Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

in Hydrology & Earth System Sciences (2011), 15(7), 2259-2274

Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to ... [more ▼]

Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result. [less ▲]

Effect of high-resolution spatial soil moisture variability on simulated runoff response using a distributed hydrologic model

in Hydrology & Earth System Sciences (2011), 15

The importance of the spatial variability of antecedent soil moisture conditions on runoff response is widely acknowledged in hillslope hydrology. Using a distributed hydrologic model, this paper aims at ... [more ▼]

The importance of the spatial variability of antecedent soil moisture conditions on runoff response is widely acknowledged in hillslope hydrology. Using a distributed hydrologic model, this paper aims at investigating the effects of soil moisture spatial variability on runoff in various field conditions and at finding the structure of the soil moisture pattern that approaches the measured soil moisture pattern in terms of field scale runoff. High spatial resolution soil moisture was surveyed in ten different field campaigns using a proximal ground penetrating radar (GPR) mounted on a mobile platform. Based on these soil moisture measurements, seven scenarios of spatial structures of antecedent soil moisture were used and linked with a field scale distributed hydrological model to simulate field scale runoff. Accounting for spatial variability of soil moisture resulted in higher predicted field scale runoff as compared to the case where soil moisture was kept constant. The ranges of possible hydrographs were delineated by the extreme scenarios where soil moisture was directly and inversely modelled according to the topographic wetness index (TWI). These behaviours could be explained by the sizes and relative locations of runoff contributing areas, knowing that runoff was generated by infiltration excess over a certain soil moisture threshold. The most efficient scenario for modeling the within field spatial structure of soil moisture appeared to be when soil moisture is directly arranged according to the TWI, especially when measured soil moisture and TWI were correlated. The novelty of this work is to benefit from a large set of high-resolution soil moisture measurements allowing to model effectively the within field distribution of soil moisture and its impact on the field scale hydrograph. These observations contributed to the current knowledge of the impact of antecedent soil moisture spatial variability on the field scale runoff. [less ▲]

Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

in Hydrology & Earth System Sciences (2010), 14

In this work we assess the uncertainty in modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) – North Chile using a novel and fully integrated multimodel approach aimed at explicitly ... [more ▼]

In this work we assess the uncertainty in modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) – North Chile using a novel and fully integrated multimodel approach aimed at explicitly accounting for uncertainties arising from the definition of alternative conceptual models. The approach integrates the Generalized Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods. For each member of an ensemble M of potential conceptualizations, model weights used in BMA for multi-model aggregation are obtained from GLUE-based likelihood values. These model weights are based on model performance, thus, reflecting how well a conceptualization reproduces an observed dataset D. GLUE-based cumulative predictive distributions for each member of M are then aggregated obtaining predictive distributions accounting for conceptual model uncertainties. For the PTA we propose an ensemble of eight alternative conceptualizations covering all major features of groundwater flow models independently developed in past studies and including two recharge mechanisms which have been source of debate for several years. Results showed that accounting for heterogeneities in the hydraulic conductivity field (a) reduced the uncertainty in the estimations of parameters and state variables, and (b) increased the corresponding model weights used for multi-model aggregation. This was more noticeable when the hydraulic conductivity field was conditioned on available hydraulic conductivity measurements. Contribution of conceptual model uncertainty to the predictive uncertainty varied between 6% and 64% for ground water head estimations and between 16% and 79% for ground water flow estimations. These results clearly illustrate the relevance of conceptual model uncertainty. [less ▲]