Global Runoff Data Centre Research Articles

MERIT Hydro: A High‐Resolution Global Hydrography Map Based on Latest Topography Dataset

AbstractHigh‐resolution raster hydrography maps are a fundamental data source for many geoscience applications. Here we introduce MERIT Hydro, a new global flow direction map at 3‐arc sec resolution (~90 m at the equator) derived from the latest elevation data (MERIT DEM) and water body data sets (G1WBM, Global Surface Water Occurrence, and OpenStreetMap). We developed a new algorithm to extract river networks near automatically by separating actual inland basins from dummy depressions caused by the errors in input elevation data. After a minimum amount of hand editing, the constructed hydrography map shows good agreement with existing quality‐controlled river network data sets in terms of flow accumulation area and river basin shape. The location of river streamlines was realistically aligned with existing satellite‐based global river channel data. Relative error in the drainage area was <0.05 for 90% of Global Runoff Data Center (GRDC) gauges, confirming the accuracy of the delineated global river networks. Discrepancies in flow accumulation area were found mostly in arid river basins containing depressions that are occasionally connected at high water levels and thus resulting in uncertain watershed boundaries. MERIT Hydro improves on existing global hydrography data sets in terms of spatial coverage (between N90 and S60) and representation of small streams, mainly due to increased availability of high‐quality baseline geospatial data sets. The new flow direction and flow accumulation maps, along with accompanying supplementary layers on hydrologically adjusted elevation and channel width, will advance geoscience studies related to river hydrology at both global and local scales.

Intraseasonal Dynamics of River Discharge and Burned Forest Areas in Siberia

This study demonstrates the dependence between the forest burning rates and abnormal decrease in Siberian river discharges under the conditions of the permafrost zone. Our study area is in Central Siberia and Eastern Siberia/Yakutia. Four rivers (Podkamennaya Tunguska, Lower Tunguska, Aldan, and Viluy) were selected for the study. We analyzed the long-term and seasonal variation of river discharges (archive of The Global Runoff Data Centre for 1939–2015) together with the forest burning dynamics within the river basins (archive of Sukachev Institute of Forest for 1996–2015). We compared the discharges per year with the 77-year average value. Abnormally low levels of discharge constituted 58–78% of the averaged annual rate. An analysis of available chronologies of extreme fire events and relative burned areas (RBAs) showed a high correlation with intra-seasonal data on the runoff minima. The most significant response of river discharges to the wildfire effect was shown for the late summer/autumn season after extreme wildfires during the summer period. The deficit of the runoff was not explained by a low precipitation. Late summer and autumn anomalies of discharge were typical (r = −0.57…−0.77, p < 0.05) for rivers of Central Siberia in seasons of extreme forest burning. The correlation was lower for rivers of Eastern Siberia/Yakutia.

Modis Snowline Elevation Changes During Snowmelt Runoff Events in Europe

Abstract This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000-2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.

LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

Land data assimilation system (LDAS)-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived surface soil moisture (SSM) and leaf area index (LAI) estimates to constrain the interactions between soil, biosphere, and atmosphere (ISBA) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the total runoff integrating pathways (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center for Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a seven-year, quarter degree spatial resolution offline reanalysis of land surface variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Taking advantage of the relatively dense data networks over CONUS, we have also evaluated the impact of the assimilation against in situ measurements of soil moisture from the USCRN (US Climate Reference Network), together with river discharges from the United States Geological Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared with an open-loop simulation (i.e., no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions, which impacts persist through time. LDAS-Monde reanalysis also has the potential to be used to monitor extreme events like agricultural drought. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries

When river and coastal floods coincide, their impacts are often worse than when they occur in isolation; such floods are examples of ‘compound events’. To better understand the impacts of these compound events, we require an improved understanding of the dependence between coastal and river flooding on a global scale. Therefore, in this letter, we: provide the first assessment and mapping of the dependence between observed high sea-levels and high river discharge for deltas and estuaries around the globe; and demonstrate how this dependence may influence the joint probability of floods exceeding both the design discharge and design sea-level. The research was carried out by analysing the statistical dependence between observed sea-levels (and skew surge) from the GESLA-2 dataset, and river discharge using gauged data from the Global Runoff Data Centre, for 187 combinations of stations across the globe. Dependence was assessed using Kendall’s rank correlation coefficient (τ) and copula models. We find significant dependence for skew surge conditional on annual maximum discharge at 22% of the stations studied, and for discharge conditional on annual maximum skew surge at 36% of the stations studied. Allowing a time-lag between the two variables up to 5 days, we find significant dependence for skew surge conditional on annual maximum discharge at 56% of stations, and for discharge conditional on annual maximum skew surge at 54% of stations. Using copula models, we show that the joint exceedance probability of events in which both the design discharge and design sea-level are exceeded can be several magnitudes higher when the dependence is considered, compared to when independence is assumed. We discuss several implications, showing that flood risk assessments in these regions should correctly account for these joint exceedance probabilities.

Assessment of Runoff Components Simulated by GLDAS against UNH–GRDC Dataset at Global and Hemispheric Scales

The current evaluations of global land data assimilation system (GLDAS) runoff were generally limited to the observation-rich areas. At the global and hemispheric scales, we assessed different runoff components performance of GLDAS (1.0 and 2.1) using the University of New Hampshire and Global Runoff Data Centre (UNH-GRDC) dataset. The results suggest that GLDAS simulations show considerable uncertainties, particularly in partition of surface and subsurface runoffs, in snowmelt runoff modeling, and in capturing the northern peak time. GLDAS1.0-CLM (common land model) produced more surface runoff almost globally; GLDAS-Noah generated more surface runoff over the northern middle-high latitudes and more subsurface runoff in the remaining areas; while the partition in GLDAS1.0-VIC (variable infiltration capacity) is almost opposite to that in Noah. Comparing to GLDAS1.0-Noah, GLDAS2.1-Noah improved the premature snow-melting tendency, but its snowmelt-runoff peak magnitude was excessively high in June and July. The discrepancies in northern primary peak times among precipitation and runoff is partly caused by the combination of rainfall and melting-snow over high-latitude, as well as the very different temporal–spatial distributions for snowmelt runoff simulated by GLDAS models. This paper can provide valuable guidance for GLDAS users, and contribute to the further improvement of hydrological parameterized schemes.

Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles

Abstract. River discharge plays an important role in earth's water cycle, but it is difficult to estimate due to un-gauged rivers, human activities and measurement errors. One approach is based on the observed flux and a simple annual water balance model (ignoring human processes) for un-gauged rivers, but it only provides annual mean values which is insufficient for oceanic modelings. Another way is by forcing a land surface model (LSM) with atmospheric conditions. It provides daily values but with uncertainties associated with the models. We use data assimilation techniques by merging the modeled river discharges by the ORCHIDEE (without human processes currently) LSM and the observations from the Global Runoff Data Centre (GRDC) to obtain optimized discharges over the entire basin. The “model systematic errors” and “human impacts” (dam operation, irrigation, etc.) are taken into account by an optimization parameter x (with annual variation), which is applied to correct model intermediate variable runoff and drainage over each sub-watershed. The method is illustrated over the Iberian Peninsula with 27 GRDC stations over the period 1979–1989. ORCHIDEE represents a realistic discharge over the north of the Iberian Peninsula with small model systematic errors, while the model overestimates discharges by 30–150 % over the south and northeast regions where the blue water footprint is large. The normalized bias has been significantly reduced to less than 30 % after assimilation, and the assimilation result is not sensitive to assimilation strategies. This method also corrects the discharge bias for the basins without observations assimilated by extrapolating the correction from adjacent basins. The “correction” increases the interannual variability in river discharge because of the fluctuation of water usage. The E (P−E) of GLEAM (Global Land Evaporation Amsterdam Model, v3.1a) is lower (higher) than the bias-corrected value, which could be due to the different P forcing and probably the missing processes in the GLEAM model.

Drought Assessment with the Community Land Model for 1951–2010 in East Asia

Severe droughts have occurred in East Asia; however, observational hydroclimate data that covers the entire region is lacking. The objective of this study is to investigate drought assessment in East Asia. This study estimated three drought indices by generating hydroclimate variables using the Community Land Model (CLM). The results of the CLM were verified by comparison with Climatic Research Unit (CRU) data for precipitation and air temperature and the Global Runoff Data Centre (GRDC) data for runoff. Spatial and temporal variations in three drought severity indices, including the standardized precipitation evapotranspiration index (SPEI), the standardized runoff index (SRI), and the Standardized Soil Moisture Index (SSMI), in East Asia were estimated using the CLM output and compared with the SPEI in the CRU. This study classified drought frequency into four classes depending on the drought severity with 5-deg gapped longitude and latitude for 1951–2010 in East Asia and found that moderately dry (D2) and severely dry (D3) drought frequency classes matched well between the CLM and CRU data. The SPEI in the CLM and CRU data showed very similar frequency magnitudes and an increasing temporal trend. The SRI and SSMI frequencies for CLM also showed an increasing temporal trend compared to the SPEI frequency trend. The results of this study show that CLM outputs are reliable for drought analysis in East Asia. Furthermore, this study suggests the possibility of CLM application to other regions to generate hydroclimate data that is otherwise insufficient.

PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model

Abstract. We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D–2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

Abstract. Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25–75.5∘ N, 11.5∘ W–62.5∘ E, at 0.5∘ resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early-autumn LAI. Forcing uncertainty impacts on simulated river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter–spring high flows and underestimate summer–autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely explained by forcing uncertainty, we suggest several research directions. These range from further investigating the discrepancies between LSMs and remote sensing retrievals to developing new model components to represent physical and anthropogenic processes.

Interoperability challenges in river discharge modelling: A cross domain application scenario

River discharge is a critical water cycle variable, as it integrates all the processes (e.g. runoff and evapotranspiration) occurring within a river basin and provides a hydrological output variable that can be readily measured. Its prediction is of invaluable help for many water-related tasks including water resources assessment and management, flood protection, and disaster mitigation. Observations of river discharge are important to calibrate and validate hydrological or coupled land, atmosphere and ocean models. This requires using datasets from different scientific domains (Water, Weather, etc.). Typically, such datasets are provided using different technological solutions. This complicates the integration of new hydrological data sources into application systems. Therefore, a considerable effort is often spent on data access issues instead of the actual scientific question.This paper describes the work performed to address multidisciplinary interoperability challenges related to river discharge modeling and validation. This includes definition and standardization of domain specific interoperability standards for hydrological data sharing and their support in global frameworks such as the Global Earth Observation System of Systems (GEOSS).The research was developed in the context of the EU FP7-funded project GEOWOW (GEOSS Interoperability for Weather, Ocean and Water), which implemented a “River Discharge” application scenario. This scenario demonstrates the combination of river discharge observations data from the Global Runoff Data Centre (GRDC) database and model outputs produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) predicting river discharge based on weather forecast information in the context of the GEOSS.

A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010

Abstract. Closing the terrestrial water budget is necessary to provide consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g., in situ observation, satellite remote sensing, land surface model, and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. Conditioned on the current limited data availability, a systematic method is developed to optimally combine multiple available data sources for precipitation (P), evapotranspiration (ET), runoff (R), and the total water storage change (TWSC) at 0.5∘ spatial resolution globally and to obtain water budget closure (i.e., to enforce P-ET-R-TWSC= 0) through a constrained Kalman filter (CKF) data assimilation technique under the assumption that the deviation from the ensemble mean of all data sources for the same budget variable is used as a proxy of the uncertainty in individual water budget variables. The resulting long-term (1984–2010), monthly 0.5∘ resolution global terrestrial water cycle Climate Data Record (CDR) data set is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This data set serves to bridge the gap between sparsely gauged regions and the regions with sufficient in situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS), and ET from FLUXNET. The data set is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.

LCA WATER FOOTPRINT AWARE CHARACTERIZATION FACTOR BASED ON LOCAL SPECIFIC CONDITIONS

A Water Footprint based on Life Cycle Assessment (LCA) methodology is a modern approach to the assessment of impacts of water use on the environment. The Available WAter REmaining (AWARE) method is the recommended characterization method by the WULCA Working group to perform a water consumption impact assessment in LCA. The published values of AWARE characterization factor were computed using the hydrological model at the country and main river basin levels only. These “average” values can be imported into the LCA software and used for LCA studies. The scale of available AWARE data could be insufficient for studies at a local level due to heterogeneous conditions in various countries or large river basins. In our study, we use runoff data from the Europe region available in the Global Runoff Data Centre for computation of “regionalized” AWARE values and compare them with AWARE values at the country and river basin levels. The analysis of computed values indicates that the variance of AWARE values can be very large in some countries and river basins. Keywords: Water footprint – life cycle assessment – characterization factor – hydrological conditions

A global-scale investigation of trends in annual maximum streamflow

This study investigates the presence of trends in annual maximum daily streamflow data from the Global Runoff Data Centre database, which holds records of 9213 stations across the globe. The records were divided into three reference datasets representing different compromises between spatial coverage and minimum record length, followed by further filtering based on continent, Köppen-Weiger climate classification, presence of dams, forest cover changes and catchment size. Trends were evaluated using the Mann-Kendall nonparametric trend test at the 10% significance level, combined with a field significance test. The analysis found substantial differences between reference datasets in terms of the specific stations that exhibited significant increasing or decreasing trends, showing the need for careful construction of statistical methods. The results were more consistent at the continental scale, with decreasing trends for a large number of stations in western North America and the data-covered regions of Australia, and increasing trends in parts of Europe, eastern North America, parts of South America and southern Africa. Interestingly, neither the presence of dams nor changes in forest cover had a large effect on the trend results, but the catchment size was important, as catchments exhibiting increasing (decreasing) trends tended to be smaller (larger). Finally, there were more stations with significant decreasing trends than significant increasing trends across all the datasets analysed, indicating that limited evidence exists for the hypothesis that flood hazard is increasing when averaged across the data-covered regions of the globe.

Evaluation of the Water Cycle in the European COSMO-REA6 Reanalysis Using GRACE

Precipitation and evapotranspiration, and in particular the precipitation minus evapotranspiration deficit ( P − E ), are climate variables that may be better represented in reanalyses based on numerical weather prediction (NWP) models than in other datasets. P − E provides essential information on the interaction of the atmosphere with the land surface, which is of fundamental importance for understanding climate change in response to anthropogenic impacts. However, the skill of models in closing the atmospheric-terrestrial water budget is limited. Here, total water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) mission are used in combination with discharge data for assessing the closure of the water budget in the recent high-resolution Consortium for Small-Scale Modelling 6-km Reanalysis (COSMO-REA6) while comparing to global reanalyses (Interim ECMWF Reanalysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)) and observation-based datasets (Global Precipitation Climatology Centre (GPCC), Global Land Evaporation Amsterdam Model (GLEAM)). All 26 major European river basins are included in this study and aggregated to 17 catchments. Discharge data are obtained from the Global Runoff Data Centre (GRDC), and insufficiently long time series are extended by calibrating the monthly Génie Rural rainfall-runoff model (GR2M) against the existing discharge observations, subsequently generating consistent model discharge time series for the GRACE period. We find that for most catchments, COSMO-REA6 closes the water budget within the error estimates. In contrast, the global reanalyses underestimate P − E with up to 20 mm/month. For all models and catchments, short-term (below the seasonal timescale) variability of atmospheric terrestrial flux agrees well with GRACE and discharge data with correlations of about 0.6. Our large study area allows identifying regional patterns like negative trends of P − E in eastern Europe and positive trends in northwestern Europe.

Water supply sensitivity to climate change: An R package for implementing reservoir storage analysis in global and regional impact studies

Whilst there are numerous global and regional studies of climate impacts on water resources, relatively few authors have incorporated reservoir storage into their earth system models. Consequently, such studies are unlikely to provide coherent estimates of how changes in climate might affect water supplies globally. This short communication describes an R package, named reservoir, which has been designed for rapid and easy routing of runoff data through storage. The package comprises tools for capacity design, release policy optimisation and performance analysis—allowing users to specify realistic reservoirs and then assess performance in terms of meeting water delivery targets. We demonstrate some of the capabilities of reservoir using 271 runoff records from the Global Runoff Data Centre. The package is freely available through the Comprehensive R Archive Network (CRAN).

Changes in the world rivers’ discharge projected from an updated high resolution dataset of current and future climate zones

Summary In this paper, an updated global map of the current climate zoning and of its projections, according to the Koppen–Geiger classification, is first provided. The map at high horizontal resolution (0.5° × 0.5°), representative of the current (i.e. 1961–2005) conditions, is based on the Climate Research Unit dataset holding gridded series of historical observed temperature and precipitation, while projected conditions rely on the simulated series, for the same variables, by the General Circulation Model CMCC-CM. Modeled variables were corrected for their bias and then projections of climate zoning were generated for the medium term (2006–2050) and long term (2056–2100) future periods, under RCP 4.5 and RCP 8.5 emission scenarios. Results show that Equatorial and Arid climates will spread at the expenses of Snow and Polar climates, with the Warm Temperate experiencing more moderate increase. Maps of climate zones are valuable for a wide range of studies on climate change and its impacts, especially those regarding the water cycle that is strongly regulated by the combined conditions of precipitation and temperature. As example of large scale hydrological applications, in this work we tested and implemented a spatial statistical procedure, the geographically weighted regression among climate zones’ surface and mean annual discharge (MAD) at hydrographic basin level, to quantify likely changes in MAD for the main world rivers monitored through the Global Runoff Data Center database. The selected river basins are representative of more than half of both global superficial freshwater resources and world’s land area. Globally, a decrease in MAD is projected both in the medium term and long term, while spatial differences highlight how some areas require efforts to avoid consequences of amplified water scarcity, while other areas call for strategies to take the opportunity from the expected increase in water availability. Also the fluctuations of trends between the medium to long term time frames is viewed in order to pay attention to the lifespan of investments and decisions. Despite preliminary and illustrative, this study suggests how large scale valuable information can be extracted even through indicators derived from statistical spatial modeling procedures, without the pretention to replace more sophisticated studies that allow process based reproduction of inter-annual and intra-annual discharge variability, and on which a robust and comprehensive assessment of future water resource reliability should be however also based.

Evaluating Global Streamflow Simulations by a Physically Based Routing Model Coupled with the Community Land Model

Abstract Accurately simulating hydrological processes such as streamflow is important in land surface modeling because they can influence other land surface processes, such as carbon cycle dynamics, through various interaction pathways. This study aims to evaluate the global application of a recently developed Model for Scale Adaptive River Transport (MOSART) coupled with the Community Land Model, version 4 (CLM4). To support the global implementation of MOSART, a comprehensive global hydrography dataset has been derived at multiple resolutions from different sources. The simulated runoff fields are first evaluated against the composite runoff map from the Global Runoff Data Centre (GRDC). The simulated streamflow is then shown to reproduce reasonably well the observed daily and monthly streamflow at over 1600 of the world’s major river stations in terms of annual, seasonal, and daily flow statistics. The impacts of model structure complexity are evaluated, and results show that the spatial and temporal variability of river velocity simulated by MOSART is necessary for capturing streamflow seasonality and annual maximum flood. Other sources of the simulation bias include uncertainties in the atmospheric forcing, as revealed by simulations driven by four different climate datasets, and human influences, based on a classification framework that quantifies the impact levels of large dams on the streamflow worldwide.

Modelling the Climatic Variability in the Niger Delta Region: Influence of Climate Change on Hydrology

With substantial variations to flood occurrence predicted as a result of climate change it becomes significant to investigate how global hydrological models process climate-forcing data. The paper presents reasonable analyses of projected hydrological effects of climate change on the Niger Delta region, Nigeria. Runoff simulated represent the present and future flood risk for the catchment of the River Niger using Global Hydrological Models (GHMs) from EU WATCH project driven by three GCMs. The simulated discharges are compared with the monthly gauge measurement along the River Niger from the Global Runoff Data Centre (GRDC). A period of (1970-2050) was chosen to understand the climatic variability across the Niger Delta region. The GHMs under Special Report on Emissions Scenarios A2 scenario were used to provide future climate scenarios over the Niger River. The hydrological models from EU WATCH project were used to calculate flood extents for different model outputs. The simulation shows clears trends of increased in river discharge over the catchment although uncertainty cannot be over role. Considering the future climate suggests that river flow from the basin could be substantially increased, especially in the long term when compare to the reference period. It is not applicable to formulate precise recommendations for management of runoff in the Niger Delta region as Todd highlighted that the degree of uncertainty in future projections of river flow is consider as a constrained towards developing applicable adaptation activities. The rationale behind this work is the need to understand in clear terms the climate change threats on the Niger Delta region; this will form a practical basis for developing adaptation strategies to manage future climate risks.

Assessing the potential global extent of SWOT river discharge observations

Despite its importance as a major element of the global hydrologic cycle, runoff remains poorly constrained except at the largest spatial scales due to limitations of the global stream gauge network and inadequate data sharing. Efforts using remote sensing to infer runoff from discharge estimates are limited by characteristics of present-day sensors. The proposed Surface Water and Ocean Topography (SWOT) mission, a joint project between the United States and France, aims to substantially improve space-based estimates of river discharge. However, the extent of rivers observable by SWOT, likely limited to those wider than 50–100 m, remains unknown. Here, we estimate the extent of SWOT river observability globally using a downstream hydraulic geometry (DHG) approach combining basin areas from the Hydro1k and Hydrosheds elevation products, discharge from the Global Runoff Data Centre (GRDC), and width estimates from a global width–discharge relationship. We do not explicitly consider SWOT-specific errors associated with layover and other phenomena in this analysis, although they have been considered in formulation of the 50–100 m width thresholds. We compare the extent of SWOT-observable rivers with GRDC and USGS gauge datasets, the most complete datasets freely available to the global scientific community. In the continental US, SWOT would match USGS river basin coverage only at large scales (>25,000 km2). Globally, SWOT would substantially improve on GRDC observation extent: SWOT observation of 100 m (50 m) rivers will allow discharge estimation in >60% of 50,000 km2 (10,000 km2) river basins. In contrast, the GRDC observes fewer than 30% (15%) of these basins. SWOT could improve characterization of global runoff processes, especially with a 50 m observability threshold, but in situ gauge data remains essential and must be shared more freely with the international scientific community.