Günter Blöschl

Observed spatial organization of soil moisture and its relation to terrain indices

We analyze the degree of spatial organization of soil moisture and the ability of terrain attributes to predict that organization. By organization we mean systematic spatial variation or consistent spatial patterns. We use 13 observed spatial patterns of soil moisture, each based on over 500 point measurements, from the 10.5 ha Tarrawarra experimental catchment in Australia. The measured soil moisture patterns exhibit a high degree of organization during wet periods owing to surface and subsurface lateral redistribution of water. During dry periods there is little spatial organization. The shape of the distribution function of soil moisture changes seasonally and is influenced by the presence of spatial organization. Generally, it is quite different from the shape of the distribution functions of various topographic indices. A correlation analysis found that ln(a), where a is the specific upslope area, was the best univariate spatial predictor of soil moisture for wet conditions and that the potential radiation index was best during dry periods. Combinations of ln(a) or ln(a/tan(β)), where β is the surface slope, and the potential solar radiation index explain up to 61% of the spatial variation of soil moisture during wet periods and up to 22% during dry periods. These combinations explained the majority of the topographically organized component of the spatial variability of soil moisture a posteriori. A scale analysis indicated that indices that represent terrain convergence (such as ln(a) or ln(a/tan(β))) explain variability at all scales from 10 m up to the catchment scale and indices that represent the aspect of different hillslopes (such as the potential solar radiation index) explain variability at scales from 80 m to the catchment scale. The implications of these results are discussed in terms of the organizing processes and in terms of the use of terrain attributes in hydrologic modeling and scale studies. A major limitation on the predictive power of terrain indices is the degree of spatial organization present in the soil moisture pattern at the time for which the prediction is made.

Preferred states in spatial soil moisture patterns: Local and nonlocal controls

In this paper we develop a conceptual and observational case in which soil water patterns in temperate regions of Australia switch between two preferred states. The wet state is dominated by lateral water movement through both surface and subsurface paths, with catchment terrain leading to organization of wet areas along drainage lines. We denote this as nonlocal control. The dry state is dominated by vertical fluxes, with soil properties and only local terrain (areas of high convergence) influencing spatial patterns. We denote this as local control. The switch is described in terms of the dominance of lateral over vertical water fluxes and vice versa. When evapotranspiration exceeds rainfall, the soil dries to the point where hydraulic conductivity is low and any rainfall that occurs essentially wets up the soil uniformly and is evapotranspired before any significant lateral redistribution takes place. As evapotranspiration decreases and/or rainfall increases, areas of high local convergence become wet, and runoff that is generated moves downslope, rapidly wetting up the drainage lines. In the wet to dry transitional period a rapid increase in potential evapotranspiration (and possibly a decrease in rainfall) causes drying of the soil and “shutting down” of lateral flow. Vertical fluxes dominate and the “dry” pattern is established. Three data sets from two catchments are presented to support the notion of preferred states in soil moisture, and the results of a modeling exercise on catchments from a range of climatic conditions illustrate that the conclusions from the field studies may apply to other areas. The implications for hydrological modeling are discussed in relation to methods for establishing antecedent moisture conditions for event models, for distribution models, and for spatially distributing bulk estimates of catchment soil moisture using indices.

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.

Panta Rhei-Everything Flows: Change in hydrology and society-The IAHS Scientific Decade 2013-2022

Abstract The new Scientific Decade 2013–2022 of IAHS, entitled “Panta Rhei—Everything Flows”, is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013–2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes. Editor Z.W. Kundzewicz Citation Montanari, A., Young, G., Savenije, H.H.G., Hughes, D., Wagener, T., Ren, L.L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S.J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V., 2013. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrological Sciences Journal. 58 (6) 1256–1275.

Toward capturing hydrologically significant connectivity in spatial patterns

Many spatial fields exhibit connectivity features that have an important influence on hydrologic behavior. Examples include high-conductivity preferred flow paths in aquifers and saturated source areas in drainage lines. Connected features can be considered as arbitrarily shaped bands or pathways of connected pixels having similar (e.g., high) values. Connectivity is a property that is not captured by standard geostatistical approaches, which assume that spatial variation occurs in the most random possible way that is consistent with the spatial correlation, nor is it captured by indicator geostatistics. An alternative approach is to use connectivity functions. In this paper we apply connectivity functions to 13 observed soil moisture patterns from the Tarrawarra catchment and two synthetic aquifer conductivity patterns. It is shown that the connectivity functions are able to distinguish between connected and disconnected patterns. The importance of the connectivity in determining hydrologic behavior is explored using rainfall-runoff simulations and groundwater transport simulations. We propose the integral connectivity scale as a measure of the presence of hydrologic connectivity. Links between the connectivity functions and integral connectivity scale and simulated hydrologic behavior are demonstrated and explained from a hydrologic process perspective. Connectivity functions and the integral connectivity scale provide promising means for characterizing features that exist in observed spatial fields and that have an important influence on hydrologic behavior. Previously, this has not been possible within a statistical framework.

Scaling issues in snow hydrology

The concept of scale can be used to quantify characteristic lengths of (a) a natural process (such as the correlation length of the spatial snow water equivalent (SWE) variability); (b) a measurement (such as the size of a snow density sample or the footprint of a satellite sensor), and (c) a model (such as the grid size of a distributed snow model). The different types of scales are denoted as process scale, measurement scale and model scale, respectively. Interpolations, extrapolations, aggregations, and disaggregations are viewed as a change in model scale and/or measurement scale. In a first step we examine, in a linear stochastic analysis, the effect of measurement scale and model scale on the data and the model predictions. It is shown that the ratio of the measurement scale and the process scale, and the ratio of the model scale and the process scale are the driving parameters for the scale effects. These scale effects generally cause biases in the variances and spatial correlation lengths of satellite images, field measurements, and simulation results of snow models. It is shown, by example, how these biases can be identified and corrected by regularization methods, At the core of these analyses is the variogram. For the case of snow cover patterns, it is shown that it may be difficult to infer the true snow cover variability from the variograms, particularly when they span many orders of magnitude. In a second step we examine distributed snow models which are a non-linear deterministic approach to changing the scale. Unlike in the linear case, in these models a change of scale may also bias the mean over a catchment of snow-related variables such as SWE There are a number of fundamental scaling issues with distributed models which include subgrid variability, the question of an optimum element size, and parameter identifiability. We give methods for estimating subgrid variability. We suggest that, in general, an optimum element size may not exist and that the model element scale may in practice be dictated by data availability and the required resolution of the predictions. The scale effects in distributed non-linear models can be related to the linear stochastic case which allows us to generalize the applicability of regularization methods. While most of the paper focuses on physical snow processes, similar conclusions apply and similar methods are applicable to chemical and biological processes.

Geostatistical characterisation of soil moisture patterns in the Tarrawarra catchment

Abstract Spatial soil moisture patterns have been measured in the 10.5 ha Tarrawarra catchment in temperate south-eastern Australia on 13 occasions using time domain reflectometry (TDR). Measurements are made on regular grids of between 500 and 2000 points for each occasion. The spatial correlation structure of these soil moisture patterns is analysed. Sample variograms are found to have a clear sill and a nugget. Exponential variogram models, including a nugget, fit the sample variograms closely. The geostatistical structure is found to evolve seasonally. High sills (15–25 (%v/v) 2 ) and low correlation lengths (35–50 m) are observed during the wet winter period. During the dry summer period sills are smaller (5–15 (%v/v) 2 ) and correlation lengths are longer (50–60 m). This seasonal evolution is explained on the basis of the importance of lateral redistribution of moisture during different seasons. Both a nugget effect due to measurement error and variability at small scales contribute to the variability at the 10 m scale, which is the smallest scale in most of the data sets. For one occasion four soil moisture patterns containing 514 samples were collected on 10 × 20 m grids. These patterns are offset by 2 m in an easterly and/or a northerly direction. Variograms for these four patterns are similar which indicates that variograms used for the structural analysis are highly reliable. An analysis based on transects subsampled from typical summer and winter soil moisture patterns indicates that a substantial number of data points (more than about 300) are needed to obtain meaningful sample variograms.

Time stability of catchment model parameters: Implications for climate impact analyses

[1] Climate impact analyses are usually based on driving hydrological models by future climate scenarios, assuming that the model parameters calibrated to past runoff are representative of the future. In this paper we calibrate the parameters of a conceptual rainfall-runoff model to six consecutive 5 year periods between 1976 and 2006 for 273 catchments in Austria and analyze the temporal change of the calibrated parameters. The calibrated parameters representing snow and soil moisture processes show significant trends. For example, the parameter controlling runoff generation doubled, on average, in the 3 decades. Comparisons of different subregions, comparisons with independent data sets, and analyses of the spatial variability of the model parameters indicate that these trends represent hydrological changes rather than calibration artifacts. The trends can be related to changes in the climatic conditions of the catchments such as higher evapotranspiration and drier catchment conditions in the more recent years. The simulations suggest that the impact on simulated runoff of assuming time invariant parameters can be very significant. For example, if using the parameters calibrated to 1976 – 1981 for simulating runoff for the period 2001 – 2006, the biases of median flows are, on average, 15% and the biases of high flows are about 35%. The errors increase as the time lag between the simulation and calibration periods increases. The implications for hydrologic prediction in general and climate impact analyses in particular are discussed.

Advances in the use of observed spatial patterns of catchment hydrological response

Over the past two decades there have been repeated calls for the collection of new data for use in developing hydrological science. The last few years have begun to bear fruit from the seeds sown by these calls, through increases in the availability and utility of remote sensing data, as well as the execution of campaigns in research catchments aimed at providing new data for advancing hydrological understanding and predictive capability. In this paper we discuss some philosophical considerations related to model complexity, data availability and predictive performance, highlighting the potential of observed patterns in moving the science and practice of catchment hydrology forward. We then review advances that have arisen from recent work on spatial patterns, including in the characterisation of spatial structure and heterogeneity, and the use of patterns for developing, calibrating and testing distributed hydrological models. We illustrate progress via examples using observed patterns of snow cover, runoff occurrence and soil moisture. Methods for the comparison of patterns are presented, illustrating how they can be used to assess hydrologically important characteristics of model performance. These methods include point-to-point comparisons, spatial relationships between errors and landscape parameters, transects, and optimal local alignment. It is argued that the progress made to date augers well for future developments, but there is scope for improvements in several areas. These include better quantitative methods for pattern comparisons, better use of pattern information in data assimilation and modelling, and a call for improved archiving of data from field studies to assist in comparative studies for generalising results and developing fundamental understanding.

Distributed Snowmelt Simulations in an Alpine Catchment: 1. Model Evaluation on the Basis of Snow Cover Patterns

This paper presents an attempt at deterministically modeling spatially distributed snowmelt in an alpine catchment. The basin is 9.4 km2 in area and elevations range from 1900 to 3050 m above sea level. The model makes use of digital terrain data with 25 m grid spacing. Energy balance components are calculated for each grid element taking topographic variations of solar radiation into account. For each grid element albedo and snow surface temperatures are simulated. Model performance is evaluated on the basis of snow cover depletion patterns as derived from weekly air photographs. The use of spatially distributed data allows for addressing individual model components. Results indicate that the basic model assumptions are realistic. Model inadequacies are shown to arise from processes not included in the model such as avalanching and long wave emission from surrounding terrain as well as inaccurate model parameters.