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This study refines the method for calibrating a glacio-hydrological model based on Hydrograph Partitioning Curves (HPCs), and evaluates its value in comparison to multidata set optimization approaches which use glacier mass balance, satellite snow cover images, and discharge. The HPCs are extracted from the observed flow hydrograph using catchment precipitation and temperature gradients. They indicate the periods when the various runoff processes, such as glacier melt or snow melt, dominate the basin hydrograph. The annual cumulative curve of the difference between average daily temperature and melt threshold temperature over the basin, as well as the annual cumulative curve of average daily snowfall on the glacierized areas are used to identify the starting and end dates of snow and glacier ablation periods. Model parameters characterizing different runoff processes are calibrated on different HPCs in a stepwise and iterative way. Results show that the HPC-based method (1) delivers model-internal consistency comparably to the tri-data set calibration method; (2) improves the stability of calibrated parameter values across various calibration periods; and (3) estimates the contributions of runoff components similarly to the tri-data set calibration method. Our findings indicate the potential of the HPC-based approach as an alternative for hydrological model calibration in glacierized basins where other calibration data sets than discharge are often not available or very costly to obtain.
Water stable isotope signatures can provide valuable insights into the catchment internal runoff processes. However, the ability of the water isotope data to constrain the internal apportionments of runoff components in hydrological models for glacierized basins is not well understood. This study developed an approach to simultaneously model the water stable isotopic compositions and runoff processes in a glacierized basin in Central Asia. The fractionation and mixing processes of water stable isotopes in and from the various water sources were integrated into a glacio-hydrological model. The model parameters were calibrated on discharge, snow cover and glacier mass balance data, and additionally isotopic composition of streamflow. We investigated the value of water isotopic compositions for the calibration of model parameters, in comparison to calibration methods without using such measurements. Results indicate that: (1) The proposed isotope-hydrological integrated modeling approach was able to reproduce the isotopic composition of streamflow, and improved the model performance in the evaluation period; (2) Involving water isotopic composition for model calibration reduced the model parameter uncertainty, and helped to reduce the uncertainty in the quantification of runoff components; (3) The isotope-hydrological integrated modeling approach quantified the contributions of runoff components comparably to a three-component tracer-based end-member mixing analysis method for summer peak flows, and required less water tracer data. Our findings demonstrate the value of water isotopic compositions to improve the quantification of runoff components using hydrological models in glacierized basins.
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.
Hydrodynamic interactions, i.e. the floodplain storage effects caused by inundations upstream on flood wave propagation, inundation areas, and flood damage downstream, are important but often ignored in large-scale flood risk assessments. Although new methods considering these effects sometimes emerge, they are often limited to a small or meso scale. In this study, we investigate the role of hydrodynamic interactions and floodplain storage on flood hazard and risk in the German part of the Rhine basin. To do so, we compare a new continuous 1D routing scheme within a flood risk model chain to the piece-wise routing scheme, which largely neglects floodplain storage. The results show that floodplain storage is significant, lowers water levels and discharges, and reduces risks by over 50%. Therefore, for accurate risk assessments, a system approach must be adopted, and floodplain storage and hydrodynamic interactions must carefully be considered.
Observed streamflow of headwater catchments of the Tarim River (Central Asia) increased by about 30% over the period 1957-2004. This study aims at assessing to which extent these streamflow trends can be attributed to changes in air temperature or precipitation. The analysis includes a data-based approach using multiple linear regression and a simulation-based approach using a hydrological model. The hydrological model considers changes in both glacier area and surface elevation. It was calibrated using a multiobjective optimization algorithm with calibration criteria based on glacier mass balance and daily and interannual variations of discharge. The individual contributions to the overall streamflow trends from changes in glacier geometry, temperature, and precipitation were assessed using simulation experiments with a constant glacier geometry and with detrended temperature and precipitation time series. The results showed that the observed changes in streamflow were consistent with the changes in temperature and precipitation. In the Sari-Djaz catchment, increasing temperatures and related increase of glacier melt were identified as the dominant driver, while in the Kakshaal catchment, both increasing temperatures and increasing precipitation played a major role. Comparing the two approaches, an advantage of the simulation-based approach is the fact that it is based on process-based relationships implemented in the hydrological model instead of statistical links in the regression model. However, data-based approaches are less affected by model parameter and structural uncertainties and typically fast to apply. A complementary application of both approaches is recommended.
One common approach to cope with floods is the implementation of structural flood protection measures, such as levees or flood-control reservoirs, which substantially reduce the probability of flooding at the time of implementation. Numerous scholars have problematized this approach. They have shown that increasing the levels of flood protection can attract more settlements and high-value assets in the areas protected by the new measures. Other studies have explored how structural measures can generate a sense of complacency, which can act to reduce preparedness. These paradoxical risk changes have been described as "levee effect", "safe development paradox" or "safety dilemma". In this commentary, we briefly review this phenomenon by critically analysing the intended benefits and unintended effects of structural flood protection, and then we propose an interdisciplinary research agenda to uncover these paradoxical dynamics of risk.
The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model
(2019)
In this paper, empirical data are used to estimate the parameters of a sociohydrological flood risk model. The proposed model, which describes the interactions between floods, settlement density, awareness, preparedness, and flood loss, is based on the literature. Data for the case study of Dresden, Germany, over a period of 200years, are used to estimate the model parameters through Bayesian inference. The credibility bounds of their estimates are small, even though the data are rather uncertain. A sensitivity analysis is performed to examine the value of the different data sources in estimating the model parameters. In general, the estimated parameters are less biased when using data at the end of the modeled period. Data about flood awareness are the most important to correctly estimate the parameters of this model and to correctly model the system dynamics. Using more data for other variables cannot compensate for the absence of awareness data. More generally, the absence of data mostly affects the estimation of the parameters that are directly related to the variable for which data are missing. This paper demonstrates that combining sociohydrological modeling and empirical data gives additional insights into the sociohydrological system, such as quantifying the forgetfulness of the society, which would otherwise not be easily achieved by sociohydrological models without data or by standard statistical analysis of empirical data.
Floods affect more people than any other natural hazard; thus flood warning and disaster management are of utmost importance.
However, the operational hydrological forecasts do not provide information about affected areas and impact but only discharge and water levels at gauges.
We show that a simple hydrodynamic model operating with readily available data is able to provide highly localized information on the expected flood extent and impacts, with simulation times enabling operational flood warning.
We demonstrate that such an impact forecast would have indicated the deadly potential of the 2021 flood in western Germany with sufficient lead time.