H. C. Winsemius

Influence of soil and climate on root zone storage capacity

The root zone water storage capacity (Sr) of a catchment is an important variable for the hydrological behaviour of a catchment; it strongly influences the storage, transpiration and runoff generation in an area. However, the root zone storage capacity is largely heterogeneous and not measurable. There are different theories about the variables affecting the root zone storage capacity; among the most debated are soil, vegetation and climate. The effect of vegetation and soil is often accounted for by detailed soil and land use maps. To investigate the effect of climate on the root zone storage capacity, an analogue can be made between the root zone storage capacity of a catchment and the human habit to design and construct reservoirs: both storage capacities help to overcome a dry period of a certain length. Humans often use the mass curve technique to determine the required storage needed to design the reservoir capacity. This mass curve technique can also be used to derive the root zone storage capacity created by vegetation in a certain ecosystem and climate (Gao et al., 2014). Only precipitation and discharge or evaporation data are required for this method. This study tests whether Sr values derived by both the mass curve technique and from soil maps are comparable for a range of catchments in New Zealand. Catchments are selected over a gradient of climates and land use. Special focus lies on how Sr values derived for a larger catchment are representative for smaller nested catchments. The spatial differences are examined between values derived from soil data and from climate and flow data.

A decade of Predictions in Ungauged Basins (PUB)—a review

Abstract The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23–25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power. This paper reviews the work that has been done under the six science themes of the PUB Decade and outlines the challenges ahead for the hydrological sciences community. Editor D. Koutsoyiannis Citation Hrachowitz, M., Savenije, H.H.G., Blöschl, G., McDonnell, J.J., Sivapalan, M., Pomeroy, J.W., Arheimer, B., Blume, T., Clark, M.P., Ehret, U., Fenicia, F., Freer, J.E., Gelfan, A., Gupta, H.V., Hughes, D.A., Hut, R.W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P.A., Uhlenbrook, S., Wagener, T., Winsemius, H.C., Woods, R.A., Zehe, E., and Cudennec, C., 2013. A decade of Predictions in Ungauged Basins (PUB)—a review. Hydrological Sciences Journal, 58 (6), 1198–1255.

On the calibration of hydrological models in ungauged basins: a framework for integrating hard and soft hydrological information.

This paper presents a calibration framework based on the generalized likelihood uncertainty estimation (GLUE) that can be used to condition hydrological model parameter distributions in scarcely gauged river basins, where data is uncertain, intermittent or nonconcomitant. At the heart of this framework is the conditioning of the model parameters such as to reproduce key signatures of the observed data within some limits of acceptability. These signatures are either based on hard or on soft information. Hard information signatures are defined as signatures for which the limits of acceptability may be objectively derived from the distribution of long series of observed values, and which effectively constrain the model parameters. Soft signatures are less effective in parameter conditioning or their limits of acceptability cannot be objectively derived. During random parameter sampling, parameter sets are accepted as equally likely if they meet all the hard limits of acceptability. This results in an intermediate parameter distribution, which can be used to reduce the sampling limits. Then, the soft information may be introduced in a second constraining step to reach a final parameter distribution. The modeler can use the final results as a guideline for a further search for information, possibly from new observations yet to collect. In an application of the framework to the Luangwa catchment in Zambia, three information signatures are retrieved from a data set of old discharge time series and used to condition the parameters of a daily conceptual rainfall-runoff model. We performed two independent calibration experiments with two significantly different satellite rainfall estimates as model input. The results show consistent parameter distributions and considerable reduction of the prior parameter space and corresponding output realizations. These results illustrate the potential of the proposed calibration framework for predictions in scarcely gauged catchments.

Assessment of Gravity Recovery and Climate Experiment (GRACE) temporal signature over the upper Zambezi

The temporal signature of terrestrial storage changes inferred from the Gravity Recovery and Climate Experiment (GRACE) has been assessed by comparison with outputs from a calibrated hydrological model (lumped elementary watershed (LEW)) of the upper Zambezi and surroundings and an inspection of the within?month ground track coverage of GRACE together with spatial?temporal rainfall patterns. The comparison of the hydrological model with GRACE reveals temporal inconsistencies between both data sets. Because the LEW model has been calibrated and validated with independent data sources, we believe that this is a GRACE artifact. The within?month ground track coverage shows an irregular orbit behavior which may well cause aliasing in the GRACE monthly deconvolutions. This aliasing is the most probable cause of observed temporal inconsistencies between GRACE and other data sets.

Monthly mean water storage variations by the combination of GRACE and a regional hydrological model: application to the Zambezi River

The lack of hydrological data and the low quality of global hydrological models’ output caused by incapability of proper calibration is the main problem when using hydrological models to study the closure of the water balance at river basin scales. Monthly GRACE gravity field solutions can be used to infer water storage variations at river basin scale. However, the estimates are erroneous due to measurement errors, aliasing effects, and limited spatial resolution, which makes it difficult to verify and improve the hydrological models.

Future scenarios for earthquake and flood risk in Eastern Europe and Central Asia

We report on a regional flood and earthquake risk assessment for 33 countries in Eastern Europe and Central Asia. Flood and earthquake risk were defined in terms of affected population and affected gross domestic product (GDP). Earthquake risk was also quantified in terms of fatalities and capital loss. Estimates of future population and GDP affected by earthquakes vary significantly among five shared socioeconomic pathways that are used to represent population and GDP in 2030 and 2080. There is a linear relationship between the future relative change in a nations exposure (population or GDP) and its future relative change in annual average population or GDP affected by earthquakes. The evolution of flood hazard was quantified using a flood model with boundary conditions derived from five different general circulation models and two representative concentration pathways, and changes in population and GDP were quantified using two shared socioeconomic pathways. There is a nonlinear relationship between the future relative change in a nations exposure (population or GDP) and its future relative change in its annual average population or GDP affected by floods. Six regions can be defined for positive and negative relative change in population that designate whether climate change can temper, counter, or reinforce relative changes in flood risk produced by changes in population or exposure. The departure from the one-to-one relationship between a relative change in a nations population or GDP and its relative change in flood risk could be used to inform further efforts at flood mitigation and adaptation.

Water Storage in Africa from the Optimised GRACE Monthly Models: Iterative Approach

Water storage variability in southern Africa and particularly in the Zambezi river basin is evaluated using optimally smoothed GRACE gravity field models recently developed at Delft University of Technology. Poor availability and low quality of hydrological in situ data make independent GRACE estimates valuable for hydrological modeling.The output of available hydrological models in the target areas is therefore used for the quantification of the sample correlation and the main discrepancy between the water storage estimates from GRACE and hydrology. Moreover, an attempt to identify the main sources of the discrepancy is made.The results of the study show the maximum sample correlation between optimal water storage estimates from GRACE and from the Lumped Elementary Watershed (LEW) regional hydrological model in the North and North-East of the Zambezi river basin. The maximum discrepancy of about 0.025 m between the mean water storage variations over the Zambezi river basin from LEW and GRACE has been observed in spring, when the water storage is the largest.The estimated signal leakage (bias) caused by the optimal filtering is practically negligible when compared with the GRACE estimates produced by other research centers, though it is considerable for hydrological applications and would require a bias correction for areas smaller then (0.5 cdot 10^6 km^2).A large discrepancy between LEW regional hydrological models of release 2008 (LEW-R2008) and 2006 (LEW-R2006) has been unexpectedly observed, especially in fall 2004 and spring 2005. This finding is presumably related to the use of the suspected higher quality of TRMM rainfall data with respect to FEWS rainfall data, respectively.It is finally concluded that the optimal GRACE estimates can be beneficially used to constrain regional hydrological models for their further improvement.