Temporal Rainfall Patterns Research Articles

Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk

Under the context of global warming and rapid urbanization, cities worldwide confront the pressing problem of urban waterlogging, hindering progress towards Sustainable Development Goals. Effective planning and mitigation of urban flooding require a comprehensive understanding of the spatial and temporal patterns of rainfall and risk heterogeneity. However, evaluating urban water-logging risk is challenged by the need for city-scale hydrological simulation and generally lacks comprehensive metrics integrating fine-scale datasets. To address these gaps, we developed a simulation method for urban flood hazards by integrating hydrological models and Random Forest algorithms. We then took Shenzhen, a megacity in China, as a case study, and investigated the spatial patterns of urban flooding risk and its determinants at the block level based on the risk assessment framework represented by Hazards-Exposure-Vulnerability (H-E-V) dimensions. We found that socio-economic indicators exhibited spatial clustering, while hazard-related indicators displayed more dispersed patterns. High-risk areas exhibited a highly heterogeneous spatial pattern, predominantly influenced by vulnerability and exposure factors, as well as the spatial mismatch among the three dimensions. Our results emphasize the importance of integrating spatial heterogeneity of exposure and vulnerability into climate adaptation resource allocation, addressing both current and future demands for effective climate mitigation.

Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China

Abstract. The atmospheric–hydrological coupling systems are essential to flood forecasting because they allow for more improved and comprehensive prediction of flood events with an extended forecast lead time. Achieving this goal requires a reliable hydrological model system that enhances both rainfall predictions and hydrological forecasts. This study evaluates the potential of coupling the mesoscale numerical weather prediction model, i.e., the weather research and forecasting (WRF) model, with different hydrological modeling systems to improve the accuracy of flood simulation. The fully distributed WRF-Hydro modeling system and the semi-distributed Hydrological Engineering Center Hydrological Modeling System (HEC-HMS) were coupled with the WRF model, and the lumped HEC-HMS model was also adopted using the observed gauge precipitation as a benchmark to test the model uncertainty. Four distinct storm events from two mountainous catchments in northern China characterized by varying spatial and temporal rainfall patterns were selected as case studies. Comparative analyses of the simulated flooding processes were carried out to evaluate and compare the performance of the coupled systems with different complexities. The coupled WRF–HEC-HMS system performed better for long-duration storm events and obtained optimal performance for storm events uniformly distributed both temporally and spatially, as it adapted to more rapid recession processes of floods. However, the coupled WRF–HEC-HMS system did not adequately capture the magnitude of the storm events as it had a larger flow peak error. On the other hand, the fully distributed WRF–WRF-Hydro system performed better for shorter-duration floods with higher flow peaks as it can adapt to the simulation of flash floods. However, the performance of the system became poor as uniformity decreased. The performance of the lumped HEC-HMS indicates some source of uncertainty in the hydrological model when compared with the coupled WRF–HEC-HMS system, but a larger magnitude error was found in the WRF output rainfall. The results of this study can help establish an adaptive atmospheric–hydrologic coupling system to improve flood forecasting for different watersheds and climatic characteristics.

Evaluation of the characteristics of Indian summer monsoon simulated by CMIP6 models

AbstractThe present research is aimed at evaluating the climate coupled models with respect to the observational datasets for the investigation of the characteristics of the Indian summer monsoon (ISM). The monthly averaged historical simulations from 12 coupled climate models that participated in Coupled Model Intercomparison Project Phase 6 (CMIP6) are compared with the ground based observational data, satellite and reanalysis datasets for the study period of 1980–2014. This study uses these high‐resolution models to investigate monsoon features, onset and withdrawal dates, primarily focusing on individual model performances rather than highly documented multi‐model mean performance. Performance evaluation of the models suggests that sea surface temperature (SST) simulations by the models are in better agreement for the Arabian Sea than the Bay of Bengal with respect to the ERA5 reanalysis data sets. Further, inter‐model differences amongst the CMIP6 models in estimating the spatial distribution of various monsoon system variables during pre‐monsoon and monsoon seasons are noted which may be attributed to the different model components and varying physics configuration, nonetheless, models like NESM3 and INM‐CN5 are able to reproduce ISM pattern reasonably well. The annual precipitation cycle demonstrates a good agreement between most climate models and IMD data. Evolution of tropospheric temperature gradient (ΔTT) estimated from the CMIP6 models mimics the temporal pattern of the annual rainfall and therefore, is used to estimate the onset and withdrawal dates from CMIP6 models; however, high variability is noted amongst the CMIP6 models in retrieving the onset and withdrawal dates when compared with the IMD observations. Most of the models show a shorter rainy season except NorESM2‐MM. Overall our results suggest that the CMIP6 models can be used for the seasonal mean evaluation of monsoon system parameters and process‐based studies to improve our present understanding of the ISM system.

New insights into the catastrophic slope failures at Sau Mau Ping: the role of antecedent rainfall pattern

Climate change is increasing the frequency and intensity of extreme rainfall events, which aggravate the threat to the safety of natural and man-made slopes. There is growing interest in the role of rainfall characteristics in these slope failures. Most of previous studies treated the rainfall individually or as cumulative value and used hypothetical rainfall temporal patterns with no association with actual physical failures. In this study, the two deadly landslides in Sau Mau Ping, Hong Kong, in June 1972 and August 1976, which caused 165 casualties, are revisited. An intriguing question that has long been overlooked is posed: why the slopes that withstood the 1972 rainfall failed in the 1976 rainfall, given that the rainfall intensity of the latter event was only half of the former? Based on an extensive review of the forensic reports and relevant studies on the failure events, numerical modeling is carried out by a combination of seepage analysis with stability analysis and unsaturated shear strength theory. Focus is placed on the geological and hydrological settings and the rainfall characteristics, particularly the temporal pattern of the antecedent and main rainfall, to look into the causes and mechanisms for these failures. Implications of the new findings for future research and practice are also highlighted.

A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness.

Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.

Historical trends and future projections of annual rainfall from CMIP6 models in Ho Chi Minh City, Vietnam

Climate risks have posed a major threat to many local communities living in low-lying coastal megacities across the globe, including Ho Chi Minh City, Vietnam. Hence, this study first aimed to contribute towards a comprehensive understanding of temporal trend patterns of annual rainfall and absolute extremes in Ho Chi Minh City over the last 4 decades (1980-2022) through multiple non-parametric statistical trend tests. We employed the quantile delta mapping (QDM) method to develop daily bias-corrected rainfall data based on the outputs in the latest Coupled Model Intercomparison Project phase 6 (CMIP6) under 8 shared socio-economic pathway (SSP) greenhouse gas emission scenarios. Evaluation of model performance was implemented by repeatedly omitting 5 successive years in turn for estimating testing errors. The outcomes indicate the high applicability of well-calibrated transfer functions, even for high quantiles, to the production of future rainfall scenarios. The projected changes in annual rainfall and absolute extremes were obtained by estimating multi-model medians from CMIP6 models for future periods (i.e. 2021-2040, 2041-2060, 2061-2080, and 2081-2100), with reference to the base period (1995-2014). In general, annual rainfall in Ho Chi Minh City is projected to increase substantially, and Thu Duc station consistently shows the highest increases in annual rainfall. Projected changes are approximately 30.9% (8.3 to 77.8%) under the high-end scenario (i.e. SSP5-8.5) by the end of the 21st century. It is expected that these findings will yield several solid arguments for mitigating climate-related risks in Ho Chi Minh City.

Hydroclimatic changes in eastern China during the Holocene based on pollen data and climate modeling

The Asian monsoon system represents one of the world's most dynamic interactions of the cryosphere-continent-ocean-atmosphere system. Understanding and responding to changes in monsoonal precipitation is a major component of environmental management in this region, given its profound influence on socioeconomic activity in monsoonal Asia. In particular, characterizing the spatiotemporal variability of the East Asian summer monsoon (EASM) is critical for comprehensively understanding its dynamics and future impacts. Although substantial progress has been made in reconstructing past changes in the EASM, the spatial pattern of EASM intensity in China on the orbital-scale remains controversial. This is mainly because of uncertainties in the climatic interpretation of EASM-related proxies. Here, we present a quantitative seasonal precipitation reconstruction for monsoonal China during the Holocene, using the Modern Analog Technique (MAT) based on pollen data. The results show that during the Holocene both the annual and summer precipitation in monsoonal China increased gradually and reached a peak during the early mid-Holocene, with respective values that were ∼ 170 mm and 60 mm higher than today, followed by a decrease in the late Holocene. On the regional scale, the temporal pattern of monsoon rainfall was discrepant: a decrease from the early through late Holocene in northern China, and an increase from the early through late Holocene in southern China.To investigate the possible forcing mechanisms of these phenomena, we used the full TraCE-21 k simulation and single-forcing transient simulations to analyze spatial and temporal rainfall patterns. The results suggest that the north-south pattern is the product of orbital forcing. Intensified summer insolation during the early Holocene enhanced the land-sea thermal contrast, which strengthened the EASM along with the northward extension of the west Pacific subtropical high. Both factors caused the increased withdrawal of oceanic water vapor and the northward advance of the EASM rain belt, delivering more rainfall to northern China and less rainfall to southern China, and vice versa. Overall, our results indicate that insolation variations were the dominant factor determining the spatial pattern of EASM precipitation during the Holocene. Our findings are relevant to the calibration of palaeoclimate models and they contribute to an improved understanding of the Asian monsoon sub-systems.

Study of changes in temporal distribution pattern of rainfall at Dapoli station in Konkan region of Maharashtra

Dapoli situated in the Konkan region of Maharashtra is having average annual rainfall of 3587 mm with average number of rainy days 75. The yield of major Kharif season crop rice is affected by the erratic behavior of rainfall. Present study is an attempt to study the rainfall variations at the Dapoli station which will be useful for forecasting the future temporal availability of water. Comprehensive statistical tools were used to investigate trends in averages and monthly rainfall over the station on decadal basis. Forty years (year 1972-2011) daily rainfall data for Dapoli station was used for the analysis. Results of study showed that, decadal mean rainfall depths of June and August were found decreasing and those for September was found increasing. Mean rainfall depth variations for July as well as annual total rainfall were found random. Seven years moving averages showed that, rainfall depths for the months of June, July and August were found decreasing and for month of September was found increasing. In annual rainfall graph a slight decline was observed.

Statistical Analysis of Temporal Variations in Patterns of Rainfall and Temperature in the Klang Valley

Malaysia is in the equatorial zone and experiences hot and humid climates throughout the year. During the rainy season, floods may occur in some parts of Malaysia, including the Klang Valley. During the flood occurrences, the residents may need to leave their houses and move to relief shelters. This disaster may happen because of the continuous heavy downpour in the area. Both incidents can be connected to climate change, and one way to investigate this is to examine differences in daily rainfall and daily temperature patterns through time. Climate change is inevitable and one of the causes is known as 'anthropogenic' in which it is caused by human activities. Geological records can provide evidence of climate change. However, there has been little statistical research on climate change in the Klang Valley. Even official organisations like the Department of Irrigation and Drainage Malaysia (DID) and the National Water Research Institute of Malaysia (NAHRIM) have not conducted this area of study. The goal of this study is to evaluate climate change in the Klang Valley using statistical analysis of temporal rainfall patterns. The data collection and statistical analysis are two critical parts of this study. Rainfall and average temperature data were obtained from their respective departments and websites. These data were sorted and filtered accordingly to get a satisfactory value for the analysis. Then, the correlation test and analysis of variance were conducted to find the relation between these data points. The results of this analysis were used to determine whether the variations in rainfall patterns in the Klang Valley are caused by climate change or otherwise. In addition to this, the statistical outputs indicate that there is a statistically significant finding in both data sets in which the decreasing temperatures may result in increasing rainfall and warmer conditions may lessen the rainfall. Surprisingly, the findings contradicted the expected outcome of this study, and it is believed that external factors are the primary contributors to these findings.

Long-term Hydrometeorological Time-series Analysis over the Central Highland of West Papua

This article presents an innovative data-driven approach for examining long-term temporal rainfall patterns in the central highlands of West Papua, Indonesia. We utilized wavelet transforms to identify signs of a negative temporal correlation between the El Niño-Southern Oscillation (ENSO) and the 12-month Standardized Precipitation Index (SPI-12). Based on this cause-and-effect relationship, we employed dynamic causality modeling using the Nonlinear Autoregressive with Exogenous input (NARX) model to predict SPI-12. The Multivariate ENSO Index (MEI) was used as an attribute variable in this predictive framework. Consequently, this dynamic neural network model effectively captured common patterns within the SPI-12 time series. The implications of this study are significant for advancing data-driven precipitation models in regions characterized by intricate topography within the Indonesian Maritime Continent (IMC).

Flood Discharge For Ungauged Catchment At Teriang River, Pahang By Using HEC-HMS

Insufficiency of meteorological data during extreme rainfall events hinders simulation and forecasting analysis in flood mitigation systems. Hence, the existence of a reliable database in hydrology is crucial in flood modelling for ungauged catchments, especially for calibration and validation purposes. This paper focused on the generation of rainfall data by using Hydrological Procedure No. 1 (HP1) from the Department of Irrigation and Drainage (DID) Malaysia. Subsequently, analysis of flood hydrographs at Teriang River, Pahang, Malaysia, using HEC-HMS 4.8 for 50 and 100-year return periods was also obtained in this study. For validation purposes, the outcomes were compared to Hydrological Procedure No. 11 (HP11), designed for rural catchments. For precipitation data generation, the design rainfall was calculated based on generalised isopleth maps from HP1 for various rainfall durations ranging from 0.25 hours to 72 hours. Based on the results, the maximum peak discharge for 50 ARI was 354.7 m3/s, while for 100 ARI it was 410 m3/s before calibration. Comparing the results from the HEC-HMS model and the outputs from HP11, the analysis for 50-year and 100-year return periods in the Teriang River showed that the relative percentage differences for peak discharge were 19.99% and 17.64%, respectively. The calibration process managed to obtain a relative percentage difference of 2.94% for 50 ARI and 4.36% for 100 ARI. In conclusion, HEC-HMS can be used as a reliable tool to produce simulated streamflow for ungauged catchments with the help of the generated rainfall temporal patterns from HP1, which is a procedure that contains estimation of design rainfall intensity based on the rainfall intensity-during-frequency relationship (IDF relationship).

Sub-daily rainfall patterns in the mountainous regions of the Island of Tahiti: Insights from a one-year rain gauge network expansion

Study regionThe Island of Tahiti in French Polynesia (17.5°S,149.5°W), with a focus on the mountainous regions of the island interior. Study focusThe high topography of Tahiti creates an obstacle for wet air masses traveling along the surface of the South Tropical Pacific Ocean, which forces air flow uplift and triggers orographic rainfall. Although the main physical processes behind orographic rain enhancement on tropical islands were identified, the ways they shape Tahitian rain fields are still not fully understood, particularly due to a lack of observations. To address the limitations posed by this shortage of rain data, this study describes the temporary expansion of the rain gauge network of Tahiti towards the mountainous island interior, and uses the resultant dataset to investigate the spatial and temporal patterns of orographic rainfall at sub-daily resolution. New hydrological insights for the regionThe analysis of the rain gauge observations on daily and shorter time-scales leads to the identification of three main types of rainfall patterns: (1) a Dry Season Trade Winds type linked to a persistent Trade Winds Inversion (TWI) and characterized by moderate orographic rainfall in windward slopes, dry conditions in most leeward areas, and localized mid-afternoon showers in the mountains downwind of the main summit of the island; (2) a Transition Season Trade Winds type linked to atmospheric disturbances weakening the TWI, and characterized by intense orographic rainfall in windward slopes and mountains, and frequent showers in leeward areas by overshoot of the main crest; and (3) a SPCZ-related type occurring when the South Pacific Convergence Zone (SPCZ) crosses Tahiti during the wet season, and characterized by intense and widespread rainfall throughout the island with clear diurnal cycle and peak intensities recorded in the mid-afternoon.

Assessing the impact of climate change on sediment discharge using a large ensemble rainfall dataset in Pekerebetsu River basin, Hokkaido

Increased rainfall associated with climate change can increase sediment discharge. The supply of fine sediment from slope failures inhibits bed armoring of mountain rivers and increases sediment discharge to the downstream reaches. Floods without slope failures lead to bed erosion and armoring and may ultimately decrease sediment discharge. Thus, it is important to consider sediment discharge from slope failure and bed erosion as factors affecting sediment production. Climate change affects not only the rainfall amount, but also the temporal rainfall pattern; consequently, the pattern affects the sediment production factors and the amount of sediment discharge. However, changes in sediment discharge due to climate change based on sediment production sources have not yet been clarified. In this study, we statistically analyzed 1200 results simulated using a physics-based sediment runoff model to assess the impact of changes in temporal rainfall patterns on sediment discharge and sediment production sources in the Pekerebetsu River Basin. In the simulations, we used the rainfall predicted in d4PDF (Database for policy decision-making for future climate change), a large ensemble climate simulation database at 5 km and 20 km resolutions. Our results showed that the climate-driven increase in sediment discharge was considerably larger than that of rainfall. An increase in short-term heavy rainfall increased the supply of fine sediments from slope failure. This resulted in the suppression of bed armoring and a large increase in sediment discharge. Thus, the increase in sediment discharge is not only caused by an increase in rainfall but also by changes in temporal rainfall patterns and sediment production factors. The sediment discharge calculated for the 20 km resolution climate projection was nearly one order of magnitude smaller than that for the 5 km resolution. This suggests that the 20 km resolution climate projections do not adequately represent orographic rainfall in the mountains and thus, do not adequately reproduce extreme sediment discharge events. An increased sediment supply causes bed aggradation and decreases the river conveyance capacity of the downstream channel. The model developed in this study will contribute to flood risk analysis and flood control planning for increased rainfall due to climate change.

Influence of Rainfall Pattern on Wetness Index for Infinite Slope Stability Analysis

Landslides are one of the riskiest disasters combining excessive rainfall and unstable slope that a wetness index can quantify. The wetness index generated by water infiltration considering the rainfall pattern such as cumulated rainfall, rainfall duration and rainfall intensity should be estimated for the slope stability analysis. Even though the infiltration capacity of soils has been largely focused to evaluate the slope stability, the temporal patterns of rainfall have commonly been ignored or assumed as a steady state for the prediction of the slope failure in the previous studies. Thus, this study focuses more on evaluating the influence of various rainfall patterns on the slope stability, and compares it with an actual landslide incident that occurred in 2011, in Korea. The factor of safety (FS) considering the time-dependent wetness index variation is used to determine the slope stability. For the various rainfall designs, the uniform rainfall distribution, Yen and Chow, Mononobe, alternating block and second quartile Huff models are adopted. Thereafter, the FS variations from five models are compared with an actual landslide incident in Seoul, Korea. Among the rainfall designs, the models that consider the abrupt rainfall intensity capture the landslide time with an FS < 1. Therefore, the appropriate adoption of a rainfall distribution model should be highlighted for landslide prediction.

The Reliability of W-flow Run-off-Rainfall Model in Predicting Rainfall to the Discharge

This research intends to predict the discharge (run-off) from rainfall for which the model is built using W-flow. The research location is in the Gajah Mungkur reservoir (Wonogiri) in Indonesia. The estimation of reservoir inflow has an important role, mainly in the scheme of reservoir operation and management. However, the heterogeneity of complex spatial and temporal patterns of rainfall and also the physiographic context of a watershed cause the development of a model of real-time run-off and rainfall that can accurately predict the reservoir inflow to become a challenge in the development of water resources. In relation to the analysis and prediction of rainfall, the constraint and problem that is still often faced is the minimal availability of observed rainfall data spatially as well as temporally; the time series of rainfall data is not long and complete enough; and the number of rainfall stations is less evenly distributed. The methodology consists of carrying out the literature study, collecting as much rainfall data as possible to build a W flow model, then carrying out the model calibration and analyzing the prediction of real-time reservoir inflow for operation. The result shows that the dependable discharge of the Wonogiri watershed shows that there are two peak discharges, which happened on February II (the second half of February) and December II (the second half of December). However, the discharge is decreasing in July and reaching its lowest level in October II (the second half of October). Doi: 10.28991/CEJ-2023-09-07-015 Full Text: PDF

Laboratory-Scaled Investigation into Combined Impacts of Temporal Rainfall Patterns and Intensive Tillage on Soil and Water Loss

Many studies have focused on the impacts of rainfall duration and intensity, while overlooking the role of rainfall patterns on intensive tillage erosion in hilly agricultural landscapes. The objective of this study was to determine the combined effects of rainfall patterns and tillage erosion on surface runoff and soil loss on sloping farmland in the purple soil area of China. Five simulated rainfall patterns (constant, rising, falling, rising–falling, and falling–rising) with the same total precipitation were designed, and the intensive tillage treatment (IT) and no-tillage treatment (NT) were subjected to simulated rainfall using rectangular steel tanks (2 m × 5 m) with a slope of 15°. To analyse the differences in the hydrological characteristics induced by tillage erosion, we calculated the flow velocity (V), Reynolds number (Re), Froude number (Fr), and Darcy–Weisbach resistance coefficient (f). The results indicate that significant differences in surface runoff and sediment yield were found among different rainfall patterns and rainfall stages (p < 0.05). The falling pattern and falling–rising pattern had a shorter time gap between the rainfall initiation and runoff occurrence as well as a larger sediment yield than those of the other rainfall patterns. The value of f varied from 0.30 to 9.05 for the IT and 0.48 to 11.57 for the NT and exhibited an approximately inverse trend to V and Fr over the course of the rainfall events. Compared with the NT, the mean sediment yield rates from the IT increased the dynamic range of 8.34–16.21% among the different rainfall patterns. The net contributions of the IT ranged from 2.77% to 46.39% in terms of surface runoff and 10.14–78.95% in terms of sediment yield on sloping farmland. The surface runoff and sediment yield were positively correlated with rainfall intensity, V, and Fr, but negatively correlated with f irrespective of tillage operation (p < 0.05). The results showed that the tillage erosion effects on soil and water loss were closely related to rainfall patterns in hilly agricultural landscapes. Our study not only sheds light on the mechanism of tillage erosion and rainfall erosion but also provides useful insights for developing tillage water erosion prediction models to evaluate soil and water loss on cultivated hillslopes.

SPATIO-TEMPORAL ANALYSIS AND MODELLING OF DENGUE INCIDENCES IN QUEZON CITY USING ORDINARY LEAST SQUARES AND SPATIAL REGRESSION

Abstract. This research examined monthly dengue incidences per barangay (village) in Quezon City, Philippines over a period of six years (2010–2015) to determine the relative significance of environmental variables on dengue prevalence. The data were subjected to correlation analysis, spatial autocorrelation assessment, multiple factor analysis, ordinary least squares (OLS) and spatial regression. Local Indicators of Spatial Autocorrelation (LISA) Moran’s I cluster maps indicate significant (p=0.05, 0.01) High-High clustering and Low-Low clustering in the northern and southern parts of the city, respectively. Monthly total cases indicated increasing trend staring from May/June, peaking at around August/September, and declining afterwards to lower levels in November/December. This corresponds to the typical temporal rainfall pattern. Dengue cases were found to be positively correlated (α=0.05) with Population (R=0.84), Informal_Settlements (IS) (R=0.716), Very_Low_Density_Residential (VLDR) (R=0.512), Open_Spaces (OS) (R=0.339), Mean_Rainfall (RFM) (R=0.637), and Mean Elevation (EM) (R=0.498). Dengue incidence (DI) was negatively correlated with Mean Air Temperature (ATM) (R=−0.3 to 0.5). Based on factor analysis, the dengue incidences were closely related to these variables, though factors F1 and F2 accounted for only 28% of the data variability. Ordinary Least Square (OLS) regression analysis of DI with general land use (LU) classes (e.g., no subclasses in residential areas) identified only IS and OS, explaining 43% of the variability of DI, with IS having twice as much influence on DI compared to OS. When residential subclasses are considered, VLDR was added to the model, slightly increasing R-squared to 0.452. Considering, in addition, EM, RFM, and ATM, the R-squared improved to 0.589, with RFM and EM considered more influential on dengue compared to IS and OS. ATM was however removed due to multicollinearity. The use of Spatial Error regression (SER) and Spatial Lag regression (SLR) produced improved models relative to the OLS model with R-squared of 0.676 and 0.667, respectively. This indicates the importance of spatial dependence. This can be explained by the fact that mosquitos fly over considerably long distances, traversing across the different barangays. The SER model (AIC=1359.89; SE=26.9584) is slightly better than the SLR model (AIC=1366.05; SE= 27.3399).

Spatial and temporal pattern of deficient Indian summer monsoon rainfall (ISMR): impact on Kharif (summer monsoon) food grain production in India.

Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901-2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3-4years) and 4 to 5 consecutive drought years (once in 120years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R2 (0.8-0.88) between observed and predicted data and low RMSE (2.6-2.7%), can be employed for advanced estimation of DDP of the country and for taking country-level policy decisions by the Indian Government. For the first time, models were formulated to estimate state-wise departure in FGP (DP). In these models, novel indices viz., (i) rainfall departure and irrigation index (RDII) and (ii) physical and socio-economic index (PSEI), were used as predictor variables. These models, with R2 (0.71-0.75) and RMSE of 11.8-14.2(< SD of observed data), hold promise for advance estimation of production loss in states, useful for regional-level planning by the Government of India, and testing them in other countries.

桢楠幼苗适应喀斯特岩溶裂隙生境及降雨时间格局变化的方式

PDF HTML阅读 XML下载 导出引用 引用提醒 桢楠幼苗适应喀斯特岩溶裂隙生境及降雨时间格局变化的方式 DOI: 10.5846/stxb202112183590 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 重庆市自然科学基金面上项目（cstc2020jcyj-msxmX0244）；中央高校基本科研业务费专项（XDJK2020B037）；国家自然科学基金项目（31500399） Phoebe zhennan S. Lee seedlings adjust the biomass allocation and root distribution to adapt to the karst fissure habitat and rainfall temporal pattern Author: Affiliation: Fund Project: General Project of Chongqing Natural Science Foundation(cstc2020jcyj-msxmX0244);Special Funds for Fundamental Scientific Research Operation of Central Universities(XDJK2020B037);National Natural Science Foundation of China(31500399) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:喀斯特生态系统是全球陆地生态系统的重要组成部分，生态环境极为脆弱。由于碳酸盐岩长期强烈的化学溶蚀作用，其基本特征体现为地表土壤和地下岩溶裂隙及洞穴的二元结构。近年来，在全球气候变化下，我国西南地区降雨格局呈现降雨频次减少且单次降雨量增加的趋势。因此，岩溶裂隙和区域降雨时间格局改变将对喀斯特地区的植物生长产生重要影响。通过模拟不同岩溶裂隙生境（S0：24 cm土壤；S1/2：12 cm土壤层+12 cm裂隙层；S3/4：6 cm土壤层+18 cm裂隙层）和不同降雨时间格局（I2d：2 d降雨间隔；I19d：19 d降雨间隔），探究二年生桢楠（Phoebe zhennan S.Lee）幼苗是否通过生物量分配及根系分布的调整来适应变化环境。结果显示：（1）短时间降雨格局下，相比全土生境，少量岩溶裂隙存在并不影响桢楠幼苗生物量的积累，然而随着岩溶裂隙层进一步增厚和降雨时间间隔延长，桢楠降低了总生物量，减少了茎且增大了根和叶的生物量分配。（2）桢楠幼苗的根系垂直分布随着深度增加而下降，且无论在何种降雨时间格局下，两种岩溶裂隙生境下桢楠均增加了岩溶裂隙之上土壤层的根系生物量分配比例。研究表明：赋存有土壤的岩溶裂隙能成为植物幼苗赖以生存的生境，但这种提供生境的能力随岩溶裂隙层增厚以及降雨时间间隔延长而减弱。桢楠幼苗以牺牲对茎生物量的分配投资为代价，提高对根系或者叶片的生物量投资，同时增大界面土层中的根系分布，来保证最大化地利用有限的土壤资源，从而适应喀斯特的岩溶裂隙（及干旱）生境。 Abstract:Karst is one of the most important fragile ecological environments in globally terrestrial ecosystems. It is basically characterized by the dual structure of surface soil and underground karst fissures and caves, due to the long-term strong chemical dissolution of carbonate rocks. In recent years, under global climate change, the rainfall pattern in southwestern China has shown a trend of decreasing rainfall frequency and greater sub-rainfall. The karst region in southwestern China, which belongs to the subtropical monsoon climate, often suffers from more obvious alternating wet and dry conditions. The underground karst fissures and variation in the temporal pattern of regional rainfall will have a significant impact on plant growth and development in this region. In this study, in order to explore whether the seedlings of Phoebe zhennan S. Lee adjust biomass allocation and root distribution to adapt to the changing environment, we simulated different karst fissure habitats (S0:24 cm soil; S1/2:12 cm soil layer+12 cm karst fractured layer; S3/4:6 cm soil layer+18 cm karst fractured layer) and different rainfall temporal patterns (I2d:rainfall interval of 2 days; I19d:rainfall interval of 19 days), and checked the plants biomass accumulation and allocation, root vertical distribution. The results showed that:(1) under the short-term rainfall pattern, compared with the whole soil habitat, the habitat with a small proportion of karst fissures did not affect the biomass accumulation of P. zhennan seedlings. With the thickening of karst fractured layer and the extension of the rainfall interval, P. zhennan reduced the total biomass, allocating less biomass to stems and more to roots and leaves. (2) The vertical distribution of root system of P. zhennan seedlings decreased with the deepen of soil layer, but increased the root biomass allocation ratio in the soil layer above the karst fissures, regardless of the rainfall temporal pattern. The results show that the fissures hosted soil can be a certain habitat for plants. However, with the thickening of fractured layer and extension of the rainfall interval, the fissures weaken the ability of the habitat. P. zhennan seedlings increase investment to root and leaf at the expense of stem biomass, and increase the root distribution in the interface soil layer above the fissures to ensure the maximum use of limited soil resources so as to adapt to the fissure (and arid) habitat in karst. 参考文献 相似文献 引证文献