Hylke E. Beck

Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

[1]xa0Numerous previous studies have constructed models to estimate base flow characteristics from climatic and physiographic characteristics of catchments and applied these to ungauged regions. However, these studies generally used streamflow observations from a relatively small number of catchments (<200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments (<10,000 km2) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long-term mean base flow to total streamflow; and (2) the base flow recession constant (k), defined as the rate of base flow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow water equivalent depth, and abundance of surface water bodies. Ensembles of artificial neural networks (ANNs; obtained by subsampling the original set of catchments) were trained to estimate the base flow characteristics from climatic and physiographic data. The catchment-scale estimation of the base flow characteristics demonstrated encouraging performance with R2 values of 0.82 for BFI and 0.72 for k. The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived models. The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl.

Global‐scale regionalization of hydrologic model parameters

Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments ( 10–10,000 km2) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Koppen-Geiger climate types and even for evaluation catchmentsu2009>u20095000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via www.gloh2o.org.

Global Maps of Streamflow Characteristics Based on Observations from Several Thousand Catchments

AbstractStreamflow Q estimation in ungauged catchments is one of the greatest challenges facing hydrologists. Observed Q from 3000 to 4000 small-to-medium-sized catchments (10–10 000 km2) around the globe were used to train neural network ensembles to estimate Q characteristics based on climate and physiographic characteristics of the catchments. In total, 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Testing coefficients of determination for the estimation of the Q characteristics ranged from 0.55 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were relatively unimportant, perhaps because of their data quality. The trained neural network ensembles were subsequently applied spatially over the entire ice-free land surface, resulting in global maps of the Q characteristics (at 0.125° resolution). These maps possess several unique feat...

Long‐term CO2 fertilization increases vegetation productivity and has little effect on hydrological partitioning in tropical rainforests

Understanding how tropical rainforests respond to elevated atmospheric CO2 concentration (eCO2) is essential for predicting Earths carbon, water, and energy budgets under future climate change. Here we use long-term (1982–2010) precipitation (P) and runoff (Q) measurements to infer runoff coefficient (Q/P) and evapotranspiration (E) trends across 18 unimpaired tropical rainforest catchments. We complement that analysis by using satellite observations coupled with ecosystem process modeling (using both “top-down” and “bottom-up” perspectives) to examine trends in carbon uptake and relate that to the observed changes in Q/P and E. Our results show there have been only minor changes in the satellite-observed canopy leaf area over 1982–2010, suggesting that eCO2 has not increased vegetation leaf area in tropical rainforests and therefore any plant response to eCO2 occurs at the leaf level. Meanwhile, observed Q/P and E also remained relatively constant in the 18 catchments, implying an unchanged hydrological partitioning and thus approximately conserved transpiration under eCO2. For the same period, using a top-down model based on gas exchange theory, we predict increases in plant assimilation (A) and light use efficiency (e) at the leaf level under eCO2, the magnitude of which is essentially that of eCO2 (i.e., ~12% over 1982–2010). Simulations from 10 state-of-the-art bottom-up ecosystem models over the same catchments also show that the direct effect of eCO2 is to mostly increase A and e with little impact on E. Our findings add to the current limited pool of knowledge regarding the long-term eCO2 impacts in tropical rainforests.

River gauging at global scale using optical and passive microwave remote sensing

Recent discharge observations are lacking for most rivers globally. Discharge can be estimated from remotely sensed floodplain and channel inundation area, but there is currently no method that can be automatically extended to many rivers. We examined whether automated monitoring is feasible by statistically relating inundation estimates from moderate to coarse (>0.05°) resolution remote sensing to monthly station discharge records. Inundation extents were derived from optical MODIS data and passive microwave sensors, and compared to monthly discharge records from over 8000 gauging stations and satellite altimetry observations for 442 reaches of large rivers. An automated statistical method selected grid cells to construct “satellite gauging reaches” (SGRs). MODIS SGRs were generally more accurate than passive microwave SGRs, but there were complementary strengths. The rivers widely varied in size, regime, and morphology. As expected performance was low (Ru2009 u20090.6. The best results (Ru2009>u20090.9) were obtained for large unregulated lowland rivers, particularly in tropical and boreal regions. Relatively poor results were obtained in arid regions, where flow pulses are few and recede rapidly, and in temperate regions, where many rivers are modified and contained. Provided discharge variations produce clear changes in inundated area and gauge records are available for part of the satellite record, SGRs can retrieve monthly river discharge values back to around 1998 and up to present.

Uncertainty of modelled flow regime for flow-ecological assessment in Southern Europe

Sustainable water basin management requires characterization of flow regime in river networks impacted by anthropogenic pressures. Flow regime in ungauged catchments under current, future, or natural conditions can be assessed with hydrological models. Developing hydrological models is, however, resource demanding such that decision makers might revert to models that have been developed for other purposes and are made available to them (off-the-shelf models). In this study, the impact of epistemic uncertainty of flow regime indicators on flow-ecological assessment was assessed at selected stations with drainage areas ranging from about 400 to almost 90,000km2 in four South European basins (Adige, Ebro, Evrotas and Sava). For each basin, at least two models were employed. Models differed in structure, data input, spatio-temporal resolution, and calibration strategy, reflecting the variety of conditions and purposes for which they were initially developed. The uncertainty of modelled flow regime was assessed by comparing the modelled hydrologic indicators of magnitude, timing, duration, frequency and rate of change to those obtained from observed flow. The results showed that modelled flow magnitude indicators at medium and high flows were generally reliable, whereas indicators for flow timing, duration, and rate of change were affected by large uncertainties, with correlation coefficients mostly below 0.50. These findings mirror uncertainty in flow regime indicators assessed with other methods, including from measured streamflow. The large indicator uncertainty may significantly affect assessment of ecological status in freshwater systems, particularly in ungauged catchments. Finally, flow-ecological assessments proved very sensitive to reference flow regime (i.e., without anthropogenic pressures). Model simulations could not adequately capture flow regime in the reference sites comprised in this study. The lack of reliable reference conditions may seriously hamper flow-ecological assessments. This study shows the pressing need for improving assessment of natural flow regime at pan-European scale.

The impact of lake and reservoir parameterization on global streamflow simulation

Highlights • The effects of lakes and reservoirs on global daily streamflow are evaluated.• Reservoirs affect model performance substantially in the global domain.• Lakes’ effects on model performance are limited to few catchments.• Lakes and reservoirs reduce return levels discharge thresholds globally.• Reservoir parameters contribute to uncertainty of model performance metrics.

Hydrological impacts of land cover change in the Dragonja catchment (Slovenia)

The hydrological effects of the conversion of forested land to other land uses (and vice versa) are to a large degree unknown. The present study investigate the effect of natural regrowth of forest on the regional hydrological cycle, and in particular the effects on streamflow. The Dragonja catchment (covering SW Slovenia and NW Croatia) was chosen because the land use changed significantly in this region over the last 50 years. Satellite data and field observations were used to study the hydrological effect of land use change. Historical remote sensing data from Landsat and ASTER revealed a significant change from agriculture to forest within the catchment. From 1973 to 2002 26% changed from agricultural field to forest. In the same period both the baseflow and the storm and flood frequency dropped significantly. A large part of the streamflow changes may probably be linked to precipitation changes in this region, making the hydrological study on reforestation a difficult task. Until now no significant link between reforestation and changes in the hydrological cycle was found and more research is needed to fully understand the hydrological system in this region.

Integrating Earth observation data in hydrological runoff models

The remote sensing and GIS communities are still separate worlds with their own tools and data formats. It is extremely difficult to easily share data among scientists representing these communities without performing some cumbersome conversions. This paper shows in a case study how these two worlds can benefit from each other by implementing online satellite derived soil moisture in a GIS based operational flood early warning system. We obtained near real time satellite data from the currently active satellite microwave sensor AQUA AMSR-E from the National Snow and Ice Data Center data pool and converted the data to soil moisture maps with the Land Parameter Retrieval Model. The soil moisture maps, with a spatial resolution of 0.1 degree and temporal resolution of approximately 1 day, were converted in a gridded format and directly added to an operational Flood Early Warning System. The developed opportunity to directly visualize soil moisture in such a system appears to be a powerful tool, because it creates the ability to study both the spatial and temporal evolution of soil moisture within the river basin. Furthermore, near real time qualitative information on soil moisture conditions prior to rainfall events, such as generated by our system, can even lead to more accurate estimations for flood hazard conditions. Finally, the current and future role and value of remote sensing products in flood forecasting systems are discussed.