Rainfall Accumulation Research Articles

Analysis of Multi-Scale Hydrometeorological Triggering Flash Flood Event of the 13 July 2020 in North Luwu, South Sulawesi

During 12-13 July 2020, heavy rainfall had caused Masamba, Rongkong, and Rada rivers to overflow, causing flash floods in the North Luwu regency. This event resulted in many casualties; at least 38 people died and displaced thousands of people. This study presents an analysis of the multi-scale hydrometeorological settings that led to the development of these intense storms in the North Luwu fl ood. Boreal Summer Intraseasonal Oscillation (BSISO) Normalized PC2 has entered phases 4 and 5 on 7 - 13 July 2020, associated with cloud growth in the Indonesian Maritime Continent. Besides, the sub-seasonal tropical disturbance of Kelvin Waves is convectively active in the Sulawesi region on 7 - 13 July, increasing the intensity of rainfall at the location. Sea Surface Temperatures (SSTs) were in warmer conditions in the Bonne Bay region south of North Luwu, supplying more water vapor into the atmosphere. The easterly wind (Australian monsoon) enhanced the diurnal cycle of the water vapor mass movement from Bonne Bay to the highlands of North Luwu. From the observation of GPM satellite imagery, the accumulation of spatial rainfall on July 12 and 13 was concentrated in the eastern region of Sulawesi, which reached >150 mm/day and >50 mm/day in North Luwu.

The Use of Laser Precipitation Monitor (LPM) of Disdrometer and Weather Radar to Determine the Microphysics Characteristics of Extreme Rainfall in Jakarta. (Jakarta Flood Case Study February 25, 2020)

Heavy rains on February 24, 2020, caused flooding in most parts of Jakarta and its surroundings. The one-day observation of accumulated rainfall from the Laser Precipitation Monitor (LPM) was recorded at 358.6 mm/day at the Kemayoran station on February 25, 2020, at 00.00 UTC (07.00 Jakarta Time). In this study, analysis of the microphysical characteristics of extreme rainfall using LPM installed at Kemayoran meteorology station and weather radar at Cengkareng meteorology station with a spatial radius of 250 km. LPM is used to measure the diameter of the raindrops, the velocity of falling raindrops, LPM reflectivity, and the amount of accumulated rainfall with time resolution per minute and stored in excel data format. While the weather radar is used to measure the reflectivity spatially and temporally in the data volume format (.vol). The method used is, first, to find the relationship between LPM reflectivity and the amount of LPM rainfall with regression analysis. Second, the radar reflectivity is converted into estimated rainfall intensity for the Jakarta area and its surroundings. The results of this study found a relationship between LPM reflectivity (X) and rainfall accumulation LPM (Y) to form a regression relationship with the formula Y = 0.013X with R2 = 0.3777. Based on the record of the LPM time series, the peak of rainfall occurred at 18.17 UTC with 1000 raindrops, the maximum fall speed was 10 m/s, and the maximum diameter is 8.5 millimeters. Based on the results of microphysical measurements of LPM, spatial plots, and vertical cross-section radar, it can be concluded that flooding in Jakarta is due to heavy rain from convective clouds.

Evaluation of CCTV Data For Estimating Rainfall Condition

Rainfall characteristics of Indonesia's tropical climate have high variability according to space and time, so to determine the rainfall pattern of a location, an in situ rainfall measuring instrument (AWS = automatic weather station) is needed with high density. The existence of AWS also requires relatively high maintenance costs and a standard placement location (according to the rules of WMO = World Meteorological Organization) which is relatively broad and is not obstructed by other objects that can make the result of rainfall data is not representative. With the concept of computer vision, research will be carried out to estimate the rainfall condition from the CCTV cameras. The CCTV camera data which have qualitative characteristic into rainfall data which have quantitative characteristics. This research is also motivated by the large number of CCTVs that are placed in a lot of locations by local governments along with the Smart City program in districts and cities throughout Indonesia. The preliminary research was conducted in Center for Atmospheric Science and Technology office in Bandung. Rainfall data from AWS was used to validate CCTV data which placed in same location. The process of converting CCTV data into rainfall data goes through 6 stages. The first is reading the image mapping data and AWS (in rainfall accumulation data form). Second, read the image data in grayscale. Third, extract the features. Fourth, split the reference and sample data. Fifth, conducts the K-NN Mapping Reference Image and rainfall accumulation data. Sixth is to praise K-NN Testing. The accuracy is calculate with comparing the estimated number of CCTV cameras that are correct with the total sample size. The evaluation result states that the highest accuracy is obtained with K = 1. When K=1, the accuracy percentage reaching 94.8%. Accuracy decreases with increasing value of K and drastically decreases with K> 2. In the 1-10 days reference data, the highest accuracy is obtained by the number of reference data for 10 days, which is around 97%, stable until the value of K = 8. While the lowest accuracy is obtained when the reference data is 1 day with an accuracy value of about 43%. Based on the results of this study, it can be concluded that rain data from CCTV can be used to estimate the rainfall data. The best result happened when K-value is equal to 1.

Four-dimensional variational (4DVAR) performance test with assimilation satellite and radar data (case study of a heavy rainfall Bengkulu March 4, 2019)

Four-dimensional variational (4DVAR) is one of the assimilation techniques considering time integration to distribute observational data at time window intervals. In this study, we aim to evaluate the 4DVAR assimilation technique using satellite and radar data to simulate a heavy rainfall case in Bengkulu on March 4, 2019. The result shows that radar data assimilation (DA-RAD) can improve rainfall pattern over Bengkulu mainland areas, while the satellite data assimilation (DA-SAT) enhances rainfall over the ocean. In addition, for temporal analysis, the DA-RAD successfully correct the initial time of the event, producing the smallest error and the best correlation in statistical verification, also a small bias and higher accuracy for discrete verification. However, DA-SAT is more capable to improve rainfall accumulation with the lowest FAR value. In conclusion, compared to others, both satellite and radar can be used as assimilation data for 4DVAR methods as they have different roles in increasing the quality of model performance.

A didactic approach to the machine learning application to weather forecast

We propose a didactic approach to use the Machine Learning protocol in order to perform weather forecast. This study is motivated by the possibility to apply this method to predict weather conditions in proximity of the Etna and Stromboli volcanic areas, located in Sicily (south Italy). Here the complex orography may significantly influence the weather conditions due to Stau and Foehn effects, with possible impact on the air traffic of the nearby Catania and Reggio Calabria airports. We first introduce a simple thermodynamic approach, suited to provide information on temperature and pressure when the Stau and Foehn effects take place. Then, in order to gain information also on the rainfall accumulation, the Machine Learning approach is presented: according to this protocol, the model is able to “learn” from a set of input data constituted by the meteorological conditions (in our case dry, light rain, moderate rain and heavy rain) associated to the rainfall, measured in mm. We observe that, since in the input dataset provided by the Salina weather station the dry condition was the most common, the algorithm is very accurate in predicting it. Further improvements can be obtained by increasing the number of considered weather stations and the time interval.

Genetic progress in grain yield radiation and nitrogen use efficiency of dryland winter wheat in Southwest China since 1965: Progress and prospect for improvements

AbstractInvestigations of critical physiological traits associated with the genetic yield gain in wheat (Triticum aestivum) are essential to determine future crop breeding and management strategies. This study grew 32 cultivars released from 1965 to 2017 for two cropping seasons (a dry year, 2016–2017, and a humid year, 2017–2018) to examine yield potential achieved through efficiencies in canopy light interception, solar energy conversion, harvest index (HI), and nitrogen (N) uptake and utilization. Yield gain for wheat (25.0 ± 1.8 kg ha−1 yr−1) resulted from increases in plant biomass, HI, and N utilization efficiency (NUtE). Modern cultivars with an erect canopy and slender flag‐leaves optimized light interception and could achieve higher biomass yield. The introduction of semi‐dwarf genes (Rht‐8) reduced plant height (0.05 yr−1) and improved HI (0.004 yr−1) and NUtE resulting from both increased pre‐anthesis accumulated biomass and plant dry matter mobilization after anthesis. Greater biomass partitioning to spikes resulted in higher fruiting efficiency and grain number. Due to greater tillering capability through increased N uptake efficiency, increased fertile spikes and grain yield were observed. Among these newer cultivars, the rate of yield gain was slow, and yield stability was more affected by accumulative rainfall than diurnal temperature. The future challenge of wheat breeding is to maintain the genetic yield gain without increasing the reliance on chemical fertilizers under an increasingly variable climate.

The Dynamic of Vegetation Growth with Regular Climate and Climatic Fluctuations in a Subtropical Mountainous Island, Taiwan

Vegetation phenology is an integrative indicator of environmental change, and remotely–sensed data provide a powerful way to monitor land surface vegetation responses to climatic fluctuations across various spatiotemporal scales. In this study, we synthesize the local climate, mainly temperature and precipitation, and large-scale atmospheric anomalies, El Niño-Southern Oscillation (ENSO)-connected dynamics, on a vegetative surface in a subtropical mountainous island, the northwest Pacific of Taiwan. We used two decadal photosynthetically active vegetation cover (PV) data (2001–2020) from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data to portray vegetation dynamics at monthly, seasonal, and annual scales. Results show that PV is positively related to both temperature and precipitation at a monthly timescale across various land cover types, and the log-linear with one-month lagged of precipitation reveals the accumulation of seasonal rainfall having a significant effect on vegetation growth. Using TIMESAT, three annual phenological metrics, SOS (start of growing season), EOS (end of growing season), and LOS (length of growing season), have been derived from PV time series and been related to seasonal rainfall. The delayed SOS was manifestly influenced by a spring drought, <40 mm during February–March. The later SOS led to a ramification on following late EOS, shorter LOS, and reduction of annual NPP. Nevertheless, the summer rainfall (August–October) and EOS had no significant effects on vegetation growth owing to abundant rainfall. Therefore, the SOS associated with spring rainfall, instead of EOS, played an advantageous role in regulating vegetation development in this subtropical island. The PCA (principal component analysis) was applied for PV time series and explored the spatiotemporal patterns connected to local climate and climatic fluctuations for entire Taiwan, North Taiwan, and South Taiwan. The first two components, PC1 and PC2, explained most of data variance (94–95%) linked to temporal dynamics of land cover (r > 0.90) which was also regulated by local climate. While the subtle signals of PC3 and PC4 explained 0.1–0.4% of the data variance, related to regional drought (r = 0.35–0.40) especially in central and southwest Taiwan and ENSO-associated rainfall variation (r = −0.40–−0.37). Through synthesizing the relationships between vegetation dynamics and climate based on multiple timescales, there will be a comprehensive picture of vegetation growth and its cascading effects on ecosystem productivity.

A Deep Learning Multimodal Method for Precipitation Estimation

To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate value with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. The ablation study, done to compare every possible combination of the three modalities, shows that the combination of rain gauges measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation.

Influence of Wind-Induced Effects on Laser Disdrometer Measurements: Analysis and Compensation Strategies

Nowadays, laser disdrometers constitute a very appealing tool for measuring surface precipitation properties, by virtue of their capability to estimate not only the rainfall amount and intensity, but also the number, the size and the velocity of falling drops. However, disdrometric measures are affected by various sources of error being some of them related to environmental conditions. This work presents an assessment of Thies Clima laser disdrometer performance with a focus on the relationship between wind and the accuracy of the disdrometer output products. The 10-min average rainfall rate and total rainfall accumulation obtained by the disdrometer are systematically compared with the collocated measures of a standard tipping bucket rain gauge, the FAK010AA sensor, in terms of familiar statistical scores. A total of 42 rainy events, collected in a mountainous site of Southern Italy (Montevergine observatory), are used to support our analysis. The results show that the introduction of a new adaptive filtering in the disdrometric data processing can reduce the impact of sampling errors due to strong winds and heavy rain conditions. From a quantitative perspective, the novel filtering procedure improves by 8% the precipitation estimates with respect to the standard approach widely used in the literature. A deeper examination revealed that the signature of wind speed on raw velocity-diameter spectrographs gradually emerges with the rise of wind strength, thus causing a progressive increase of the wrongly allocated hydrometeors (which reaches 70% for wind speed greater than 8 m s−1). With the aid of reference rain-gauge rainfall data, we designed a second simple methodology that makes use of a correction factor to mitigate the wind-induced bias in disdrometric rainfall estimates. The resulting correction factor could be applied as an alternative to the adaptive filtering suggested by this study and may be of practical use when dealing with disdrometric data processing.

Naïve Bayes ensemble models for groundwater potential mapping

Groundwater potential maps are important tools for the sustainable management of water resources, especially in agricultural producing countries like Vietnam. Here, we describe the development and application of a spatially explicit ensemble modeling framework that allows for analyzing spatially explicit data for estimating groundwater potential across the Kon Tum Province, Vietnam. Based on this framework, the Naïve Bayes (NB) method was integrated with the Bagging (B), AdaBoost (AB), and Rotation Forest (RF) ensemble learning techniques to develop three ensemble models, namely BNB, ABNB, and RFNB. A suite of well yield data and thirteen explanatory variables (i.e., elevation, aspect, slope, curvature, river density, topographic wetness index, sediment transport index, soil type, geology, land use, rainfall, and flow direction and accumulation) were incorporated into the modeling processes over the independent training and validation levels of the single NB model and its three ensembles. Several performance metrics (i.e., area under the receiver operating characteristic curve (AUC), root mean square error (RMSE), accuracy, sensitivity, specificity, negative predictive value, and positive predictive value) demonstrated that the three ensemble models successfully surpassed the single NB model in groundwater potential mapping. The ensemble RFNB model with AUC = 0.849, accuracy = 83.33%, sensitivity = 100%, specificity = 75%, and RMSE = 0.406 exhibited the most accurate performance for mapping groundwater potential in the Kon Tum Province, followed by the ABNB (AUC = 0.844), BNB (AUC = 0.815), and single NB (AUC = 0.786) models, respectively. Further, the correlation based feature selection method identified elevation, slope, land use, rainfall, and STI as the most useful explanatory variables for explaining the distribution of groundwater potential in the Kon Tum Province. The methodology proposed in this case study and the produced potential maps enable managers to align water use patterns with the shared benefits and costs of different users and to develop strategies for sustainable groundwater exploitation, preservation, and management.

Study on phosphorus loss and influencing factors in the water source area

Maintaining drinking water security is a global issue, and phosphorus is a limiting factor affecting drinking water quality. Hence, this study took Fushi Reservoir as a test area, and set up field runoff observation plots around the reservoir catchment, which is covered by moso bamboo stands. Through field observation, the vertical variation of phosphorus loss in different stands of moso bamboo was initially studied. The results showed that: (1) For the vertical dimensions (atmospheric rainfall, stemflow, throughfall, surface runoff) from high to low, the loss of total phosphorus (TP) increased, and the proportion of dissolved phosphorus increased from 29.29% (atmospheric rainfall) to 62.76% (surface runoff). (2) Different rainfall factors had various impacts on phosphorus loss at the different vertical levels. The accumulation of rainfall had the greatest impact on surface runoff TP loss, with the correlation coefficient reaching 0.994 (P < 0.01), while surface runoff particulate phosphorus loss was mostly affected by the average rainfall intensity. (3) Modifying the forest structure in water source areas can reduce the loss of TP via stemflow and throughfall, but the effect on surface runoff TP loss is variable. Thus, it is expected that this novel study can serve as a reference for improving the environmental quality of water source areas, and help in reducing phosphorus loss and controlling non-point source pollution.

Trends of daily extreme and non‐extreme rainfall indices and intercomparison with different gridded data sets over Mexico and the southern United States

AbstractTrends of 15 extreme and non‐extreme daily rainfall indices for Mexico and the southern United States are analyzed for the extended boreal winter (November–March) and summer (June–October) during 1981–2010. The analysis includes gridded observations (CHIRPS and Livneh), reanalyses (ERA5, ERA‐Interim, MERRA2, and CFSR), the regional climate model RegCM4.7 (forced by ERA‐Interim), and four general circulation models (GCMs) from the CMIP5 (HadGEM2‐ES, CNRM‐CM5, MPI‐ESM‐MR, and GFDL‐ESM2M). The domain is divided into 13 regions with similar seasonal rainfall patterns according to the k‐means algorithm applied to CHIRPS monthly precipitation, as a reference. Gridded observations and reanalyses (O&amp;R) show similar regional annual cycles and metrics of precipitation. The two observational data sets show the largest differences in the magnitude of some indices in the wettest region (southeastern Mexico) and in the monsoon. During summer, O&amp;R show positive significant trends in extreme rainfall indices in regions commonly affected by tropical cyclones (southern Baja California Peninsula, northeastern Mexico, and the southeastern United States), while negative significant trends of seasonal accumulation of rainfall are seen in the Mediterranean Californias and the Sonoran Desert region. There is an agreement among most data sets that the frequency and intensity of extreme winter precipitation indices have declined in northwest Mexico and the southwestern United States, but trends are not statistically significant, while southeastern Mexico shows significant increases in 5‐days accumulation (rx5d). ERA5 followed by MERRA2 show the best performance. RegCM4.7 reproduces the annual cycle of precipitation in most of the regions and captures the main trend signals especially in winter, but it has difficulties in reproducing the summer interannual variability of few indices. The GCMs tend to overestimate (underestimate) the annual cycle of precipitation and the median values of extreme indices in the driest (wettest) regions. They are not able to capture the regional trends of the indices.

Detection The Spatiotemporal Association between Climatic Factors and Vegetation Cover (NDVI) Incorporate MODIS and TRMM Product (Case study: CharmahalvaBakhtiary Province of Iran) Behzad Amraei

Background and Objective: the climate factors are main determinant of vegetation spatiotemporal dynamics. A visual comparison of climate and vegetation on a global scale immediately reveals a strong association between climate and vegetation. The main object of this study is to reveal the spatiotemporal association between climatic factors and Modis derived NDVI in Charmahal&Bakhtiary province of Iran.Material and Methods: In this study, we use MOD13A3 of MODIS product as NDVI layer for study area. MOD11A2 as land surface temperature and mean monthly accumulative rainfall of synoptic station for study area during 2008 to 2018. We used the correlation analysis in 0.95 confident level (P_value =0.5) to reveal the spatiotemporal association between the NDVI and climatic factors.Results: The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months, the pixels which have the high value of NDVI are spatially associated with the pixels which have highest value of LST (6 to 12C°). In winter the spatial correlation between NDVI and LST is so high which is statistical significant in 0.95 confident level. In transient months such as May, October and November, the spatial correlation between NDVI and LST is falling to 0.30 to 0.35, which is not statistical, significant in 0.95 confident level. Finally, in summer season or warm months including Jun to September, we found the minimum spatial association between the NDVI and LST.Conclusion: we found that the maximum correlation between NDVI and LST simultaneously appears and no lag time has been observed. The spatial correlation of NDVI and monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time but in other months we do not find any significant correlation between NDVI and rainfall.

Multi-year analyses on three populations reveal the first stable QTLs for tolerance to rain-induced fruit cracking in sweet cherry (Prunus avium L.)

Rain-induced fruit cracking is a major problem in sweet cherry cultivation. Basic research has been conducted to disentangle the physiological and mechanistic bases of this complex phenomenon, whereas genetic studies have lagged behind. The objective of this work was to disentangle the genetic determinism of rain-induced fruit cracking. We hypothesized that a large genetic variation would be revealed, by visual field observations conducted on mapping populations derived from well-contrasted cultivars for cracking tolerance. Three populations were evaluated over 7–8 years by estimating the proportion of cracked fruits for each genotype at maturity, at three different areas of the sweet cherry fruit: pistillar end, stem end, and fruit side. An original approach was adopted to integrate, within simple linear models, covariates potentially related to cracking, such as rainfall accumulation before harvest, fruit weight, and firmness. We found the first stable quantitative trait loci (QTLs) for cherry fruit cracking, explaining percentages of phenotypic variance above 20%, for each of these three types of cracking tolerance, in different linkage groups, confirming the high complexity of this trait. For these and other QTLs, further analyses suggested the existence of at least two-linked QTLs in each linkage group, some of which showed confidence intervals close to 5 cM. These promising results open the possibility of developing marker-assisted selection strategies to select cracking-tolerant sweet cherry cultivars. Further studies are needed to confirm the stability of the reported QTLs over different genetic backgrounds and environments and to narrow down the QTL confidence intervals, allowing the exploration of underlying candidate genes.

Convection-Permitting Regional Climate Change Simulations for Understanding Future Climate and Informing Decision-Making in Africa

AbstractPan-Africa convection-permitting regional climate model simulations have been performed to study the impact of high resolution and the explicit representation of atmospheric moist convection on the present and future climate of Africa. These unique simulations have allowed European and African climate scientists to understand the critical role that the representation of convection plays in the ability of a contemporary climate model to capture climate and climate change, including many impact-relevant aspects such as rainfall variability and extremes. There are significant improvements in not only the small-scale characteristics of rainfall such as its intensity and diurnal cycle, but also in the large-scale circulation. Similarly, effects of explicit convection affect not only projected changes in rainfall extremes, dry spells, and high winds, but also continental-scale circulation and regional rainfall accumulations. The physics underlying such differences are in many cases expected to be relevant to all models that use parameterized convection. In some cases physical understanding of small-scale change means that we can provide regional decision-makers with new scales of information across a range of sectors. We demonstrate the potential value of these simulations both as scientific tools to increase climate process understanding and, when used with other models, for direct user applications. We describe how these ground-breaking simulations have been achieved under the U.K. Government’s Future Climate for Africa Programme. We anticipate a growing number of such simulations, which we advocate should become a routine component of climate projection, and encourage international coordination of such computationally and human-resource expensive simulations as effectively as possible.

The 2019/2020 floods in the UK: a hydrological appraisal

The 2019/2020 floods in the <scp>UK</scp>: a hydrological appraisal

CHARACTERIZATION OF TEMPORAL RAINFALL DISTRIBUTION IN FLORIANÓPOLIS, SANTA CATARINA, BRAZIL

Temporal rainfall distribution is an important information for the preparation of the design hyetograph, directly influencing peak flow. This study analyzes the temporal rainfall distribution of Florianópolis city. We used rainfall data from 1986 to 2012 obtained from the Brazilian National Meteorological Institute (code 83897). Heavy rainfall events were classified and individualized, and intensities were determined at each 5% interval of the duration. Rainfall was classified in Huff quartiles and identified by season and duration. Rainfall accumulation curves in the 10-90 percentiles were determined as well as 50% probability curves for each season and duration. The results showed that type I rainfall is the most frequent and that summer rains have a more anticipated pattern. Important differences were found between rainfall durations. The advance coefficient varies with rainfall duration, and averaged 0.389

Alkalinity Production Coupled to Pyrite Formation Represents an Unaccounted Blue Carbon Sink

AbstractBlue carbon ecosystems, including mangroves, saltmarshes, and seagrasses, mitigate climate change by storing atmospheric carbon. Previous blue carbon research has focused on organic carbon stocks. However, recent studies suggest that lateral inorganic carbon export might be equally important. Lateral export is a long‐term carbon sink if carbon is exported as alkalinity (TAlk) produced via sulfate reduction coupled to pyrite formation. This study evaluates drivers of pyrite formation in blue carbon ecosystems, compares pyrite production to TAlk outwelling rates, and estimates global pyrite stocks in mangroves. We quantified pyrite stocks in mangroves, saltmarshes, and seagrasses along a latitudinal gradient on the Australian East Coast, including a mangrove dieback area, and in the Everglades (Florida, USA). Our results indicate that pyrite stocks were driven by a combination of biomass, tidal amplitude, sediment organic carbon, sediment accumulation rates, rainfall, latitude, temperature, and iron availability. Pyrite stocks were three‐times higher in mangroves (103 ± 61 Mg/ha) than in saltmarshes (30 ± 30 Mg/ha) and seagrasses (32 ± 1 Mg/ha). Mangrove pyrite stocks were linearly correlated to TAlk export at sites where sulfate reduction was the dominant TAlk producing process. However, pyrite generation could not explain all TAlk outwelling. We present the first global model estimating pyrite stocks in mangroves, giving a first‐order estimate of 197 Mg/ha (RMSE = 24 Mg/ha). In mangroves, estimated global TAlk production coupled to pyrite formation (∼3 mol/m2/y) is equal to ∼24% of their global carbon burial rate, highlighting the importance of including TAlk export in future blue carbon budgets.

Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan

This study explores the impact of rainfall on the followed-up landslides after a severe typhoon and the relationship between various rainfall events and the occurrence, scale, and regional characteristics of the landslides, including second landslides. Moreover, the influence of land disturbance was evaluated. The genetic adaptive neural network was used in combination with the texture analysis of the geographic information system for satellite image classification and interpretation to analyze land-use change and retrieve disaster records and surface information after five rainfall events from Typhoon Morakot (2009) to Typhoon Nanmadol (2011). The results revealed that except for extreme Morakot rains, the greater the degree of slope disturbance after rain, the larger the exposed slope. Extreme rainfall similar to Morakot strikes may have a greater impact on the bare land area than on slope disturbance. Moreover, the relationship between the bare land area and the index of land disturbance condition (ILDC) is positive, and the ratio of the bare land area to the quantity of bare land after each rainfall increases with the ILDC. With higher effective accumulative rainfall on the slope in the study area or greater slope disturbance, the landslide area at the second landslide point tended to increase.

A random field model of external metal-loss corrosion on buried pipelines

This paper proposes a random field model to characterize the corrosion depth on the external surface of buried steel oil and gas pipelines. The model addresses the intermingling of corroded and corrosion-free areas on the pipe surface, similar to the spatial intermittency of rainfall accumulation within a geographic area, by using a latent homogeneous Gaussian random field and a spatial position-dependent threshold associated with the latent Gaussian field. High-resolution corrosion measurement data obtained from corroded pipe segments removed from in-service pipelines are used to estimate parameters of the proposed model, including the probability of corrosion at a given point, marginal distribution of the nonzero corrosion depth and correlation structure of the latent Gaussian field. The results indicate that the nonzero corrosion depth follows a shifted lognormal distribution. The isotropic γ-exponential correlation function is adequate to characterize the correlation structure of the latent Gaussian field with the correlation length varying from 17 to 50 mm and γ from 0.69 to 0.82. A comparison of simulated and measured corrosion fields suggests that the proposed model is able to capture the characteristics of naturally-occurring corrosion field on the pipe surface. The proposed model provides a useful tool for developing fitness-for-service assessment methodologies for corroded pipelines.