Franklin Paredes-Trejo

Droughts impact the water cycle, ecological balance, and socio-economic development in various regions around the world. The Orinoco River Basin is a region highly susceptible to droughts. The basin supports diverse ecosystems and supplies valuable resources to local communities. We assess the spatiotemporal patterns and impacts of droughts in the basin using remote sensing data and surface observations. We use monthly precipitation (P), air temperature near the surface (T2M), enhanced vegetation index (EVI) derived from Earth observations, and average daily flow (Q) data to quantify drought characteristics and impacts. We also investigated the association between drought and global warming by correlating the drought intensity and the percentage of dry area with sea surface temperature (SST) anomalies in the Pacific (Niño 3.4 index), Atlantic (North Atlantic Index [NATL]), and South Atlantic Index [SATL]) oceans. We evaluate the modulating effect of droughts on the hydrological regime of the most relevant tributaries by calculating the trend and significance of the regional standardized precipitation index (SPI) and percentage area affected by dry conditions. El Niño events worsen the region’s drought conditions (SPI vs. Niño 3.4 index, r = −0.221), while Atlantic SST variability has less influence on the basin’s precipitation regime (SPI vs. NATL and SATL, r = 0.117 and −0.045, respectively). We also found that long-term surface warming trends aggravate drought conditions (SPI vs. T2M anomalies, r = −0.473), but vegetation greenness increases despite high surface temperatures (SPI vs. EVI anomalies, r = 0.284). We emphasize the irregular spatial-temporal patterns of droughts in the region and their profound effects on the ecological flow of rivers during prolonged hydrological droughts. This approach provides crucial insights into potential implications for water availability, agricultural productivity, and overall ecosystem health. Our study underlines the urgent need for adaptive management strategies to mitigate the adverse effects of droughts on ecosystems and human populations. The insights derived from our study have practical implications for developing strategies to address the impacts of droughts and ensure the protection of this ecologically significant region.

The Brazilian semiarid region (BSR) has faced severe drought over the last three decades, which has led to a significant decline in land productivity, posing a considerable threat to food security and the local economy and communities. The United Nations Convention to Combat Desertification (UNCCD) has proposed the use of Earth observation-derived vegetation indices for monitoring land degradation across regions. In this study, we aim to evaluate three comprehensive UNCCD-recommended land productivity dynamic (LPD) approaches in the BSR by utilizing the standardized precipitation–evapotranspiration index (SPEI) at 12-month time scales as a benchmark drought index obtained from ground-based measurements. Our findings indicate that the LPD methods utilizing residual trends analysis (RESTREND), Trends.Earth (TE), and the Food and Agricultural Organization’s World Overview of Conservation Approaches and Technologies (FAO-WOCAT) are best suited for identifying degraded land areas in the BSR region compared to other approaches. However, it is advisable to use these methods with caution, since they do not fully capture the impact of drought on vegetation and may result in underestimating the extent of degraded areas. The RESTREND-based LPD, TE, and FAO-WOCAT estimate that the BSR region reached 213,248 km², 248,075 km², and 246,783 km² of degraded land, respectively, between 2001 and 2015. These findings may be valuable for decision-makers involved in land management and conservation efforts in the Sertão region of Brazil.

The impacts of climate change will not be homogeneous in all countries or between regions within each country. Mapping projected changes in temperature and precipitation is crucial for formulating region-specific agricultural adaptation measures. The spatial variation of projected changes in temperature and annual precipitation for 1970–2000 and 2041–2060 in Venezuela was analyzed using the WorldClim 2.1 data. Both variables have been analyzed in fourteen physiographic regions that differ in climate, geology, geomorphology, soils, and land use. The results reveal that western regions experience higher temperature increases, while the regions located in the east and center of the country are projected to experience greater decreases in rainfall. Likewise, temperature and precipitation will increase from north to south. Thus, there are differences in how different regions will be affected by variations in temperature and annual precipitation associated with climate change. It is concluded that physiographic regions can be used as large spatial units to plan future land use and design agricultural adaptation measures to climate change at the national scale.

In order to better understand the extent to which global climate variability is linked to long-term mean and extreme sea level patterns, correlations between average sea levels at coastal sites throughout North America and low-frequency oscillations of several climate indices (CIs) were analyzed for the entire period of 1948–2018 as well as three equal-length sub-periods using correlation analysis. Correlation strength was assessed using Pearson’s correlation coefficient, while significance was estimated using Leave-One-Out Cross-Validation and a bootstrapping technique (p-value). The sliding window size, lag time, and beginning month were varied for optimal correlation; 60-month sliding windows, along with 0 lag time, resulted in the strongest correlations. Strong (r >= 0.60) and significant (p-value <= 0.05) correlations were identified. The Western Hemisphere Warm Pool Eastern Asia/Western Russia index and ENSO exhibited the strongest and most widespread correlation with coastal sea levels. Further analysis was performed to identify and quantify the magnitude of any sea level trends using the Theil–Sen estimator, while the Mann–Kendall (MK) test was used to estimate the significance of said trends. The results revealed that a complex set of ocean–atmosphere interactions govern long-term coastal sea level variability in large coastal regions of North America. The final results of this study allow a greater understanding of potential links between climate variability and long-term sea levels along the coasts of North America, as well as insights into sudden shifts in these relationships, which will contribute toward more accurate long-term forecasts.

Water has become more important in agricultural implementations over the years, as has the need for water management. Thus, Water Use Efficiency (WUE) has been used as an alternative form of detecting the variability of water management based on the carbon–water cycle. The study aimed to map and quantify the spatio-temporal distribution of WUE based on its interactions with environmental changes. It focused on an agricultural area in the westernmost region of Bahia, Northeast Brazil (NEB). For WUE estimation, data from Collection 6 MODIS Gross Primary Productivity (GPP) and Evapotranspiration (ET) products with a spatial resolution of 0.05° × 0.05° were obtained from the Earth Explorer website. Subsequently, annual WUE anomalies were calculated based on the 2001–2019 period. The results obtained indicated that the highest values of GPP (580 gC/m²), ET (3000 mm), and WUE (3.5 gC/mm·m²) occurred in agricultural areas, associated with cultural treatments and insertion of irrigation, which helped in the higher WUE values and consequently increased agricultural productivity in the study region. In addition, there was a marked influence of the phases of the climate variability mode—El Niño-Southern Oscillation (ENSO)—on the annual variability of the WUE, with a reduction of 96% during the La Niña of 2016 (an increase of 89% during El Niño of 2005). During El Niños, vegetation had greater efficiency resulting from the adaptation of vegetation in maintaining the carbon–water balance, using water more efficiently. However, unlike Las Niñas, with excessive precipitation there is an interference in the WUE, which interferes with the absorption of radiation and nutrients for the biophysical processes of vegetation and agriculture and, consequently, agricultural production. The use of WUE for agriculture is extremely important, especially for Brazil and countries with an economy based on primary production. This information on the way vegetation (native or agricultural) responds to interactions with the environment aids in decision-making about water management, possibly lowering losses or agricultural damage caused by a lack of water.

The Amazon River Basin (ARB) plays an essential role in global climate regulation. Recent studies have revealed signs of increasing drought conditions in different parts of the basin. Although human activities have degraded large areas, little work has been done to assess whether prolonged drought may exacerbate land degradation. Among different methods for monitoring land degradation, the Sustainable Development Goal (SDG) indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) allows a comprehensive assessment of the impacts of land degradation due to its multi-factor nature and scalability. The aims of this study are twofold: 1) to assess the status of land degradation using the SDG indicator 15.3.1 from 2001 to 2020 in the Amazon basin; and 2) to explore the relationship between the detectability of land degradation using the UNCCD approach and long-term drought severity. The Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and self-calibrating Palmer Drought Severity Index (scPDSI) were used as drought indices. The results revealed 757,704 km² (12.67% of the basin) as degraded land, which was reflected by a downward trend in land productivity dynamics followed by the combined downward trend in land productivity, Soil Organic Carbon (SOC) degradation, and land cover degradation. The largest land degradation hotspot was identified along the southwestern boundary of the Amazon River Basin. Furthermore, there was strong evidence that the detection of land degradation through SDG indicator 15.3.1 is sensitive to long-term drought, particularly when applied to rainfed croplands.

Agricultural land drainage is an instrument for growing production and a tool for the conservation of land resources. The performance of land drainage systems is thus critical for achieving sustainable agricultural production Recently, many types of software have been developed in this field for modeling and simulating the performance of these systems. SISDRENA is a simulation model of the performance of underground drainage systems. The main objectives of this paper are to simulate different combination of depths and spaces between drains and to analyze their impact on potential sugarcane productivity in the western plains of Venezuela using a land drainage system model. Therefore, three climatic scenarios were defined by annual precipitation: dry years (25% below average), normal (mean) and humid (75% above average). The scenarios were implemented in three different soil types: sandy loam, loam and silt loam, with a hydraulic conductivity of 0.19, 0.26 and 0.04 m day⁻¹, respectively. The simulation of the yield related to soil deficit (YRD) and water stress (YRW) indicated that the highest yields were reached for the larger spacing between drains and the high conductivity hydraulic of soils. In relation to the average relative productivity (YT), it was shown that in soils with a greater water retention capacity there is an inversely proportional relationship between the spacing between drains and the productivity. We concluded that in order to reach the maximum sugarcane yield, the effect of hydraulic conductivity is more important than the changes in the precipitation pattern.

The São Francisco River Basin (SFRB) plays a key role for the agricultural and hydropower sectors in Northeast Brazil (NEB). Historically, in the low part of the SFRB, people have to cope with strong periods of drought. However, there are incipient signs of increasing drought conditions in the upper and middle parts of the SFRB, where its main reservoirs (i.e., Três Marias, Sobradinho, and Luiz Gonzaga) and croplands are located. Therefore, the assessment of the impacts of extreme drought events in the SFRB is of vital importance to develop appropriate drought mitigation strategies. These events are characterized by widespread and persistent dry conditions with long-term impacts on water resources and rain-fed agriculture. The purpose of this study is to provide a comprehensive evaluation of extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and soil moisture over different time windows between 1980 and 2020. The Standardized Precipitation-Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI) at 3- and 12-month time scales derived from ground data were used as benchmark drought indices. The self-calibrating Palmer Drought Severity Index (scPDSI) and the Soil Moisture and Ocean Salinity-based Soil Water Deficit Index (SWDIS) were used to assess the agricultural drought. The Water Storage Deficit Index (WSDI) and the Groundwater Drought Index (GGDI) both derived from the Gravity Recovery and Climate Experiment (GRACE) were used to assess the hydrological drought. The SWDISa and WSDI showed the best performance in assessing agricultural and hydrological droughts across the whole SFRB. A drying trend at an annual time scale in the middle and south regions of the SFRB was evidenced. An expansion of the area under drought conditions was observed only during the southern hemisphere winter months (i.e., JJA). A marked depletion of groundwater levels concurrent with an increase in soil moisture content was observed during the most severe drought conditions, indicating an intensification of groundwater abstraction for irrigation. These results could be useful to guide social, economic, and water resource policy decision-making processes.

At present, satellite rainfall products, such as the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) product, have become an alternative source of rainfall data for regions where rain gauge stations are sparse, e.g., Northeast Brazil (NEB). In this study, continuous scores (i.e., Pearson’s correlation coefficient, R; percentage bias, PBIAS; and unbiased root mean square error, ubRMSE) and categorical scores (i.e., probability of detection, POD; false alarm ratio, FAR; and threat score, TS) were used to assess the CHIRPS rainfall estimates against ground-based observations on a pixel-to-station basis, during 01 January 1981 to 30 June 2019 over NEB. Results showed that CHIRPS exhibits better performance in inland regions (R, PBIAS, and ubRMSE median: 0.51, −3.71%, and 9.20 mm/day; POD, FAR, and TS median: 0.59, 0.44, and 0.40, respectively) than near the coast (R, PBIAS, and ubRMSE median: 0.36, −5.66%, and 12.43 mm/day; POD, FAR, and TS median: 0.32, 0.42, and 0.26, respectively). It shows better performance in the wettest months (i.e., DJF) than in the driest months (i.e., JJA) and is sensitive to both the warm-top stratiform cloud systems and the sub-cloud evaporation processes. Overall, the CHIRPS rainfall data set could be used for some operational purposes in NEB.