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This study analyzes the influence of local and regional climatic factors on the stable isotopic composition of rainfall in the Vietnamese Mekong Delta (VMD) as part of the Asian monsoon region. It is based on 1.5 years of weekly rainfall samples. In the first step, the isotopic composition of the samples is analyzed by local meteoric water lines (LMWLs) and single-factor linear correlations. Additionally, the contribution of several regional and local factors is quantified by multiple linear regression (MLR) of all possible factor combinations and by relative importance analysis. This approach is novel for the interpretation of isotopic records and enables an objective quantification of the explained variance in isotopic records for individual factors. In this study, the local factors are extracted from local climate records, while the regional factors are derived from atmospheric backward trajectories of water particles. The regional factors, i.e., precipitation, temperature, relative humidity and the length of backward trajectories, are combined with equivalent local climatic parameters to explain the response variables delta O-18, delta H-2, and d-excess of precipitation at the station of measurement.
The results indicate that (i) MLR can better explain the isotopic variation in precipitation (R-2 = 0.8) compared to single-factor linear regression (R-2 = 0.3); (ii) the isotopic variation in precipitation is controlled dominantly by regional moisture regimes (similar to 70 %) compared to local climatic conditions (similar to 30 %); (iii) the most important climatic parameter during the rainy season is the precipitation amount along the trajectories of air mass movement; (iv) the influence of local precipitation amount and temperature is not sig-nificant during the rainy season, unlike the regional precipitation amount effect; (v) secondary fractionation processes (e.g., sub-cloud evaporation) can be identified through the d-excess and take place mainly in the dry season, either locally for delta O-18 and delta H-2, or along the air mass trajectories for d-excess. The analysis shows that regional and local factors vary in importance over the seasons and that the source regions and transport pathways, and particularly the climatic conditions along the pathways, have a large influence on the isotopic composition of rainfall. Although the general results have been reported qualitatively in previous studies (proving the validity of the approach), the proposed method provides quantitative estimates of the controlling factors, both for the whole data set and for distinct seasons. Therefore, it is argued that the approach constitutes an advancement in the statistical analysis of isotopic records in rainfall that can supplement or precede more complex studies utilizing atmospheric models. Due to its relative simplicity, the method can be easily transferred to other regions, or extended with other factors.
The results illustrate that the interpretation of the isotopic composition of precipitation as a recorder of local climatic conditions, as for example performed for paleorecords of water isotopes, may not be adequate in the southern part of the Indochinese Peninsula, and likely neither in other regions affected by monsoon processes. However, the presented approach could open a pathway towards better and seasonally differentiated reconstruction of paleoclimates based on isotopic records.
This study analyzes the influence of local and regional climatic factors on the stable isotopic composition of rainfall in the Vietnamese Mekong Delta (VMD) as part of the Asian monsoon region. It is based on 1.5 years of weekly rainfall samples. In the first step, the isotopic composition of the samples is analyzed by local meteoric water lines (LMWLs) and single-factor linear correlations. Additionally, the contribution of several regional and local factors is quantified by multiple linear regression (MLR) of all possible factor combinations and by relative importance analysis. This approach is novel for the interpretation of isotopic records and enables an objective quantification of the explained variance in isotopic records for individual factors. In this study, the local factors are extracted from local climate records, while the regional factors are derived from atmospheric backward trajectories of water particles. The regional factors, i.e., precipitation, temperature, relative humidity and the length of backward trajectories, are combined with equivalent local climatic parameters to explain the response variables delta O-18, delta H-2, and d-excess of precipitation at the station of measurement. The results indicate that (i) MLR can better explain the isotopic variation in precipitation (R-2 = 0.8) compared to single-factor linear regression (R-2 = 0.3); (ii) the isotopic variation in precipitation is controlled dominantly by regional moisture regimes (similar to 70 %) compared to local climatic conditions (similar to 30 %); (iii) the most important climatic parameter during the rainy season is the precipitation amount along the trajectories of air mass movement; (iv) the influence of local precipitation amount and temperature is not sig-nificant during the rainy season, unlike the regional precipitation amount effect; (v) secondary fractionation processes (e.g., sub-cloud evaporation) can be identified through the d-excess and take place mainly in the dry season, either locally for delta O-18 and delta H-2, or along the air mass trajectories for d-excess. The analysis shows that regional and local factors vary in importance over the seasons and that the source regions and transport pathways, and particularly the climatic conditions along the pathways, have a large influence on the isotopic composition of rainfall. Although the general results have been reported qualitatively in previous studies (proving the validity of the approach), the proposed method provides quantitative estimates of the controlling factors, both for the whole data set and for distinct seasons. Therefore, it is argued that the approach constitutes an advancement in the statistical analysis of isotopic records in rainfall that can supplement or precede more complex studies utilizing atmospheric models. Due to its relative simplicity, the method can be easily transferred to other regions, or extended with other factors. The results illustrate that the interpretation of the isotopic composition of precipitation as a recorder of local climatic conditions, as for example performed for paleorecords of water isotopes, may not be adequate in the southern part of the Indochinese Peninsula, and likely neither in other regions affected by monsoon processes. However, the presented approach could open a pathway towards better and seasonally differentiated reconstruction of paleoclimates based on isotopic records.
Flood events can be expressed by a variety of characteristics such as flood magnitude and extent, event duration or incurred loss. Flood estimation and management may benefit from understanding how the different flood characteristics relate to the hydrological catchment conditions preceding the event and to the meteorological conditions throughout the event. In this study, we therefore propose a methodology to investigate the hydro-meteorological controls on different flood characteristics, based on the simulation of the complete flood risk chain from the flood triggering precipitation event, through runoff generation in the catchment, flood routing and possible inundation in the river system and floodplains to flood loss. Conditional cumulative distribution functions and regression tree analysis delineate the seasonal varying flood processes and indicate that the effect of the hydrological pre-conditions, i.e. soil moisture patterns, and of the meteorological conditions, i.e. weather patterns, depends on the considered flood characteristic. The methodology is exemplified for the Elbe catchment. In this catchment, the length of the build-up period, the event duration and the number of gauges undergoing at least a 10-year flood are governed by weather patterns. The affected length and the number of gauges undergoing at least a 2-year flood are however governed by soil moisture patterns. In case of flood severity and loss, the controlling factor is less pronounced. Severity is slightly governed by soil moisture patterns whereas loss is slightly governed by weather patterns. The study highlights that flood magnitude and extent arise from different flood generation processes and concludes that soil moisture patterns as well as weather patterns are not only beneficial to inform on possible flood occurrence but also on the involved flood processes and resulting flood characteristics.
Variability of the Cold Season Climate in Central Asia. Part II: Hydroclimatic Predictability
(2019)
Central Asia (CA) is subjected to a large variability of precipitation. This study presents a statistical model, relating precipitation anomalies in three subregions of CA in the cold season (November-March) with various predictors in the preceding October. Promising forecast skill is achieved for two subregions covering 1) Uzbekistan, Turkmenistan, Kyrgyzstan, Tajikistan, and southern Kazakhstan and 2) Iran, Afghanistan, and Pakistan. ENSO in October is identified as the major predictor. Eurasian snow cover and the quasi-biennial oscillation further improve the forecast performance. To understand the physical mechanisms, an analysis of teleconnections between these predictors and the wintertime circulation over CA is conducted. The correlation analysis of predictors and large-scale circulation indices suggests a seasonal persistence of tropical circulation modes and a dynamical forcing of the westerly circulation by snow cover variations over Eurasia. An EOF analysis of pressure and humidity patterns allows separating the circulation variability over CA into westerly and tropical modes and confirms that the identified predictors affect the respective circulation characteristics. Based on the previously established weather type classification for CA, the predictors are investigated with regard to their effect on the regional circulation. The results suggest a modification of the Hadley cell due to ENSO variations, with enhanced moisture supply from the Arabian Gulf during El Nino. They further indicate an influence of Eurasian snow cover on the wintertime Arctic Oscillation (AO) and Northern Hemispheric Rossby wave tracks. Positive anomalies favor weather types associated with dry conditions, while negative anomalies promote the formation of a quasi-stationary trough over CA, which typically occurs during positive AO conditions.
A better understanding of precipitation dynamics in the Indian subcontinent is required since India's society depends heavily on reliable monsoon forecasts. We introduce a non-linear, multiscale approach, based on wavelets and event synchronization, for unravelling teleconnection influences on precipitation. We consider those climate patterns with the highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple timescales. We find substantial variation across India and across timescales. In particular, El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the south-east at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation, particularly in the northern regions. The effect of the Pacific Decadal Oscillation (PDO) stretches across the whole country, whereas the Atlantic Multidecadal Oscillation (AMO) influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improve precipitation forecasting.
Statistical distributions of flood peak discharge often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior.
This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage.
Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented.
In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice.
Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment, and the river system.
We then discuss to which extent the current knowledge supports or contradicts these hypotheses.
We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms.
We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research toward testing the hypotheses and improving the prediction of heavy tails.
We analyze trends in compound flooding resulting from high coastal water levels (HCWLs) and peak river discharge over northwestern Europe during 1901-2014. Compound peak discharge associated with 37 stream gauges with at least 70 years of record availability near the North and Baltic Sea coasts is used. Compound flooding is assessed using a newly developed index, compound hazard ratio, that compares the severity of river flooding associated with HCWL with the at-site, T-year (a flood with 1/T chance of being exceeded in any given year) fluvial peak discharge. Our findings suggest a spatially coherent pattern in the dependence between HCWL and river peaks and in compound flood magnitudes and frequency. For higher return levels, we find upward trends in compound hazard ratio frequency at midlatitudes (gauges from 47 degrees N to 60 degrees N) and downward trends along the high latitude (>60 degrees N) regions of northwestern Europe. Plain Language Summary Compound floods in delta areas, that is, the co-occurrence of high coastal water levels (HCWLs) and high river discharge, are a particular challenge for disaster management. Such events are caused by two distinct mechanisms: (1) HCWLs may affect river flows and water levels by backwater effects or by reversing the seaward flow of rivers, particularly in regions with elevation less than 10 m in northwestern Europe. (2) The correlation between HCWL and river flow peaks may also stem from a common meteorological driver. Severe storm periods may be associated with high winds leading to storm surges, and at the same time with high precipitation followed by inland flooding. Understanding the historical trends in compound flooding, owing to changes in relative sea levels, in river flooding and in the dependence between these two drivers, is essential for projecting future changes and disaster management. The risk assessment frameworks are often limited to assessing flood risk from a single driver only. We present a new approach to assess compound flood severity resulting from extreme coastal water level and peak river discharge. We find upward trends in compound flooding for midlatitude regions and downward trends for high latitudes in northwestern Europe.
Reliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage-influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector-specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector-specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector-specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector-specific data acquisition and a consideration of sector-specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data.