Spatial Distribution Of Precipitation Research Articles

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.

Spatial and Temporal Evolution of Precipitation in the Bahr el Ghazal River Basin, Africa

Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives for hydrological applications in the Bahr el Ghazal River Basin in South Sudan, Africa. This includes examining the spatial and temporal evolution of regional precipitation using relatively accurate precipitation datasets. The findings indicate that CRU TS is the best precipitation dataset in the Bahr el Ghazal Basin. The spatial and temporal distributions of precipitation from CRU TS reveal that precipitation in the Bahr el Ghazal Basin has a clear wet season, with June–August accounting for half of the annual precipitation and peaking in July and August. The long-term annual total precipitation exhibits a gradual increasing trend from the north to the south, with the southwestern part of the Basin having the largest percentage of wet season precipitation. Notably, the Bahr el Ghazal Basin witnessed a significant precipitation shift in 1967, followed by an increasing trend. Moreover, the spatial and temporal precipitation evolutions reveal an ongoing risk of flooding in the lower part of the Basin; therefore, increased engineering counter-measures might be needed for effective flood prevention.

Precipitation Changes on the Northern Slope of the Kunlun Mountains in the Past 42 Years

The precipitation on the northern slope of the Kunlun Mountains significantly impacts the green economy of the Tarim Basin’s southern edge. Observations have noted an expansion of the surface water area in this region, though the reasons for this are not yet fully understood. Due to limited instrumental data, this study leverages field measurements from the third Xinjiang comprehensive expedition and multiple gridded datasets. Through trend analysis and a geographical detector model, it examines the precipitation’s decadal, interannual, and seasonal variations across key areas (Hotan River Basin, Keriya River Basin, Qarqan River Basin, and Kumukuli Basin), identifying factors behind the spatial and temporal distribution of regional precipitation. The findings reveal the following: (1) An increase in annual precipitation across the region from 187.41 mm in the 1980s to 221.23 mm in the early 21st century, at a rate of 10.21 mm/decade, with the most significant rise in the eastern Kunlun-Kumukuli Basin. (2) Precipitation exhibits clear seasonal and spatial patterns, predominantly occurring in spring and summer, accounting for 90.27% of the annual total, with a general decrease from the mountains towards downstream areas. (3) Rising average annual temperatures contribute to an unstable atmospheric structure and increased water-holding capacity, facilitating precipitation. Significant influences on precipitation changes include the North Atlantic Oscillation and solar flux, explaining 43.98% and 31.21% of the variation, respectively.

Revealing hidden diversity and community dynamics of land snails through DNA barcoding: implications for conservation and ecological studies

IntroductionLand snails play a crucial role in maintaining ecosystem sustainability within their habitats. Therefore, understanding the characteristics of their communities is vital for ecological studies and the development of effective conservation strategies. In this study, land snail communities inhabiting the Hyrcanian forest were identified and the variations in their community composition along elevational gradients were investigated.MethodsSnail samples were collected from three distinct elevations in three different forest locations within the Hyrcanian area of Iran. This study utilized DNA barcoding to identify land snail species. By employing statistical analyses such as ANOVA and PERMANOVA, significant differences in the features of snail communities across different elevations were examined. Concurrently, soil samples were collected from each site to assess soil physicochemical parameters about snail presence.ResultThrough this comprehensive analysis, a total of 10 OTUs, were further classified into seven families, and nine genera were identified. Five of these genera had never been reported in the study region before. We observed a decline in OTU richness with increasing elevation; however, the maximum abundance of snails was found at higher elevations. CCA demonstrated that Ca, Mg, and moisture saturation predominantly shape snail community composition.DiscussionThe unique climatic conditions and spatial distribution of precipitation from lowlands to highlands, as well as from west to east, make the Hyrcanian forests an ideal case study area for understanding the dynamics of land snail communities. In summary, this study provides new insights into the land snail communities thriving in the Hyrcanian forests. The findings from our research can contribute to the development of effective conservation management strategies for forest ecosystems. By understanding the factors influencing the distribution and composition of land snail communities, we can make informed decisions to protect and preserve land snails and the balance they maintain within their habitats.

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

The Inner Mongolia Reach of the Yellow River Basin is characterized by a relative scarcity of meteorological stations. While satellite precipitation products can complement observations from meteorological stations, their limited spatial resolution restricts their efficacy in regional studies. This study utilizes the GPM IMERG precipitation dataset, considering various factors that influence the spatial distribution of precipitation, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, aspect, and topographical relief, to construct a multiscale geographically weighted regression (MGWR) model. A spatial downscaling method for the GPM IMERG precipitation dataset is proposed, and its reliability is validated through an accuracy assessment. Moreover, the scale differences in the impact of different factors on the spatial pattern of precipitation in the Inner Mongolia Reach of the Yellow River Basin are scrutinized. The results indicate that: 1) The downscaled GPM IMERG precipitation data (1 km × 1 km) exhibit enhanced accuracy compared to the pre-downscaled data (approximately 11 km × 11 km). The correlation coefficient, Bias, and RMSE of the annual precipitation data after downscaling of GPM IMERG are 0.865, 6.05%, and 68.50 mm/year, respectively. For the monthly downscaled precipitation data, the correlation coefficient, Bias, and RMSE are 0.895, 6.09%, and 16.25 mm/month, respectively. The downscaled GPM IMERG precipitation dataset exhibit high accuracy on both annual and monthly temporal scales. 2) Different factors demonstrate localized effects on precipitation in both dry and wet years. Elevation is the dominant factor influencing the spatial heterogeneity of annual precipitation. The findings from this study can provide technical support for hydrological modeling, drought monitoring, and water resource management in data-scarce areas of the Inner Mongolia Reach of the Yellow River Basin.

Rainfall Repercussions: Assessing Climate Change Influence on Iraq Precipitation Patterns

The unequal spatial and temporal distribution of precipitation, exacerbated by climate change, has received significant attention. Rainfall is pivotal for crop growth and environmental health, crucial in the water cycle, and essential to replenishing surface water sources vital for drinking water supplies. Consequently, understanding this phenomenon is critical for future planning. Evaluating spatial and temporal variations in rainfall is essential for the effective management of water resources. This study employs a statistical analysis of rainfall data from 16 rain gauge stations to identify annual rainfall trends, Linear Regression Equations, Coefficients of Determination (R²), Precipitation Concentration Index (PCI), and Rainfall Variability Index (RVI). The findings of the study come to show as the PCI has indicated strongly irregular rainfall concentration in Iraq. RVI identifies 2017 and 1983 as notably dry years, while 2018 stands out as a particularly wet year within the 40-year period from 1980 to 2019. RVI also highlights that normal rainfall years predominate, with very dry years ranging from 2 to 13, dry years from 3 to 11, wet years from 1 to 7, and very wet years from 6 to 10 within the study period.

Concurrent and dynamical interdependency of compound precipitation and wind speed extremes over India

Compound precipitation and wind speed extremes have the potential to cause significant socio-economic losses. Therefore, this study aims to investigate the association of compound precipitation and wind speed extremes in observed period (1981–2020) and future periods (2021–2060 (F1) and 2061–2100 (F2)) over India. In this study, we have used IMD precipitation and ERA5 windspeed gridded (0.250 × 0.250) data for the observed period. For future analysis, we have used CMIP6-based EC-Earth3 GCM (SSP2–4.5 and SSP5–8.5) and CORDEX CMIP5 RCM (RCP-4.5 and RCP-8.5) climate models for precipitation and wind speed outputs. We have analyzed the characteristics of precipitation and wind speed extremes using three approaches: empirical method, tail dependence analysis, and event coincidence analysis. The empirical analysis revealed that the proportion of the area with >240 Compound Precipitation and Wind speed Extremes (CPWE) on the same day is 2.8% in observed period. However, it increased to 22.22% in future period F2, particularly in the SSP5–8.5 scenario of the CMIP6 case, and this increase is observed in regions like Am, eastern parts of Aw and Cwa, Bwk, and Dsb. The tail dependence analysis revealed that the area with tail dependence >0.5 constitutes approximately 0.72% in the observed period across the study area. However, under climate change scenarios, this percentage varies between 2.45% to 3.66% and notably, the increase is observed in regions such as Am, Bwk, and Dsb. The results of precursor coincidence rate (pcr) shows a high value of 3.52% in the entire study area for the observed period. The future analysis reveals an increase of areas in between 3.18% to 5.14% for different climate change scenarios. Consequently, the trigger coincidence rate (tcr) results also revealed the occurrence of high tcr in the range of 0.52% to 5.79% across the study area within the temporal window. However, the tcr range increases to 2.17% to 23.32% during the monsoon season during the observed period with respect to the days in the temporal window. The overall results showed that the high values of tail dependence, pcr and tcr are concentrated in the Am, coastal areas of Aw and Cwa, as well as in the Bwk and Dsb regions. This will lead to increase the extreme precipitation driven by extreme wind speed in those regions consequently, the risk of flash floods, landslides and natural hazards may rise in the future. Overall, this study provides valuable insights into the spatial distribution of compound precipitation and wind speed extremes across the study area.

Spatial Inhomogeneity of Synoptic‐Induced Precipitation in a Region of Steep Topographic Relief: A Case Study

AbstractThe topography plays an essential role in the initiation and development of precipitating clouds, therefore has a profound effect on the ultimate spatial distribution of precipitation. This study investigates the fine‐scale characteristics of a synoptic‐induced precipitation event in Southwest China, a region characterized by a sequence of steep mountains aligned roughly north‐south. The convection‐permitting simulation successfully reproduces the observed rainband induced by a synoptic‐scale shear line. The spatial distribution of accumulated precipitation over three small‐scale mountains (named M1, M2, and M3 from east to west) exhibits distinct inhomogeneity. The accumulated precipitation is significantly enhanced on the western slope of M1, the high‐altitude area of M2, and the eastern slope of M3. The low‐level vortex generated on the western slope of M1, as well as the convergence established over M2 and the eastern slope of M3, dynamically contributes to the enhanced precipitation over the various mountain locations. As the highest of the three mountains, M2 exhibits pronounced blocking effects on the mesoscale circulations. Additional sensitivity experiment demonstrates that the mesoscale circulations and corresponding precipitation are sensitive to the highest elevation in the continuous mountains. The rainfall accumulation over M2 (M3) could decrease (increase) by 54.3% (63.4%), when the terrain of M2 is reduced to the comparable height of surrounding mountains. With more upslope wind flowing over M2, the convergence over M3 is strengthened. The more intense upward motion and stronger potential instability both contribute to the notable increase of precipitation over M3. This study implies the possible mechanisms of the inhomogeneous precipitation over Southwest China and could deepen the current understanding of the topographic effects on precipitation over complex terrains.

Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia

Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation gauge data, whether due to improper gauge coverage or gaps in the recorded data. Several alternatives are available to compensate for deficiencies in terrestrial rain gauge records, such as satellite data or utilizing geostatistical interpolation. However, adequate assessment of these alternatives is mandatory to avoid the dramatic effect of using improper data in the design of flood protection works and water resource management. The current study covers 75% of the Kingdom of Saudi Arabia’s area and spans the period from 1967 to 2014. Seven satellite precipitation datasets with daily, 3-h, and 30-min temporal resolutions, along with 43 geostatistical interpolation techniques, are evaluated as supplementary data to address the gaps in terrestrial gauge records. The Normalized Root Mean Square Error by the mean value of observation (NRMSE) is selected as a ranking criterion for the evaluated datasets. The geostatistical techniques outperformed the satellite datasets with 0.69 and 0.8 NRMSE for the maximum and total annual records, respectively. The best performance was found in the areas with the highest gauge density. PERSIANN-CDR and GPM IMERG V7 satellite datasets performed better than other satellite datasets, with 0.8 and 0.82 NRMSE for the maximum and total annual records, respectively. The spatial distributions of maximum and total annual precipitation for every year from 1967 to 2014 are generated using geostatistical techniques. Eight Probability Density Functions (PDFs) belonging to the Gamma, Normal, and Extreme Value families are assessed to fit the gap-filled datasets. The PDFs are ranked according to the Chi-square test results and Akaike information criterion (AIC). The Gamma, Extreme Value, and Normal distribution families had the best fitting over 56%, 34%, and 10% of the study area gridded data, respectively. Finally, the selected PDF at each grid point is utilized to generate the maximum annual precipitation for 2, 5, 10, 25, 50, and 100-year rasters that can be used directly as a gridded precipitation input for hydrological studies.

Interannual Variation of Half-Hourly Extreme Precipitation in Africa: The Assessment of the Warmest Decade (2011–2020) Since the Beginning of Observations

AbstractAfrica is experiencing widespread extreme precipitation. However, the continent remains understudied regarding extreme precipitation variation, particularly at high temporal resolution. This study assessed the changes in spatial distribution, frequency, and intensity of half-hourly violent precipitation (≥ 50 mm/h) in Africa during 2011–2020. The data were sourced from the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) program. The results reflect that the 50–60 mm/h precipitation category was the most densely spatially distributed with less-discernable interannual changes. There was a strong interannual variation in the spatial distribution of the 60–70 mm/h and ≥ 70 mm/h precipitation categories. There was a general decrease in the frequency of occurrence of violent precipitation in all regions, except in North Africa (NA). Among the study regions, NA had the broadest precipitation intensity distribution extended to high values and consequently, on average, the most intense violent precipitation, followed by Southern Africa (SA) and Southwest Indian Ocean (SWIO). The regions in the tropics, i.e., West Africa (WA), Central Africa (CA), and East Africa (EA), had a relatively narrow intense violent precipitation distribution. The results further illustrate that the strength of climate variability modes has a strong influence in the distribution, frequency, and intensity of violent precipitation over the continent particularly the ≥ 70 mm/h precipitation category. This study underscores a need for a strengthened capacity in forecasting climate variability modes impacting on Africa, augmentation of continental precipitation observation network, and improved extreme precipitation adaptation infrastructure.

Stochastic in Space and Time: 1. Characterizing Orographic Gradients in Mean Runoff and Daily Runoff Variability

AbstractMountain topography alters the phase, amount, and spatial distribution of precipitation. Past efforts focused on how orographic precipitation can alter spatial patterns in mean runoff, with less emphasis on how time‐varying runoff statistics may also vary with topography. Given the importance of the magnitude and frequency of runoff events to fluvial erosion, we evaluated whether orographic patterns in mean runoff and daily runoff variability can be constrained using the global WaterGAP3 water model data. Model runoff data are validated against observational data in the contiguous United States, showing agreement with mean runoff in all settings and daily runoff variability in settings where rainfall‐runoff predominates. In snowmelt‐influenced settings, runoff variability is overestimated by the water model data. Cognizant of these limitations, we use the water model data to develop relationships between mean runoff and daily runoff variability and how these are mediated by snowmelt fraction in mountain topography globally. A global analysis of topographic controls on hydroclimatic variables using a random forest model was ambiguous. Instead, relationships between topography and runoff parameters are better assessed at the mountain range scale. Rulesets linking topography to mean runoff and snowmelt fraction are developed for three mid‐latitude mountain landscapes—British Columbia, European Alps, and Greater Caucasus. Increasing topographic elevation and relief together leads to higher mean runoff and lower runoff variability due to the increasing contribution of snowmelt. The three sets of empirical relationships developed here serve as the basis for a suite of numerical experiments in our companion manuscript (Part 2, Forte & Rossi, 2024a, https://doi.org.10.1002/2023JF007327).

Applicability of Precipitation Products in the Endorheic Basin of the Yellow River under Multi-Scale in Time and Modality

Continuous and accurate precipitation data are critical to water resource management and eco-logical protection in water-scarce and ecologically fragile endorheic or inland basins. However, in typical data-scarce endorheic basins such as the endorheic basin of the Yellow River Basin (EBYRB) in China, multi-source precipitation products provide an opportunity to accurately capture the spatial distribution of precipitation, but the applicability evaluation of multi-source precipitation products under multi-time scales and multi-modes is currently lacking. In this context, our study evaluates the regional applicability of seven diverse gridded precipitation products (APHRODITE, GPCC, PERSIANN-CDR, CHIRPS, ERA5, JRA55, and MSWEP) within the EBYRB considering multiple temporal scales and two modes (annual/monthly/seasonal/daily precipitation in the mean state and monthly/daily precipitation in the extreme state). Furthermore, we explore the selection of suitable precipitation products for the needs of different hydrological application scenarios. Our research results indicate that each product has its strengths and weaknesses at different time scales and modes of coupling. GPCC excels in capturing annual, seasonal, and monthly average precipitation as well as monthly and daily extreme precipitation, essentially meeting the requirements for inter-annual or intra-annual water resource management in the EBYRB. CHIRPS and PERSIANN-CDR have higher accuracy in extreme precipitation assessment and can provide near real-time data, which can be applied as dynamic input precipitation variables in extreme precipitation warnings. APHRODITE and MSWEP exhibit superior performance in daily average precipitation that can provide data for meteorological or hydrological studies at the daily scale in the EBYRB. At the same time, our research also exposes typical problems with several precipitation products, such as MSWEP’s abnormal assessment of summer precipitation in certain years and ERA5 and JRA55’s overall overestimation of precipitation assessment in the study area.

Assessment of mean precipitation and precipitation extremes in Iran as simulated by dynamically downscaled RegCM4

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991-2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000s compared to the 1990s.

Comparative Analysis of the Latest Global Oceanic Precipitation Estimates from GPM V07 and GPCP V3.2 Products

Abstract Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understanding how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM) Core Observatory instruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the Merged CloudSat, TRMM, and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have a higher precipitation rate than the previous version, except for the radar-only product. Within ∼65°S–65°N, covered by all of the instruments, this increase ranges from about 9% for the combined radar–radiometer product to about 16% for radiometer-only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar-only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics (25°S–25°N), passive microwave sounders show the highest precipitation rate among all of the precipitation products studied and the highest increase (∼19%) compared to their previous version. Precipitation products are least consistent in midlatitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.

Modification of Microphysical Parameterization Schemes and Their Application in the Simulation of an Extremely Heavy Rainfall Event in Zhengzhou

AbstractIn the present study, the Morrison and Thompson microphysics schemes in the Weather Research and Forecasting model were improved and used to simulate a record‐breaking heavy rainfall event occurred in Henan Province over central China during 19–21 July 2021. In the modified schemes, the terminal velocity of graupel and initial cloud droplet concentration were adjusted based on sensitivity tests. Results showed that the modified Morrison scheme (MORR_MOD) better captured the spatial distribution and temporal evolution of precipitation than the original scheme (MORR). Specifically, it produced a 40‐hr cumulative rainfall amount of 963 mm and an extreme hourly rainfall rate of 157.7 mm, which were closer to observations than MORR. Analysis of the dynamic and microphysical structures of the extreme‐rain‐producing convective clusters revealed that MORR_MOD predicted stronger updrafts, higher rain mass content and larger mean mass diameter of raindrops than MORR. By analyzing the tendencies of rain mass and number concentration, it was found that MORR_MOD predicted stronger accretion rates of cloud droplets by rain and weaker auto‐conversion rates of cloud droplets than MORR, which contributed to the high rain mixing ratio and low number concentration, respectively. In addition, MORR_MOD predicted stronger diabatic heating rates than MORR, which were primarily attributed to stronger condensation of water vapor, and may have been the reason why MORR_MOD simulated stronger updrafts.

Mapping cultivation potential index of rainfed wheat from climatic perspective using Shannon entropy catastrophe scheme

Mapping the Cultivation Potential Index (CPI) for a specific crop is a topical research activity in agro-climate studies, which can lead to environmental and water resources protection and agriculture efficiency increase. This study formulates a framework to delineate the CPI of rainfed wheat from the climatic perspective of precipitation and temperature. Four precipitation layers were prepared using the frequency analysis of annual, autumn, spring, and June precipitation time series; also, three temperature layers comprise autumn, spring, and June temperatures obtained by remote sensing techniques. The Shannon-Entropy Catastrophe Scheme (ECS) was also formulated to combine seven layers of precipitation and temperature besides the land use layer while decreasing the inherent subjectivity. The formulated methodology was implemented in the Maragheh city, northwest of Iran. Some contrasts exist between the spatial distributions of precipitation in different time scales, which necessitates the formulation of ECS as a multi-criteria decision-making technique. ECS identifies the eastern half of the study area as the medium to relatively high CPI, which can be employed as a decision support system for planning and managing agriculture.

Analysis on spatio-temporal evolution and influencing factors of ecosystem service in the Changsha-Zhuzhou-Xiangtan urban agglomeration, China

Introduction: It is of great significance to strengthen the evaluation research and driving force analysis of ecosystem services value for the rational utilization and protection of the ecological environment of Changsha-Zhuzhou-Xiangtan (CZT) urban agglomeration and the promotion of the integration of urban agglomeration.Methods: Based on the remote sensing image data, the spatial and temporal evolution characteristics and influencing factors of the ecosystem services value of CZT urban agglomeration were analyzed by the methods of ArcGIS10.2, Geoda, value equivalent and spatial statistics.Results: The results showed that: 1) From 2000 to 2020, the total value of ecosystem service in the CZT urban agglomeration decreased gradually, with an overall decrease of 4,381.07 × 106 yuan. In the past 20 years, the ecosystem service Value (ESV) of cultivated land, forest land and grassland had declined, but the ESV of water area and unused land had fluctuated, and the single ESV had declined. 2) From 2000 to 2020, the spatial distribution of ESV in the CZT urban agglomeration showed an obvious pattern of “low in the middle and high in the surrounding areas”, and the changes were quite different in different periods. 3) The spatial correlation between ESV and distance from county government, distance from railway, proportion of construction land, NDVI, population density, economic density, slope and precipitation were significant. The spatial distribution of the distance from the county government, the distance from the railway, NDVI and ESV were similar; the population density and economic density were consistent with the spatial distribution of ESV; and the spatial distribution of construction land proportion, slope, precipitation and ESV were different.Discussion: The results of the study can provide some reference for the further development of ecological protection policies and related planning in urban agglomerations.

Application of TRMM for Spatio-Temporal Analysis of Precipitation in the Taiwan Strait and Its Adjacent Regions

Precipitation patterns are highly valued in the fields of weather forecasting, water resource management, and estuary environment research. In this study, daily and monthly precipitation TRMM data from 1998 to 2019 were selected, and EOF analysis was employed to analyze the precipitation patterns of the Taiwan Strait and its neighboring regions. We obtained the following results: (1) The rainy season (May–June) is the main contributor to precipitation in the study area. The EOF first mode reflected the overall consistency of the precipitation spatial distribution. However, within each river basin, the magnitude of precipitation variation is spatially different. The magnitude of precipitation variation is significant in the northwestern part of the Minjiang River basin, the southwestern part of the Jiulong River basin, and the southwestern corner of the Hanjiang River basin. These areas happen to correspond to the mountain areas, revealing that topographic precipitation plays a role in the spatial distribution of precipitation in the three river basins. (2) The spatial distributions of the EOF first mode and of precipitation during El Niño in the Minjiang River basin are consistent. This reveals that ENSO is probably the dominant factor in precipitation in the Minjiang River basin. The significant increase in precipitation during El Niño compared with a normal year in the Minjiang River basin confirms this point. (3) In all three strong El Niño years, 1998, 2010, and 2016, the Minjiang River basin experienced significant heavy precipitation in the fall and winter, whereas the Jiulong River and Hanjiang River basins did not (except in 2016). In other words, the Minjiang River basin is more affected by ENSO, while the Jiulong River and Hanjiang River basins are only limitedly impacted by ENSO.

Entropy based approach for precipitation monitoring network in Bihar, India

Study regionBihar State, located in India’s eastern region, displays significant spatial and temporal variation in rainfall during the Indian Summer Monsoon period with subsequent flooding problems. Study focusRecent severe flooding problems highlight the need for improved spatial precipitation monitoring to enable effective flood management and reduce water-related disasters. To address this challenge, we employed Shannon entropy theory to assess the spatial distribution of precipitation and identify critical areas for rain gauge network improvements. We used Principal of Maximum Entropy (POME) to compute entropy measures and Value of Monitoring (VOM) with Thiessen polygons, and Adjacent Station Groups (ASGs). New hydrological insights for the regionThe results showed that the Marginal Entropy (ME) values lie between 0.039 and 0.048. The maximum values of ME are in the northeast area of the study region, exhibiting larger complexity and variability in the environmental conditions typical for northeast Bihar. The VOM was in the range of − 1 to + 1 suggesting strategic placement of additional 12 rain gauge stations to improve the existing monitoring network. The new locations were in the south mountainous area, the east, and the northwest, enhancing network coverage and addressing spatial and temporal precipitation variability. These findings support the design of a more effective monitoring network and have significant implications in hydrological modelling, flood prediction, and water resources management.

Analysis of Changes in Rainfall Concentration over East Africa

Understanding the spatial and temporal distribution of precipitation is important in agriculture, water management resources, and flood disaster management. The present study analyzed the changes in rainfall concentration over East Africa (EA). Three matrices—the precipitation concentration index (PCI), the precipitation concentration degree (PCD), and the precipitation concentration period (PCP)—were used to examine the changes in rainfall during 1981–2021. The changes in spatial variance annually and during two seasons, namely, “long rains” (March to May [MAM]) and “short rain” (October to December [OND]), were estimated using an empirical orthogonal function (EOF). The study employed the robust statistical metrics of the Theil–Sen estimator to detect the magnitude of change and modified Mann–Kendall (MMK) to examine possible changes in rainfall concentration. The localized variation of the power series within the series for PCI, PCD, and PCP variability was performed using the continuous wavelet transform. The findings showed that the concentration of rainfall patterns of EA occurred in four months of the total months in a year over most parts, with the western sides experiencing uniform rainfall events throughout the year. The EOF analysis revealed a homogeneous negative pattern during the MAM season over the whole region for PCD, PCI, and PCP for the first mode, which signified reduced rainfall events. Moreover, the MMK analysis showed evidence of declining trends in the PCD annually and during the MAM season, while the opposite tendency was noted for the OND season where an upward trend in the PCD was observed. Interestingly, areas adjacent to Lake Victoria in Uganda and Lake Tanganyika in Tanzania showed increasing trends in the PCD for annual and seasonal time scales. The analysis to characterize the rainfall cycle and possible return period, considering the indices of PCD, PCI, and PCP, showed higher variability during the year 2000, while much variability was presented in the PCP for the annual period. During the MAM and OND seasons, a 1-year band as a dominant period of variability was observed in all the indices. Overall, the findings of the present study are crucial in detecting the observed changes in rainfall concentration for avoiding the loss of life and property, as well as for coping with potential changes in water resources.