Effects of management practices on water yield in small headwater catchments at Cordillera de los Andes in southern Chile

In several parts of the world, drinking water is obtained from springs in natural and managed mountainous forests. Since forests regulate quality as well as quantity of water, the effects of forest-management activities on water yield are an important subject of study. The objective of this study was to evaluate the effects of forest management on water yield in managed and unmanaged temperate native rainforests in the Andean range of southern Chile. The study area is located in San Pablo, a forest reserve of 2,184 ha located at the Andean range of southern Chile (39º 35’ S, 72º 07’ W, 600-925 m a.s.l.). From April 2003 to October 2008, seven experimental small catchments were monitored for rainfall, throughfall, stemflow, soil water infiltration, soil water percolation and runoff. In 2002, one catchment with a secondary deciduous forest was managed, through thinning, causing a reduction in basal area by 35% whereas the other one remained unthinned as control. Both watersheds are adjacent and are located at 600 – 720 m of elevation on deep loam textured volcanic soils (100 - 120 cm). In November 2006, a watershed covered with evergreen old-growth forests was thinned extracting 40% of the total basal area whereas another adjacent catchment remained unthinned as control. Both watersheds are located at 725 – 910 m a.s.l. and have the same aspects. The effects of management of deciduous secondary forests showed that for the period April 2003-March 2007, the mean value of the increase in total annual streamflow was 12.7%, ranging from 10.9% to 14.6%. Thinning of the evergreen old-growth forest increased the streamflow for the period November 2006-October 2008 with 6.1%, ranging from 4.4% to 7.8%, with greater differences during summertime (15.7 to 206%)

A stochastic design rainfall generator based on copulas and mass curves

The use of design storms can be very useful in many hydrological and hydraulic practices. In this study, the concept of a copula-based secondary return period in combination with the concept of mass curves is used to generate point-scale design storms. The analysis is based on storms selected from the 105 year rainfall time series with a 10 min resolution, measured at Uccle, Belgium. In first instance, bivariate copulas and secondary return periods are explained, together with a focus on which couple of storm variables is of highest interest for the analysis and a discussion of how the results might be affected by the goodness-of-fit of the copula. Subsequently, the fitted copula is used to sample storms with a predefined secondary return period for which characteristic variables such as storm duration and total storm depth can be derived. In order to construct design storms with a realistic storm structure, mass curves of 1st, 2nd, 3rd and 4th quartile storms are developed. An analysis shows that the assumption of independence between the secondary return period and the internal storm structure could be made. Based on the mass curves, a technique is developed to randomly generate an intrastorm structure. The coupling of both techniques eventually results in a methodology for stochastic design storm generation. Finally, its practical usefulness for design studies is illustrated based on the generation of a set of statistically identical design storm and rainfall-runoff modelling

Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling

Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moisture retention curve

Flood mapping in vegetated areas using an unsupervised clustering approach on Sentinel-1 and-2 imagery

The European Space Agency's Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available

An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics

Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e. g. water stage in rivers) or agricultural (e. g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett-Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett-Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett-Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period

Effective drought communication : using the past to assess the present and anticipate the future

Especially during drought events, it is important that water gets properly allocated and is not misused or wasted. For an effective drought management, it is thus of utmost importance to raise the awareness of water managers as well as the general public about the drought’s severity. In this paper, we provide two possible sources of information that can be used to communicate about drought events. To illustrate our approach, we make use of drought events that were identified in preceding work as connected components in space and time through the use of operators from the field of mathematical morphology and summarized in terms of characteristics such as affected area, duration and intensity. We demonstrate how these drought characteristics can be used to query for historical drought events that are most similar to an ongoing event, such that lessons learnt from the management of past events can be incorporated in the management of the ongoing event. Further, we also demonstrate how a probabilistic model describing the dependence structure between the drought characteristics is identified and how such model can serve as a basis to estimate the severity of the event. Both approaches provide information that can be used to communicate to laymen about the severity of the ongoing drought, which will help them to anticipate the future