Spatial patterns of rainfall regimes related to levels of double cropping agriculture systems in Mato Grosso (Brazil)

Abstract

Assessing the impact/adaptation of human activities on/to climate change is a key issue, especially in the tropics that concentrate major anthropogenic dynamics such as deforestation and nearly two-thirds of the planetary rainfall. However, this task is often made tough because human activities such as agricultural dynamics are usually analysed at local or regional scale whereas climate related studies are led at large to global scales due to a lack of reliable data, especially in the tropics. In this article we argue that the increased spatial resolution of remote sensing-based rainfall estimates enables assessing the spatiotemporal variability of rainfall regimes at regional and local scales, thus allowing fine analysis of the interactions with human activities. We processed Tropical Rainfall Measuring Mission (TRMM) 3B42 daily rainfall estimates over the state of Mato Grosso (southern Brazilian Amazon) for the 1998-2012 study period in order to compute rainfall metrics such as annual rainfall and duration, onset and end dates of the rainy season based on the Anomalous Accumulation methodology (at a 0.25◦ spatial resolution). We then crossed these metrics with agricultural maps (produced at a 250m spatial resolution) and proved that the adoption of intensive agricultural practices such as double cropping systems is partly the result of a strategy to adapt practices to local climatic conditions. Finally, we discuss how such results raise important issues regarding the sustainability of the agricultural development model in the Southern Amazon

Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

“…The beginning of the rainy season is later in western Mato Grosso (Chapada dos Parecis) compared to other areas of the same state. The differential onset of the rainy season across the state of Mato Grosso described here confirms previous observations and partially explains why double-cropping systems have been less widely adopted in this agricultural region (western Mato Grosso) than in central Mato Grosso [47]. In general, the onset date is characterized by high interannual variability (Figure 8, top) especially in the northern part of the study area compared to Mato Grosso, which has more pronounced wet/dry seasons ( Figure 3).…”

“…While such an approach may be appropriate from a climatological point of view and is easy to implement, it may not be appropriate for other applications. For example, from an agricultural point of view, farmers expect specific rainfall conditions to cultivate crops, e.g., daily precipitation higher than a threshold to start planting soybean or a long rainy season with a minimum average daily precipitation to adopt double cropping systems [47,48]. From this point of view, the adaptiveR threshold complicates the comparisons between time series of different pixels or years, thus making it difficult to characterize the spatio-temporal variability of the rainy season.…”

“…The intense land use changes, mainly characterized by high deforestation rates, that have taken place in this pioneer active frontier region in the last four decades partly explain the observed trend toward increased seasonality [20] and a shortening of the rainy season [16,17]. Such trends may have irreversible ecological impacts [44] and affect agricultural activities, which depend on the quantity of rainfall and temporal patterns [15,[47][48][49]. …”

“…As a consequence, the minimum value of the AA time series may occur after the maximum value (or even at the end of the time series), thus leading to inconsistent onset dates that appear to occur after the demise date. To get around this problem, we computed the derivative of the AA time series, like in Arvor et al [47], and identified the middle of the rainy season as the day when this derivative is at a maximum. The onset (demise) date was finally computed as the day of the minimum (maximum) AA value before (after) this middle date.…”

Efficient Dissemination of Rainfall Forecasting to Safeguard Farmers from Crop Failure Using Optimized Neural Network Model

“…A precise quantitative precipitation forecast can recognize the potential for substantial precipitation and conceivably related glimmer flooding and in addition giving data to hydrologic interests [3]. Likewise, precipitation bears different human biology co-operations, for example, horticulture, warm solace, and dissipation [4]. The presentations of both stochastic and deterministic precipitation forecast models [5] are influenced as precipitation is being a standout amongst the most convoluted segments of the hydrological cycle to forecast, and tremendous suspicious [6].…”