Total Monthly Precipitation Research Articles

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.

Assessing Polarisation of Climate Phenomena Based on Long-Term Precipitation and Temperature Sequences

This article presents an analysis of monthly precipitation totals based on data from the Global Precipitation Climatology Centre and monthly mean temperatures from the National Oceanic and Atmospheric Administration for 377 catchments located worldwide. The data sequences, spanning 110 years from 1901 to 2010, are analysed. These long-term precipitation and temperature sequences are used to assess the variability in climate characteristics, referred to here as polarisation. This article discusses the measures of polarisation used in the natural sciences. This study adopts two measures to evaluate the phenomenon of polarisation. The first measure is defined based on a stationary time series, calculated as the ratio of the amplitude of values to the standard deviation. The second measure is proposed as the difference in trends of these values. Based on the analysis of monthly precipitation data in the studied catchments, polarisation components are confirmed in 25% of the cases, while in the remaining 75%, they are not. For temperature data, polarisation is confirmed in 12.2% of the cases and not in the remaining 88.8%. The trend analysis employs Mann–Kendall tests at a 5% significance level. The Pettitt test is used to determine the point of trend change for precipitation and temperature data. This article underscores the complex relationship between climate polarisation and sustainable development, reaffirming that sustainable development cannot be pursued in isolation from the challenges posed by climate change. It emphasises the importance of integrating environmental, social, and economic strategies to adapt to extreme climatic events and mitigate their effects. This research is supported by detailed graphical analyses, with the results presented in tabular form.

Relationship of litterfall anomalies with climatic anomalies in a mangrove swamp of the Yucatan Peninsula, Mexico.

Among the set of phenological traits featuring mangrove ecosystems, litterfall production stands out with marked intra-annual and longer-term variation. Furthermore, mangrove forests resilience is one of the most important ecological attribute, reconciling the juxtaposed terrestrial and marine environment such transitional systems occupy. However, world's mangroves are nowadays facing recurrent climatic events, reflected in anomalies depicted by major drivers, including temperature and precipitation. This physical-environmental setting may either constrain or favor overall forest productivity. A combination of time series analysis (spectral density and cross-correlation techniques) and statistical model fitting (General additive model) was implemented to explore trends in total litterfall of a well-developed mangrove forest in southeastern Gulf of Mexico (Celestun Lagoon, SE Mexico) and potential association with the varying behavior of temperature (°C) and precipitation (mm month-1), highlighting their anomalies. The results are consistent with a synchronous response between litterfall production and climatic variables (mean monthly temperature and total monthly precipitation). Concurrent peak litterfall production in Celestun lagoon with high temperatures and precipitation occurred during June and October, featuring a two-month time lag for the response time. More than half of the litterfall anomalies (53.5%) could be reflecting either multiple sources of climatic anomalies (maximum, minimum, and monthly average temperature and monthly total precipitation) or single point events (cyclone landfall). This relationship dynamics showed an interannual persistence (1999-2010). The structure portrayed by the litterfall time-series was not unequivocally related to climatic anomalies. Arguably, climatic anomalies behave with different intensities and even may exhibit complex interactions among them. The study of anomalies provides a baseline for a better grasp of: i) mangrove anomalies responses and ii) their vulnerability to these extremes.

Application of Deep Learning for the Analysis of the Spatiotemporal Prediction of Monthly Total Precipitation in the Boyacá Department, Colombia

Application of Deep Learning for the Analysis of the Spatiotemporal Prediction of Monthly Total Precipitation in the Boyacá Department, Colombia

A Quality Control System for Historical In Situ Precipitation Data

ABSTRACT In this study, a comprehensive quality control (QC) system for in-situ precipitation data records was developed and applied to Canadian in situ precipitation datasets. The system includes a pair of screening procedures to screen for two types of random errors: one procedure is applied to the untransformed monthly total precipitation data series, which is good at finding erroneous data of unusually large values; another is applied to the log-transformed monthly precipitation data (in mm) series, log(P + 0.1), which is good at identifying erroneous zero or near-zero monthly total precipitation amounts. The system then applies three QC (threshold, kriging, and temporal) tests and a decision-making process to confirm whether the screened suspects are erroneous. There is generally good agreement between all the QC tests, while the decision-making process yields the most accurate results when compared to the manually reviewed results. The QC work on Canadian precipitation data sets revealed that it is necessary to apply a pair of screening procedures to identify both types of random errors. All the monthly values identified to be erroneous are set to missing, and so are the corresponding daily values, while keeping records of the original data.

Regional variation in branch morphological and physical traits in Abies sachalinensis associated with winter climatic conditions

ABSTRACT In boreal forests, overwintering strategies, including snow adaptation, can be expected in the branches of coniferous trees. Although it is difficult to detect resistance to snow disturbances and few studies have quantified the functional traits related to snowfall resistance, the regional variation associated with winter conditions may provide cues for identifying adaptive traits against snow damage. In common garden trials, we examined the regional variation in branch functional traits in Abies sachalinensis, one of the most important conifers in boreal forest silviculture. Morphological traits reflecting susceptibility to snow accumulation, such as branch estimated area, Young’s modulus and flexural rigidity, which represent deformability to snow loads, and base diameter and xylem density were evaluated in five-year-old branches from eight provenances. The length and estimated area of a branch showed regional variation. Significant regional variation in flexural rigidity rather than Young’s modulus and no significant correlation between xylem density and mechanical traits indicated that the physical properties of branches depend on their size rather than on their materials. A significant correlation with winter climatic conditions was observed between flexural rigidity and total monthly precipitation in January. Our results showed that the thicker the branch is, the larger the branch area and the more rigid it is. This indicates that the branch size-dependent resistance to snow loads might be associated with winter adaptation to local snowfall conditions.

The Global Historical Climatology Network Monthly Precipitation Dataset, Version 4

The Global Historical Climatology Network (GHCN) monthly precipitation dataset contains historical time series for thousands of land surface stations worldwide. Initially released in 1992 and revised in 1998, the dataset has been employed in a variety of applications over the past three decades, including operational monitoring, applied research, and international assessments. This paper describes the data and methods used to compile the latest edition (version 4), which has three major enhancements. The first enhancement is to the station network, which increased in size by a factor of five due to the inclusion of dozens of new source datasets, most notably GHCN Daily (GHCNd). The second improvement is the application of a rule-based algorithm to compare and merge records representing the same location. The third enhancement is to the quality assurance approach, now consisting of 18 new checks based on GHCNd and other operational systems. Updated monthly, the resulting dataset consists of time series of monthly precipitation totals at more than 120,000 worldwide stations, including more than 33,000 active observing sites.

Changes in precipitation and air temperature over Turkey using innovative trend pivot analysis method

Changes in precipitation and air temperature over Turkey using innovative trend pivot analysis method

Evaluation of long-term meteorological drought in the Aras and Coruh Basins with Crossing Empirical Trend Analysis

Drought is one of the most devastating disasters. Although the devastating effects of drought appear after many years, it is possible to evaluate drought based on meteorological parameters to control these negativities. In recent years, graphical trend analysis methods have been used to evaluate drought using meteorological parameters. The Crossing Empirical Trend Analysis method is the most recent trend analysis method and one of these graphical methods. Using monthly total precipitation data measured between 1969 and 2020 at 12 different meteorological observation stations within the borders of the Aras and Coruh basins, located northeast of Turkey, the basin's meteorological drought analysis was analyzed through the standardized precipitation index. In the study, the trend of meteorological drought in the basin was determined using the CETA method. As a result of the study, it was determined that there was an increasing trend in the majority of drought values in the Slope Upper and CETA Pivot analysis and a decreasing trend in the Slope Lower values. According to the Sen Slope analysis, there was no significant change in trends (34% increased, 35% decreased, and 31% no trend). Ardahan station is the most sensitive station regarding meteorological drought among the stations examined. It was concluded that the months with the highest increase trend in the Aras and Coruh basins are May, June and November.

Assessment of the state of bridge crossings and geoinformation spatial analysis of regional engineering and geological conditions of their operation

The article is devoted to the assessment of engineering and geological conditions of the Ivano-Frankivsk region, as a possible factor influencing the safe operation of bridges and bridge overpasses. An analysis of the condition of the bridges was carried out according to age characteristics and the location of the structure in accordance with the engineering and geological zoning of the study area. The possible impact of landslide processes on bridges and bridge crossings was investigated using GIS methods. Reconstruction of bridges is associated with the emergency state of the bridge crossing, which arose as a result of external influences, including as a result of natural phenomena – floods, mudflows, landslides, coastal erosion, changes in the watercourses. These phenomena are especially relevant for the territory of the Carpathian region. The distribution of bridges in relation to their total number on the territory of Ivano-Frankivsk region by year is given, and the connection of their location with engineering and geological zoning is indicated. It is shown that the number of reconstructed bridges is related to the maximum total monthly precipitation. The influence of the development of dangerous exogenous geological processes on the operation of bridge crossings was assessed using the methods of geoinformation analysis. The impact of landslide processes on the state of bridges was studied. The analysis of the histograms of the distribution of the distance from the bridge to the landslide object measured by map layers in the GIS environment shows the differences in the distributions in connection with the distance to the landslides for the reconstructed bridges, which indicates the need to differentiate the assessment of damage threats. The applied statistical non-parametric Mann-Whitney U-test for the series of distances to landslides and meandering coefficients of watercourses in the area of the bridges, according to the indicator of grouping - the presence or absence of reconstructions, revealed that the samples by grouping for two types of bridges, reconstructed and not reconstructed, according parameters of distances to landslides and tortuosity coefficients, are dependent, that is, they belong to the same general population. Therefore, the distance to the landslide and the meandering coefficient of the channel are not the determining factors that affect the fact of the reconstruction of the bridge. Further analysis of the stability of bridges and bridge crossings through a detailed study of the engineering and geological conditions of the research area and the dynamics of the state of watercourses with the involvement of GIS tools should be considered logical and justified. Keywords: exogenous geological processes, landslides, engineering-geological zoning, bridge crossings, geoinformation analysis.

Drought Assessment and Monitoring for Rajshahi District Using Meteorological Drought Indices

An analysis for drought condition has been made in the Rajshahi district of Bangladesh. The assessment of drought for the country has become an urgent need. The agriculture of the country is dependent on the amount of precipitation. As drought and precipitation are inversely related so it is desired to know the condition of Rajshahi. The main characteristics that define drought are the absence of precipitation for a lengthy period and a low distribution of rainfall in contrast to the normal climatic condition. The monthly maximum average temperature data, monthly minimum average temperature data and total monthly precipitation data for the last 48 years (1971-2019) from Bangladesh Meteorological Department (BMD) are utilized for the analysis process. An online software named DrinC is preferred due to its open-source platform and its user-friendly interface. The meteorological data serve as inputs for the software. The Deciles Index (DI), the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) have been used to identify the drought condition of Rajshahi. The values of the DI and SPI are generated from the precipitation data. However, for the generation of the RDI, the precipitation data as well as the potential evapo-transpiration (PET) data, which is an intermediate data that is generated from the two different types of temperature data via DrinC software are used. The values of SPI, RDI and DI values are presented in a tabular as well as in graphical form. From the comparison of these data and from the study of the recurrence interval the state and the repetition of drought in Rajshahi district can be determined.

Rainfall erosivity in the Republic of North Macedonia: Fournier index approach

Rainfall erosivity is a crucial factor influencing soil erosion, which results from the detachment, transportation, and deposition of soil particles caused by rainfall and runoff. Rainfall erosivity assessment is a fundamental process aimed at quantifying the erosive power of rainfall events in a given region. The paper provides an overview of rainfall erosivity assessment in several heterogeneous regions of the Republic of North Macedonia based on rainfall data from meteorological stations characterized by different climatic and geographical conditions. Having into consideration that rainfall erosivity can be quantified by using various indices, this study uses two common ones: Fournier Index (FI) and the Modified Fournier Index (MFI) based on monthly and annual precipitation totals for the period 1951-2020. The results imply that in general, the studied stations belong to a low class of erosivity. It is found that FI has greater sensitivity and provides more details, also showing years with high and even very high rainfall erosivity while MFI gives more information for moderate rainfall erosivity.

SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States

Abstract. The annual area burned due to wildfires in the western United States (WUS) increased by more than 300 % between 1984 and 2020. However, accounting for the nonlinear, spatially heterogeneous interactions between climate, vegetation, and human predictors driving the trends in fire frequency and sizes at different spatial scales remains a challenging problem for statistical fire models. Here we introduce a novel stochastic machine learning (SML) framework, SMLFire1.0, to model observed fire frequencies and sizes in 12 km × 12 km grid cells across the WUS. This framework is implemented using mixture density networks trained on a wide suite of input predictors. The modeled WUS fire frequency matches observations at both monthly (r=0.94) and annual (r=0.85) timescales, as do the monthly (r=0.90) and annual (r=0.88) area burned. Moreover, the modeled annual time series of both fire variables exhibit strong correlations (r≥0.6) with observations in 16 out of 18 ecoregions. Our ML model captures the interannual variability and the distinct multidecade increases in annual area burned for both forested and non-forested ecoregions. Evaluating predictor importance with Shapley additive explanations, we find that fire-month vapor pressure deficit (VPD) is the dominant driver of fire frequencies and sizes across the WUS, followed by 1000 h dead fuel moisture (FM1000), total monthly precipitation (Prec), mean daily maximum temperature (Tmax), and fraction of grassland cover in a grid cell. Our findings serve as a promising use case of ML techniques for wildfire prediction in particular and extreme event modeling more broadly. They also highlight the power of ML-driven parameterizations for potential implementation in fire modules of dynamic global vegetation models (DGVMs) and earth system models (ESMs).

Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016-2018: a spatial temporal analysis.

Temperature, precipitation, relative humidity (RH), and Normalized Different Vegetation Index (NDVI), influence malaria transmission dynamics. However, an understanding of interactions between socioeconomic indicators, environmental factors and malaria incidence can help design interventions to alleviate the high burden of malaria infections on vulnerable populations. Our study thus aimed to investigate the socioeconomic and climatological factors influencing spatial and temporal variability of malaria infections in Mozambique. We used monthly malaria cases from 2016 to 2018 at the district level. We developed an hierarchical spatial-temporal model in a Bayesian framework. Monthly malaria cases were assumed to follow a negative binomial distribution. We used integrated nested Laplace approximation (INLA) in R for Bayesian inference and distributed lag nonlinear modeling (DLNM) framework to explore exposure-response relationships between climate variables and risk of malaria infection in Mozambique, while adjusting for socioeconomic factors. A total of 19,948,295 malaria cases were reported between 2016 and 2018 in Mozambique. Malaria risk increased with higher monthly mean temperatures between 20 and 29°C, at mean temperature of 25°C, the risk of malaria was 3.45 times higher (RR 3.45 [95%CI: 2.37-5.03]). Malaria risk was greatest for NDVI above 0.22. The risk of malaria was 1.34 times higher (1.34 [1.01-1.79]) at monthly RH of 55%. Malaria risk reduced by 26.1%, for total monthly precipitation of 480 mm (0.739 [95%CI: 0.61-0.90]) at lag 2 months, while for lower total monthly precipitation of 10 mm, the risk of malaria was 1.87 times higher (1.87 [1.30-2.69]). After adjusting for climate variables, having lower level of education significantly increased malaria risk (1.034 [1.014-1.054]) and having electricity (0.979 [0.967-0.992]) and sharing toilet facilities (0.957 [0.924-0.991]) significantly reduced malaria risk. Our current study identified lag patterns and association between climate variables and malaria incidence in Mozambique. Extremes in climate variables were associated with an increased risk of malaria transmission, peaks in transmission were varied. Our findings provide insights for designing early warning, prevention, and control strategies to minimize seasonal malaria surges and associated infections in Mozambique a region where Malaria causes substantial burden from illness and deaths.

Radial growth responses of three coniferous species to climate change on the southern slope of Funiu Mountains, China.

Funiu Mountains are located in a transition region between warm temperate zone and northern subtropical region, where a variety of plant species are distributed with sensitive response to climate change. Their response characteristics to climate change are still unclear. We developed the basal area increment (BAI) index chronologies of Pinus tabuliformis, P. armandii, and P. massoniana in the Funiu Mountains to examine their growth trend and their sensitivity to climatic change. The results showed that the BAI chronologies gave a clue that the three conife-rous species had similar radial growth rate. The large Gleichlufigkeit (GLK) indices among the three BAI chronologies also indicated that the three species had a similar growth trend. Results of correlation analysis showed that the three species also had similar response to climatic change to a certain extent. Radial growth of all the three species was significantly positively correlated with the total monthly precipitation in December of previous year and June of the current year, but negatively correlated with the precipitation in September and the mean monthly temperature in June of the current year. There were some differences in the responses of the three coniferous to climate change. P. massoniana had a significant negative correlation with the mean temperature in March, and a significant positive correlation with the precipitation in March, while P. armandii and P. massoniana were affected negatively by the maximum temperature in August. Results of the moving correlation analysis showed that the three coniferous species had some similar sensitivity to climate change. Their positive responses to precipitation in previous December consistently increased, as well as the negative correlation with precipitation in current September. As to P. masso-niana, they had a relatively stronger climatic sensitivity and higher stability than the other two species. It would be more suitable for P. massoniana trees on the southern slope of the Funiu Mountains under global warming.

Climate and topography rather than tree species affect mite communities (Mesostigmata) in severe conditions—Studies from sandstone rocky islands of the Szczeliniec Wielki Plateau (Poland)

AbstractRelationships between certain plant species and soil fauna assemblages in severe climatic conditions require scientific attention due to reported climatic changes. We investigated density, diversity, and community structure of soil mites (Mesostigmata) in tree biogroups of Betula pendula, Picea abies, and Pinus sylvestris with microhabitats represented by Calluna vulgaris, Dicranum scoparium, Vaccinium myrtillus, and Vaccinium vitis‐idaea plant patches and accumulated litter in humid and cold rock crevices a few meters deep. We analyzed the effects of climatic (light availability, precipitation, temperature) and environmental (litter pH, organic matter thickness, microhabitat type) conditions on mite communities based on 1080 samples collected during two vegetation periods (2004–2005). Results revealed that litter pH was shaped by microhabitat type (plant patches and rock crevices) whereas organic matter thickness depended on tree species and microhabitat type. Also, light availability differed between tree biogroups and microhabitats. This study documented that Mesostigmata abundance was shaped by maximum daily temperature while species richness was also affected by total monthly precipitation, tree species (P. sylvestris) and microhabitat (rock crevices). Results revealed that mite density ranged from 3001 ± 213 ind./m2 in the P. abies biogroup to 3310 ± 350 ind./m2 in P. sylvestris. On the microhabitat level, mite density ranged from 2571 ± 170 ind./m2 in V. myrtillus patches to 4137 ± 463 ind./m2 in rock crevices. Total number of recorded species was highest in rock crevices (41 species), and lowest in C. vulgaris (19). In conclusion, results indicated that climatic conditions modulate mite communities on an isolated rocky island. This study documented that maximum daily temperature during the vegetation period affected Mesostigmata abundance, whereas species richness and diversity were also shaped by total monthly precipitation and tree species (P. sylvestris). In addition, the community was also affected by litter acidity, organic matter and tree species.

Evaluation of Rainfall Erosivity in the Western Balkans by Mapping and Clustering ERA5 Reanalysis Data

The Western Balkans (WB) region is highly prone to water erosion processes, and therefore, the estimation of rainfall erosivity (R-factor) is essential for understanding the complex relationships between hydro-meteorological factors and soil erosion processes. The main objectives of this study are to (1) estimate the spatial-temporal distribution R-factor across the WB region by applying the RUSLE and RUSLE2 methodology with data for the period between 1991 and 2020 and (2) apply cluster analysis to identify places of high erosion risk, and thereby offer a means of targeting suitable mitigation measures. To assess R-factor variability, the ERA5 reanalysis hourly data (0.25° × 0.25° spatial resolution) comprised 390 grid points were used. The calculations were made on a decadal resolution (i.e., for the 1990s, the 2000s, and the 2010s), as well as for the whole study period (1991–2020). In order to reveal spatial patterns of rainfall erosivity, a k-means clustering algorithm was applied. Visualization and mapping were performed in python using the Matplotlib, Seaborn, and Cartopy libraries. Hourly precipitation intensity and monthly precipitation totals exhibited pronounced variability over the study area. High precipitation values were observed in the SW with a >0.3 mm h−1 average, while the least precipitation was seen in the Pannonian Basin and far south (Albanian coast), where the mean intensity was less than an average of 0.1 mm h−1. R-factor variability was very high for both the RUSLE and RUSLE2 methods. The mean R-factor calculated by RUSLE2 was 790 MJ mm ha−1·h−1·yr−1, which is 58% higher than the mean R-factor obtained from RUSLE (330 MJ mm ha−1·h−1·yr−1). The analysis of the R-factor at decadal timescales suggested a rise of 14% in the 2010s. The k-means algorithm for both the RUSLE and RUSLE2 methods implies better spatial distribution in the case of five clusters (K = 5) regarding the R-factor values. The rainfall erosivity maps presented in this research can be seen as useful tools for the assessment of soil erosion intensity and erosion control works, especially for agriculture and land use planning. Since the R-factor is an important part of soil erosion models (RUSLE and RUSLE2), the results of this study can be used as a guide for soil control works, landscape modeling, and suitable mitigation measures on a regional scale.

ВОЗМОЖНОЕ РАСШИРЕНИЕ НА ЮГЕ РОССИИ КЛИМАТИЧЕСКОГО АРЕАЛА СРЕДИЗЕМНОМОРСКОЙ ПЛОДОВОЙ МУХИ - ОПАСНОГО ВРЕДИТЕЛЯ ПЛОДОВЫХ КУЛЬТУР

The Mediterranean fruit fly is a significant agroclimatic threat to the fruit production. It can damage up to 80-100% of fruits on 45-78% of agricultural land in some countries. The modern climatic range of the pest is analyzed in the present paper using computational data generated by the Voeikov Main Geophysical Observatory regional climate model and the global climate model of Marchuk Institute of Numerical Mathematics. The species requirements for climate conditions are: the sum of active temperatures (a threshold is 10°C) above 2700°C; monthly mean temperatures for December-February are positive; monthly precipitation totals in December-February exceed 13 mm. In Russia, such conditions are observed on the Black Sea coast and in a small part of the southern Caspian lowland. The assessment of changes in the climatic range of the pest in 2050-2059 versus 1990-1999 under the RCP8.5 scenario showed the expansion of the climatically suitable territory. The territory will almost completely include such traditional fruit growing regions as the Krasnodar krai, the Republic of Crimea, and the Republic of Adygea. Strict quarantine measures and early resource allocation that quickly suppresses infestations of the Mediterranean fruit fly may be proposed as adaptation measures.

Analysis of Precipitation Data Using Innovative Trend Pivot Analysis Method and Trend Polygon Star Concept: A Case Study of Soan River Basin, Potohar Pakistan

Abstract The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors. Significance Statement The monthly polygonal trends with risk graphs of total monthly precipitation data depicted a clear picture of climate change effects in the Potohar region of Pakistan. The monsoonal rainfall showed a significant decreasing trend in highland stations and an increasing trend in lowland stations, indicating a shifting pattern of monsoonal rainfall from highland to lowland areas.

Precipitation amount and event intervals interact to change plant diversity during dry years in a desert shrubland

Variations in species diversity and soil properties in response to future climate change is a key focus area in ecological research. The influence of precipitation amount on plant community diversity and soil properties, in sandy desert ecosystems, is poorly studied. We chose the Artemisia ordosica community to examine how diversity, and the physical and chemical properties of soil vary in response to changes in precipitation. An in situ experimental platform was used to simulate total monthly precipitation at specific intervals in a desert-grassland region of northern China. The influence of precipitation on the community was investigated using three precipitation amount levels and two intervals. The relative density, coverage, and frequency of species in each experimental quadrat was analyzed for evaluating the community structure. Results showed that variability in precipitation amounts and intervals altered the species diversity in the A. ordosica communities. Increased precipitation amount enhanced whole community’s species diversity via improving the richness of perennial forbs. Lengthening the precipitation intervals enhanced whole community’s species diversity in the context of low precipitation amount but reduced species diversity in high precipitation amount condition. The precipitation patterns affected community species composition and diversity mainly by controlling soil moisture, secondly by changing the nitrogen and organic matter content of the surficial soils. This study revealed that effects of precipitation interval on the species diversity depended on annual precipitation amount and offer an innovative insight to evaluating the succession of desert ecosystems in the context of precipitation change in the future.