Thresholds of hydrologic flow regime of a river and investigation of climate change impact—the case of the Lower Brahmaputra river Basin

The sustainability of social-ecological systems depends on river flows being maintained within a range to which those systems are adapted. In order to determine the extent of this natural range of variation, we assess ecological flow thresholds and the occurrence of potentially damaging flood events to society in the context of the Lower Brahmaputra river basin. The ecological flow threshold was calculated using twenty-two ‘Range of Variability (RVA)’ parameters, considering the range between?±?1 standard deviation from the mean of the natural flow. Damaging flood events were calculated using flood frequency analysis of Annual Maxima series and using the flood classification of Bangladesh. The climate change impacts on future river flow were calculated by using a weighted ensemble analysis of twelve global circulation models (GCMs) outputs driving a large-scale hydrologic model. The simulated climate change induced altered flow regime of the Lower Brahmaputra River Basin was then investigated and compared with the calculated threshold flows. The results demonstrate that various parameters including the monthly mean of low flow (January, February and March) and high flow (June, July and August) periods, the 7-day average minimum flow, and the yearly maximum flow will exceed the threshold conditions by 1956–1995 under the business-as-usual A1B and A2 future scenarios. The results have a number of policy level implications for government agencies of the Lower Brahmaputra River Basin, specifically for Bangladesh. The calculated thresholds may be used as a good basis for negotiations with other riparian countries of the basin. The methodological approach presented in this study can be applied to other river basins and provide a useful basis for transboundary water resources management.     

Unique isoprene oxidation products demonstrate chlorine atom chemistry occurs in the Houston,Texas urban area

As part of the 2000 Texas Air Quality Study (TexAQS), we studied the isoprene oxidation process under ambient conditions to discern the presence of chlorine atom (Cl) chemistry in the Houston, Texas urban area. By measuring chloromethylbutenone (CMBO) and an isomer of chloromethylbutenal (CMBA), we clearly observed sixteen episodes of active Cl chemistry during the 24-day experiment. Estimated median Cl concentration during each of these episodes was between the detection limit of ~10² atoms cm⁻³ and 50 _{- 30} ^{+ 70} ×10⁴ 50_{ - 30}^{ + 70} \times {10^4} atoms cm⁻³. Cl concentration during all the episodes averaged 7.6 _{- 2.0} ^{+ 4.7} ×10⁴ 7.6_{ - 2.0}^{ + 4.7} \times {10^4} atoms cm⁻³ and thus amounted to less than 3% of the OH concentration during the same periods. During the episodes, the fraction of oxidation chemistry initiated by Cl ranged from 3–43% and was strongly dependent on the quantity and type of hydrocarbons present in the atmosphere. Because of its intermittent presence and low concentration, Cl is not a broadly influential oxidant in the Houston, Texas urban area.     

Uncertainties in climate change prediction: El Niño-Southern Oscillation and monsoons

The sensitivity of climate phenomena in the low latitudes to enhanced greenhouse conditions is a scientific issue of high relevance to billions of people in the poorest countries of the globe. So far, most studies dealt with individual model results. In the present analysis, we refer to 79 coupled ocean–atmosphere simulations from 12 different climate models under 6 different IPCC scenarios. The basic question is as to what extent various state-of-the-art climate models agree in predicting changes in the main features of El Niño-Southern Oscillation (ENSO) and the monsoon climates in South Asia and West Africa. The individual model runs are compared with observational data in order to judge whether the spatio-temporal characteristics of ENSO are well reproduced. The model experiments can be grouped into multi-model ensembles. Thus, climate change signals in the classical index time series, in the principal components and in the time series of interannual variability can be evaluated against the background of internal variability and model uncertainty.There are large differences between the individual model predictions until the end of the 21st century, especially in terms of monsoon rainfall and the Southern Oscillation index (SOI). The majority of the models tends to project La Niña-like anomalies in the SOI and an intensification of the summer monsoon precipitation in India and West Africa. However, the response barely exceeds the level of natural variability and the systematic intermodel variations are larger than the impact of different IPCC scenarios. Nonetheless, there is one prominent climate change signal, which stands out from model variations and internal noise: All forced model experiments agree in predicting a substantial warming in the eastern tropical Pacific. This oceanic heating does not necessarily lead to a modification of ENSO towards more frequent El Niño and/or La Niña events. It simply represents a change in the background state of ENSO. Indeed, we did not find convincing multi-model evidence for a modification of the wavelet spectra in terms of ENSO or the monsoons. Some models suggest an intensification of the annual cycle but this signal is fairly model-dependent. Thus, large model uncertainty still exists with respect to the future behaviour of climate in the low latitudes. This has to be taken into account when addressing climate change signals in individual model experiments and ensembles.     

Identification of groundwater mean transit times of precipitation and riverbank infiltration by two‐component lumped parameter models

Groundwater transit time is an essential hydrologic metric for groundwater resources management. However, especially in tropical environments, studies on the transit time distribution (TTD) of groundwater infiltration and its corresponding mean transit time (mTT) have been extremely limited due to data sparsity. In this study, we primarily use stable isotopes to examine the TTDs and their mTTs of both vertical and horizontal infiltration at a riverbank infiltration area in the Vietnamese Mekong Delta (VMD), representative of the tropical climate in Asian monsoon regions. Precipitation, river water, groundwater, and local ponding surface water were sampled for 3 to 9 years and analysed for stable isotopes (δ¹⁸O and δ²H), providing a unique data set of stable isotope records for a tropical region. We quantified the contribution that the two sources contributed to the local shallow groundwater by a novel concept of two‐component lumped parameter models (LPMs) that are solved using δ¹⁸O records. The study illustrates that two‐component LPMs, in conjunction with hydrological and isotopic measurements, are able to identify subsurface flow conditions and water mixing at riverbank infiltration systems. However, the predictive skill and the reliability of the models decrease for locations farther from the river, where recharge by precipitation dominates, and a low‐permeable aquitard layer above the highly permeable aquifer is present. This specific setting impairs the identifiability of model parameters. For river infiltration, short mTTs (<40 weeks) were determined for sites closer to the river (<200 m), whereas for the precipitation infiltration, the mTTs were longer (>80 weeks) and independent of the distance to the river. The results not only enhance the understanding of the groundwater recharge dynamics in the VMD but also suggest that the highly complex mechanisms of surface–groundwater interaction can be conceptualized by exploiting two‐component LPMs in general. The model concept could thus be a powerful tool for better understanding both the hydrological functioning of mixing processes and the movement of different water components in riverbank infiltration systems.     

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Background

Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.

Results

Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m^?2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R² ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha^?1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha^?1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R² ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha^?1 [between 0.69–0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha^?1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R² was between 0.58–0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha^?1 for the echo-based model, whereas for the CHM R² was between 0.37–0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha^?1.

Conclusions

Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m^?2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m^?2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m^?2.

     

Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest

Biophysical and biochemical plant foliage parameters play a key role in assessing vegetation health. Those plant parameters determine the spectral reflectance and transmittance properties of vegetation; therefore, hyperspectral remote sensing, particularly imaging spectroscopy, can provide estimates of leaf and canopy chemical properties. Based on the relationship between spectral response and biochemical/biophysical properties of the leaves and canopies, the PROSPECT radiative transfer model simulates the interaction of light with leaves. In this study, more than 1100 leaf samples from the Amazon forest of Ecuador were collected at several study sites, some of which are affected by petroleum pollution, and across the vertical profile of the forest. For every sample, field spectroscopy at leaf level was conducted with a spectroradiometer. The goal of this study was to assess leaf optical properties of polluted and unpolluted rainforest canopies across the vertical profile and identify vegetation stress expressed in changes of biophysical and biochemical properties of vegetation. An ANOVA followed by Holme’s multiple comparisons of means and a principal component analysis showed that photosynthetic pigments, chlorophyll and carotenoids have significantly lower levels across the vertical profile of the forest, particularly in sites affected by petroleum pollution. On the other hand, foliar water content showed significantly higher levels in the polluted site. Those findings are symptoms of vegetation stress caused by reduced photosynthetic activity and consequently decreased transpiration and water-use efficiency of the plants. Cross-comparison between SPAD-502 chlorophyll content meter index and chlorophyll content showed strong positive correlation coefficients (r = 0.71 and r ² = 0.51) which suggests that using the SPAD-502 chlorophyll index itself is sensitive enough to detect vegetation stress in a multispecies tropical forest. Therefore, the SPAD-502 can be used to assess chlorophyll content of vegetation across polluted and non-polluted sites at different canopy layers. The results presented in this paper contribute to the very limited literature on field spectroscopy and radiative transfer models applied to the vertical profile of the Amazon forest.     

Flood risk analyses—how detailed do we need to be?

Applied flood risk analyses, especially in urban areas, very often pose the question how detailed the analysis needs to be in order to give a realistic figure of the expected risk. The methods used in research and practical applications range from very basic approaches with numerous simplifying assumptions up to very sophisticated, data and calculation time demanding applications both on the hazard and on the vulnerability part of the risk. In order to shed some light on the question of required model complexity in flood risk analyses and outputs sufficiently fulfilling the task at hand, a number of combinations of models of different complexity both on the hazard and on the vulnerability side were tested in a case study. The different models can be organized in a model matrix of different complexity levels: On the hazard side, the approaches/models selected were (A) linear interpolation of gauge water levels and intersection with a digital elevation model (DEM), (B) a mixed 1D/2D hydraulic model with simplifying assumptions (LISFLOOD-FP) and (C) a Saint-Venant 2D zero-inertia hyperbolic hydraulic model considering the built environment and infrastructure. On the vulnerability side, the models used for the estimation of direct damage to residential buildings are in order of increasing complexity: (I) meso-scale stage-damage functions applied to CORINE land cover data, (II) the rule-based meso-scale model FLEMOps+ using census data on the municipal building stock and CORINE land cover data and (III) a rule-based micro-scale model applied to a detailed building inventory. Besides the inundation depths, the latter two models consider different building types and qualities as well as the level of private precaution and contamination of the floodwater. The models were applied in a municipality in east Germany, Eilenburg. It suffered extraordinary damage during the flood of August 2002, which was well documented as were the inundation extent and depths. These data provide an almost unique data set for the validation of flood risk analyses. The analysis shows that the combination of the 1D/2D model and the meso-scale damage model FLEMOps+ performed best and provide the best compromise between data requirements, simulation effort, and an acceptable accuracy of the results. The more detailed approaches suffered from complex model set-up, high data requirements, and long computation times.     

Third-order depositional sequences reflecting Milankovitch cyclicity

The origin of third-order depositional sequences remains debatable, and in many cases it is not clear whether they were controlled by tectonic activity and/or by eustatic sea-level changes. In Oxfordian and Berriasian–Valanginian carbonate-dominated sections of Switzerland, France, Germany and Spain, high-resolution sequence-stratigraphic and cyclostratigraphic analyses show that the sedimentary record reflects Milankovitch cyclicity. Orbitally induced insolation changes translated into sea-level fluctuations, which in turn controlled accommodation changes. Beds and bedsets formed in rhythm with the precession and 100-kyr eccentricity cycles, whereas the 400-kyr eccentricity cycle contributed to the creation of major depositional sequences. Biostratigraphical data allow the correlation of many of the 400-kyr sequence boundaries with third-order sequence boundaries recognized in European basins. This implies that climatically controlled sea-level changes contributed to the formation of third-order sequences. Furthermore, this cyclostratigraphical approach improves the relative dating of stratigraphic intervals.