River Routing Research Articles

Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model Using Satellite-based Products and Streamflow Observations.

The purpose of this study was to evaluate snow and snowmelt simulated by version 4 of the Community Land Model (CLM4). We performed uncoupled CLM4 simulations, forced by Modem-Era Retrospective Analysis for Research and Applications Land-only (MERRA-Land) meteorological fields. GlobSnow snow cover fraction (SCF), snow water equivalent (SWE) and satellite-based passive microwave (PMW) snowmelt-off day of year (MoD) data were used to evaluate SCF, SWE, and snowmelt simulations. Simulated runoff was then fed into a river routing scheme and evaluation was performed at 408 snow-dominated catchments using gauge observations. CLM4 and GlobSnow snow cover extent showed a strong agreement, especially during the peak snow cover months. Overall there was a good correlation between simulated and observed SWE (correlation coefficient, R = 0.6). Simulated and observed SWE were similar over areas with relatively flat terrain and moderate forest density. The simulated MoD agreed (MoD differences (CLM4-PMW) = +/-7 days) with observations over 39.4% of the study domain. Snowmelt-off occurred earlier in the model compared to the observations over 39.5 % of the domain and later over 21.1% of the domain. Large differences of MoD were seen in the areas with complex terrain and dense forest cover. We also found that, although streamflow seasonal phase was accurately modeled (R=0.9), the peaks controlled by snowmelt were underestimated. Routed CLM4 streamflow tended to occur early (by 10 days on average).

A Thermally Stratified Reservoir Module for Large‐Scale Distributed Stream Temperature Models With Application in the Tennessee River Basin

AbstractRiver temperatures affect water quality, power plant cooling, and freshwater ecosystems. Stream temperature models that treat river reaches and reservoirs as well‐mixed segments do not capture thermal stratification in reservoirs. To account for the effects of reservoir stratification on downstream water temperatures, we developed a two‐layer stratified reservoir module, which was integrated into the River Basin Model (RBM) to simulate river temperature across a river network with multiple large thermally stratified reservoirs. To evaluate the performance of this model configuration compared to RBM without thermally stratified reservoirs, we simulated river temperature in the Tennessee River Basin in the southeastern United States. We simulated land surface hydrologic fluxes using the Variable Infiltration Capacity (VIC) model and routed runoff using the river routing model RVIC. The two‐layer model configuration reduced the bias in simulated summer river temperature from 6.7 to −1.2 °C downstream of a reservoir with a residence time of 92 days and from 3.0 to −0.7 °C downstream of a reservoir with a residence time of 8 days. Improvement in fall and winter, when reservoirs tend to be well mixed, is minimal. RBM with the two‐layer module also captured the observed longitudinal river temperature variation downstream of a reservoir, with cool temperatures immediately downstream of the reservoir and gradual warming of the river as it flows downstream. Incorporating a simple stratified reservoir module into RBM improves model performance and increases the ability to apply the river temperature model to large basins with multiple large reservoirs.

Estimating the eco-toxicological risk of estrogens in China's rivers using a high-resolution contaminant fate model

The contamination of freshwater systems arises in many river basins due to industrialization and population growth, posing risks to ecosystems and human health. Despite these concerns, the fate and potential impact of many emerging pollutants are currently unknown, especially when the compounds are released into surface waters from populations distributed across large spatial scales. In order to address this shortcoming, a spatially-explicit contaminant fate model was developed as an extension of the global, vector-based river routing model HydroROUT. HydroROUT operates at very high spatial resolution (∼500 m), simulates river and stream chemical transport with in-stream removal, and contains links to a set of lakes and reservoirs, which act as a partial sink during the transport. The chemical fate model was applied to China and includes a consumption and release module based on county-level population demographics, considers point-source contributions from 2547 wastewater treatment plants, and accumulates contributions of rural and urban populations not connected to sewage treatment plants. As a case study, the sources and fates of the estrogens estrone (E1), 17β-estradiol (E2), estriol (E3), as well as the synthetic estrogenic steroid hormone 17α-ethinylestradiol (EE2) were modelled in Chinese surface water bodies. Preliminary validation of the results showed predictions to be within the ranges of concentrations reported in literature, with exception of EE2. The total estrogenic mass in the entire river and lake system amounted to 370 tonnes of estrogens, with about 1.3 tonnes per year discharged to the ocean, neighboring countries or to inland sinks. Under a selected baseline scenario, eco-toxicological risk—i.e., contaminant concentrations in excess of the predicted no effect concentration (PNEC)—is found in 23.6% of all analyzed rivers of China with an average flow > 0.1 m3/s. Out of these, about 4% of rivers showed a high level of risk of 10 times or more above PNEC. Medium-to-large rivers are disproportionally affected; for example, 23.6%, 37.3%, 29.0% and 21.6% of river length are at risk in rivers of 1–10, 10–100, 100–1,000, and 1,000–10,000 m3/s of discharge, respectively, whereas no risk was predicted in the largest rivers (i.e., >10,000 m3/s) of China. Wastewater treatment plants process 22.5% of the total hormone load and thus play an important role in water quality control by reducing the risk in substantial portions of the river network, which would otherwise show elevated risk. Releases from untreated population dominate by far the overall contribution to risk.

Implementation of a vector-based river network routing scheme in the community WRF-Hydro modeling framework for flood discharge simulation

Continental-scale flood discharge modeling requires a high level of efficiency and flexibility. To this end, this study documents the implementation and application of a vector-based river routing model in the community WRF-Hydro modeling framework. Using Hurricane Ike as a case study, the hybrid vector–grid modeling framework's sensitivity to the land grid resolution and the coupling interface is assessed. Results show the model is more sensitive to the coupling interface than the grid resolution, and a 1-km land grid with an area-weighted coupling interface exhibits the optimal simulation results. A geographic information system (GIS) based approach is adopted to improve the regional representativeness of the flow travel time estimation. The model's computational efficiency and complexity are compared to a grid-based routing scheme, demonstrating its advantages for large-scale “offline” hydrological applications with GIS-supported features. Trade-offs between the modeling efficiency and complexity are then discussed to inform future large-scale flood prediction applications.

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.

A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula

Abstract. This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000–2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km 1 h−1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20–99 and 44–88 %, respectively, when considering the ensemble mean.

The UKC2 regional coupled environmental prediction system

Abstract. It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere–land–ocean–wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential benefits of coupling between environmental model components. Results also illustrate that the coupling itself is not sufficient to address all known model issues. Priorities for future development of the UK Environmental Prediction framework and component systems are discussed.

Evaluation of the Runoff and River Routing Schemes in the Community Land Model of the Yellow River Basin

AbstractRunoff and river routing schemes play important roles in land surface models (LSMs) because they regulate the soil moisture, heat flux, and vegetation dynamics of land surface processes and account for the carbon and nutrient transport in river channels. However, these schemes are often simplistic and conceptual. Hence, in this study, we focused on (1) evaluating these schemes in the Community Land Model (CLM) and (2) altering the model representations using physically based schemes based on a study of the Yellow River basin. For runoff simulation, CLM exhibits limitations in water‐limited areas, especially areas with aridity index values greater than 2. Additionally, CLM greatly overestimates runoff, and produces negative Nash‐Sutcliffe efficiency (NSE) coefficients, few quick flow variations, high quick flow index values, and large root mean square errors of total water storage. These issues were greatly improved by implementing schemes of physically based overland flow, lateral soil water flow, and interchange between groundwater and river water. Furthermore, for the river routing scheme, the use of 1‐D kinematic wave routing improved previously unrealistic aspects of the flood hydrographs (e.g., incorrect flood peak values and times) of the original CLM. Moreover, compared with using the grid element‐based river routing approach in CLM, the computational costs were reduced by up to 45% by implementing the flow interval element method. Finally, our results indicate that parameter calibration in CLM minimally improved the simulation performance in water‐limited areas, while physically based schemes generated better results.

Upper Blue Nile basin water budget from a multi-model perspective

Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, in-situ streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

Coupling a global hydrodynamic algorithm and a regional hydrological model for large-scale flood inundation simulations

Abstract To bridge the gap between 1D and 2D hydraulic models for regional scale assessment and global river routing models, we coupled the CaMa-Flood (Catchment-based Macro-scale Floodplain) model and the regional hydrological model SWIM (Soil and Water Integrated Model) as a tool for large-scale flood risk assessments. As a proof-of-concept study, we tested the coupled models in a meso-scale catchment in Germany. The Mulde River has a catchment area of ca. 6,171 km2 and is a sub-catchment of the Elbe River. The modified CaMa-Flood model routes the sub-basin-based daily runoff generated by SWIM along the river network and estimates the river discharge as well as flood inundation areas. The results show that the CaMa-Flood hydrodynamic algorithm can reproduce the daily discharges from 1991 to 2003 well. It outperforms the Muskingum flow routing method (the default routing method in the SWIM) for the 2002 extreme flood event. The simulated flood inundation area in August 2002 is comparable with the observations along the main river. However, problems may occur in upstream areas. The results presented here show the potential of the coupled models for flood risk assessments along large rivers.

A user-friendly software package for VIC hydrologic model development

The Variable Infiltration Capacity (VIC) hydrologic and river routing model simulates the water and energy fluxes that occur near the land surface and provides useful information regarding the quantity and timing of available water within a watershed system. However, despite its popularity, wider adoption is hampered by the considerable effort required to prepare model inputs and calibrate the model parameters. This study presents a user-friendly software package, named VIC-Automated Setup Toolkit (VIC-ASSIST), accessible through an intuitive MATLAB graphical user interface. VIC-ASSIST enables users to navigate the model building process through prompts and automation, with the intention to promote the use of the model for practical, educational, and research purposes. The automated processes include watershed delineation, climate and geographical input set-up, model parameter calibration, sensitivity analysis, and graphical output generation. We demonstrate the package's utilities in various case studies.

A coupled metabolic‐hydraulic model and calibration scheme for estimating whole‐river metabolism during dynamic flow conditions

AbstractConventional methods for estimating whole‐stream metabolic rates from measured dissolved oxygen dynamics do not account for the variation in solute transport times created by dynamic flow conditions. Changes in flow at hourly time scales are common downstream of hydroelectric dams (i.e., hydropeaking), and hydrologic limitations of conventional metabolic models have resulted in a poor understanding of the controls on biological production in these highly managed river ecosystems. To overcome these limitations, we coupled a two‐station metabolic model of dissolved oxygen dynamics with a hydrologic river routing model. We designed calibration and parameter estimation tools to infer values for hydrologic and metabolic parameters based on time series of water quality data, achieving the ultimate goal of estimating whole‐river gross primary production and ecosystem respiration during dynamic flow conditions. Our case study data for model design and calibration were collected in the tailwater of Glen Canyon Dam (Arizona, U.S.A.), a large hydropower facility where the mean discharge was 325 m3 s−1 and the average daily coefficient of variation of flow was 0.17 (i.e., the hydropeaking index averaged from 2006 to 2016). We demonstrate the coupled model's conceptual consistency with conventional models during steady flow conditions, and illustrate the potential bias in metabolism estimates with conventional models during unsteady flow conditions. This effort contributes an approach to solute transport modeling and parameter estimation that allows study of whole‐ecosystem metabolic regimes across a more diverse range of hydrologic conditions commonly encountered in streams and rivers.

The impact of reservoir construction on riverine sediment and carbon fluxes to the Mediterranean Sea

Riverine suspended particulate matter (SPM) is an important vector for the transfer of nutrients and contaminants to the Sea. Mediterranean rivers are naturally rich in SPM, but their fluxes have been largely altered in the past decades because of damming. Based on empirical modeling, combining literature estimates on SPM fluxes with the environmental characteristics of the corresponding drainage basins, we present for the first time a spatially explicit quantification of the natural fluxes at the level of the Mediterranean Sea and its different sub-basins. These fluxes are further combined with the evolution of major reservoir constructions during the last century in order to quantify the sediment retention by dams. Our results confirm that natural riverine sediment fluxes generally result from a combination of hydroclimatic, geomorphologic and land use parameters in the corresponding drainage basin. Among these factors, the combination of lithology and land elevation appears to be a key factor in the control of mechanical erosion rates. According to our model, the natural flux of SPM by all Mediterranean rivers cumulates to about 997Mtyr−1, which corresponds to a specific SPM flux of 211tkm−2yr−1. Besides the Nile River, which holds a particular place among the Mediterranean rivers, coastal rivers (<5000km2) are particularly important for the overall SPM transport. Although they only occupy 38% of the Mediterranean drainage basin area, they export about 58% of the natural SPM flux. We further quantified the potential impact of damming on these fluxes. Using the dams which are geolocalized in the global GRandD data base, and combining them with a high resolution (5′) river routing scheme developed in this study, we find that after 2003, about 35% of the river sediments are retained by dams (26% without the Nile). These values are clearly minimum values, since GRandD is far from being complete in the Mediterranean drainage basin. According to our simulations, about 22% of the storage capacity is currently lost in these dams, and about 30% of them have a life expectancy that does not exceed 200years. We finally also combined our modeling with a global model for the fluxes of particulate organic carbon (POC), representative for particulate organic nutrients in general. Although POC is non-linearly coupled to the fluxes of SPM, our results show that the retention of POC in dams is almost identical to the retention of sediments.

Compound simulation of fluvial floods and storm surges in a global coupled river‐coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh

AbstractWater‐related disasters, such as fluvial floods and cyclonic storm surges, are a major concern in the world's mega‐delta regions. Furthermore, the simultaneous occurrence of extreme discharges from rivers and storm surges could exacerbate flood risk, compared to when they occur separately. Hence, it is of great importance to assess the compound risks of fluvial and coastal floods at a large scale, including mega‐deltas. However, most studies on compound fluvial and coastal flooding have been limited to relatively small scales, and global‐scale or large‐scale studies have not yet addressed both of them. The objectives of this study are twofold: to develop a global coupled river‐coast flood model; and to conduct a simulation of compound fluvial flooding and storm surges in Asian mega‐delta regions. A state‐of‐the‐art global river routing model was modified to represent the influence of dynamic sea surface levels on river discharges and water levels. We conducted the experiments by coupling a river model with a global tide and surge reanalysis data set. Results show that water levels in deltas and estuaries are greatly affected by the interaction between river discharge, ocean tides and storm surges. The effects of storm surges on fluvial flooding are further examined from a regional perspective, focusing on the case of Cyclone Sidr in the Ganges‐Brahmaputra‐Meghna Delta in 2007. Modeled results demonstrate that a &gt;3 m storm surge propagated more than 200 km inland along rivers. We show that the performance of global river routing models can be improved by including sea level dynamics.

The critical role of the routing scheme in simulating peak river discharge in global hydrological models

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Streamflows over a West African Basin from the ALMIP2 Model Ensemble

Abstract Comparing streamflow simulations against observations has become a straightforward way to evaluate a land surface model’s (LSM) ability in simulating water budget within a catchment. Using a mesoscale river routing scheme (RRS), this study evaluates simulated streamflows over the upper Ouémé River basin resulting from 14 LSMs within the framework of phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2). The ALMIP2 RRS (ARTS) has been used to route LSM outputs. ARTS is based on the nonlinear Muskingum–Cunge method and a simple deep water infiltration formulation representing water-table recharge as previously observed in that region. Simulations are performed for the 2005–08 period during which ground observations are largely available. Experiments are designed using different ground-based rainfall datasets derived from two interpolation methods: the Thiessen technique and a combined kriging–Lagrangian methodology. LSM-based total runoff (TR) averages vary from 0.07 to 1.97 mm day−1, while optimal TR was estimated as ~0.65 mm day−1. This highly affected the RRS parameterization and streamflow simulations. Optimal Nash–Sutcliffe coefficients for LSM-averaged streamflows varied from 0.66 to 0.92, depending on the gauge station. However, individual LSM performances show a wider range. A more detailed rainfall distribution provided by the kriging–Lagrangian methodology resulted in overall better streamflow simulations. The early runoff generation related to reduced infiltration rates during early rainfall events features as one of the main reasons for poor LSM performances.

A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data

Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online NFMs adopt a local learning approach and are able to adapt to changes continuously. The DENFIS model however requires upper/lower bound for normalization and also the number of rules increases monotonically. This requirement makes the model unsuitable for use in basins with limited data, since a priori data is required. In order to address this and other drawbacks of current online models, the Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) is adopted in this study for forecast applications in basins with limited data. GSETSK is a fully-online NFM which updates its structure and parameters based on the most recent data. The model does not require the need for historical data and adopts clustering and rule pruning techniques to generate a compact and up-to-date rule-base. GSETSK was used in two forecast applications, rainfall-runoff (a catchment in Sweden) and river routing (Lower Mekong River) forecasts. Each of these two applications was studied under two scenarios: (i) there is no prior data, and (ii) only limited data is available (1year for the Swedish catchment and 1 season for the Mekong River). For the Swedish Basin, GSETSK model results were compared to available results from a calibrated HBV (Hydrologiska Byråns Vattenbalansavdelning) model. For the Mekong River, GSETSK results were compared against the URBS (UnifiedRiverBasin Simulator) model. Both comparisons showed that results from GSETSK are comparable with the physically based models, which were calibrated with historical data. Thus, even though GSETSK was trained with a very limited dataset in comparison with HBV or URBS, similar results were achieved. Similarly, further comparisons between GSETSK with DENFIS and the RBF (Radial Basis Function) models highlighted further advantages of GSETSK as having a rule-base (compared to opaque RBF) which is more compact, up-to-date and more easily interpretable.

Evaluation of streamflow simulation results of land surface models in GLDAS on the Tibetan plateau

AbstractThe Global Land Data Assimilation System (GLDAS) project estimates long‐term runoff based on land surface models (LSMs) and provides a potential way to solve the issue of nonexistent streamflow data in gauge‐sparse regions such as the Tibetan Plateau (TP). However, the reliability of GLDAS runoff data must be validated before being practically applied. In this study, the streamflows simulated by four LSMs (CLM, Noah, VIC, and Mosaic) in GLDAS coupled with a river routing model are evaluated against observed streamflows in five river basins on the TP. The evaluation criteria include four aspects: monthly streamflow value, seasonal cycle of streamflow, annual streamflow trend, and streamflow component partitioning. The four LSMs display varying degrees of biases in monthly streamflow simulations: systematic overestimations are found in the Noah (1.74 ≤ bias ≤ 2.75) and CLM (1.22 ≤ bias ≤ 2.53) models, whereas systematic underestimations are observed in the VIC (0.36 ≤ bias ≤ 0.85) and Mosaic (0.34 ≤ bias ≤ 0.66) models. The Noah model shows the best performance in capturing the temporal variation in monthly streamflow and the seasonal cycle of streamflow, while the VIC model performs the best in terms of bias statistics. The Mosaic model provides the best performance in modeling annual runoff trends and runoff component partitioning. The possible reasons for the different performances of the LSMs are discussed in detail. In order to achieve more accurate streamflow simulations from the LSMs in GLDAS, suggestions are made to further improve the accuracy of the forcing data and parameterization schemes in all models.

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions

Abstract. The hydrological cycle over the Yellow River has been altered by the climate change and human interventions greatly during past decades, with a decadal drying trend mixed with a large variation of seasonal hydrological extremes. To provide support for the adaptation to a changing environment, an experimental seasonal hydrological forecasting system is established over the Yellow River basin. The system draws from a legacy of a global hydrological forecasting system that is able to make use of real-time seasonal climate predictions from North American Multimodel Ensemble (NMME) climate models through a statistical downscaling approach but with a higher resolution and a spatially disaggregated calibration procedure that is based on a newly compiled hydrological observation dataset with 5 decades of naturalized streamflow at 12 mainstream gauges and a newly released meteorological observation dataset including 324 meteorological stations over the Yellow River basin. While the evaluation of the NMME-based seasonal hydrological forecasting will be presented in a companion paper to explore the added values from climate forecast models, this paper investigates the role of initial hydrological conditions (ICs) by carrying out 6-month Ensemble Streamflow Prediction (ESP) and reverse ESP-type simulations for each calendar month during 1982–2010 with the hydrological models in the forecasting system, i.e., a large-scale land surface hydrological model and a global routing model that is regionalized over the Yellow River. In terms of streamflow predictability, the ICs outweigh the meteorological forcings up to 2–5 months during the cold and dry seasons, but the latter prevails over the former in the predictability after the first month during the warm and wet seasons. For the streamflow forecasts initialized at the end of the rainy season, the influence of ICs for lower reaches of the Yellow River can be 5 months longer than that for the upper reaches, while such a difference drops to 1 month during the rainy season. Based on an additional ESP-type simulation without the initialization of the river routing model, it is found that the initial surface water state is the main source of streamflow predictability during the first month, beyond which other sources of terrestrial memory become more important. During the dry/wet periods, the dominance of ICs on the streamflow predictability can be extended by a month even in the rainy season, suggesting the usefulness of the ESP forecasting approach after the onset of the hydrological extreme events. Similar results are found for the soil moisture predictability but with longer influences from ICs. And the simulations indicate that the soil moisture memory is longer over the middle reaches than those over the upper and lower reaches of the Yellow River. The naturalized hydrological predictability analysis in this study will provide a guideline for establishing an operational hydrological forecasting system as well as for managing the risks of hydrological extremes over the Yellow River basin.

Integrating Runoff Generation and Flow Routing in Susquehanna River Basin to Characterize Key Hydrologic Processes Contributing to Maximum Annual Flood Events

The Susquehanna River basin (SRB) is the largest U.S. watershed (71,250 km2) draining to the Atlantic Coast. It encompasses portions of New York, Pennsylvania, and Maryland. Given that annual maximum flood events commonly result from either rain-on-snow or hurricanes/tropical storms, determining the potential impacts of climate change on flooding behavior is especially challenging. This paper presents a modeling system that captures these dominant flooding processes, which is well-suited for future research investigating the impacts of regional climate change. For this study, a coupled hydrologic-hydraulic model is developed and used to estimate hourly streamflow for the period from 2000 to 2008, capturing a range annual maximum discharge phenomenon (e.g., rain-on-snow, localized convective events, and hurricanes/tropical storms). The three-layer variable infiltration capacity (VIC-3L) model is used to generate surface runoff and the vertical flux of water through the root zone at a scale of 0.025° (about 2.8 km), which are used as inputs to the Hillslope River Routing (HRR) model that operates on an irregular grid with a mean length scale of 4.7 km to simulate lateral surface and subsurface transport and channel hydraulics. The coupled model is validated using USGS daily streamflow, snow water equivalent (SWE) derived from the advanced microwave scanning radiometer for EOS (AMSR-E) satellite and snow depth from in situ measurements. The coupled model (VIC-HRR) shows good performance for both seasonal baseflow patterns and large flood events (e.g., rain-on-snow and hurricane/tropical storms). Given the SRB is commonly subjected to two types of flood events, the role of snow processes is investigated. Comparing synthetic model scenarios with and without snow processes suggests that if future climate conditions reduce winter snowfall due to warmer temperatures, but maintain total precipitation levels, annual runoff will increase and mean annual peak discharge will decrease.