Refine
Has Fulltext
- no (129)
Year of publication
Document Type
- Article (129) (remove)
Is part of the Bibliography
- yes (129) (remove)
Keywords
- climate change (4)
- Connectivity (3)
- meteorological drought (3)
- runoff (3)
- Brazil (2)
- Climate change (2)
- Extreme events (2)
- Hydrological modelling (2)
- Lake Malawi (2)
- Precipitation (2)
Knowledge on the response of sediment export to recent climate change in glacierized areas in the European Alps is limited, primarily because long-term records of suspended sediment concentrations (SSCs) are scarce. Here we tested the estimation of sediment export of the past five decades using quantile regression forest (QRF), a nonparametric, multivariate regression based on random forest. The regression builds on short-term records of SSCs and long records of the most important hydroclimatic drivers (discharge, precipitation and air temperature - QPT). We trained independent models for two nested and partially glacier-covered catchments, Vent (98 km(2)) and Vernagt (11.4 km(2)), in the upper otztal in Tyrol, Austria (1891 to 3772 m a.s.l.), where available QPT records start in 1967 and 1975. To assess temporal extrapolation ability, we used two 2-year SSC datasets at gauge Vernagt, which are almost 20 years apart, for a validation. For Vent, we performed a five-fold cross-validation on the 15 years of SSC measurements. Further, we quantified the number of days where predictors exceeded the range represented in the training dataset, as the inability to extrapolate beyond this range is a known limitation of QRF. Finally, we compared QRF performance to sediment rating curves (SRCs). We analyzed the modeled sediment export time series, the predictors and glacier mass balance data for trends (Mann-Kendall test and Sen's slope estimator) and step-like changes (using the widely applied Pettitt test and a complementary Bayesian approach).Our validation at gauge Vernagt demonstrated that QRF performs well in estimating past daily sediment export (Nash-Sutcliffe efficiency (NSE) of 0.73) and satisfactorily for SSCs (NSE of 0.51), despite the small training dataset. The temporal extrapolation ability of QRF was superior to SRCs, especially in periods with high-SSC events, which demonstrated the ability of QRF to model threshold effects. Days with high SSCs tended to be underestimated, but the effect on annual yields was small. Days with predictor exceedances were rare, indicating a good representativity of the training dataset. Finally, the QRF reconstruction models outperformed SRCs by about 20 percent points of the explained variance.Significant positive trends in the reconstructed annual suspended sediment yields were found at both gauges, with distinct step-like increases around 1981. This was linked to increased glacier melt, which became apparent through step-like increases in discharge at both gauges as well as change points in mass balances of the two largest glaciers in the Vent catchment. We identified exceptionally high July temperatures in 1982 and 1983 as a likely cause. In contrast, we did not find coinciding change points in precipitation. Opposing trends at the two gauges after 1981 suggest different timings of "peak sediment". We conclude that, given large-enough training datasets, the presented QRF approach is a promising tool with the ability to deepen our understanding of the response of high-alpine areas to decadal climate change.
The spatial variability of landscape features such as topography, soils and vegetation defines the spatial pattern of hydrological state variables like soil moisture. Spatial variability thereby controls the functional behaviour of the landscape in terms of its runoff response. A consequence of spatial variability is that exchange processes between landscape patches can occur at various spatial scales ranging from the plot to the basin scale. In semi-arid areas, the lateral redistribution of surface runoff between adjacent landscape patches is an important process. For applications to large river basins of 10(4)-10(5) km(2) in size, a multi-scale landscape discretization scheme is presented in this paper. The landscape is sub-divided into modelling units within a hierarchy of spatial scale levels. By delineating areas characterized by a typical toposequence, organised and random variability of landscape characteristics is captured in the model. Using runoff-runon relationships with transition frequencies based on areal fractions of modelling units, lateral surface and subsurface water fluxes between modelling units at the hillslope scale are represented. Thus, the new approach allows for a manageable description of interactions between fine-scale landscape features for inclusion in coarse-scale models. Model applications for the State of Ceara (148,000 km(2)) in the north- east of Brazil demonstrate the importance of taking into account landscape variability and interactions between landscape patches in a semi-arid environment. Using mean landscape characteristics leads to a considerable underestimation of infiltration-excess surface runoff and total simulated runoff. Re-infiltration of surface runoff and lateral redistribution processes between landscape patches cause a reduction of runoff volumes at the basin scale and contribute to the amplification of variations in runoff volumes relative to variations in rainfall volumes for semi-arid areas. (C) 2004 Elsevier B.V. All rights reserved
Timber harvesting by clear cutting is known to impose environmental impacts, including severe disturbance of the soil hydraulic properties which intensify the frequency and magnitude of surface runoff and soil erosion. However, it remains unanswered if harvest areas act as sources or sinks for runoff and soil erosion and whether such behavior operates in a steady state or evolves through time. For this purpose, 92 small-scale rainfall simulations of different intensities were carried out under pine plantation conditions and on two clear-cut harvest areas of different age. Nonparametrical Random Forest statistical models were set up to quantify the impact of environmental variables on the hydrological and erosion response. Regardless of the applied rainfall intensity, runoff always initiated first and yielded most under plantation cover. Counter to expectations, infiltration rates increased after logging activities. Once a threshold rainfall intensity of 20mm/h was exceeded, the younger harvest area started to act as a source for both runoff and erosion after connectivity was established, whereas it remained a sink under lower applied rainfall intensities. The results suggest that the impact of microtopography on surface runoff connectivity and water-repellent properties of the topsoil act as first-order controls for the hydrological and erosion processes in such environments. Fast rainfall-runoff response, sediment-discharge-hystereses, and enhanced postlogging groundwater recharge at catchment scale support our interpretation. At the end, we show the need to account for nonstationary hydrological and erosional behavior of harvest areas, a fact previously unappreciated in predictive models.
Ecohydrology analyses the interactions of biotic and abiotic aspects of our ecosystems and landscapes. It is a highly diverse discipline in terms of its thematic and methodical research foci. This article gives an overview of current German ecohydrological research approaches within plant-animal-soil-systems, meso-scale catchments and their river networks, lake systems, coastal areas and tidal rivers. It discusses their relevant spatial and temporal process scales and different types of interactions and feedback dynamics between hydrological and biotic processes and patterns. The following topics are considered key challenges: innovative analysis of the interdisciplinary scale continuum, development of dynamically coupled model systems, integrated monitoring of coupled processes at the interface and transition from basic to applied ecohydrological science to develop sustainable water and land resource management strategies under regional and global change.
The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach.
Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality.
Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.
Deforestation is a prominent anthropogenic cause of erosive overland flow and slope instability, boosting rates of soil erosion and concomitant sediment flux. Conventional methods of gauging or estimating post-logging sediment flux often focus on annual timescales but overlook potentially important process response on shorter intervals immediately following timber harvest. We resolve such dynamics with non-parametric quantile regression forests (QRF) based on high-frequency (3 min) discharge measurements and sediment concentration data sampled every 30-60 min in similar-sized (similar to 0.1 km(2)) forested Chilean catchments that were logged during either the rainy or the dry season. The method of QRF builds on the random forest algorithm, and combines quantile regression with repeated random sub-sampling of both cases and predictors. The algorithm belongs to the family of decision-tree classifiers, which allow quantifying relevant predictors in high-dimensional parameter space. We find that, where no logging occurred, similar to 80% of the total sediment load was transported during extremely variable runoff events during only 5% of the monitoring period. In particular, dry-season logging dampened the relative role of these rare, extreme sediment-transport events by increasing load efficiency during more efficient moderate events. We show that QRFs outperform traditional sediment rating curves (SRCs) in terms of accurately simulating short-term dynamics of sediment flux, and conclude that QRF may reliably support forest management recommendations by providing robust simulations of post-logging response of water and sediment fluxes at high temporal resolution.
Shaking water out of soil
(2015)
Moderate to large earthquakes can increase the amount of water flowing in streams. Previous interpretations and models assume that the extra water originates in the saturated zone. Here we show that earthquakes may also release water from the unsaturated zone when the seismic energy is sufficient to overcome the threshold of soil water retention. Soil water may then be released into aquifers, increasing streamflow. After the M8.8 Maule, Chile, earthquake, the discharge in some headwater catchments of the Chilean coastal range increased, and the amount of extra water in the discharge was similar to the total amount of water available for release from the unsaturated zone. Assuming rapid recharge of this water to the water table, a groundwater flow model that accounts for evapotranspiration and water released from soils can reproduce the increase in discharge as well as the enhanced diurnal discharge variations observed after the earthquake. Thus the unsaturated zone may play a previously unappreciated, and potentially significant, role in shallow hydrological responses to earthquakes.
Simple water balance modelling of surface reservoir systems in a large data-scarce semiarid region
(2004)
Water resources in dryland areas are often provided by numerous surface reservoirs. As a basis for securing future water supply, the dynamics of reservoir systems need to be simulated for large river basins, accounting for environmental change and an increasing water demand. For the State of Ceara in semiarid Northeast Brazil, with several thousands of reservoirs, a simple deterministic water balance model is presented. Within a cascade-type approach, the reservoirs are grouped into six classes according to storage capacity, rules for flow routing between reservoirs of different size are defined, and water withdrawal and return flow due to human water use is accounted for. While large uncertainties in model applications exist, particularly in terms of reservoir operation rules, model validation against observed reservoir storage volumes shows that the approach is a reasonable simplification to assess surface water availability in large river basins. The results demonstrate the large impact of reservoir storage on downstream flow and stress the need for a coupled simulation of runoff generation, network redistribution and water use