chirps: API Client for the CHIRPS Precipitation Data in R

Sousa, Kauê de; Sparks, Adam H.; Ashmall, William; Etten, Jacob van; Solberg, Svein Øivind

doi:10.21105/joss.02419

Cited by 27 publications

(20 citation statements)

References 10 publications

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“…For comparison, we extracted point‐level data for coordinates using bi‐linear interpolation in R with the raster (Hijmans, 2020), ncdf4 (Pierce, 2019), and chirps (de Sousa et al ., 2020) packages or using the ArcGIS Spatial Analyst multivalue to points tool. For precipitation datasets, we calculated annual and monthly normals for comparison with WorldClim (https://www.worldclim.org/data/worldclim21.html) and Chelsa (https://chelsa-climate.org/downloads/).…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

Newell

Ausprey

Robinson

2022

Intl Journal of Climatology

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Tropical montane cloud forest may be especially sensitive to climate change. However, our ability to understand effects of climate on montane biodiversity remains limited by the resolution of climate data. We compared 5 years of in situ weather data from cloud forests in northern Peru, regional weather stations, and gridded datasets to examine how climatologies reflect (a) forest microclimate buffering and (b) local rainfall in a sparse data region; we also examined spatiotemporal variability and regional trends. Across a 1,700–3,100 m gradient in which temperature did not covary with relative humidity (RH), in situ data showed interactions between climate and land‐use. Forest humidity buffered warming‐induced evaporative drying across elevations, and inside forest maximum vapour pressure deficit (VPDmax) did not change with elevation, whereas with a 22% reduction in RHmin at stations, VPDmax increased >10‐fold from high to low elevations. Cloud forest dried out on sunny days after 3 days without rain, especially during ENSO‐related drought concurrent with peak solar insolation. Climatologies were twice as precise for temperature as rainfall. Chelsa captured a 3.9°C reduction in maximum temperatures inside forest (MAE 1.6°C, R2 = 0.95) whereas WorldClim reflected drier lapse rates and higher Tmax outside forest. CHIRPS provided the best fit for monthly rainfall (MAE 23 mm, R2 = 48), capturing regional drought but underestimating rainfall >150 mm·month−1. Consistent with stations, CHIRPS showed strong support for regional increases in wet‐season rainfall. Reduced variability and more regular dry seasons were only detected by montane stations, especially south of 6°S, where rainfall seasonality shifted to earlier wet‐season peaks and reduced dry‐season rainfall as part of a transition from the Northern to Central Andes. Our results show that cloud forests may be partly buffered from warming but are likely to become extremely vulnerable under reduced humidity either through forest loss or drought.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

Newell

Ausprey

Robinson

2022

Intl Journal of Climatology

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“…All relative humidity variables were averaged over the evaluation period of each trial (i.e., either planting to shooting or planting to harvest). Precipitation and temperature variables were used as inputs with R package climatrends (de Sousa, van Etten et al., 2023) to compute climatic indices (Table 5).…”

Section: Methodsmentioning

confidence: 99%

“…We used the following R (R Core Team, 2022) packages for data management and preparation: readr , readxl, janitor , dplyr, gosset , and caret (de Sousa, Brown et al., 2023; Firke, 2021; Kuhn, 2022; Wickham & Bryan, 2022; Wickham, François et al., 2022, 2022). Climatic variables were obtained using the packages ag5Tools and climatrends (Brown et al., 2023; de Sousa, van Etten et al., 2023). The statistical modeling was performed with the packages PlackettLuce , stablelearner , gosset , and qvcalc (de Sousa, Brown et al., 2023; Firth, 2020; Philipp et al., 2016; Turner et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

Rank‐based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of Musa genotypes to black leaf streak disease

et al. 2023

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Synthesis of crop trial data can generate insights that are not available from the analysis of individual studies, but such synthesis is often constrained by the heterogeneity of data among studies. Rank‐based data synthesis provides the flexibility to combine data of heterogeneous types and from different sources. We demonstrate the application of rank‐based data synthesis of heterogeneous trial data to assess the effect of climatic factors on the reaction of several Musa genotypes to black leaf streak disease (BLSD; caused by Pseudocercospora fijiensis [sexual morph: Mycosphaerella fijiensis]). We aggregated data from the main public repositories of Musa trial data. We applied model‐based recursive partitioning with the Plackett‐Luce model, using climatic data as covariates. The model identified the maximum length of the dry spell as the main variable influencing differences in genotypic response to BLSD, dividing the aggregated trial dataset into humid and dry environments. We found differences in the reaction of genotypes to BLSD between these environments. In humid environments, NARITA 8 was found to be the most resistant genotype, while in dry environments FHIA‐01 was the best performing improved genotype. We also assessed reliability, which is the probability of outperforming the reference genotype (Calcutta 4). In humid environments NARITA 2, NARITA 8 and FHIA‐01 had the highest reliability, while in dry environments only the landrace Saba surpassed 50% reliability. The information generated by our data synthesis approach supports selecting Musa genotypes for further evaluations at new locations.This article is protected by copyright. All rights reserved

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“…CHIRPS provides gaugecorrected global rainfall data (period of record: 1981 -present) at 0.05° resolution; it incorporates satellite imagery with in-situ station data to create gridded rainfall time series. We use the CHIRPS pentad collection available on Google Earth Engine (GEE) to derive annual rainfall for the period of interest (de Sousa et al, 2020). This also allows us to compare with the self-reported shock survey data.…”

Section: Remote-sensing Weather and Conflict Event Datamentioning

confidence: 99%

Seeds for recovery: The long-term impacts of a complex agricultural intervention on welfare, behaviour and stability in Syria (SEEDS)

Baliki¹,

Weiffen²,

Daccache³

et al. 2023

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There is scarce evidence on whether and how assistance in humanitarian emergencies and conflict settings impacts household well-being and behaviour. Conducting rigorous impact evaluations in such settings poses multiple challenges in design and data collection. In SEEDS, we evaluate the impact of a complex large-scale multi-arm agricultural intervention on productivity, food security, and resilience in the context of an on-going humanitarian crisis in Syria. Specifically, we identify the causal impacts of agricultural asset transfers over various time horizons (the short-, medium-, and long-run), and across different conditions and subgroups (gender and conflict intensity) at the household-level. We evaluate the effectiveness of irrigation rehabilitation separately at the community-level. We use and combine various data sources, including a unique survey panel dataset collected over a period of four years from multiple governorates in Syria, satellite remote-sensing data, and publicly available violent conflict incidence and weather data. Our findings from using cutting-edge machine and deep learning approaches together with innovative balancing and analytical methods can be summarised as follows: For average treatment effects at the household-level, we find that the provision of agricultural asset support leads to significant improvements in food security in the short- and long-term, three years after the intervention. The positive and significant effect on food security is driven mainly by the increased consumption of healthy food items such as vegetables. In the long-run, livestock support reduces the use of harmful coping strategies households employ to deal with food shortages. Interestingly, we find that households who received vegetable kits are not just less likely to sell their productive assets in the long-term but also are less likely to marry off their young daughters or send their children to work. Overall, we find that both agricultural and livestock asset support is key to improving households’ resilience in the long-term. The irrigation rehabilitation interventions at the community-level positively affected agricultural productivity compared to the pre-intervention and pre-conflict periods. However, these effects were only significantly pronounced in the spring season. As for the heterogeneity analysis, we find that female-headed households benefit remarkably more in terms of food security in the medium-term compared to male-headed families. Moreover, households residing in areas that are moderately affected by violent conflict show stronger food security improvements compared to households from peaceful or conflict-intense settings. Overall, we draw three overarching lessons from our findings in SEEDS: First, agricultural support in protracted conflict settings effectively improves the long-term welfare and resilience of vulnerable households. In fact, the presence of an ongoing humanitarian operation acts as a social safety net if circumstances deteriorate suddenly. Second, not all interventions are equally effective, and not all households equally benefit, underscoring the need to design and implement inclusive context-specific interventions with detailed targeting. Third, methodologically, using multiple remote data sources and machine learning methods help overcome challenges in conducting rigorous impact evaluations in hard-to-reach humanitarian emergency settings.

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chirps: API Client for the CHIRPS Precipitation Data in R

Cited by 27 publications

References 10 publications

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

Rank‐based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of Musa genotypes to black leaf streak disease

Seeds for recovery: The long-term impacts of a complex agricultural intervention on welfare, behaviour and stability in Syria (SEEDS)

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