It has been suggested that rainfall in the Amazon decreases if forest loss exceeds some threshold, but the specific value of this threshold remains uncertain. Here, we investigate the relationship between historical deforestation and rainfall at different geographical scales across the Southern Brazilian Amazon (SBA). We also assess impacts of deforestation policy scenarios on the region’s agriculture. Forest loss of up to 55–60% within 28 km grid cells enhances rainfall, but further deforestation reduces rainfall precipitously. This threshold is lower at larger scales (45–50% at 56 km and 25–30% at 112 km grid cells), while rainfall decreases linearly within 224 km grid cells. Widespread deforestation results in a hydrological and economic negative-sum game, because lower rainfall and agricultural productivity at larger scales outdo local gains. Under a weak governance scenario, SBA may lose 56% of its forests by 2050. Reducing deforestation prevents agricultural losses in SBA up to US$ 1 billion annually. Deforestation in the Amazon region has suggested to influence precipitation in a non-linear way. Here, the authors show that forest loss is associated with decreasing precipitation after a scale-dependent threshold is crossed, which can cause stress on agriculture if deforestation is expanded.

/pdf/deforestation-reduces-rainfall-and-agricultural-revenues-in-3pxb2wsvj8.pdf

Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon.

Spatial prediction of precipitation with high resolution is a challenging task in regions with strong climate variability and scarce monitoring. For this purpose, the quasi-continuous supply of information from satellite imagery is commonly used to complement in situ data. However, satellite images of precipitation are available at coarse resolutions, and require adequate methods for spatial downscaling and calibration. The objective of this paper is to introduce and evaluate a 2-step spatial downscaling approach for monthly precipitation applied to TRMM 3B43 (from 0 . 25 ∘ ≈ 27 km to 5 km resolution), resulting in 5 downscaled products for the period 01-2001/12-2011. The methodology was evaluated in 3 contrasting climatic regions of Ecuador. In step 1, bilinear resampling was applied over TRMM, and used as a reference product. The second step introduces further variability, and consists of four alternative gauge-satellite merging methods: (1) regression with in situ stations, (2) regression kriging with in situ stations, (3) regression with in situ stations and auxiliary variables, and (4) regression kriging with in situ stations and auxiliary variables. The first 2 methods only use the resampled TRMM data set as an independent variable. The last 2 methods enrich these models with auxiliary environmental factors, incorporating atmospheric and land variables. The results showed that no product outperforms the others in every region. In general, the methods with residual kriging correction outperformed the regression models. Regression kriging with situ data provided the best representation in the Coast, while regression kriging with in situ and auxiliary data generated the best results in the Andes. In the Amazon, no product outperformed the resampled TRMM images, probably due to the low density of in situ stations. These results are relevant to enhance satellite precipitation, depending on the availability of in situ data, auxiliary satellite variables and the particularities of the climatic regions.

/pdf/two-step-downscaling-of-trmm-3b43-v7-precipitation-in-3kges67ugr.pdf

Two-Step Downscaling of Trmm 3b43 V7 Precipitation in Contrasting Climatic Regions With Sparse Monitoring: The Case of Ecuador in Tropical South America

In this study, we validate precipitation estimates remotely sensed by the Tropical Rainfall Measuring Mission (TRMM) at monthly and seasonal timescales, during the period 1998–2015, by calculating and analyzing diverse error metrics between the 3B43 V7 product and in situ measurements from 1,180 rain gauges over Colombia, of which at least 987 are fully independent of TRMM. We explore the existence of spatiotemporal patterns to assess the performance of 3B43 V7 over the five major natural regions of Colombia: Caribbean, Pacific, Andes, Orinoco and Amazon. The results show that 3B43 V7 product is able to capture the phase of the annual cycle of monthly mean precipitation, but the performance is not good for the amplitude, in particular over the Andes and Pacific regions owing to complex climatic and topographic conditions. In general, 3B43 V7 exhibits good performance in the low‐lying and plain Amazon, Orinoco and Caribbean regions. Over the Andes region, characterized by complex topography, overestimation errors are identified [root mean squared error (RMSE) ≥83.59 mm·month−1 and relative bias (BIAS) ≥4.69%], whereas the extremely wet rainfall regime of the Pacific region is largely underestimated (RMSE ≥253.52 mm ·month−1 and BIAS ≤−11.75%). These errors are greater during the wet seasons when the metrics reach worse scores than those reported in similar studies worldwide. Occurrence analyses showed that 3B43 V7 misses very frequent light rainfall events and less frequent but very heavy storms, which contribute to the overall underestimation (overestimation) observed over the Pacific (Andes) region. The error characteristics identified and quantified in this study confirm the well‐documented limitations of remote precipitation sensing and constitute a warning about major challenges that complex climatic and physiographic features can impose on satellite rainfall missions.

Ground Validation of TRMM 3B43 V7 Precipitation Estimates over Colombia. Part I: Monthly and Seasonal Timescales

In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.

Evaluation of ERA5 Precipitation Accuracy Based on Various Time Scales over Iran during 2000–2018

No upward shift of alpine grassland distribution on the Qinghai-Tibetan Plateau despite rapid climate warming from 2000 to 2014.

. Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

/pdf/validation-of-satellite-precipitation-trmm-3b43-in-1wllbrh4hm.pdf

Validation of satellite precipitation (trmm 3b43) in ecuadorian coastal plains, andean highlands and amazonian rainforest

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Validation of satellite precipitation (trmm 3b43) in ecuadorian coastal plains, andean highlands and amazonian rainforest