Hydrometeorology of Anthropogenic Erosion in the Metropolitan Region of São Paulo

Erosive processes play an important role in environmental degradation. Rain is the main erosive agent in the Metropolitan Region of Sao Paulo. This study characterized the erosion events caused by precipitation leveraging the accumulated daily precipitation estimate generated by the Climate Prediction Center Morphing Method (CMORPH) and integrating the surface telemetric network using the Statistical Objective Analysis method (SOAS). From the Civil Defense database, 400 events were identified in the Metropolitan region of Sao Paulo (MRSP) area between 2000 and 2013 and, of these, 3 were chosen to carry out meteorological and climatological analyses. In an initial observation, 58% of them were found to occur in summer. Two regions with the highest number of erosion events were observed, in the Serra do Mar and Serra da Cantareira. In the Serra do Mar core, the municipality of São Bernardo do Campo was the one with the greatest amount of erosion. Precipitation volumes were estimated for accumulations of 30 minutes, 1 day, 1 month, and 1 year. The results, from the 3 events, indicate accumulated precipitation in 30 minutes from 10 mm to 19.8 mm, daily from 30.8 mm to 69.5 mm, and 1 month from 369.7 mm to 742.5 mm, and 1 year (2010) from 1712.9 mm to 1961.8 mm. In these events, it was noted that there were heavy rains in December 2009 and January 2010. It was also noted that the CMORPH and SOAS identify the rain events found by the São Paulo meteorological radar. The meteorological analyzes of the events based on images from the São Paulo meteorological radar and the Meteosat-9 satellite indicate that the active precipitation systems are associated with cold fronts, instability lines, and isolated convection.

Study of Hydrometeorology in a Hard Rock Terrain, Kadirischist Belt Area, Anantapur District, Andhra Pradesh

Hydrometeorological studies reveal that the study area comes under semi-arid zone with moderate drought conditions. The rainfall confirms that erratic nature of rainfall in the study area. The rainfall data for 14 years of the study area reveal that only three years had sufficient rainfall and rest of the 11 years are drought-prone to varying intensities. It is also observed that the area receives maximum amount of rainfall from south-west monsoon (June-December).

The heterogeneity of hydrometeorological changes during the period of 1961-2016 in the source region of the Yellow River, China

Runoff in the source region of a river makes up most of water resources in the whole basin in arid and semi-arid areas. It is very important for water resources management to timely master the latest dynamic changes of the runoff and quantitatively reveal its main driving factors. This paper aims to discover the variation heterogeneity of runoff and the impacts of climatic factors on this runoff in the source region of the Yellow River(SRYR) in China from 1961 to 2016. We divided SRYR into four sub-regions, and analyzed changes of their contributions to total runoff in SRYR. We also revealed the impacts of precipitation, temperature and potential evapotranspiration on runoff in each sub-region by constructing the regression relationships between them at multiple temporal scales. The changes of runoff in the four sub-regions and their contributions to the total runoff were not exactly consistent. The climatic variables’ changes also have heterogeneity, and runoff was mainly affected by precipitation compared to influences of temperature or potential evapotranspiration. Their impacts on runoff have spatiotemporal heterogeneity and can be reflected by very significant-linear regression equations.It provided a simple method to predict headwater runoff for better water management in the whole basin.

On the Application of Probabilistic Hydrometeorological Simulation of Soil Moisture across Different Stations in India

An application of a proposed hydrometeorological approach for probabilistic simulation of soil moisture is carried out. The time series of in-situ soil moisture and meteorological variables at monthly scale from a few monitoring stations having different soil-hydrologic properties across India are utilized. Preliminary investigation with both precipitation and near-surface air-tempera- ture as meteorological variables to establish that the strength of association between soil moisture and precipitation is more significant as compared to that between soil moisture and temperature. Precipitation-based probabilistic estimation of soil moisture using the proposed hydrometeorological approach is tested with in-situ observed soil moisture, CPC model output and with soil moisture data of the Climate Change Initiative (CCI) project. The parameter of the developed model is linked to the soil-hydrologic characteristics through Hydrologic Soil Group (HSG) classification. Higher values of model parameter (dependence parameter (θ) for the selected copula) correspond to HSG A and B having higher soil porosity, whereas, lower values correspond to HSG B and C having lower soil porosity.

Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India

The magnitude and frequency of precipitation is of great significance in the field of hydrologic and hydraulic design and has wide applications in varied areas. However, the availability of precipitation data is limited to a few areas, where the rain gauges are successfully and efficiently installed. The magnitude and frequency of precipitation in ungauged sites can be assessed by grouping areas with similar characteristics. The procedure of grouping of areas having similar behaviour is termed as Regionalization. In this paper, RCDA cluster ensemble algorithm is employed to identify the homogeneous regions of rainfall in India. Cluster ensemble methods are commonly used to enhance the quality of clustering by combining multiple clustering schemes to produce a more robust scheme delivering similar homogeneous regions. The goal is to identify, analyse and describe hydrologically similar regions using RCDA cluster ensemble algorithm. RCDA cluster ensemble algorithm, which is based on discriminant analysis. The algorithm takes H base clustering schemes each with K clusters, obtained by any clustering method, as input and constructs discriminant function for each one of them. Subsequently, all the data tuples are predicted using H discriminant functions for cluster membership. Tuples with consistent predictions are assigned to the clusters, while tuples with inconsistent predictions are analyzed further and either assigned to clusters or declared as noise. RCDA algorithm has been compared with Best of K-means and Clue cluster ensemble of R software using traditional clustering quality measures. Further, domain knowledge based comparison has also been performed. All the results are encouraging and indicate better regionalization of the rainfall in different parts of India.

Statistical Analysis of Precipitation Extremes in São Francisco River Basin, Brazil

This study comprises a climatology of the spatial variability of precipitation over the São Francisco River Basin (SFRB), characterized by its geographic heterogeneity. The different rainfall regimes in the region were analyzed through statistical and spectral analyses. Measured precipitation data, Pacific Decennial Climate indexes, ENSO, Atlantic Multidecadal Oscillation, North Atlantic Oscillation, Atlantic dipole, and the sunspot cycle over 65 years were used. The rainfall data were filtered and filled in using the regional weighting method. The spatial and temporal variability of precipitation along the SFRB is remarkable. A pattern was observed along with the time series of precipitation over the SFRB. The cluster analysis identified four homogeneous regions in the SFRB and explained 87.4% of the total variance of the average monthly rainfall of the 199 rain gauges. The Cross-wavelet analysis identified the relationship between the precipitation data series and the climatic indexes that are analyzed in this work.

Reconstruction of debris-flow activity in a temperate mountain forest catchment of central Mexico

The use of dendrochronology to study and date geomorphic processes in volcanic environments is still incipient, even more so on the volcanic slopes covered by temperate forests in central Mexico. Mass movements, such as debris flows, often impact forest stands where they cause damage to individual trees, thereby generating growth disturbances(GD) in the tree-ring records. The identification and dating of GD enables reconstruction of the age of trees colonizing bare surfaces after major events, but also allows the assessment of the frequency or spatial distribution of past geomorphic process activity. Here we used increment cores from 65 Pinus leiophylla, Abies religiosa, and Alnus jorullensis trees growing in the Axal gorge, on the southern slopes of La Malinche volcano, to unravel past debris-flow activity both temporally and spatially. Based on the combination of GD records, a weighted tree response index(Wit), field evidence and hydrometeorological records, we reconstructed 23 debris flows since 1933.Interestingly, almost two-thirds of the reconstructed years with debris-flow activity in Axal gorge match with events recorded in Axaltzintle gorge located on the NE slopes of La Malinche. These findings suggest a regional triggering mechanism, most likely related to the occurrence of hurricanes. This research could be useful for disaster risk management of the La Malinche National Park.

Climate Change Impacts on Water Resource and Air Pollution in Ka­bul Sub-basins,Afghanistan

This Climate Change Impacts on Water Resources and Air Pollution,research is carried out to analysis Hydro-meteorological and groundwater data in Kabul Sub-basins,Afghanistan.The main objective of this research is to find out natural causes of climate change effects on surface and to,groundwater resources and air pollutions,these data are collected from diferent Hydrometeoroiogical stations and observations in Kabul Sub-basins for different years(1957 to 2017).For completion this research they used two categories of data analysis;one is hydro meteorological analysis,and the other is groundwater level analysis.In hydro meteorological analysis air temperature,rainfall and discharge have been recovered by this research in Kabul Sub-basins,a number of air temperature,rainfall,discharge of surface water and groundwater are changes due to climate changes from 1957 to 2017.For climate changes effects this article used air pollution data of national,international development bank of Asia,WHO standards and parameters;PM_(2.5),PM_(10),TSP,NO_(2),SO_(2),O_(3),CO and Pb.From comparing PM_(10) are very higher in the air of Afghanistan.The discharge of Panjsher river due to glacier melting and climate changes increasing.The challenges during this research are lack of equipment.

Characteristics of climate and melt runoff in the Koxkar Glacier River Basin, south slope of the Tianshan Mountains, Northwest China

Hydrology of the high glacierized region in the Tianshan Mountains is an important water resource for arid and semiarid areas of China,even Central Asia.The hydrological process is complex to understand,due to the high variability in cli mate and the lack of hydrometeorological data.Based on field observations,the present study analyzes the meteorological and hydrological characteristics of the Koxkar Glacier River Basin during 20082011;and the factors influencing climate impact on glacier hydrology are discussed.The results show that precipitation at the terminus of the glacier was 426.2 mm,471.8 mm,624.9 mm,and 532 mm in 2008,2009,2010,and 2011,respectively.Discharge increases starting in May,reaches its highest value in July and August,and then starts to decrease.The mean annual discharge was 118.23×106 m3 during the four years observed,with 87.0%occurring in the ablation season(May September).During the study period,the runoff in August accounted for 29%of total streamflow,followed by July(22%)and June(14%).The runoff exhibited obviously high interannual variability from April to September,induced by drastic changes in climate factors.Discharge autocorrelations are very high for all the years.The climate factors show different influences on discharge.The highest correlation R between daily temperature and discharge was for a time lag of 23 days 2on the Koxkar Glacier(0.660.76).The daily depth of runoff to daily temperature and daily water vapor pressure had an R value of 0.56 and 0.69,respective ly,which could be described by an exponential function.A closer relationship is found between runoff and either tempera ture or water vapor pressure on a monthly scale;the R2 values are 0.65 and 0.78,respectively.The study helps us to under stand the mechanisms of the hydrological meteorological system of typical regional glaciers and to provide a reference for glacier-runoff simulations and water-resource management.

EU and international policies for hydrometeorological risks:Operational aspects and link to climate action

Changes in hydrometeorological characteristics and risks have been observed and are projected to increase under climate change. These considerations are scientifically well studied and led to the development of a complex policy framework for adaptation and mitigation for hydrometeorological risks. Awareness for policy actions is growing worldwide but no legal framework is in place to tackle climate change impacts on water at a global scale. With the example of international frameworks and the legislation on EU-level, this article elaborates that hydrometeorological risks are not considered in the framework of one single policy. However, various policy instruments are directly or indirectly considering these risks at different operational levels. It is discussed that a tailor-made framework for hydrometeorological risks would improve coordination at international or national level. A major drawback for a single operational framework is that hydrometeorological risks are scientifically tackled in two large communities: the disaster risk reduction community and the climate change adaptation community, both of which are bound to different research and operational funding budgets. In future, disaster risk reduction and climate change adaptation will need been seen as a complementary set of actions that requires collaboration.

Precipitation Case Prediction Experiment Based on Improved Downscaling Method

The common downscaling methods refer to statistical downscaling, dynamic downscaling and hybrid downscaling. In the work, an improved downscaling method was proposed based on the hybrid downscaling of dynamics and statistics. After that, a precipitation process was selected to compare the actual precipitation with the precipitation results predicted by the downscaling method. Results showed that the prediction results of this improved method basically met the computational needs of hydrological model.

A Hydrometeorological Analysis of the Severe Rainstorm of 20-22 September 1900 over the Gangetic West Bengal

A hydrometeorological study is made of the September, 1900 severe rainstorm which led up to the record rainfalls over Gangetic West Bengal with subsequent disastrous flooding in the Damodar and the Hooghly rivers. The spatial extent of the rainstorm for different durations has been examined by constructing the isohyetal patterns based on rainfall records of stations affected by the storm. Areal rainfalls for 1,2 and 3-day periods are calculated and the values have been compared with similar values from other major rainstorms of the region. The comparison revealed that the September, 1900 rainstorm was the heaviest for 1,2 and 3-day durations for all the areas. The storm contributed rainfalls of 33.0 cm, 52.0 cm and 62.0 cm over an area of 10,000 km2 in 1,2 and 3 days respectively. This rainstorm could, therefore, be considered as an important input in flood and design storm studies in the Gangetic West Bengal region. A relationship between point to areal rainfall has also been developed with a view to evaluate areal PMP estimates.

Identifying Possible Climate Change Signals Using Meteorological Parameters in Short-Term Fire Weather Variability for Russian Boreal Forest in the Republic of Sakha (Yakutia)

The Boreal forest is a terrestrial ecosystem highly vulnerable to the impacts of short-term climate and weather variabilities. Detecting abrupt, rapid climate-induced changes in fire weather and related changes in fire seasonality can provide important insights to assessing impacts of climate change on forestry. This paper, taking the Sakha Republic of Russia as study area, aims to suggest an approach for detecting signals indicating climate-induced changes in fire weather to express recent fire weather variability by using short-term ranks of major meteorological parameters such as air temperature and atmospheric precipitation. Climate data from the “Global Summary of the Day Product” of NOAA (the United States National Oceanic and Atmospheric Administration) for 1996 to 2018 were used to investigate meteorological parameters that drive fire activity. The detection of the climate change signals is made through a 4-step analysis. First, we used descriptive statistics to grasp monthly, annual, seasonal and peak fire period characteristics of fire weather. Then we computed historical normals for WMO reference period, 1961-1990, and the most recent 30-year period for comparison with the current means. The variability of fire weather is analyzed using standard deviation, coefficient of variation, percentage departures from historical normals, percentage departures from the mean, and precipitation concentration index. Inconsistency and abrupt changes in the evolution of fire weather are assessed using homogeneity analysis whilst a Mann-Kendall test is used to detect significant trends in the time series. The results indicate a significant increase of temperature during spring and fall months, which extends the fire season and potentially contributes to increase of burned areas. We again detected a significant rainfall shortage in September which extended the fire season. Furthermore, this study suggests a new approach in statistical methods appropriate for the detection of climate change signals on fire weather variability using short-term climate ranks and evaluation of its impact on fire seasonality and activity.

Seasonal responses of monthly mean sea levels in the Bohai Sea to hydrometeorological forcing and their double screening regression models

-In this paper, the maximum entropy spectral, the cross-spectral and the frequency response analyses are madeon the basis of the data of monthly mean sea levels at coastal stations in the Bohai Sea during 1965-1986. The results show that the annual fluctuations of the monthly mean sea levels in the Bohai Sea are the results of the coupling response of seasonal variations of the marine hydrometeorological factors. Furthermore, the regression prediction equation is obtained by using the double screening stepwise regression analysis method . Through the prediction test , it is proved that the obtained results are desirable.

Towards hydrometeorological thresholds of reservoir-induced landslide from subsurface strain observations

Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displacements have been widely investigated.However,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration.In this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February 2021.The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective.Considering the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift.To identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time series.The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,<169.700 m)and large-amount and high-intensity rainfalls(i.e.,daily rainfall>57.9 mm and rainfall intensity>24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset.Standing on the shoulder of this landslide case,our study informs a practical and reliable pathway for georisk early warning based on subsurface observations,particularly in the context of enhanced extreme weather events.

Feedbacks between vegetation restoration and local precipitation over the Loess Plateau in China

The implementation of large-scale vegetation restoration over the Chinese Loess Plateau has achieved clear improvements in vegetation fraction,as evidenced by large areas of slopes and plains being restored to grassland or forest.However,such large-scale vegetation restoration has altered land-atmosphere exchanges of water and energy,as the land surface characteristics have changed.These variations could affect regional climate,especially local precipitation.Quantitatively evaluating this feedback is an important scientific question in hydrometeorology.This study constructs a coupled land-atmosphere model incorporating vegetation dynamics,and analyzes the spatio-temporal changes of different land use types and land surface parameters over the Loess Plateau.By considering the impacts of vegetation restoration on the water-energy cycle and on land-atmosphere interactions,we quantified the feedback effect of vegetation restoration on local precipitation across the Loess Plateau,and discussed the important underlying processes.To achieve a quantitative evaluation,we designed two simulation experiments,comprising a real scenario with vegetation restoration and a hypothetical scenario without vegetation restoration.These enabled a comparison and analysis of the net impact of vegetation restoration on local precipitation.The results show that vegetation restoration had a positive effect on local precipitation over the Loess Plateau.Observations show that precipitation on the Loess Plateau increased significantly,at a rate of 7.84 mm yr^(-2),from 2000 to 2015.The simulations show that the contribution of large-scale vegetation restoration to the precipitation increase was about 37.4%,while external atmospheric circulation changes beyond the Loess Plateau contributed the other 62.6%.The average annual precipitation under the vegetation restoration scenario over the Loess Plateau was 12.4%higher than that under the scenario without vegetation restoration.The above research results have important theoretical and practical significance for the ecological protection and optimal development of the Loess Plateau,as well as the sustainable management of vegetation restoration.

Application of profile likelihood function to the uncertainty analysis of hydrometeorological extreme inference

Profile likelihood function is introduced to analyze the uncertainty of hydrometeorological extreme inference and the theory of estimating confidence intervals of the key parameters and quantiles of extreme value distribution by profile likelihood function is described.GEV(generalized extreme value)distribution and GP(generalized Pareto)distribution are used respectively to fit the annual maximum daily flood discharge sample of the Yichang station in the Yangtze River and the daily rainfall sample in10 big cities including Guangzhou.The parameters of the models are estimated by maximum likelihood method and the fitting results are tested by probability plot,quantile plot,return level plot and density plot.The return levels and confidence intervals of flood and rainstorm in different return periods are calculated by profile likelihood function.The results show that the asymmetry of the profile likelihood function curve increases with the return period,which can reflect the effect of the length of sample series and return periods on confidence interval.As an effective tool for estimating confidence interval of the key parameters and quantiles of extreme value distribution,profile likelihood function can lead to a more accurate result and help to analyze the uncertainty of extreme values of hydrometeorology.

Reviewing the Oceanic Nino Index(ONI) to Enhance Societal Readiness for El Nino’s Impacts

NOAA’s Oceanic Nino Index(ONI)is used to record for historical purposes the occurrence and duration of El Nino episodes,based on the monitoring of sea surface temperatures(SSTs)in the central Pacific Ocean.The ONI is used to identify the onset of an above average SST threshold that persists for several months,encompassing both the beginning and end of an El Nino episode.The first appearance of an anomalous seasonal value of 0.5℃suggests with a high probability that an El Nino could emerge,but for heightened warnings,one must wait for several months.In this article,we proposed that the ONI value of 0.7℃identifies a tipping point at which the El Nino event becomes locked in,which can provide additional lead time for mitigative actions to be taken by societal decision makers.Our preliminary findings suggest that a first appearance of 0.7℃value could serve as a credible marker of El Nino’s locked-in phase,which can provide additional credibility to the current 0.5℃El Nino onset indicator for at-risk societies to get ready for El Nino’s foreseeable societal and ecological impacts.

Disaster Risk Reduction Efforts in the Greater Horn of Africa

This article assesses the current state of disaster risk reduction(DRR) in the Greater Horn of Africa(GHA),and focuses on interventions and policies to mitigate hydrometeorological risks. The research analyzes, as main case study, the program 'Regional Climate Prediction and Risk Reduction in the Greater Horn of Africa'funded by the Office of U.S. Foreign Disaster Assistance(USAID OFDA) in the early 2000 that targeted risk preparedness.The research method combines a desk review of relevant documents and research papers with surveys and interviews directed to key proponents of DRR across the GHA. Results highlight current strengths and weaknesses in the way DRR is implemented in the GHA. Significant improvements in the climate-forecasting capabilities in the GHA since the 2000 s are acknowledged, but the practice of DRR remains technology driven and impacts on the ground are limited. The key findings highlight the significant communication gaps that exist between the producers of climate information and their end users, the communities at risk. The article urges the establishment of bridges that connect climate experts, policymakers, and representatives of the local communities, and for the implementation of a feedback loop from forecast users to their producers, in order to strengthen risk resilience across the GHA.

Systems Analysis of Vulnerability to Hydrometeorological Threats: An Exploratory Study of Vulnerability Drivers in Northern Zimbabwe

Disasters result from complex interactions of hazards and vulnerability conditions. Reducing human exposure and sensitivity to threats can reduce disaster impact. Prior knowledge about community vulnerability levels is crucial to minimizing potential losses from future threats. Most vulnerability studies focus on high-impact disasters and their temporal and spatial analyses. Yet highfrequency, low-impact disasters have a cumulative potential to severely disrupt or damage socioeconomic systems.There is limited knowledge especially in the global south about the creation of vulnerability to hydrometeorological threats. Using a systems approach, this study explores ways in which communities in the northern semiarid tropics of Zimbabwe are vulnerable to hydrometeorological threats.This predominantly qualitative study used literature review, interviews, transect walks, and focus groups to gather data from selected samples involving smallholder farmers with in-depth knowledge about community vulnerability. The results show that the communities are vulnerable to multiple hydrometeorological threats due to multiple interacting factors including rainfed and floodbased farming, land tenure, topography, climate, and other socioeconomic conditions such as inadequate income sources and high poverty. In order to reduce vulnerability,this study provides five policy options for government and nongovernmental organization interventions, including the need to transform rural economies beyond the traditional rainfed and flood-based farming systems.