Hydroclimatic Regions Research Articles

A Global Hydrological Drought Index Dataset Based on Gravity Recovery and Climate Experiment (GRACE) Data

The Gravity Recovery and Climate Experiment (GRACE) satellites have been used in drought/flood monitoring by observing terrestrial water storage (TWS) change. Meteorological drought indicators or other identified disaster information were usually adopted in association with GRACE-observed changes in TWS for the determination of the occurrence and severity of droughts/floods. Inter-comparisons of dry conditions based on TWS change on a global scale, however, were very difficult because TWS anomalies are not comparable for different hydro-climatic regions. In this paper, we established a global dataset of GRACE-based dimensionless drought index, the Total Storage Deficit Index (TSDI), which is spatially comparable and capable of independently examining the characteristics of dry/wet spells globally. The globally mapped GRACE-based TSDI was examined with some reported extreme hydrologic events, which suggested that the results were fairly consistent with documented drought/flood disaster information. Moreover, comparisons of the GRACE-based TSDI with other frequently used drought indicators, such as the Standardized Precipitation Index (SPI), the Palmer Drought Severity Index (PDSI), and the Palmer Hydrological Drought Index (PHDI), suggested that the TSDI was significantly correlated with the SPI at three different time scales, the PDSI, and the PHDI over most parts of the global surface. The longer the time scale of the selected SPI, the stronger the correlation tended to be with the TSDI. Moreover, the correlation of the TSDI with the PHDI was higher than that with the PDSI over almost the whole global surface. With regard to its performance, this study suggested that the TSDI derived from GRACE-based TWS could be a useful dimensionless index for global and regional hydrological drought monitoring, especially for areas where meteo-hydrological observations are insufficient or human activities are intensive.

Medium-Resolution Soil Moisture Retrieval Using the Bayesian Merging Method

The National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission, launched in January 2015, was designed to provide a global soil moisture product at medium resolution (~9 km), by combining observations from its radar and radiometer. Several downscaling methods have been proposed by the SMAP team for this purpose. This paper evaluates another candidate downscaling method, namely, the Bayesian merging approach. While this has been tested using a synthetic data set across the USA, it is imperative that it can also be tested using the experimental data for a comprehensive range of land surface conditions (i.e., in different hydro-climatic regions) prior to a global application. Consequently, this paper applies this method using the data collected from SMAP experiments field campaigns in south-eastern Australia that closely simulated the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. The method studied here differs from the linear downscaling methods of the SMAP mission, in that it uses a nonlinear method based on Bayes’ theorem. The medium-resolution soil moisture product is obtained using background soil moisture estimates that are updated according to the difference between the observed and predicted brightness temperatures and backscatter coefficients, relating the high- and low-resolution data. Results were assessed against a reference soil moisture map derived from high-resolution airborne radiometer observations. The root-mean-square-error and $R^{2}$ for the Bayesian merging method were found to be 0.02 cm3/cm3 and 0.55, respectively, at 9-km resolution, being similar to the SMAP’s “optional” downscaling method.

A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff Forecasting

Considering network topologies and structures of the artificial neural network (ANN) used in the field of hydrology, one can categorize them into two different generic types: feedforward and feedback (recurrent) networks. Different types of feedforward and recurrent ANNs are available, but multilayer perceptron type of feedforward ANN is most commonly used in hydrology for the development of wavelet coupled neural network (WNN) models. This study is conducted to compare performance of the various wavelet based feedforward artificial neural network (ANN) models. The feedforward ANN types used in the study include the multilayer perceptron neural network (MLPNN), generalized feedforward neural network (GFFNN), radial basis function neural network (RBFNN), modular neural network (MNN) and neuro-fuzzy neural network (NFNN) models. The rainfall-runoff data of four catchments located in different hydro-climatic regions of the world is used in the study. The discrete wavelet transformation (DWT) is used in the present study to decompose input rainfall data using db8 wavelet function. A total of 220 models are developed in this study to evaluate the performance of various feedforward neural network models. Performance of the developed WNN models is compared with their counterpart simple models developed without applying wavelet transformation (WT). The results of the study are further compared with - multiple linear regression (MLR) model which suggest that the WNN models outperformed their counterpart simple models. The hybrid wavelet models developed using MLPNN, the GFFNN and the MNN models performed best among the six selected data driven models explored in the study. Moreover, performance of the three best models is found to be similar and thus the hybrid wavelet GFFNN and the MNN models can be considered as an alternative to the most commonly used hybrid WNN models developed using MLPNN. The study further reveals that the wavelet coupled models outperformed their counterpart simple models only with the parsimonious input vector.

Characterization of floods in the United States

Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and flood duration with geospatial and geomorphologic information. The availability of a representative and long archive of flooding events spanning 78years over a variety of hydroclimatic regions results in a spatially and temporally comprehensive flood characterization over the continental U.S. This study, for the first time, employs a large-event database that is based on actual National Weather Service (NWS) definitions of floods instead of the frequently-adopted case study or frequentist approach, allowing us to base our findings on real definitions of floods. It examines flooding characteristics to identify how space and time scales of floods vary with climatic regimes and geomorphology. Flood events were characterized by linking flood response variables in gauged basins to spatially distributed variables describing climatology, geomorphology, and topography. The primary findings of this study are that the magnitude of flooding is highest is regions such as West Coast and southeastern U.S. which experience the most extraordinary precipitation. The seasonality of flooding varies greatly from maxima during the cool season on the West Coast, warm season in the desert Southwest, and early spring in the Southeast. The fastest responding events tend to be in steep basins of the arid Southwest caused by intense monsoon thunderstorms and steep terrain. The envelope curves of unit peak discharge are consistent with those reported for Europe and worldwide. But significant seasonal variability was observed in floods of the U.S. compared to Europe that is attributed to the diversity of causative rainfall ranging from synoptic scales with orographic enhancements in the West Coast, monsoon thunderstorms in the desert Southwest, to land-falling tropical storms and localized, intense thunderstorms in the Southeast.

Sediment delivery modeling in practice: Comparing the effects of watershed characteristics and data resolution across hydroclimatic regions.

Geospatial models are commonly used to quantify sediment contributions at the watershed scale. However, the sensitivity of these models to variation in hydrological and geomorphological features, in particular to land use and topography data, remains uncertain. Here, we assessed the performance of one such model, the InVEST sediment delivery model, for six sites comprising a total of 28 watersheds varying in area (6-13,500km2), climate (tropical, subtropical, mediterranean), topography, and land use/land cover. For each site, we compared uncalibrated and calibrated model predictions with observations and alternative models. We then performed correlation analyses between model outputs and watershed characteristics, followed by sensitivity analyses on the digital elevation model (DEM) resolution. Model performance varied across sites (overall r2=0.47), but estimates of the magnitude of specific sediment export were as or more accurate than global models. We found significant correlations between metrics of sediment delivery and watershed characteristics, including erosivity, suggesting that empirical relationships may ultimately be developed for ungauged watersheds. Model sensitivity to DEM resolution varied across and within sites, but did not correlate with other observed watershed variables. These results were corroborated by sensitivity analyses performed on synthetic watersheds ranging in mean slope and DEM resolution. Our study provides modelers using InVEST or similar geospatial sediment models with practical insights into model behavior and structural uncertainty: first, comparison of model predictions across regions is possible when environmental conditions differ significantly; second, local knowledge on the sediment budget is needed for calibration; and third, model outputs often show significant sensitivity to DEM resolution.

A Sub-Catchment Based Approach for Modelling Nutrient Dynamics and Transport at a River Basin Scale

The prediction of nutrient pollution at realistic details is difficult due to lack of proper description of inherent processes in modelling tools. To overcome that this study has adopted a process based approach to build a semi-distributed model to simulate nutrient pollution in changing environment. The model was built to describe: (1) nutrient generation process in the catchment with consideration of different aspects of external and internal sources, (2) nutrient release from surface to the waterways via runoff and soil erosion, and (3) in-stream transport and chemical reaction process. The key novelty of this research is the linking of the nutrient generation process with transport mechanism for modelling nutrient dynamics at a basin scale. A flow capacity based approach was introduced to determine nutrient export from catchment to the waterways, which was useful to achieve the high resolution outputs from the model. The model performed reasonably well to represent the behaviour of nutrient in high flow events as well as in seasonal flow in two catchments located in distinct hydro-climatic regions. The study has shown that the nutrient model is suitable for predicting actual nutrient pollution in rivers for both high flow and seasonal flow under different hydro-climatic conditions. By simulating organic and inorganic nutrients separately, the model allows to estimate river water quality status in detail.

How streamflow has changed across Australia since the 1950s: evidence from the network of hydrologic reference stations

Abstract. Streamflow variability and trends in Australia were investigated for 222 high-quality stream gauging stations having 30 years or more continuous unregulated streamflow records. Trend analysis identified seasonal, inter-annual and decadal variability, long-term monotonic trends and step changes in streamflow. Trends were determined for annual total flow, baseflow, seasonal flows, daily maximum flow and three quantiles of daily flow. A distinct pattern of spatial and temporal variation in streamflow was evident across different hydroclimatic regions in Australia. Most of the stations in southeastern Australia spread across New South Wales and Victoria showed a significant decreasing trend in annual streamflow, while increasing trends were retained within the northern part of the continent. No strong evidence of significant trend was observed for stations in the central region of Australia and northern Queensland. The findings from step change analysis demonstrated evidence of changes in hydrologic responses consistent with observed changes in climate over the past decades. For example, in the Murray–Darling Basin, 51 out of 75 stations were identified with step changes of significant reduction in annual streamflow during the middle to late 1990s, when relatively dry years were recorded across the area. Overall, the hydrologic reference stations (HRSs) serve as critically important gauges for streamflow monitoring and changes in long-term water availability inferred from observed datasets. A wealth of freely downloadable hydrologic data is provided at the HRS web portal including annual, seasonal, monthly and daily streamflow data, as well as trend analysis products and relevant site information.

Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture

Several inversion modeling-based approaches have been developed/used to extract soil hydraulic properties (α, n, θres, θsat, Ksat) from remotely sensed (RS) soil moisture footprints. Hydrological models with shallow ground water (SGW) table depths in soils simulate daily root zone soil moisture dynamics based on the extracted soil parameters. The presence of SGW table depths in soils significantly influences model performances; however, SGW table depths are usually unknown in the field, thus, unknown SGW table depths might cause uncertainties in the model outputs. In order to overcome these drawbacks, we developed a dynamic ground water (DGW) data assimilation approach that can consider SGW table depths across time for quantifying effective soil hydraulic properties in the unsaturated zone. In order to verify the DGW data assimilation scheme, numerical experiments comprising synthetic and field validation experiments were conducted. For the numerical studies, the Little Washita (LW) watershed in Oklahoma and Olney (OLN)/Bondville (BOND) sites in Illinois were selected as different hydroclimatic regions. For the synthetic conditions, we tested the DGW scheme using various soil textures and vegetation covers with fixed and dynamically changing SGW table depths across time in homogeneous and heterogeneous (layered) soil columns. The DGW-based soil parameters matched the observations under various synthetic conditions better than those that only consider fixed ground water (FGW) table depths in time. For the field validations, our proposed data assimilation scheme performed well in predicting the soil hydraulic properties and SGW table depths at the point, airborne sensing, and satellite scales, even though uncertainties exist. These findings support the robustness of our proposed DGW approach in application to regional fields. Thus, the DGW scheme could improve the availability and applicability of pixel-scale soil moisture footprints based on satellite platforms.

Multi-Basin Modelling of Future Hydrological Fluxes in the Indian Subcontinent

The impact of climate change on the hydro-climatology of the Indian subcontinent is investigated by comparing statistics of current and projected future fluxes resulting from three RCP scenarios (RCP2.6, RCP4.5, and RCP8.5). Climate projections from the CORDEX-South Asia framework have been bias-corrected using the Distribution-Based Scaling (DBS) method and used to force the HYPE hydrological model to generate projections of evapotranspiration, runoff, soil moisture deficit, snow depth, and applied irrigation water to soil. We also assess the changes in the annual cycles in three major rivers located in different hydro-climatic regions. Results show that conclusions can be influenced by uncertainty in the RCP scenarios. Future scenarios project a gradual increase in temperature (up to 7 °C on average), whilst changes (both increase and decrease) in the long-term average precipitation and evapotranspiration are more severe at the end of the century. The potential change (increase and decrease) in runoff could reach 100% depending on the region and time horizon. Analysis of annual cycles for three selected regions showed that changes in discharge and evapotranspiration due to climate change vary between seasons, whereas the magnitude of change is dependent on the region’s hydro-climatic gradient. Irrigation needs and the snow depth in the Himalayas are also affected.

Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin

Abstract Evapotranspiration (ET) mapping at the Landsat spatial resolution (100 m) is essential to fully understand water use and water availability at the field scale. Water use estimates in the Colorado River Basin (CRB), which has diverse ecosystems and complex hydro-climatic regions, will be helpful to water planners and managers. Availability of Landsat 8 images, starting in 2013, provides the opportunity to map ET in the CRB to assess spatial distribution and patterns of water use. The Operational Simplified Surface Energy Balance (SSEBop) model was used with 528 Landsat 8 images to create seamless monthly and annual ET estimates at the inherent 100 m thermal band resolution. Annual ET values were summarized by land use/land cover classes. Croplands were the largest consumer of “blue” water while shrublands consumed the most “green” water. Validation using eddy covariance (EC) flux towers and water balance approaches showed good accuracy levels with R2 ranging from 0.74 to 0.95 and the Nash–Sutcliffe model efficiency coefficient ranging from 0.66 to 0.91. The root mean square error (and percent bias) ranged from 0.48 mm (13%) to 0.60 mm (22%) for daily (days of satellite overpass) ET and from 7.75 mm (2%) to 13.04 mm (35%) for monthly ET. The spatial and temporal distribution of ET indicates the utility of Landsat 8 for providing important information about ET dynamics across the landscape. Annual crop water use was estimated for five selected irrigation districts in the Lower CRB where annual ET per district ranged between 681 mm to 772 mm. Annual ET by crop type over the Maricopa Stanfield irrigation district ranged from a low of 384 mm for durum wheat to a high of 990 mm for alfalfa fields. A rainfall analysis over the five districts suggested that, on average, 69% of the annual ET was met by irrigation. Although the enhanced cloud-masking capability of Landsat 8 based on the cirrus band and utilization of the Fmask algorithm improved the removal of contaminated pixels, the ability to reliably estimate ET over clouded areas remains an important challenge. Overall, the performance of Landsat 8 based ET compared to available EC datasets and water balance estimates for a complex basin such as the CRB demonstrates the potential of using Landsat 8 for annual water use estimation at a national scale. Future efforts will focus on (a) use of consistent methodology across years, (b) integration of multiple sensors to maximize images used, and (c) employing cloud-computing platforms for large scale processing capabilities.

Runoff forecasting using hybrid Wavelet Gene Expression Programming (WGEP) approach

This study presents a novel approach of using the hybrid Wavelet Gene Expression Programming (WGEP) model to forecast the runoff using rainfall data. The rainfall-runoff data from four different catchments located in the different Hydro-Climatic regions of the world is used in the study. The WGEP model is developed by integrating the discrete wavelet transform (DWT) with the Gene Expression Programming (GEP) models so that individual strengths of each approach can be exploited in a synergistic manner. It is implemented by transforming the input rainfall data using the DWT in order to reveal the temporal and the spectral information contained in the data and subsequently this transformed data is used as input for the GEP models. Ten different mother wavelet functions from different wavelet families are employed in the study in order to transform the data. The mother wavelet functions used in the study include the simple mother wavelet function Haar, the db2, db4, db8 wavelet functions of the most popular wavelet family Daubechies, sym2, sym4, sym8 wavelet functions with sharp peaks, coif2, coif4 wavelet functions and the discrete approximation of meyer (dmey) wavelet. The study further investigates the selection of the optimum input vector to be used in conjunction with the WGEP models by considering nine different input vectors. The first five input vectors contain only one rainfall data series ranging from lagged-1 day to lagged-5 day rainfall data series. The remaining four input vectors are selected using the most common approach in which selection of the input vector comprising of the sequential time series data. The performance of the hybrid WGEP models are compared with the simple GEP models developed without the wavelet transformation for the four selected catchments. The study found that the performance of the WGEP models is superior relative to the simple GEP model for all the nine input vectors considered. However, the WGEP models outperformed their respective simple GEP models only for the first five input vectors. Furthermore, the WGEP models exhibits better results with the dmey wavelet function for all the four catchments during both training and testing.

Prospects and challenges of simulating river channel response to future climate change

Due to the predicted impacts of future climate on hydrology, morphological changes to river channels are expected. Quantifying the magnitudes and rates of future channel change is important for sustainable river channel management. To date, reviews of simulation approaches for investigating river channels and the modelling of environmental change impacts on channel form and process have focused on contemporary process or palaeo perspectives. Hence, herein we review numerical modelling approaches available for reach-scale simulation of future river channels and the predicted in-channel hydro- and morphodynamic changes modelled. We found that despite their widespread availability, hydrodynamic, morphodynamic and cellular models have yet to be used routinely in future in-channel simulations, with cellular models in particular under-represented. Our review shows that predictions of within-channel changes vary greatly between hydro-climatic regions and under contrasting climate change scenarios, mainly due to varying input discharge scenarios; however, increased sediment transport and flood risk are usually predicted. Key challenges in simulating future channel change include representations of external forcing conditions, adequate temporal and spatial scales, transport equations, changing channel materials and lateral erosion; calibration and validation; simulation chains with multiple models; and identification of feedback systems and non-linearity. Nevertheless, despite these challenges, models with increasing complexity have recently been developed and so there is increasing potential in their application. One-dimensional hydro- and morphodynamic simulations, and cellular models, can be modified to reflect the requirements of future representations, such as grain size properties, whilst there is also now an increasing capability to include a greater quantity of external forcing conditions. Some studies, however, have demonstrated the need to develop two-dimensional models for application in centennial-scale studies. We recommend that a wider range of scenarios and the combined effects of multiple external forcing factors should be included, whilst studies are also needed from more hydrologically diverse reaches.

Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China

There is an increasing focus on the usefulness of climate model–based seasonal precipitation forecasts as inputs for hydrological applications. This study reveals that most models from the North American Multi-Model Ensemble (NMME) have potential to forecast seasonal precipitation over 17 hydroclimatic regions in continental China. In this paper, we evaluated the NMME precipitation forecast against observations. The evaluation indices included the correlation coefficient (R), relative root-mean-square error (RRMSE), rank histogram (RH), and continuous ranked probability skill score (CRPSS). We presented the RRMSE-R diagram to distinguish differences between the performances of individual models. We find that the predictive skill is seasonally and regionally dependent, exhibiting higher values in autumn and spring and lower values in summer. Higher predictive skill is observed over most regions except the southeastern monsoon regions, which may be attributable to local climatology and variability. Among the 11 NMME models, CFS, especially CFSv2, exhibits the best predictive skill. The GFDL and NASA models, which are followed by CMC, perform worse than CFS. The performances of IRI and CCSM3 are relatively worse than that of the other models. The forecast skills are significantly improved in multi-model mean forecasts based on simple model averaging (SMA). The improvement is more obvious for Bayesian model averaging (BMA), which is employed to further improve the forecast skill and address model uncertainty using multiple model outputs, than individual model and SMA.

Sensitivity of lake thermal and mixing dynamics to climate change

Warming-induced changes in lake thermal and mixing regimes present risks to water quality and ecosystem services provided by U.S. lakes and reservoirs. Modulation of responses by different physical and hydroclimatic settings are not well understood. We explore the potential effects of climate change on 27 lake “archetypes” representative of a range of lakes and reservoirs occurring throughout the U.S. Archetypes are based on different combinations of depth, surface area, and water clarity. LISSS, a one-dimensional dynamic thermal simulation model, is applied to assess lake response to multiple mid-21st century change scenarios applied to nine baseline climate series from different hydroclimatic regions of the U.S. Results show surface water temperature increases of about 77 % of increase in average air temperature change. Bottom temperature changes are less (around 30 %) for deep lakes and in regions that maintain mid-winter air temperatures below freezing. Significant decreases in length of ice cover are projected, and the extent and strength of stratification will increase throughout the U.S., with systematic differences associated with depth, surface area, and clarity. These projected responses suggest a range of future challenges that lake managers are likely to face. Changes in thermal and mixing dynamics suggest increased risk of summer hypoxic conditions and cyanobacterial blooms. Increased water temperatures above the summer thermocline could be a problem for cold water fisheries management in many lakes. Climate-induced changes in water balance and mass inputs of nutrients may further exacerbate the vulnerability of lakes to climate change.

Evaluation of the SMAP brightness temperature downscaling algorithm using active–passive microwave observations

The baseline radiometer brightness temperature (Tb) downscaling algorithm for NASA's Soil Moisture Active Passive (SMAP) mission, scheduled for launch in January 2015, is tested using an airborne simulation of the SMAP data stream. The algorithm synergistically uses 3km Synthetic Aperture Radar (SAR) backscatter (σ) to downscale a 36km radiometer Tb to 9km. While the algorithm has already been tested using experimental datasets from field campaigns in the USA, it is imperative that it is tested for a comprehensive range of land surface conditions (i.e. in different hydro-climatic regions) before global application. Consequently, this study evaluates the algorithm using data collected from the Soil Moisture Active Passive Experiments (SMAPEx) in south-eastern Australia, that closely simulate the SMAP data stream for a single SMAP radiometer pixel over a 3-week interval, with repeat coverage every 2–3days. The results suggest that the average root-mean-square error (RMSE) in downscaled Tb is 3.1K and 2.6K for h- and v-polarizations respectively, when downscaled to 9km resolution. This increases to 8.2K and 6.6K when applied at 1km resolution. Downscaling over the relatively homogeneous grassland areas resulted in 2K lower RMSE than for the heterogeneous cropping area. Overall, the downscaling error was around 2.4K when applied at 9km resolution for five of the nine days, which meets the 2.4K error target of the SMAP mission.

Evaluating Skill of Seasonal Precipitation and Temperature Predictions of NCEP CFSv2 Forecasts over 17 Hydroclimatic Regions in China

Abstract Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.

Quantifying Northern Hemisphere freshwater ice

The areal extent and volume of peak freshwater (river and lake) ice are quantified across the Northern Hemisphere for the period 1957–2002. Quantification is conducted using a degree‐day ice growth model and ice growth coefficients defined for 14 ice‐specific hydroclimatic regions. The model is driven by ERA‐40 gridded daily air temperature data, and the Global Lakes and Wetlands Database is employed to spatially define rivers and lakes. Results indicate that the total area covered by freshwater ice, at peak thickness, north of the January 0°C isotherm (excluding the Greenland ice sheet) is 1.7 × 106 km2 and the total freshwater ice volume is 1.6 × 103 km3. This area is approximately equal to that of the Greenland ice sheet and the volume to snow on land (Northern Hemisphere). Such values now permit a more complete quantification of the cryosphere (evaluations already having been completed for other components, such as snow, glaciers, and sea ice) and provide a reference data set for assessing future climate‐related changes.

A comparative analysis of the effectiveness of flood management measures based on the concept of "retaining water in the landscape" in different European hydro-climatic regions

Abstract. In this paper, we analyse the effectiveness of flood management measures based on the concept known as "retaining water in the landscape". The investigated measures include afforestation, micro-ponds and small-reservoirs. A comparative and model-based methodological approach has been developed and applied for three meso-scale catchments located in different European hydro-climatological regions: Poyo (184 km2) in the Spanish Mediterranean, Upper Iller (954 km2) in the German Alps and Kamp (621 km2) in Northeast-Austria representing the Continental hydro-climate. This comparative analysis has found general similarities in spite of the particular differences among studied areas. In general terms, the flood reduction through the concept of "retaining water in the landscape" depends on the following factors: the storage capacity increase in the catchment resulting from such measures, the characteristics of the rainfall event, the antecedent soil moisture condition and the spatial distribution of such flood management measures in the catchment. In general, our study has shown that, this concept is effective for small and medium events, but almost negligible for the largest and less frequent floods: this holds true for all different hydro-climatic regions, and with different land-use, soils and morphological settings.

When are multiobjective calibration trade‐offs in hydrologic models meaningful?

This paper applies a four‐objective calibration strategy focusing on peak flows, low flows, water balance, and flashiness to 392 model parameter estimation experiment (MOPEX) watersheds across the United States. Our analysis explores the influence of model structure by analyzing how the multiobjective calibration trade‐offs for two conceptual hydrologic models, the Hydrology Model (HYMOD) and the Hydrologiska Byråns Vattenbalansavdelning (HBV) model, compare for each of the 392 catchments. Our results demonstrate that for modern multiobjective calibration frameworks to identify any meaningful measure of model structural failure, users must be able to carefully control the precision by which they evaluate their trade‐offs. Our study demonstrates that the concept of epsilon‐dominance provides an effective means of attaining bounded and meaningful hydrologic model calibration trade‐offs. When analyzed at an appropriate precision, we found that meaningful multiobjective trade‐offs are far less frequent than prior literature has suggested. However, when trade‐offs do exist at a meaningful precision, they have significant value for supporting hydrologic model selection, distinguishing core model deficiencies, and identifying hydroclimatic regions where hydrologic model prediction is highly challenging.

On the spatio-temporal analysis of hydrological droughts from global hydrological models

Abstract. The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river basin models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are classified as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA). The NCDA presents time series of percentages of areas in drought at the global scale and for pre-defined regions of known hydroclimatology. The CDA is introduced as a complementary method that generates information on the spatial coherence of drought events at the global scale. Spatial drought events are found through CDA by clustering patterns (contiguous areas). In this study the global hydrological model WaterGAP was used to illustrate the methodology development. Global gridded time series of subsurface runoff (resolution 0.5°) simulated with the WaterGAP model from land points were used. The NCDA and CDA were developed to identify drought events in runoff. The percentages of area in drought calculated with both methods show complementary information on the spatial and temporal events for the last decades of the 20th century. The NCDA provides relevant information on the average number of droughts, duration and severity (deficit volume) for pre-defined regions (globe, 2 selected hydroclimatic regions). Additionally, the CDA provides information on the number of spatially linked areas in drought, maximum spatial event and their geographic location on the globe. Some results capture the overall spatio-temporal drought extremes over the last decades of the 20th century. Events like the El Niño Southern Oscillation (ENSO) in South America and the pan-European drought in 1976 appeared clearly in both analyses. The methodologies introduced provide an important basis for the global characterization of droughts, model inter-comparison of drought identified from global hydrological models and spatial event analyses.