Variability Of Annual Precipitation Research Articles

Rainfall Potential and Consequences on Structural Soil Degradation of the Most Important Agricultural Region of Mexico

This study investigates the historical variability in annual average precipitation in the northwest region of Mexico, aiming to evaluate the cumulative impact of precipitation on soil degradation and associated risks posed by rainfall. Despite being known as “The Agricultural Heart of Mexico”, the region’s soil has experienced significant damage to its granulometric structure due to unpredictable rainfall patterns attributed to climate change. Sixteen historical series of average annual rainfall were analyzed as stationary stochastic processes for spectral analysis. The results revealed exponential decay curves in each radial spectrum, indicating a linear relationship between frequency and amplitude. These curves identified initial impulses correlated with moments of severity for structural damages caused by rainfall-induced degradation. The degradation process, exacerbated by water stress, accelerates, as evidenced by maps illustrating approximately 75% soil damage. In the context of climate change and the uncertainty surrounding soil responses to extreme meteorological events, understanding this phenomenon becomes crucial. Recognizing the dynamic nature of soil responses to environmental stressors is essential for effective soil management. Emphasizing the need to employ numerical processes tailored to new environmental considerations related to observed soil damages is crucial for sustainable soil management practices in any region.

Changes in Surface Runoff and Temporal Dispersion in a Restored Montane Watershed on the Qinghai–Tibetan Plateau

Surface runoff is a major component of the hydrological cycle, and it is essential for supporting the ecosystem services provided by grassland and forest ecosystems. It is of practical importance to understand the mechanisms and the dynamic processes of runoff in a river’s basin, and in this study, we focused on the restored montane Pailugou Basin in the Qilian Mountains, Gansu Province, China, since its water status is extremely important for the large arid area and local economies therein. Our purpose was to determine the annual variation in the surface runoff in the Pailugou Basin because it is important to understand the influence of climate fluctuations on surface water resources and the economy of the basin. In addition, little is known about the annual variations in precipitation and runoff in this region of the world. Daily atmospheric precipitation, air temperature and runoff data from 2000 to 2019 were analyzed by the calculation of the uneven annual distribution of surface runoff, the calculation of the complete adjustment coefficient, and the vector accumulation expressed by the concentration degree. We also used the cumulative anomaly approach to determine the interannual variation trend of runoff, while the change trend was quantified by the sliding average method. Finally, we used the Mann–Kendall mutation test method and regression analysis to establish the time-series trend for precipitation and runoff and to determine the period of abrupt runoff changes. The results indicated concentrated and positive distributions of surface runoff on an annual basis, with a small degree of dispersion, and an explicit concentration of extreme flows. The relative variation ranges exhibited a decreasing trend, and the distribution of the surface runoff gradually was uniform over the year. The runoff was highest from July to September (85% of the annual total). We also determined that annual surface runoff in the basin fluctuated over the 20-year period but showed an overall increasing trend, increasing by 3.94 × 105 m3, with an average increase rate of 0.42 × 105 m3 every ten years. From 2005 to 2014, the annual runoff and the proportion of runoff in the flood season (July to September) to the annual runoff fluctuated greatly. The correlation between the runoff and precipitation was significant (r = 0.839, p < 0.05), whereas the correlation between air temperature and surface runoff was low (r = 0.421, p < 0.05).

Winter Insolation Modulates Boreal Tropical Monsoonal Temperatures in the Late Pleistocene

AbstractDuring past glacial‐interglacial cycles, the boreal summer insolation is the most crucial external forcing for climate change. However, the question of whether summer insolation is a key forcing on temperatures in the boreal tropics remains under debate, hampering our understanding of climate change at low latitudes. To shed further light on this issue, we performed a series of equilibrium simulations with the NorESM‐L model over the past 425 ka. Our simulations show that in the boreal tropical monsoon region, the simulated annual temperature is anti‐phased towards the boreal summer insolation. This antiphase relation is also supported by some available geological data. Additional diagnostic analyses reveal that the tropical warmth throughout the year is more reliant on winter temperatures than on summer temperatures. This stands in contrast to the situation in middle to high latitudes, particularly in the Mediterranean region. Further correlation analysis and spectrum analysis suggest that the annual temperature in boreal tropics is highly linked to local winter insolation. Our results highlight complex hydrothermal configurations in the boreal tropics, suggesting a decoupling of temperature and precipitation. Specifically, variations in annual temperature and precipitation in the boreal tropics are driven by distinct patterns of seasonal insolation. We deduce that the unique hydrothermal configurations in North Africa may have influenced the dispersal of early humans out of Africa.

Influence of seasonal hydrological regimes on benthic macroinvertebrates in two the Brazilian biodiversity hotspots

Global warming has changed climate patterns worldwide causing an increase of extreme weather conditions that have altered annual seasonal hydrological regimes. These extreme climate-driven shifts modify habitat availability and can influence freshwater communities in disruptive ways. Our study investigates how changes in annual seasonal hydrological regimes affect the community structure and the Functional Feeding Groups (FFG) of benthic macroinvertebrates in two Brazilian biodiversity hotspots, the Brazilian Tropical Savannas (a.k.a., Cerrado) and Atlantic Forest biomes. We investigate whether annual variation in precipitation between biomes influence composition, richness, and abundance of benthic macroinvertebrates and their proportions of FFG. We demonstrate that differences in annual precipitation rates affect the composition and abundance, but not richness of benthic macroinvertebrates. Changes in community structure are related to changes in annual precipitation, which modify stream variables. Our findings suggest that annual seasonal changes in hydrological precipitation modify benthic macroinvertebrate communities, especially in Cerrado, where dry seasons are more pronounced. Therefore, annual changes in precipitation rates may disrupt the benthic macroinvertebrate communities in tropical savannas, potentially leading to biodiversity loss.

Characterizing variability of spatial patterns of annual and seasonal precipitation of Turkey and identifying the probable driving factors including teleconnection patterns

Abstract This study aimed to identify primary modes of annual and seasonal precipitation in Turkey using a rotated Empirical Orthogonal Function (EOF) method, and the resulting patterns were described concerning the prevalent atmospheric circulations, orography, and continentality. The varimax rotation of the EOF determines modes that are more localized in space than the conventional EOF modes The first three EOFs accounted for approximately 67% and 62% of the total variance in the annual and wet season precipitation series, respectively, whereas only 50% of the variance was captured by the first three EOFs for dry season precipitation. Spatially different atmospheric circulation mechanisms are major drivers of variability in Turkish precipitation on annual and seasonal timescales. The spatial coherence of the highest negative and positive EOF1 loadings of the annual data was observed in the western and southern regions where westerly and northwesterly circulations prevailed during the wet season. A lower spatial coherency was observed with the dry season precipitation. The contribution of atmospheric moisture advection to precipitation variability diminishes in summer, whereas that of local land surface processes increases. Some regional teleconnection patterns, such as the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO), also contributed to the annual variability in precipitation.

Trend analysis and forecast of annual precipitation and temperature series in the Eastern Mediterranean region

The present study aims to examine the current trend of annual precipitation and temperature series referred to the Eastern Mediterranean basin on a national basis, including the annual variability of sea surface temperature anomalies and the connection effect with the North Atlantic Oscillation and the Mediterranean Oscillation indices (NAOI and MOI, respectively). The period under consideration is mainly the last 32 years, from 1990 to 2021. Additionally, the prediction of monthly and yearly temperature and precipitation series, based on the autoregressive integrated moving average model on a national base, for the next four or eight years, is another interesting feature of the present study. Results indicate rising trends in the annual precipitation during 1990-2021, mostly non-significant and significant warming trends in the annual temperature, including sea surface temperature. The NAOI is highly correlated mostly with annual temperature, whereas the MOI does not affect the variation of annual precipitation and temperature. The results of the present study are in general agreement with the results of available studies in the literature, and they could be of high interest to national authorities and environmental unions/organizations, to help decision-makers face climate change.

Characterization of landuse and landcover dynamics and their impact on runoff generation patterns in dam catchments of Northern Ghana

Landuse alteration is a critical factor that exerts influence over hydrological mechanisms and the oversight of water resources across the globe. Northern Ghana is characterized by erratic rainfall, leading to the intense construction of small dams and dugouts. This study investigated historical landuse/cover (LULC) changes and physical factors associated with surface runoff generation within eight (8) sub-catchments namely; Guno, Gbahali, Sambu, Duago, Denugo, Kwisini, Sandu, and Nyeko, with areas of 29.5, 110, 38.5, 106.04, 67.81, 2.59, 10.56 and 120.65 km2, respectively, located in four (4) regions of northern Ghana. For LULC change detection, historical satellite imagery and geospatial data were used with the aid of remote sensing and applying supervised maximum likelihood classification to Landsat images of 1995, 2005, 2015, and 2023. Runoff factors and depths were identified based on LULC classes, hydrologic soil groups, and precipitation conditions prevailing in the areas, based on the curve number (CN) method. With regards to LULC, the results from the overall analysis gave an insight into the rampant conversion, where grassland/mix vegetation witnessed significant declines, with decreases ranging from 150 to 395.25% in different catchments. Agricultural and arable lands emerged as the dominant landuse category, indicating a remarkable expansion of 11.61–51.17 km2, from 1995 to 2023 respectively, followed by built-up areas. While grassland decreased in all catchments over the same period, in some places, open tree (savannah woodland) increased, which confirmed the implementation of the afforestation policy. The CN values offered essential insights into the hydrological characteristics of each catchment. In addition to the impact of LULC and watershed characteristics on runoff, the observed variations in runoff patterns can be attributed to interannual variability in total annual precipitation which is critical driver of runoff. The findings hold significant importance for developing effective strategies and integrated watershed management policies to contribute to better water resource management and inform sustainable landuse planning in the face of changing environmental conditions.

Impacts of climate variability on the vegetable production of urban farmers in the Addis Ababa metropolitan area: Nexus of climate-smart agricultural technologies

This study was aimed at examining the impacts of climate variability on vegetable production and nexus climate-smart agriculture technologies. The study was conducted on vegetable farmers along the little Akaki River in Addis Ababa. Field data was collected from 156 randomly selected vegetable farmers via semi-structured survey questions. Climate data from 1996 to 2020 was analysed using qualitative and quantitative descriptive statistics methods. The results of monthly and annual precipitation variability indicated a coefficient of variation (CV) ranging from 23% to 73% and 49% to 98%, respectively. Seasonally, CV ranges between 34% and 99%, 50%–97%, and 20%–84% in Belg, Bega, and Kiremt, respectively. The results of respondents' perceptions indicated an increasing trend in temperature and precipitation variability. Vegetable urban farmers perceived an increase in the frequency of floods and rain falls (44.9%), drought frequency (13.5%), temperature (89.7%), and a decrease in the trend of vegetable productivity (86.5%) as the major impacts of climate variability. However, changing vegetable varieties (31.4%), early planting (26.9%), mixed farming (26.6%), late planting (5.1%), using agrochemicals (4.5%), and agroforestry (1.9%) are the major on-farm climates where smart agriculture technologies were identified for adaptation. Shift occupation (37.8%), nonadaptation (36.5%), and non-farm activations (24.4%) were employed by the farmers as off-farm adaptation options. In addition, vulnerability analysis indicated that the absence of direct access to the market, inadequate access to weather information, land fragmentation, and tenure complications are the major determinants of being vulnerable. Finally, high precipitation and temperature variability affect vegetable productivity. Practical ImplicationsClimate extreme phenomena are substantial pressures on urban agricultural production systems in risk-prone cities, where climate service challenges are rising globally (Sanfo et al., 2022; Kifle et al., 2022; Ebissa and Desta, 2022); besides, the requirement to produce more urban vegetables to feed residents, an ever-increasing and vulnerable group, is undeniable (Martinez et al., 2022). Additionally, climate variability and change threaten urban and pre-urban farmers’ livelihoods and agricultural farming, particularly in semi-arid areas in Africa (Magesa et al., 2023). Furthermore, 64 % of the world’s poorest people lived in sub-Saharan Africa in 2020, which strongly requires the implementation of Sustainable Development Goals 1 (no poverty), 2 (zero hunger) and 11 (make cities and human settlements inclusive, safe, resilient, and sustainable) in the region (Magesa et al., 2023; Chitakira and Ngcobo, 2021; Degefu et al., 2021c).Thus, cities are exposure to compatible climate information services is vital for anticipating climate variability risks in vegetable production, optimizing the training of practitioners (urban farmers), and adapting to climate change through climate smart agriculture technologies (Degefu et al., 2021a; Chitakira and Ngcobo, 2021; Kifle et al., 2022). Moreover, it is compulsory to ascertain and analyze impact insights and their origins, vulnerabilities, and adaptive potential among urban farmers before beginning with the exercise of any given climate service to understand the demands of urban vegetable farmers and the possible exploitation of nexuses (Dendir and Simane, 2021).Conversely, in Ethiopia, the agriculture sector is identified as one of the sectors most vulnerable to climate change and vulnerability (Kifle et al., 2022). On top of these, the productivity and the concern of urban agriculture (vegetable production) is highly ignored and reduced the potential, productivity and suitability of ecological land (Kifle et al., 2022; Degefu et al., 2021b; Degefu et al., 2021c). Besides, the combined effect of climate variability, population pressure, and urban ecosystem dynamics reduced urban vegetable production and leads the farming community into a viscous of poverty circle and worsens food security (Amberber et al., 2020; Degefu et al., 2021c).However, previous studies have revealed that the effectiveness of climate information services on urban ecosystems depends on (i) the ability of urban farmers to access, understand, and overcome institutional constraints (Kiplagat et al., 2022) (ii) the capacity of end-users to translate the information and knowledge into effective decision-making options (Dendir and Simane, 2021), and (iii) the capacity of end-users to translate the information and knowledge into effective decision-making options (Martinez et al., 2022). To that end, the disparity between the awareness of urban farmers and policymakers towards the benefits of CSA and their practices implies that indigenous knowledge-based research on CSA farming and land management technology should be conducted. Therefore, it is crucial to appreciate the practices and adoption of CSA at the city level to realise triple-win outcomes: increased productivity, enhanced resilience, and mitigating climate variability and change.

The long-term economic effects of aridification

We conduct a disaggregated empirical analysis of the economic effects of desertification, exploiting a novel grid-cell global dataset from 1990 to 2015. Our measure of desertification combines annual variation in precipitation and potential evapotranspiration of the soil. To ensure accuracy, we employed advanced spatial econometric techniques to account for the interdependence between economic development and both time and location. Our results indicate that a one standard deviation increase in desertification is associated with a 0.6% to 0.9% decrease in GDP per capita. Based on these estimates, we have predicted the potential impact of future desertification on economic development, with a particular focus on Africa and Southeast Asia.

Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China

The study aimed to investigate the spatiotemporal variation of annual precipitation and extreme precipitation within the Songhua River Basin (SRB). It utilized precipitation data collected from 60 meteorological stations within the SRB during the period 1968–2019. Employing Empirical Orthogonal Function (EOF) analysis, it decomposed spatiotemporal characteristics of annual precipitation in the SRB. Through Pearson correlation analysis, application of the cross-wavelet transform, and wavelet coherence analysis, the current study explored the correlation between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation. The results indicated an increasing trend for annual precipitation and for most indices of extreme precipitation within the SRB, apart from the consecutive dry days (CDD). Spatially, a general pattern of “more in the east and less in the west” was observed. Annual precipitation types in the basin were resolved into two modes with the first mode showing a general tendency of more (or less) precipitation over the entire basin, while the second mode exhibited less (or more) precipitation in the western areas and more (or less) in the eastern areas. Longitude, latitude, and altitude significantly impacted annual precipitation and extreme precipitation. Local air temperature notably affected the consecutive wet days (CWD). The West Pacific Subtropical High (WPSH) exerts a strong influence on the annual precipitation and extreme precipitation within the basin.

Glacier Retreat Leads to the Expansion of Alpine Lake Karakul Observed Via Remote Sensing Water Volume Time Series Reconstruction

Due to high altitudes, Central Asian alpine lakes can serve as indicators of localized climate change. This article monitored the water volume time series trends of the ungauged alpine Lake Karakul, which is typical because of the abundance of glaciers in the basin, from 1990 to 2020 via multiple source remote sensing data. The “Global-Local” multi-scale lake extraction method is used to delineate the boundary of Lake Karakul. Consistency analysis was performed on the altimetry data of CryoSat-2, ICESat-1 and ICESat-2, assuming that the lake surface was flat; a threshold value was set to remove gross error, and then 3σ was used to remove the surface elevation anomaly. Based on the pyramid volume model, the lake area and surface elevation information were used to reconstruct the water volume time series of Lake Karakul. The influencing factors of water volume temporal variation were discussed. The results show that Lake Karakul has been on an expansionary trend in recent years: The lake area increased from 394.9 km2 in 1988 to 411.4 km2 in 2020; the rate of increase is 0.74 m/year. The surface elevation increased from 3886.6 m in 2003 to 3888.6 m in 2020; the rate of increase is 0.11 km2/year. The lake water volume accumulated was 0.817 km3 in 2003–2020, with an accumulation rate of 0.059 km3/year. The Lake Karakul basin is developing towards dry heat, with a cumulative temperature variation rate of +0.38 °C/year; the average rate of variation in annual cumulative precipitation is −3.37 mm/year; the average evapotranspiration in the watershed is on a fluctuating increasing trend, with a rate of variation of +0.43 mm/year; glaciers in the lake basin have a retreating trend, with an average annual rate of variation of −0.22 km2/year from 1992 to 2020. Lake Karakul is more sensitive to temperature variations, and the runoff from retreating glaciers in the basin is an important contribution to the expansion of Lake Karakul.

Hybrid interpolation approach for estimating the spatial variation of annual precipitation in the Macta basin, Algeria

Hybrid interpolation approach for estimating the spatial variation of annual precipitation in the Macta basin, Algeria

Land use change analysis and modeling of its future trajectories in Morogoro Region, Tanzania: Implication for conservation

In Sub-Saharan Africa, where people's livelihoods depend largely on natural resources, understanding land use change dynamics and its implications for the sustainability of social-ecological systems is critical. This study analyzed both historic land use and land cover (LULC) change and trajectories for future change in the Morogoro Region, Tanzania. We mapped LULC in the study area for 1994, 2007 and 2020 using Random Forest in Google Earth Engine and projected future LULC in 2033 using Land Change Modeler. Results revealed that conversion of natural vegetation and wetlands has occurred at a high rate due to cropland expansion and will likely continue over the next decade. Conversion to cropland occurred on the edges of protected areas, in remnant forests and near existing cropland, and was associated with slope, elevation, proximity to settlements and variation in annual precipitation. In landscapes where wildlife shares areas with humans, converting natural vegetation into crop production increases tension and human-wildlife conflicts. Given compounding impacts of a growing human population in Tanzania, and reduced crop yields due to unpredictable rains and prolonged droughts, both human wellbeing and biodiversity conservation require understanding the resulting pressure on land and ecosystems.

Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China

AbstractProjecting precipitation changes is essential for researchers to understand climate change impacts on hydrological cycle. This study projected future precipitation over the Hanjiang River Basin (HRB) based on the multimodel ensemble (ME) of six global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). An ME method using the fusion of four machine learning (ML) algorithms (random forest [RF], K‐nearest neighbors [KNN], extra tree [ET], and gradient boosting decision tree [GBDT]) was proposed in this study. The future precipitation changes were investigated during 2023–2042 (Near‐term), 2043–2062 (Mid‐term), and 2081–2100 (Long‐term) periods, with reference to the base period 1995–2014, under three integrated scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5) of the Shared Socioeconomic Pathways (SSPs) and the representative concentration pathways (RCPs). The results show that: (1) the proposed ME method performs better than the ME mean and individual ML algorithms, with a correlation coefficient value reaching 0.88 and Taylor skill score reaching 0.764. (2) The precipitation under SSP5‐8.5 has the largest upward trend with the annual precipitation variation range of −9.27% to 112.84% from 2023 to 2100, followed by SSP2‐4.5 with −30.48% to 44.67%, and the smallest under SSP1‐2.6 with −37.19% to 37.78%, which show a significant trend of humidification over the HRB in the future. (3) The precipitation changes over the HRB are projected to increase over time, with the largest in the Long‐term, followed by Mid‐term, and the smallest in the Near‐term. (4) The northeastern parts of the HRB are projected to experience a large precipitation in the future, and the southeastern parts are smaller. (5) Uncertainties in the projected precipitation over the HRB still exist, which can be reduced by ME. The findings obtained in this study have important implications for hydrological policymakers to make adaptive strategies to reduce the risks of climate change.

Spatio-temporal variation of annual precipitation in China and its response to ENSO

Abstract The results showed that the precipitation in the study area was mainly in a downward trend before the mid-1930s, and then turned upward. In the 1950s, the precipitation generally showed a distribution of rising in the west and falling in the east, and this trend continued until the early 21st century. By 2007, except for the central part of the Continental Basin, the overall trend was mainly upward. In this study, 65 El Niño Southern Oscillation (ENSO) events were identified, including 24 El Niño events and 41 La Niña events. The precipitation was generally less when ENSO occurred. However, there were differences in the type and intensity of the event. For example, El Niño had a greater impact on precipitation than La Niña did, and extremely strong or strong El Niño/La Niña events had a more significant impact than moderate, weak, or extremely weak ones. The correlation between precipitation and El Niño or La Niña events had some similarities and differences. For example, precipitation was mainly negatively correlated with El Niño and La Niña at the same time, and both correlations were proportional to intensity, but the correlation between precipitation and El Niño was significantly stronger than that of La Niña.

Interactions between thresholds and spatial discretizations of snow: insights from estimates of wolverine denning habitat in the Colorado Rocky Mountains

Abstract. Thresholds can be used to interpret environmental data in a way that is easily communicated and useful for decision-making purposes. However, thresholds are often developed for specific data products and time periods, changing findings when the same threshold is applied to datasets or periods with different characteristics. Here, we test the impact of different spatial discretizations of snow on annual estimates of wolverine denning opportunities in the Colorado Rocky Mountains, defined using a snow water equivalent (SWE) threshold (0.20 m) and threshold date (15 May) from previous habitat assessments. Annual potential wolverine denning area (PWDA) was thresholded from a 36-year (1985–2020) snow reanalysis model with three different spatial discretizations: (1) 480 m grid cells (D480), (2) 90 m grid cells (D90), and (3) 480 m grid cells with implicit representations of subgrid snow spatial heterogeneity (S480). Relative to the D480 and S480 discretizations, D90 resolved shallower snow deposits on slopes between 3050 and 3350 m elevation, decreasing PWDA by 10 %, on average. In years with warmer and/or drier winters, S480 discretizations with subgrid representations of snow heterogeneity increased PWDA, even within grid cells where mean 15 May SWE was less than the SWE threshold. These simulations increased PWDA by upwards of 30 % in low-snow years, as compared to the D480 and D90 simulations without subgrid snow heterogeneity. Despite PWDA sensitivity to different snow spatial discretizations, PWDA was controlled more by annual variations in winter precipitation and temperature. However, small changes to the SWE threshold (±0.07 m) and threshold date (±2 weeks) also affected PWDA by as much as 82 %. Across these threshold ranges, PWDA was approximately 18 % more sensitive to the SWE threshold than the threshold date. However, the sensitivity to the threshold date was larger in years with late spring snowfall, when PWDA depended on whether modeled SWE was thresholded before, during, or after spring snow accumulation. Our results demonstrate that snow thresholds are useful but may not always provide a complete picture of the annual variability in snow-adapted wildlife denning opportunities. Studies thresholding spatiotemporal datasets could be improved by including (1) information about the fidelity of thresholds across multiple spatial discretizations and (2) uncertainties related to ranges of realistic thresholds.

Evaluation of Urban Flood Governance Efficiency Based on the Data Envelopment Analysis Model and Malmquist Index: Evidence from 30 Provincial Capitals in China

Urban flooding disasters endanger people’s lives and property while causing significant economic damage to cities. To further improve the efficiency of urban flooding governance and promote the optimal allocation of resources, this article selects the number of people in flood control, medical and health security, financial expenditure on flood control, affected population, and direct economic loss as evaluation indicators from the input–output perspective; it measures the efficiency of urban flooding governance in 30 provincial capitals in China from 2012 to 2021 using the DEA model and Malmquist index method and identifies the key factors affecting the efficiency of urban flooding governance. The research results show that in 2021, the average value of the comprehensive technical efficiency of flood disaster governance in 30 provincial capitals in China was 0.408, the development trend was not optimistic, and the constraint factor was scale efficiency. The interannual average value of the total factor productivity index of urban flood disaster governance from 2012 to 2021 was 0.976, and the overall trend was decreasing year by year, during which some cities were able to achieve a yearly increase in governance efficiency, but most cities still faced a severe situation in flood disaster governance. The total factor productivity index varied enormously across towns with the variation in annual precipitation; the greater the annual precipitation, the greater the total factor productivity index of urban flooding disaster governance in the urban agglomerations, and the comprehensive technical efficiency change was consistent with its trend change. This consistency has a positive contribution to the total factor productivity index of urban flooding disaster governance.

Variability and Changes in Temperature, Precipitation and Snow in the Desaguadero-Salado-Chadileuvú-Curacó Basin, Argentina

We analyze the variability and changes in mean temperature, annual precipitation, and snow water equivalent (SWE) in the Desaguadero-Salado-Chadileuvú-Curacó (DSCC) basin, observed in a recent period, and their projected future values. The current climate and observed changes are studied based on the scarce observation records available in the region. Simulations from the High-Resolution Model Intercomparison Project (HighResMIP v1.0) are used for the analysis of future climate projections. The mean annual temperature presents a major rise between 1 and 2 °C throughout the region in the 1961–2020 period. The annual precipitation has nonlinear positive trends of varied importance. The SWE has decreased noticeably since 2010. Interannual variability also plays a key role, with dominant cycles between 2.8 and 4 years and between 7.5 and 10 years. The projected changes, computed as the difference between the 1985–2014 and 2021–2050 averages, reveal a rise in mean annual temperature in the entire basin, with an eastward magnitude increase from 1 to more than 2 °C. The annual precipitation is projected to increase up to 10% over most of the basin, except in the Andes, where it will decrease. The maximum annual SWE will also continue to decrease, in agreement with the trend observed in recent years.

Rapid drought-recovery of gas exchange in Caragana species adapted to low mean annual precipitation.

While variation in mean annual precipitation (MAP) of the native habitat of a species has been shown to determine the ability of a species to resist a hydraulic decrease during drought, it remains unknown whether these variations in MAP also influence the ability of a species to recover and survive drought. Leaf hydraulic and gas exchange recovery following drought and the underlying mechanisms of these responses in six Caragana species from habitats along a large precipitation gradient were investigated during rehydration in a common garden. The gas exchange of species from arid habitats recovered more rapidly during rehydration after mild, moderate and severe drought stress treatments than species from humid habitats. The recovery of gas exchange was not associated with foliar abscisic acid concentration, but tightly related to the recovery of leaf hydraulic conductance (Kleaf ). The recovery of Kleaf was associated with the loss of Kleaf during dehydration under mild and moderate drought stress, and to leaf xylem embolism formation under severe drought stress. Results pointed to the different ability to recover in gas exchange in six Caragana species post-drought is associated with the MAP of the species in its native habitat.

Precipitation variability using GPCC data and its relationship with atmospheric teleconnections in Northeast Brazil

The present study investigates the influence of different atmospheric teleconnections on the annual precipitation variability in Northeast Brazil (NEB) based on the annual precipitation data from the Global Precipitation Climatology Center (GPCC) from 1901 to 2013. The objective of this study is to analyze the influence of different atmospheric teleconnections on the total annual precipitation of NEB for the 1901–2013 period, considering the physical characteristics of four subregions, i.e., Mid-north, Backwoods, Agreste, and Forest zone. To analyze the influence of different atmospheric teleconnections, GPCC data were used, and the behavior of the teleconnections was assessed using Pearson correlation coefficient, Rainfall Anomaly Index (RAI), and cross-wavelet analysis. The Pearson correlation was used to analyze the influence on the annual precipitation for the studied region. RAI was used to calculate the frequency of atmospheric patterns and drought episodes. The cross-wavelet analysis was applied to identify similarity signals between precipitation series and atmospheric teleconnections. The results of the Pearson correlation assessed according to Student's t test and cross-wavelet analysis showed that the Atlantic Multidecadal Oscillation (AMO) exerts a more significant influence on the Backwoods region at an interannual scale. In contrast, the Pacific Decadal Oscillation (PDO) exerts greater control over the modulation of the climatic patterns in NEB. The results of the study are insightful and reveal the differential impacts of teleconnections such as the AMO, PDO, MEI, and NAO on precipitation in the four sub-regions of NEB. The Atlantic circulation patterns strongly influence the interannual and interdecadal precipitation in the Agreste, Backwoods, and Mid-north regions, possibly associated with the Intertropical Convergence Zone (ITCZ) position. Finally, this study contributes to understanding internal climatic variability in NEB and planning of water resources and agricultural activities in such a region.Graphic abstract