Observed data versus climatological normals showed a significant decrease in normal 1 (1961-1990) in the rainy season, while in relation to normal 2 (1981-2010) there was an increase in the months of February, March and April (between 10 to 20%) and October and December (between 5 to 15%). The spatial distribution of monthly rainfall via the CHELSA product showed the formation of a pluviometric gradient between the coast and the upper part of Maceió. The topography influences the rainfall regime in neighborhoods belonging to the administrative regions (AR) - (R4, R5 and R6) with the highest rainfall records. The ENOS phases are directly responsible for the variability of interannual rain, while the decadal variability corresponded to the PDO phase change and changes in land use and occupation in Maceió.

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Land surface model photosynthesis and parameter calibration for boreal sites with adaptive population importance sampler

10.5194/gmd-2018-313 ◽

2019 ◽

Author(s):

Jarmo Mäkelä ◽

Jürgen Knauer ◽

Mika Aurela ◽

Andrew Black ◽

Martin Heimann ◽

...

Keyword(s):

Stomatal Conductance ◽

Land Surface ◽

Carbon Fixation ◽

Gross Primary Production ◽

Effect Of Temperature ◽

Coefficient Of Determination ◽

Surface Model ◽

Drought Event ◽

Forest Site ◽

Delayed Effect

Abstract. We calibrated the JSBACH model with six different stomatal conductance formulations using measurements from 10 FLUXNET coniferous evergreen sites in the Boreal zone. The parameter posterior distributions were generated by adaptive population importance sampler and the optimal values by a simple stochastic optimisation algorithm. The observations used to constrain the model are evapotranspiration (ET) and gross primary production (GPP). We identified the key parameters in the calibration process. These parameters control the soil moisture stress function and the overall rate of carbon fixation. We were able to improve the coefficient of determination and the model bias with all stomatal conductance formulations. There was no clear candidate for the best stomatal conductance model, although certain versions produced better estimates depending on the examined variable (ET, GPP) and the used metric. We were also able to significantly enhance the model behaviour during a drought event in a Finnish Scots pine forest site. The JSBACH model was also modified to use a delayed effect of temperature for photosynthetic activity. This modification enabled the model to correctly time and replicate the springtime increase in GPP (and ET) for conifers throughout the measurements sites used in this study.

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Climatologies at high resolution for the earth’s land surface areas

Scientific Data ◽

10.1038/sdata.2017.122 ◽

2017 ◽

Vol 4 (1) ◽

Cited By ~ 526

Author(s):

Dirk Nikolaus Karger ◽

Olaf Conrad ◽

Jürgen Böhner ◽

Tobias Kawohl ◽

Holger Kreft ◽

...

Keyword(s):

High Resolution ◽

Land Surface ◽

Surface Areas

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The new face of large dams in the 21st century

10.53014/eqfi5945 ◽

2021 ◽

Author(s):

Atal Ahmadzai

Keyword(s):

Surface Area ◽

Aquatic Ecosystems ◽

Energy Demand ◽

Environmental Aspects ◽

Water Insecurity ◽

Surface Areas ◽

Large Dams ◽

Physical Attributes ◽

Under Construction ◽

Ecological Disturbances

Alerted by increasing water insecurity and energy demand, countries, mainly in the Global South, are building dams of unprecedented magnitude. Hundreds of large dams (≥ 100 metres) have been constructed since 2000, with hundreds more under construction. Analyses of the physical attributes of these dams present a concerning image. While they create expansive reservoirs with large surface areas, they have inefficient surface area-to-volume ratios ('S2VR'). Their unprecedented size and the reservoirs’ expansive surface area, indicate severe environmental costs, mainly through ecological disturbances to the (riverine) aquatic ecosystems; and greenhouse gas emissions (GHG). Other ecological costs due to the larger S2VR include a high evaporation rate and compromised biodiversity of a wider area, both up- and downstream. The safety and environmental aspects of these large dams should be robustly scrutinised.

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A synthetic view of rainfall intensiﬁcation in the West African Sahel

Environmental Research Letters ◽

10.1088/1748-9326/ac4a9c ◽

2022 ◽

Author(s):

Guillaume Chagnaud ◽

Geremy Panthou ◽

Theo Vischel ◽

Thierry Lebel

Keyword(s):

Decadal Variability ◽

Regional Scale ◽

Daily Rainfall ◽

Extreme Value Distribution ◽

Extreme Rainfall ◽

West African ◽

Rainfall Regime ◽

The West ◽

West African Sahel ◽

African Sahel

Abstract The West African Sahel has been facing for more than 30 years an increase in extreme rainfalls with strong socio-economic impacts. This situation challenges decision-makers to define adaptation strategies in a rapidly changing climate. The present study proposes (i) a quantitative characterization of the trends in extreme rainfalls at the regional scale, (ii) the translation of the trends into metrics that can be used by hydrological risk managers, (iii) elements for understanding the link between the climatology of extreme and mean rainfall. Based on a regional non-stationary statistical model applied to in-situ daily rainfall data over the period 1983-2015, we show that the region-wide increasing trend in extreme rainfalls is highly significant. The change in extreme value distribution reflects an increase in both the mean and variability, producing a 5%/decade increase in extreme rainfall intensity whatever the return period. The statistical framework provides operational elements for revising the design methods of hydraulic structures which most often assume a stationary climate. Finally, the study shows that the increase in extreme rainfall is more attributable to an increase in the intensity of storms (80%) than to their occurrence (20%), reflecting a major disruption from the decadal variability of the rainfall regime documented in the region since 1950.

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BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-7-3-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 3-8 ◽

Cited By ~ 2

Author(s):

M. Matsuoka ◽

M. Takagi ◽

S. Akatsuka ◽

R. Honda ◽

A. Nonomura ◽

...

Keyword(s):

Surface Properties ◽

Land Surface ◽

Goodness Of Fit ◽

Near Infrared ◽

Temporal Frequency ◽

Atmospheric Correction ◽

Time Of Day ◽

Coefficient Of Determination ◽

Near Infrared Reflectance ◽

Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

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Ecohydrological Concepts of Water-Vegetation Interaction in the Drylands of Africa

10.1093/acrefore/9780190228620.013.554 ◽

2017 ◽

Author(s):

A. Johannes Dolman ◽

Luis U. Vilasa-Abad ◽

Thomas A. J. Janssen

Keyword(s):

Land Surface ◽

Hydraulic Redistribution ◽

Rainfall Regime ◽

Climate Feedbacks ◽

Stable States ◽

Human Disturbances ◽

Socioeconomic System ◽

System P ◽

Adaptive Mechanisms ◽

Dryland Ecosystems

Drylands cover around 40% of the land surface on Earth and are inhabited by more than 2 billion people, who are directly dependent on these lands. Drylands are characterized by a highly variable rainfall regime and inherent vegetation-climate feedbacks that can enhance the resilience of the system, but also can amplify disturbances. In that way, the system may get locked into two alternate stable states: one relatively wet and vegetated, and the other dry and barren. The resilience of dryland ecosystems derives from a number of adaptive mechanisms by which the vegetation copes with prolonged water stress, such as hydraulic redistribution. The stochastic nature of both the vegetation dynamics and the rainfall regime is a key characteristic of these systems and affects its management in relation to the feedbacks. How the ecohydrology of the African drylands will change in the future depends on further changes in climate, human disturbances, land use, and the socioeconomic system.

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Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels

Hydrology and Earth System Sciences ◽

10.5194/hess-23-949-2019 ◽

2019 ◽

Vol 23 (2) ◽

pp. 949-969

Author(s):

Fugen Li ◽

Xiaozhou Xin ◽

Zhiqing Peng ◽

Qinhuo Liu

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Heihe River ◽

Natural Ecosystems ◽

Evaporative Fraction ◽

Mixed Pixel ◽

Mixed Pixels

Abstract. Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300 m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47 MJ m−2(decreased approximately to 0.64 from 0.99 mm) and the mean bias error (MBE) decreased from 1.92 to 1.18 MJ m−2 (decreased from approximately 0.77 to 0.47 mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.

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Performance Evaluation of the Multiple Quantile Regression Model for Estimating Spatial Soil Moisture after Filtering Soil Moisture Outliers

Remote Sensing ◽

10.3390/rs12101678 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1678

Author(s):

Chunggil Jung ◽

Yonggwan Lee ◽

Jiwan Lee ◽

Seongjoon Kim

Keyword(s):

Soil Moisture ◽

Quantile Regression ◽

Outlier Detection ◽

Land Surface ◽

Removal Rate ◽

Distribution Functions ◽

Cumulative Distribution ◽

Soil Types ◽

Coefficient Of Determination ◽

Random Errors

The spatial distribution of soil moisture (SM) was estimated by a multiple quantile regression (MQR) model with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and filtered SM data from 2013 to 2015 in South Korea. For input data, observed precipitation and SM data were collected from the Korea Meteorological Administration and various institutions monitoring SM. To improve the work of a previous study, prior to the estimation of SM, outlier detection using the isolation forest (IF) algorithm was applied to the observed SM data. The original observed SM data resulted in IF_SM data following outlier detection. This study obtained an average data removal rate of 20.1% at 58 stations. For various reasons, such as instrumentation, environment, and random errors, the original observed SM data contained approximately 20% uncertain data. After outlier detection, this study performed a regression analysis by estimating land surface temperature quantiles. The soil characteristics were considered through reclassification into four soil types (clay, loam, silt, and sand), and the five-day antecedent precipitation was considered in order to estimate the regression coefficient of the MQR model. For all soil types, the coefficient of determination (R2) and root mean square error (RMSE) values ranged from 0.25 to 0.77 and 1.86% to 12.21%, respectively. The MQR results showed a much better performance than that of the multiple linear regression (MLR) results, which yielded R2 and RMSE values of 0.20 to 0.66 and 1.08% to 7.23%, respectively. As a further illustration of improvement, the box plots of the MQR SM were closer to those of the observed SM than those of the MLR SM. This result indicates that the cumulative distribution functions (CDF) of MQR SM matched the CDF of the observed SM. Thus, the MQR algorithm with outlier detection can overcome the limitations of the MLR algorithm by reducing both the bias and variance.

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Global observation-based climatology of precipitation occurrence and peak intensity

10.5194/egusphere-egu2020-7837 ◽

2020 ◽

Author(s):

Hylke Beck ◽

Seth Westra ◽

Eric Wood

Keyword(s):

Land Surface ◽

Regression Models ◽

Cross Validation ◽

Climate Models ◽

Daily Precipitation ◽

State Of The Art ◽

Coefficient Of Determination ◽

Peak Intensity ◽

Uncertainty Estimates ◽

Fold Cross Validation

We introduce a unique set of global observation-based climatologies of daily precipitation (P) occurrence (related to the lower tail of the P distribution) and peak intensity (related to the upper tail of the P distribution). The climatologies were produced using Random Forest (RF) regression models trained with an unprecedented collection of daily P observations from 93,138 stations worldwide. Five-fold cross-validation was used to evaluate the generalizability of the approach and to quantify uncertainty globally. The RF models were found to provide highly satisfactory performance, yielding cross-validation coefficient of determination (R2) values from 0.74 for the 15-year return-period daily P intensity to 0.86 for the >0.5 mm d-1 daily P occurrence. The performance of the RF models was consistently superior to that of state-of-the-art reanalysis (ERA5) and satellite (IMERG) products. The highest P intensities over land were found along the western equatorial coast of Africa, in India, and along coastal areas of Southeast Asia. Using a 0.5 mm d-1 threshold, P was estimated to occur 23.2 % of days on average over the global land surface (excluding Antarctica). The climatologies including uncertainty estimates will be released as the Precipitation DISTribution (PDIST) dataset via www.gloh2o.org/pdist. We expect the dataset to be useful for numerous purposes, such as the evaluation of climate models, the bias correction of gridded P datasets, and the design of hydraulic structures in poorly gauged regions.

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Simulating Human Water Regulation: The Development of an Optimal Complexity, Climate-Adaptive Reservoir Management Model for an LSM

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0056.1 ◽

2016 ◽

Vol 17 (3) ◽

pp. 725-744 ◽

Cited By ~ 9

Author(s):

Kurt C. Solander ◽

John T. Reager ◽

Brian F. Thomas ◽

Cédric H. David ◽

James S. Famiglietti

Keyword(s):

Land Surface ◽

Large Scale ◽

Storage Capacity ◽

Model Performance ◽

Reservoir Management ◽

Coefficient Of Determination ◽

Surface Model ◽

Essential Components ◽

Model Equations ◽

And Storage

Abstract The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM.

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