Hydrological Sciences Journal

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Evolution of river-routing schemes in macro- scale models and their potential for watershed management

Kashif Shaad

To cite this article: Kashif Shaad (2018): Evolution of river-routing schemes in macro-scale models and their potential for watershed management, Hydrological Sciences Journal, DOI: 10.1080/02626667.2018.1473871 To link to this article: https://doi.org/10.1080/02626667.2018.1473871

Accepted author version posted online: 08 May 2018. Published online: 01 Jun 2018.

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Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=thsj20 HYDROLOGICAL SCIENCES JOURNAL, 2018 https://doi.org/10.1080/02626667.2018.1473871

Evolution of river-routing schemes in macro-scale models and their potential for watershed management Kashif Shaad Moore Center for Science, Conservation International, Arlington, Virginia, USA

ABSTRACT ARTICLE HISTORY Macro-scale river-routing schemes first emerged to channel runoff generated as a by-product Received 27 February 2017 from land surface models to oceans. In the past decade, as discharge of major rivers was Accepted 19 April 2018 identified as a suitable parameter to test the performance of the macro-scale land surface models, EDITOR river-routing received significant attention, with development of multiple schemes. As resolution R. Woods improves, the possibility of river-routing schemes connecting the global models with watershed ASSOCIATE EDITOR issues has emerged as an option. Yet, even as results from these schemes become easily G. Di Baldassarre available, a comprehensive overview of their scope and limitation when considering regional or watershed-centric applications is lacking. To address this gap, 18 published river-routing schemes KEYWORDS are compared by examining their structure, rationale and limitations. Due to the diverse nature of river routing; land surface scheme implementations, a direct comparison of performance is not yet possible. However, models; basin hydrology; features and studies geared towards watershed-scale applications are highlighted. Issues of model validation global to local integration are discussed.

1 Introduction quantity and quality of for decision makers, high data requirements and local technical expertise Emerging water management challenges, stemming required to develop, run and maintain these models fromtheneedtomitigatetheimpactsofclimatic results in slow proliferation. On the other hand, initi- and anthropogenic forcing, cut across several scienti- ally, global climate models (GCMs) primarily relied fic disciplines and multiple spatial scales. While at the on simple hydrological assumptions to compute run- fore are the local water resources availability and off as a means of balancing the water budget. The extreme event concerns, modified and shaped by focus has been on vertical rather than horizontal social–ecological interaction, regional pressures and movement of water on the surface, with runoff rout- global changes impact the terrestrial water budgets ing primarily coming into place to close the water that underscore the system. Traditionally, the model- budgets. While, clearly, the water budgets derived ling approaches for local and global scales of water from these global models are less than ideal for resources have evolved separately (Archfield et al. local-scale water resource management, given the 2015) with either simplifying assumptions or unidir- technical and logistical requirements for developing, ectional forcing substituting for the processes at the maintaining and sharing distributed hydrological and other scale. Based on downscaling method pathways hydraulic models for watersheds, increasingly multi- reviewed in Xu (1999), Figure 1 shows the pathways lateral, bilateral, regional and local organizations turn that may be followed for calculating streamflow based towards macro-scale models, or their results available on information flowing between global and local on web-based platforms (e.g. NASA, Giovanni 2018), scales. Thus, for local-scale management, the empha- for preliminary information about the local water sis has been on developing and applying distributed cycle that is otherwise inaccessible to them, or gener- hydrological and hydraulic models for watersheds ally unavailable. (Overgaard et al. 2006) that rely on downscaled cli- In this context, the improvement and evolution in matic information from global or regional models to modelling of land surface processes in GCMs (Pitman obtain climatic forcing datasets. While the local 2003, Overgaard et al. 2006) and development of hydrological models can provide the most detailed macro-scale hydrological models (Sood and Smakhtin assessment of the conditions in basins on both

CONTACT Kashif Shaad [email protected] © 2018 IAHS 2 K. SHAAD

Global Climate Models Section 4 identifies gaps and opportunities for future developments in RRMs that would continue to make Precipitation, Land Surface Models Precipitation, Evapotranspiration Evapotranspiration them relevant to the water resources challenges faced Statistical / Dynamic Downscaling Global Hydrological Models around the world.

Surface & Subsurface Runoff Local/Watershed Hydrological Models River Routing Methods Flow 2 Evolution of routing schemes

Flow Over the past two decades a number of RRMs have River Discharge been developed – some tightly coupled to particular land surface models or macro-scale hydrological mod- Figure 1. Pathways of deriving river discharge information els (referred to collectively hereafter as global models from GCMs. Based on downscaling method pathways reviewed or GMs) – other, more general, open systems are used in Xu (1999). to route runoff at variable grid resolutions. This review was able to identify at least 18 RRM schemes published 2015) are important advances. In response to questions between 1996 and the present. This list is not exhaus- on global water security (Griffiths and Lambert 2013), tive, but aims to cover the basic assumption, compo- its impact on the nutrient cycle (Rabalais et al. 2009, nents and limitations of the current state of the art Fekete et al. 2010) and food security (Islam et al. 2007), available for routing flow derived from GMs. Table 1 a class of global models that are attempting to use provides a summary of the conceptual underpinnings globally and consistently collected remotely sensed of the 18 RRMs in chronological order, namely the data to address these issues at the global, regional and main technical steps and set-up for performing routing. local scales has started to take shape. Besides precipita- Table 2 outlines the structural elements of the RRMs tion and evapotranspiration, runoff and discharge have including scale/resolution and other processes, besides become variables of interest from these models and, direct surface flow considered by the RRM. Table 2 also over the past decades, their capability to route surface tabulates any known application – especially for water has received significant attention from the watershed-scale studies – and the GM that the RRM research community. Although, at a global scale, river has been developed for or demonstrated with. and streamflow may only form a small proportion of Early land surface models (LSM) focused almost the water cycle, locally, they have a direct impact on exclusively on the partitioning of energy over the land human society (Li et al. 2015). Besides their role in surface into latent and sensible heat and, thus, led to transport of sediment and nutrient, maintenance of equations focused on a vertical solution scheme of terrestrial and freshwater ecosystems, as well as cycles energy transfer between ground and atmosphere, with of and , rivers regulate the balance no horizontal exchange between neighbouring cells of between water supply and demand. River-routing the LSM. Arnell (1995) recognized that this partition- schemes and methods (RRMs) linked to global/regional ing capability can be validated against observed preci- models have thus emerged as possibly a key component pitation and streamflow data. However, this required in connecting the global and local-scale modelling the development of runoff routing schemes that could approaches, as well as providing a pathway for asses- preserve the horizontal transfer time over the model sing local surface water resources and water transport domain to obtain discharge estimates in streams in the rivers of the world (Miller et al. 1994, Yamazaki derived from LSM-generated runoff estimates. One of et al. 2011) in a globally consistent manner. While the first most widely adopted approaches in this direc- Mizukami et al.(2016) and Clark et al.(2015), among tion was developed by Lohmann et al.(1996). The others, have provided a brief overview of the types of underlying assumptions of the Lohmann routing schemes used for river-routing, as the model results scheme, which sets the basis of the RRMs that follow, from RRMs and maps generated therefrom become is that all the nonlinear processes associated with flow widely and easily available to influence decision mak- generation can be resolved into the vertical solution ing, a comprehensive analysis of the capabilities, fea- scheme of the LSM – leaving the RRM to deal only tures and assumptions used in RRMs is missing to help with routing water over the surface. Lohmann et al. users make informed judgement of the skill of the (1996) then expressed the routing process as a linear models used to produce outputs. The focus of this and time-invariant transfer function modelled on unit review is to fill that gap, with Sections 2 and 3 looking hydrographs (also referred to as impulse response at the evolution of the routing schemes, their under- function, or IRFs) firstly within each grid and then lying mechanics, as well as their structure, while connecting each grid through a river network. This is Table 1. Conceptual underpinnings of the river-routing models arranged in chronological order. River-routing method Routing stage In-grid routing/storage Subsurface contribution Channel routing method 1 Lohmann routing [Lohmann et al. (1) In-grid routing to Routing using impulse response function (IRF) The IRF has a “slow” component to reflect IRF derived from linearized Saint Venant equations 1996] outflow point; for grid box baseflow (1D diffusive wave equation) (2) Channel/river routing 2 Total runoff integration pathways Change in storage per grid Storage for grid cell based on linear Deep drainage from LSM Linear cell-by-cell transport using continuity equation (TRIP) river runoff model [Oki and cell and grid routing linked to grid routing with fixed constant velocity (v = 0.5 m/s) and fixed Sud 1998] river meandering ratio (1.4) 3 River transport model (RTM) Change in storage per grid Storage for grid cell based on liquid or ice LSM drainage Linear cell-by-cell transport using continuity equation [Branstetter 2001] cell and grid routing water considering inflow from neighboring with fixed constant velocity (v = 0.35 m/s) based on slope cells and runoff from LSM 4 Grid-to-grid routing Model (G2G) (1) Land-pathways routing Grid-cells classified as “Land” under land- 2D kinematic flow for below Grid-cell classified as river under river-routing [Bell et al. 2007] (2) River routing pathways routing parameters using linear both land and river – connected via a parameters using linear form of continuity form of continuity equation in 2D with “return” flow variable equation in 2D with constant velocity constant velocity 5 Total runoff integration pathways Change in storage per grid Storage for grid cell based on linear reservoir Deep drainage from LSM and linear 1D kinematic wave routing with variable velocity (TRIP2) river-routing model cell and grid routing linked to grid routing groundwater storage reservoir (determined by Manning’s equation based on [Pappenberger et al. 2010] rectangular cross-section) 6 TRIP CHS river-routing model (1) Change in storage per Storage for grid cell based on linear reservoir Deep drainage from LSM and linear 1D kinematic wave routing with variable velocity [Decharme et al. 2010] grid cell and grid linked to grid routing groundwater storage reservoir (determined by Manning’s equation based on routing rectangular cross-section) (2) Floodplain interaction 7 Catchment-based macro-scale (1) Change in floodplain “Unit catchment” derived for outlet point for No Diffuse wave approximation of the 1D Saint Venant floodplain model (CaMa-) and river storage for each grid cell using 1-km resolution DEM. equations [Yamazaki et al. 2011] each grid Change in storage for river and flood plain (2) Main channel routing solved for each unit catchment (thus, for each grid) 8 Paiva et al.(2011) (1) Routing in each sub- Linear to route baseflow and Linear reservoirs to route subsurface flow Hydrodynamic model using full Saint Venant basin/HRU surface flow from HRU to stream network from HRU equations (2) River routing 9 RAPID [David et al. 2011] River routing Surface runoff from LSM summed into lateral Subsurface runoff from LSM added as Muskingum method with parameters calibrated using flow to channel lateral flow gauge data 10 Wen et al.(2012) (1) Overland routing 1D kinematic wave routing with variable No 1D kinematic wave routing with variable velocity and (2) Main channel routing velocity and statistical distribution derived multidirectional flow possible in river channel from DEM used to describe “length” of flow for in-grid overland flow to reach channel YRLGCLSINE JOURNAL SCIENCES HYDROLOGICAL 11 WaterGAP routing [Verzano et al. Change in storage of river Storage for river segment in each grid cell No 1D kinematic wave routing with variable velocity 2012] segment per grid cell based on linear reservoir linked to grid (determined by Manning’s equation assuming and grid routing routing trapezoidal cross-section) 12 Model for scale-adaptive river (1) Hillslopes to 1D kinematic wave routing with variable No 1D kinematic wave routing with variable velocity for transport (MOSART) [Li et al. tributaries; velocity for hillslopes and tributaries main channel 2013] (2) Tributaries to main channel; (3) Main channel routing 13 HydroROUT [Lehner and Grill 2013] Main channel routing No No Multidirectional routing with network tracing using weights, barriers and decay functions 14 Kinematic wave routing (KWR CLM) (1) Catchment based slope Grids are mapped to catchments; in each No Kinematic approximation of 1D Saint Venant [Ye et al. 2013] routing catchment, the kinematic approximation of equations (2) Main channel routing the Saint Venant equations is used to route water over slopes till it enters river (Continued) 3 4 K. SHAAD

achieved by using a linear model of “fast” and “slow” components of the measured discharge as the basis for the in-grid flow’s IRF and linearized one-dimensional (1D) diffusive wave equation for the inter-grid flow’s IRF. While the model does not explicitly account for subsurface flow, the baseflow contribution from each grid cell is reflected via a “slow” flow component of the in-grid routing step. The main parameters of the rout- ing scheme are the ratio between the “fast” and “slow”

Channel routing method flow components, wave velocity and diffusivity esti- mates for each grid cell – fixed values for which are derived during an optimization procedure for the and/or (2) impulse responseprocedure function routing constant (user-defined/calibrated) velocity region/watershed being studied. As discharge at the Nonlinear Muskingum-Cunge method Kinematic wave equations (1) Kinematic wave tracking (KWT) routing procedure; river outlet (or main gauging stations) was the key output required from this scheme, IRFs proved to be a computationally efficient approach to adopt – leading to the development of more sophisticated “source-to- sink” schemes (Naden et al. 1999, Olivera et al. 2000, Gong et al. 2009). Storage-based routing schemes demonstrate the first attempts to capture spatial distribution of flow. Table 3

Subsurface contribution summarizes the governing equations employed by these models. These schemes relied on various simplifications and deep groundwater infiltration reservoir Linear reservoir for baseflow generation Adds to in-grid routing No Depends on runoff model selected Geomorphologically-based accumulation using of the 1D kinematic wave routing (KWR) equation without distinguishing between overland and channel flow. Oki and Sud (1998)demonstrateasimplesto- rage-based scheme (referred to as TRIP here) with a priori constant velocity for routing water between grids along the “TRIP network”–a global river channel net- work developed at 1-degree (1°) resolution. Under this

(sub-division of grids) scheme, flow is modelled based on 1D KWR, which ” reduces to a linear reservoir equation with the constant

In-grid routing/storage velocity assumption. The cell-by-cell calculation macrocell “ increases the computational load considerably compared “ ” delay to capture in-grid routing tributaries only); assumes overlandrunoff surface and baseflow entercorresponding the dominant river intervalstributaries and within each time step (gamma-distribution-based unit hydrograph) each identified as land drainingmacrocells into channel to source-to-sink schemes; however, the formulation Storage using a linear reservoir with time Routing using kinematic wave equations (for Routing using impulse response function Routing using impulse response function for of the equations is simpler to implement and has been widely used in LSMs. (Pappenberger et al. 2010 identi- fied at least 14 studies using variants of this scheme.) The river transport model (RTM; Branstetter 2001) – the standard routing method in the community land – Routing stage model (CLM) uses a similar simplified 1D KWR and deep groundwater in each grid level between grids approach; however, it allows for user-derived flow net- (2) Main channel routing (2) Main channel routing (2) Main channel routing (2) Main channel routing (1) Surface, subsurface (1) Tributaries at in-grid (1) Hillslope routing works at finer scales resulting in more detailed channel . ] (1) Hillslope routing networks. The imposed linearity, with lack of represen- ] et al . 2016

. tation of spatio-temporal variability due to the calibrated et al 2014 . or prescribed constant velocity approach, was replaced et al

et al by a Manning’s equation-based approach developed by, among others, Ngo-Duc et al.(2007) and Arora and George (1999). Implemented in TRIP2 (Pappenberger ] ] River-routing method et al. 2010), TRIP CHS (Decharme et al. 2010)and (Continued). (ARTS) [Getirana routing (DRTR) model [Wu 2014 2016 WaterGAP (Verzano et al. 2012), the temporally vari- able routing velocity is now estimated using Manning’s 15 ALMIP-2 river-routing scheme 16 Dominant river tracing-based runoff 17 mizuRoute [Mizukami 18 HYPERstream [Piccolroaz

Table 1. equation dependent on channel width and roughness Table 2. Structural components and known applications of the river-routing models (RRM). RRM Resolution River network Intervention, flooding or transport Applications/coupled with 1 Lohmann Spatial: Multiscale; reported for 0.5° in Derived river flow network Basic capability of representing Standard routing scheme used with variable infiltration capacity (VIC) LSM routing Lohmann et al.(1998) interventions on river Temporal: Hourly 2 TRIP RRM Spatial:1° TRIP network No Standard coupling with Interactions between Soil, Biosphere and Temporal: Daily Atmosphere (ISBA) Demonstrated with Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) LSM [Pokhrel et al. 2012] 3 RTM Spatial: 0.5° Based on slope of terrain No Standard routing scheme used with Community Land Model (CLM) Temporal: Daily or lower model 4 G2G Spatial:1km Derived from HYDRO1K digital No Demonstrated for a simple LSM forced by RCM/Observed data Temporal: Daily or sub-daily (computational elevation data Applied for the British Isles time step: 15 min) 5 TRIP2 RRM Spatial:1° TRIP network No Demonstrated as coupled with hydrological component of European Temporal: Daily/hourly Centre for Medium-Range Weather Forecasts (ECMWF) 6 TRIP CHS Spatial:2° TRIP network Floodplain reservoir for two-way Demonstrated as coupled with ISBA [Decharme et al. 2010, 2012] RRM Temporal: 3-hourly interaction with LSM 7 CaMa-Flood Spatial: 0.25° Based on Global Drainage Exchange with storage for flood plain Demonstrated for the Amazon Basin [Yamazaki et al. 2012] using runoff Temporal: Daily Basin Database [Masutomi from MATSIRO land surface model. et al. 2009] Hirabayashi et al.(2013) demonstrated on 29 major basins globally 8 Paiva routing Spatial: Multiscale Derived from SRTM data Floodplain interchange Demonstrated on the Purus River Basin (tributary of the Amazon River) Temporal: Daily 9 RAPID Spatial: Vector river layer NHDPlus network [Horizon No Applicable to contiguous United States, demonstrated using runoff input Temporal: 900 s Systems Corporation 2007] from Noah LSM 10 Wen routing Spatial: Multiscale; Reported for 1/32° to 1° Multidirectional network No Demonstrated on the Blue River Basin, the Illinois River near Watts Basin, Temporal: Daily and hourly scheme from Guo et al. and the Elk River Basin while coupled with VIC −3L LSM (2004) 11 WaterGAP Spatial: 0.0833° HydroSHEDS river network Retention storage representing lakes, Developed for WaterGAP Global Hydrology Model; demonstrated for routing Temporal: Daily reservoirs, Europe 12 MOSART Spatial: Multiscale; Reported for 1/16–1/2° HydroSHEDS data with DRT No Columbia River basin with VIC [Li et al. 2013] Temporal: Hourly or daily algorithm Global streamflow with CLM4 [Li et al. 2015] 13 HydroROUT Spatial: Vector river layer at 15 s resolution, HydroSHEDS river network Accounts for lake volume; Coupled with runoff generated from WaterGAP runoff downscaled to 500 m resolution includes contaminant fate model Applied for studying fragmentation of river globally and specifically for Temporal: Monthly averages the Mekong, contaminant transport in China and Saint Lawrence River

Basin, Canada JOURNAL SCIENCES HYDROLOGICAL 14 KWR CLM Spatial: Catchment size depends on Derived No Demonstrated on China while coupled with Community Land Model (CLM) topography; reported minimum sub- basin LSM area of >100 km2 Temporal: Not reported; study used data at 3- h resolution 15 ARTS Spatial: 0.05° Derived from SRTM data No Ouémé River basin (in Benin) with Interactions between Soil, Biosphere, Temporal: Daily (computational time steps: a and Atmosphere (ISBA) [Getirana et al. 2014] few min to several hours) 16 DRTR Spatial: ~12 km 1 km hydrographic inputs Two-way exchange with LSM is Global Flood Monitoring System (GFMS) with VIC [Wu et al. 2014] Temporal: 3-hourly through hierarchical DRT possible but not activated 17 mizuRoute Spatial: Multiscale; applicable for fine (~1 km) User defined; Grid-based or No Developed as a stand-alone routing model, demonstrated for spatially to coarse (>10 km) resolutions vector networks distributed streamflow simulations over Contiguous United States Temporal: Hourly or daily (CONUS) 18 HYPERstream Spatial: Multiscale User defined; hybrid grid- No Demonstrated on the Upper Tiber Basin, Italy Temporal: Reported; sub-hourly based or vector networks Developed as a possible stand-alone routing method 5 6 K. SHAAD

Table 3. Governing equations for storage-based routing schemes. Equation type Applied by Kinematic wave routing (KWR) with constant/simplified velocity @q þ @h ¼ RTM @x @t r TRIP RRM a v ¼ 1:4=v ¼ max β0:5 G2G β < 0:25 KWR with variable velocity @q þ @h ¼ WaterGAP routing @x @t r Wen routing b v ¼ 1 R0:66β0:5 MOSART n DRTRb KWR CLMb KWR with variable velocity and other storage in cell

@q @h G @ þ @ ¼ r þ TRIP2 RRM x t ac TRIP CHS RRM @G ¼ G @t Qsb τ

¼ 1 0:66β0:5 v n R Diffusion wave @q þ @h ¼ CaMa-Flood @x @t r @y þ ¼ @x So Sf 0 Dynamic wave @ q þ @h ¼ qcat qfl Paiva routing @x @t b

@q þ @q þ ðÞ 2 @h 2 @A j ¼ ðÞ b @t 2vb @x gA v b @x v @x h¼cte gA So Sf x is river segment [L] estimated differently for different methods, q is discharge per unit length [L2 T−1], h is height above datum [L], r is per unit surface runoff from LSM [L T−1], v is mean velocity [L T−1], β is grid cell topographic slope [-], n is Manning’s roughness coefficient [-], R 3 is channel hydraulic radius [L], ac is grid cell area, G is groundwater storage [L ], Qsb is deep 3 −1 drainage from LSM [L T ], So and Sf are bed slope [-] and friction slope, respectively [-], qcat 2 −1 2 −1 is the lateral flow into the river [L T ], qfl is the river–floodplain flow exchange [L T ], b is river cross-section width at free surface elevation [L], A is cross-section area [L2], g is acceleration due to gravity [L T−2]. a G2G splits the KWR into sets for land, river and subsurface with constant velocity. b These routing schemes divide routing by stages (hillslope/overland/river, etc.). parameters. The channel width is, in turn, estimated by the subgrid-scale routing (Getirana et al. 2012), includ- geomorphological functions assuming a rectangular ing convective time delay, is calibrated as a single value (TRIP2 and TRIP CHS) or trapezoidal (WaterGAP) for the entire model. The G2G model (Bell et al. 2007) cross-section. TRIP2 and TRIP CHS also introduced attempts to use hyper-resolution (1-km resolution grid) an element of groundwater dynamics by incorporating – albeit with a pre-assigned wave celerity assumption – a linear reservoir to represent exchange with the subsur- to improve flow representation and account for convec- face in each grid. Gong et al.(2009) strongly critiqued tive time delay. G2G is among the first schemes to storage-based schemes due to their inability to account distinguish overland and channel flow in its set-up for convective time delay over river reaches and scale (with complementary groundwater storage) and solves dependency inherent to slope and reach length calcula- a 2D KWR using finite difference with different celeri- tion being dependent on spatial resolution used for ties for overland and channel flow. However, this comes channel network derivation. The ARTS model at a high computational cost, requiring a 15-min time (Getirana et al. 2014) attempts to incorporate a time step throughout the domain. Scale dependency remains delay parameter, τ, for a grid cell’s flow release from an issue for simple storage-based cell-by-cell routing storage while using a nonlinear Muskingum-Cunge schemes. Nevertheless, the spatial distribution of flow (MC) method for inter-grid routing. The parameters (and, in some cases, depth and velocity) provided by for the nonlinear MC routing method attempt to them has opened up possibilities for regional and local account for the physical constraints of the flow network application in water resource management. and are re-calculated for each reach and discharge; how- All the above schemes for flow routing are tightly ever, the time delay parameter, τ, which attempts to coupled to the scale and grid, with the GM providing characterize the physically-based process occurring at the runoff input. Considering recognition of scale HYDROLOGICAL SCIENCES JOURNAL 7 dependency, improving computational efficiency and Two recent models, mizuRoute (Mizukami et al. demonstration of the potential to obtain spatially-dis- 2016) and HYPERstream (Piccolroaz et al. 2016), tributed flow directly from GMs has led to the gradual attempt to provide “general” flow routing models that decoupling of routing schemes from GMs in two ways. can couple to any GM runoff results available. The first is in terms of the grid and scale they operate Additionally, they can leverage both raster and vector on. These routing methods using a different grid from data with the computationally efficient IRF type meth- the GM to route flow, incorporating flow length dis- ods of the early noted “source-to-sink” methods. tributions, sub-basin polygons, stream vectors, terrain Discharge values can be reported for river segments raster, etc. to capture flow and channel dynamics in (mizuRoute) or points defined by the user more detail, while being able to provide routing infor- (HYPERstream). Both models use an IRF-based mation an any point within the basin. Secondly, these method to route flow from hillslope/overland to the methods explicitly account for at least two different channel over units (sub-basins or grids) derived from routing processes – overland flow and channel flow – the highest quality terrain/network data available. solving different sets of equation for each at the grid/ While HYPERstream uses a constant velocity assump- inter-grid level. The MOSART (Li et al. 2013), KWR tion to aggregate flows along the channel network, CLM (Ye et al. 2013) and DRTR models (Wu et al. mizuRoute allows for a Lohmann type 1D diffusive 2014) are examples of routing schemes using these wave approximation based IRF or a kinematic wave approaches while still primarily using the 1D KWR tracking (KWT) method. The KWT method computes equations for routing flow, however, now solving wave celerity for each sub-basin as the basis of routing. these equations at various levels. The KWR CLM and However, Mizukami et al.(2016) reported that the DRTR models solve the 1D KWR at two levels: the KWT component’s sensitivity to parametrization was hillslope and the main channel for KWR CLM, and substantial. Another significant attempt to use a vector tributaries and the main channel for the DRTR model. river network with an added component of parameter The MOSART model applies the 1D KWR at three estimation was through RAPID (David et al. 2011). levels: hillslopes, tributaries and main channel. All The RAPID model uses over 20 000 gauges situated three schemes rely on higher-resolution data to deter- across river basins in the contiguous USA to automa- mine the characteristics used to solve the 1D KWR, tically optimize Muskingum parameters for the RRM such as delineating sub-basins (for KWR CLM), calcu- and perform discharge computations. lating average hillslope, average length of tributaries The work by Paiva et al.(2011) on coupling a full and main channel, etc. The MOSART and DRTR mod- 1D hydrodynamic model to a large-scale hydrological els rely on a hierarchical dominant river tracing (DRT) model, and the 1D diffusive equation of CaMa-Flood algorithm (Wu et al. 2011, 2012) to generate spatial (Yamazaki et al. 2011) represent attempts to move scale-consistent river network generation. The novelty beyond the simpler representation of flow hydrody- of the DRT algorithm is in its attempt to utilize infor- namics seen so far and expand the application in the mation on global and local drainage patterns from direction of flood inundation mapping. These models available hydrography at finer spatial scales/resolution require significant data and computational resources. to determine flow directions, basin area, basin shape However, CaMa-Flood has been successfully applied and river lengths at coarser scales. The original baseline for flood risk assessment on major basins of the hierarchical drainage structure is preserved by tracing world by Hirabayashi et al.(2013), albeit without the entire flow path from headwater to river mouth at accounting for human intervention in river systems. fine scale while prioritizing successively higher-order On this front, HydroROUT (beta version discussed in basins and rivers for tracing, allowing consistent sub- Lehner and Grill 2013) hopes to build with it the grid parameterization of RRMs. The routing scheme by resources on inundation areas, dams and reservoirs, Wen et al.(2012) – referred to here as “Wen routing”– etc. into a global database that can be harnessed for introduces a novel element for the overland flow com- detailed global-scale modelling. ponent by using a higher-resolution DEM to derive statistical distributions for overland flow paths within each LSM grid cell using a multidirectional flow algo- 3 Comparison of features rithm. Wen routing then uses 1D KWR for in-grid 3.1 Resolution and scale overland flow alongside the distributions, where the range of flow lengths derived from the distribution One of the main distinctions between RRMs, besides the helps account for different arrival times to the channel routing equations employed, is the grid used for routing while keeping the computational load low. the flow. Table 2 summarizes the key aspects of this. The 8 K. SHAAD spatial resolutions of the routing schemes are derived as CHS, MOSART and DRTR models have been used at a combination of the resolution of the GM that drives it the global scale by coupling with the European Centre and the river network description used to route the flow. for Medium-Range Weather Forecasts (ECMWF) Nine of the 18 schemes reviewed have a fixed spatial model, ISBA, CLM4 and VIC. Six of the schemes resolution reported with the scheme developed around a have been demonstrated on a regional multi-basin pre-defined network. Five of these nine schemes run at level, with G2G in the British Isles, WaterGAP routing resolutions greater than or equal to 0.5°. The remaining over Europe, HydroROUT and KWR CLM over China, schemes are able to run at multiple spatial scales mizuRoute and RAPID over the contiguous United depending on the requirements of the case study (global, States and the CaMa-Flood over 29 major basins regional or basin), or the forcing data available. While around the world. The three remaining schemes have most schemes currently use a grid-based format for been demonstrated on single, or parts of, large water- routing flow, vector data have also been demonstrated sheds, such as the Amazon Basin by Paiva routing. in at least four schemes – HydroROUT, RAPID, mizuRoute and HYPERstream. Use of vector data has 3.2 Subsurface and lateral flows allowed these schemes to post results at 1-km resolution and finer. Only one grid-based method, G2G, reports a Beyond routing of surface flow, a few of the RRMs spatial resolution of up to 1 km. attempt to incorporate a more complete description of Closely linked to spatial resolution is the river net- surface–subsurface dynamics (Table 2). Nine schemes work description used to route the flow. In the coarse have some representation of subsurface dynamics within spatial resolution models, TRIP or TRIP-like eight- the grids that will impact the generation of flow into the directional drainage networks derived from the under- channel. IRF-based methods, such as Lohmann routing, lying terrain model are used. The HydroSHEDS river use a calibrated “slow” component to capture the time network, derived from SRTM data, has been used in delay in subsurface flow releasing water into the chan- three of the schemes. Nine of the 18 schemes use their nel. TRIP, RTM and DRTR allow the deep drainage/ own method of deriving river flow networks from the baseflow values from their LSM to flow into the surface, terrain data available, ranging from using SRTM and while TRIP2, TRIP CHS and ARTS add another linear HYDRO1K terrain models as a base to GMs underlying reservoir to represent groundwater storage that links to terrain information. The MOSART and DRTR models the LSM’s drainage. This linear reservoir then releases propose the use of a hierarchical dominant river tra- baseflow into the surface routing schemes. G2G cing (DRT) algorithm that aims to maintain a scale- attempts to use a subsurface 2D kinematic flow with consistent upscaling of the river network (described constant pre-defined velocity below overland and river briefly in Section 2). The HYPERstream and cells that can mimic two-directional exchange of water mizuRoute models allow users to provide the best between surface and subsurface. available topographic data – a hybrid of grid and vector Seven schemes report capability to incorporate some layers – to derive the river-routing network. effects of lateral flow from other surface storage fea- Almost all the schemes are able to report discharges tures that will impact flow dynamics, such as reser- at the outlet at a daily time step. Nine of the 18 voirs, wetlands, lakes and flood plains. WaterGAP uses schemes reported capability of producing outputs at a linear and nonlinear storage to represent lakes, reser- sub-daily time step – typically hourly or 3-hourly. Also, voirs and (further described in Döll et al. almost all the reviewed schemes are able to report 2003), which connects to its routing scheme. discharge at multiple points within the watersheds, HydroROUT also intends to account for lake volume albeit limited by the constraints of spatial resolution. and obstruction to flow caused by dams and reservoirs. At least 10 of the 18 schemes are able to calculate TRIP CHS and DRTR have a floodplain reservoir that variable flow velocity within their domain and a further allows a two-way interaction with the LSM – where the four – TRIP CHS, DRTR, Paiva routing and CaMa- water entering the floodplain can impact the evapo- Flood – attempt to estimate some measure of water transpiration and infiltration over the surface. depth or area of flood inundation as part of their However, this capability has been reported as “not schemes. Eight of the routing schemes reviewed have activated” in DRTR at the current stage. CaMa-Flood been applied at the global scale, with Lohmann routing, and Paiva routing allow for overflow from the channel TRIP and RTM available as standard routing methods into the floodplain (without interacting with the LSM), in variable infiltration capacity (VIC), interaction soil– thus allowing for floodplain inundation dynamics. biosphere–atmosphere (ISBA) and community land Only HydroROUT reports attempts to couple a con- model (CLM) LSMs, respectively. The TRIP2, TRIP taminant fate model to the routing scheme – an HYDROLOGICAL SCIENCES JOURNAL 9 application that can open a new avenue of application representation can be reduced to simple calibrated for routing schemes, as discussed in Section 4. parameters. Nazemi and Wheater (2015b)provideda detailed review of algorithms used for water supply and allocation modelling in GMs and noted consider- 4 Gaps, opportunities and trends for regional able limitations in representing streamflow in regu- application lated catchments. For local and regional application, With advances in the routing methods documented in moving towards the ability to (1) represent interven- the previous two sections, the prospect of applying them tions, and (2) incorporate local knowledge on their in regional and local management of water resources – in operationisthekeyopportunityforRRMstomake the context of (1) filling gaps in information where none them relevant for long-term water resource manage- exists, and (2) bridging local, regional and global forcing ment and planning. data on the water resources – has emerged as a distinct In general, most studies rely on goodness of fit of possibility. In the domain of flood risk management, model prediction and observed discharge at the out- GFMS (Global Flood Monitoring System) and flow to validate the GM–RRM coupling. Some stu- Aqueduct Global Flood Analyzer are examples of web- dies extend this and use an instance of a model based dissemination platforms that provide access to calibrated to discharge from a regulated watershed processed flood risk data derived from RRMs routing as a proxy for representing current conditions. As runoff obtained from GMs. Ward et al.(2015)gavean Vogel and Sankarasubramanian (2003)demonstrated, illustrative example of how these results have been used fitness can be influenced by model error and the to engage stakeholders and identify local regions at high advocated using a model’s ability to reproduce the risk that require further detailed study. It is clear that statistical characteristics of the input and output data these routing schemes are not yet capable of providing as the quantitative method to accept or reject a actionable input on interventions for changing or mod- model’s performance rather than a goodness of fit ifying the scale of flood protection (e.g. height of dykes validation which may produce misleading results. A required); nevertheless, these developments are encoura- simple water balance model coupled to a storage- ging as they have the ability to highlight challenges in based hydrological routing model (to mimic the disaster risk management. GM–RRM coupling), with and without basic reser- Therefore, considering their potential to provide voir operations for the Dongjiang River Basin timely information for more holistic local and regional (China), is used here to test the difference in ability freshwater management, three areas are identified to represent hydrological processes by the approach where continued integration and development in advocated by Vogel and Sankarasubramanian (2003). RRM and linked tools may lead to actionable and This illustrative set-up for Dongjiang (Fig. 2(a)) uses relevant information. the HydroBASIN Level 8 product (Lehner and Günther 2013) to describe the drainage network – where each sub-basin is analogous to a cell/HRU/ 4.1 Improving local controls on flow and sub-basin used in GMs. The water balance compo- validation nent of the set-up uses the “abcd” model (Thomas With basins representing 59% of global rivers having 1981) for each sub-basin. The runoff thus generated large dams (Grill et al. 2015) and many more having for each sub-basin is then routed through the sto- smaller regulating structures on their stream network, rage-based routing method along the stream net- reservoir storage and operation directly influence work, transferring flow to the basin outlet. In the instream flow (Vogel et al. 2007), and are a significant setup “with basic reservoir operation”,therelease factor in shaping the overall behaviour of the hydro- from the two main reservoirs in the basin is mod- logical regime. While Lohmann routing implicitly is elled based on a policy of “fixed target release”.This able to incorporate some influence of these interven- requires the reservoirs to release a fixed volume per tions, none of the newer andmoredetailedmethods month to fulfil downstream demand, however, with report their inclusion; instead (if at all) they rely on the ability for overspill or reduced discharge depend- the underlying GMs to account for their influence. Of ing on reservoir storage status. In the set-up “with- the 12 macro-scale hydrological models that Sood and out basic reservoir operation”, no steps are taken to Smakhtin (2015)reviewed,50%hadsomecapability represent the impact of the reservoirs on the flow. to include reservoir dynamics. Even when applied in The model set-ups are then evaluated in their ability the global models, due to varying levels of operation to reproduce the serial correlation between discharge (ρ1 information available at the global level, the of Q) and the cross-correlation between precipitation 10 K. SHAAD

With Basic Reservoir Operation 1 0.9 0.8 Dongjiang Basin 0.7 0.6 0.5 [P,Q] ρ 0.4 Reservoir 1: 0.3 Xinfengjiang 0.2 0.1 0 0.4 0.6 0.8 1 ρ1 Reservoir 2: Without Basic Reservoir Operation Fengshuba 1

0.8

0.6

0.4 [P,Q]

ρ 0.2

0 0.4 0.6 0.8 1 -0.2

Boluo [Gauge] -0.4 ρ1

Simulated Moments Observed Moment

Figure 2. Dongjiang basin and validation of model with and without basic reservoir representation for the two main reservoirs based on discharge at downstream gauge at Boluo.

(P) and discharge (Q). The ability to maintain these (b). As seen in Figure 3, the goodness of fit achieved statistical characteristics reflects on the model’s fit for between discharge observed at Boluo and a calibrated long-term water resource planning. Using 10 000 Monte simulation using the “without basic reservoir operation” Carlo experiments, which generate parameter values set-up appears fairly similar to the other set-up; how- from the range documented in Table 4, model simula- ever, based on its failure to reproduce the observed tions are realized and the moments plotted in Figure 2 moment, the set-up is rejected. Alongside developing

Table 4. Range of parameters of the abcd model for Monte Carlo simulations. Parameter Definition Min value Max value a Propensity to generate runoff before the soil layer is fully saturated 0.7 1.0 b Estimate of the upper bound of storage in the unsaturated zone 0.0 300 mm/d c Ratio of to surface runoff 0.0 1.0 d Proportion of groundwater that seeps back into the stream 0.0 1.0

Gauge_Boluo Without R Gauge_Boluo With R 3000 3000 Correlation: 0.902 Correlation: 0.857 2500 NSE: 0.327 2500 NSE: 0.468 2000 2000 1500 1500 1000 1000 500 500 Discharge (cumecs) Discharge (cumecs) 0 0 0 20406080 0 20406080 Months Months

Figure 3. Average monthly discharge at Boluo with Pearson correlation coefficient and NSE between monitored data and simulated results. HYDROLOGICAL SCIENCES JOURNAL 11

Figure 4. High-resolution tracking of surface water dynamics using remotely sensed optical imagery, demonstrated using 30-m resolution LandSAT imagery over the Pearl River Estuary and Lower Dongjiang River Basin. Map extracted from results of an algorithm tracking change of Water Occurrence Change Intensity between 1984 and 2015 developed by Pekel et al. (2016).

Figure 5. Annual average total phosphorus (TP) load generation and routing through the river network. the idea of locally-sensible control representation, the may be satisfactory, this analysis indicates that that may analysis advises caution on using calibration and valida- not necessarily be due to the skill of the model to tion on runoff estimates produced directly from GMs at simulate the basin hydrology. a daily time step or lower, without adequately consider- Thus, a dynamic link between global runoff genera- ing any routing scheme to derive discharge from runoff. tion and local discharge routing will likely produce the While the “fit” observed through the calibration process best results. Development and proliferation of cloud- 12 K. SHAAD based computing platforms opens the option where improve performance. Among these, error correction GMs relying on global data sources to generate runoff models appear to have had the most success in estimates are given a spatially-bounded interface for improving predictions, with just state/input updates local user input on the regulation of the stream net- at time of availability of new information dampened work which can influence and improve the streamflow out after a few time steps and parameter updates not estimates derived from RRMs. The web-based adoption having performed as well as expected (with the lim- of METRIC, in the form of EEflux (Kilic et al. 2016) for ited number of case studies carried out so far). calculating actual evapotranspiration, using the Google Studies by Durand et al.(2008) and Biancamaria Earth Engine as the computing platform and some user et al.(2011), among others, have demonstrated sig- input for calibration, in some respects can serve as a nificant improvements in the root mean square error template for the future direction of RRM development (RMSE) of simulated discharge from river hydraulic that incorporates local input into a global dataset. models assimilating remotely sensed river elevation and slope data. These studies used a synthetic ver- sion of radar-based (Ka-band, ~35 GHz) satellite 4.2 Continuing integration with remotely sensed data altimetry data of surface water height – of the form With data assimilation (DA) algorithms being that can be expected to be available globally once the applied in a number of GMs for improved model Surface Water and Ocean Topography (SWOT) mis- prediction by integrating available observations of sion launches in 2020 (Biancamaria et al. 2016). Even the state of the system, refining the estimates of with its limitations on stream width for stream ele- riverflowisoneofthekeyareasinwhichGMs/ vation measurements (at least 100 m width) and RRMs can leverage and benefit from the develop- orbital measurements every ~11 days, the estimated ments in remote sensing of freshwater systems. high vertical elevation and water-surface slope accu- While no published study was found on attempts to racy (1 cm/km2 and 0.1 cm/km river length, respec- useDAwithRRMs,thenumberofapplications tively) will open up avenues for monitoring demonstrating the DA algorithm applied on hydrau- freshwater systems continually and in most weather lic models with remotely sensed data has been grow- conditions. ing over the past decade (Grimaldi et al. 2016). Other approaches for obtaining discharge esti- Among these, remotely sensed observations of extent mates from remotely sensed data revolve around and level of water – derived from optical imagery, using SAR and optical imagery. Here, from the radar altimeters, LiDAR, SAR and other active/pas- data, “effective channel width” can be established sive microwave imagery – are assimilated in models and, with estimated hydraulic geometry relationships, using filtering approaches. The selection of the filter- dischargeestimatescanbederivedfromwidthalone. ingalgorithmsisbasedon(a)thefilter’sabilityto Gleason and Smith (2014)outlinedsuchan account for uncertainty of the remotely sensed data, approach, named “At-Many-Station-Hydraulic and (b) the physical dimensions of the model Geometry” (AMGH), and demonstrated the process domain. The ensemble Kalman filter (EnKF) or par- for three rivers using LandSAT imagery, obtaining ticle filter (PF), or variants thereof, have emerged as RMSE of discharge estimates in the 20–40% range. prominent algorithms (see the review by Grimaldi With a number of optical instrument-carrying satel- et al. 2016). Both global and local formulations of lites (such as LandSAT, SPOT, SENTINEL 2) mon- weighting are possible in the filter-based DA algo- itoring land surface at high resolution, the change in rithms. For RRMs, a global filter is the more likely wetted area is trackable and becoming more easily mode of application where, in general terms, the accessible (Fig. 4; source: Pekel et al. 2016)forfuture likelihood of model realization is based on its ability development and incorporation in DA algorithms. to correctly predict state variables (water level, depth, velocity)overtheentireriverdomain.Intheappli- 4.3 Evaluating impact on coupled human and cations of DA reviewed, the updating process of the natural systems model consists of either (i) updating state variables only or, state variable and input conditions, at the Even with the limitations in reservoir and other regu- time step when new information is made available by lating infrastructure representation in GMs (discussed remotely sensed data; (ii) constant error correction in Section 4.1), a number of attempts have been made of state variables only or, state variable and input to use these models for estimating water supply and conditions, based on an error forecast model; or (iii) demand for human, agricultural and industrial con- parameter updating of the simulating model to sumption. Nazemi and Wheater (2015a, 2015b) HYDROLOGICAL SCIENCES JOURNAL 13 provided a detailed overview of the algorithms and temperature model by Yearsley (2012), which applies approaches that have been developed for this, high- a 1D semi-Lagrangian model using time-dependent lighting their potential as well as challenges for a equations for conservation of thermal energy on flow greater role in determining water supply from ecosys- estimates derived from VIC. The presented case studies tems. They concluded that the current capability of show potential in analysing the impact of climate GMs to account for water demand is limited and esti- change on stream temperatures despite significant mates of water supply uncertain due to limitations of bias in simulated stream temperatures due to uncer- data, model uncertainty and propagation of bias across tainty in estimation of headwater temperature and integrated models. However, they identified that con- magnitude of stream speed during low-flow periods. sidering water resources jointly in terms of demand The second approach is WorldQual (Voß et al. 2012), and supply was the right direction to take, with avail- which couples with WaterGap to gauge both conserva- ability of regional-scale data for model development, tive and non-conservative pollutants. Applied for esti- diagnosis and validation the next steps needed in this mates of in-stream biological oxygen demand (BOD) domain of research. and faecal coliform for Latin America, Africa and Asia Besides water supply and demand, sediment and (WWAP, 2017), the WorldQual model attempts to use water quality regulation remain two key areas where simple equations that are reflective of data availability routing methods along with their GMs can make sig- at continental scales to gauge impacts on river water nificant contributions in improving management. quality in response to anthropogenic loading and flow These are also the areas where, generally, local exper- dilution. Voß et al.(2012) reported overall good fit of tise in modelling is limited. Of the reviewed literature, the spatial/temporal water quality pattern based on only one of the RRMs mentioned attempts to build in observed and simulated values in Europe, despite the capability for stream water quality projections. The model parameters for water quality not having been main challenge with water quality, similar to reservoir calibrated for local conditions. operation, is that the forcing datasets are context dependent and in many cases highly localized. However, a generic framework to track sources and 5 Conclusions transport patterns in a river basin may be possible using the information available globally. The simple The growing sophistication of process representation water balance model coupled to a storage-based hydro- in land surface models and macro-scale hydrological logical routing model used in Section 4.1 has been models, higher accuracy of atmospheric data available extended to include a water quality module based on to force them, alongside improving the resolution of the InVEST ecosystem services model (Tallis et al. the models, have highlighted their potential in local 2013) for non-conservative pollutants. The loadings and regional water management. River-routing meth- for this model are generated based on population den- ods that were first introduced as a validation approach sity (SEDAC global population layer; CIESIN – for the global and macro-scale models based on dis- Columbia University 2015), agricultural land (land charge comparisons for major rivers have continued to cover map; ESA Climate Change Initiative – Land evolve over the past two decades, improving the repre- Cover project 2014) and per capita load estimates sentation of river flow dynamics. Structurally, the mod- (Wu and Chen 2013). Figure 5 depicts the estimated els differ in channel routing methods, resolution and load from each sub-basin for total phosphorus (TP) characterization of the drainage network, as well as in and its routing through the river network. The critical the way they account for and integrate surface and drawback of this approach is the gap between the subsurface drainage from the global models. granularity at which the adsorption, absorption and Most of the routing methods have been applied decay processes work and the scale of the routing globally, or at least regionally, and validated against model. Hence, these processes are controlled by a local discharge. Almost all the approaches produce “decay-rate” parameter, which may require to be cali- flows at least at a daily time step and have the ability brated to local data – although general patterns of to provide discharge not just at the basin outlet, but at water quality will likely be primarily driven by magni- any point within the drainage network. However, as tude of input loads. Clearly, this remains an area that the analysis based on the method of Vogel and will need more effort in the coming years. Two model- Sankarasubramanian (2003) suggests, the calibration– ling approaches that have used hydrological data from validation approach alone may not be sufficient to GMs to make inroads into this domain should be establish the fitness of the modelling set-up to repre- considered at this point. The first is a stream sent flow dynamics, especially in the absence of 14 K. SHAAD representation of regulating structures in the model or Research: Atmospheres, 104 (D24), 30965–30979. routing schemes. doi:10.1029/1999JD900905 Incorporating representation of regulating structures Bell, V.A., et al., 2007. Development of a high resolution grid-based river flow model for use with regional climate and continuing integration of remotely sensed data in model output. Hydrology and Earth System Sciences,11 global models with DA algorithms can help improve the (1), 532–549. doi:10.5194/hess-11-53two-2007 flow representation, to be locally and regionally more Biancamaria, S., et al., 2011. Assimilation of virtual wide suitable for water supply, demand and extreme event swath altimetry to improve Arctic river modeling. – analysis and risk management. While improvement in Remote Sensing of Environment, 115 (2), 373 381. Biancamaria, S., Lettenmaier, D.P., and Pavelsky, T.M., 2016. the model output using DA is something that can be The SWOT mission and its capabilities for land hydrology. carried out centrally, to truly make the representation of Surveys in Geophysics, 37 (2), 307–337. doi:10.1007/ regulating structures relevant to local management, s10712-015-9346-y approaches that allow tiered input into these set-ups Branstetter, M.L., 2001. Development of a parallel river trans- will be required, where local stakeholders can modify port algorithm and applications to climate studies PhD the operation rules to reflect ground conditions and Dissertation. University of Texas. Center for International Earth Science Information Network requirements. Emergence of programmable GIS plat- - CIESIN - Columbia University, 2015. Gridded popula- forms such as Google Earth Engine and the tools being tion of the world, version 4 (GPWv4): population density, developed on them sets a precedent of how a hybrid beta release. Palisades, NY: NASA Socioeconomic Data global-to-local set-up can be achieved. and Applications Center (SEDAC). doi:10.7927/ Moving beyond routing of flows to include other H46T0JKB Clark, M.P., et al., 2015. Improving the representation of hydro- issues related to delivery of ecosystem services by logic processes in earth system models. Water Resources water, such as regulation of water quality and sediment Research,1–28. doi:10.1002/2015WR017096.Received transport, can be envisioned to be the next step in the David, C.H., et al., 2011. River network routing on the development of these schemes, with initial attempts in NHDPlus dataset. Journal of Hydrometeorology, 12 (5), this direction already hinted at in published literature. 913–934. doi:10.1175/2011JHM1345.1 These developments will help fill some critical gaps in Decharme, B., et al. 2010. Global evaluation of the ISBA- TRIP continental hydrological system. Part II: uncertain- local and regional management of water resources. ties in river routing simulation related to flow velocity and groundwater storage. Journal of Hydrometeorology, 11 (3), 601–617. doi:10.1175/2010JHM1212.1 Disclosure statement Decharme, B., et al., 2012. Global off-line evaluation of the ISBA-TRIP flood model. Climate Dynamics,38(7–8), No potential conflict of interest was reported by the author. 1389–1412. doi:10.1007/s0038two-011-1054-9 Döll, P., Kaspar, F., and Lehner, B., 2003. A global hydro- Funding logical model for deriving water availability indicators: model tuning and validation. Journal of Hydrology, 270 – This paper was written as part of the Freshwater Health (1), 105 134. doi:10.1016/S0022-1694(02)00283-4 Index development, funded by grants from the Victor and Durand, M., et al., 2008. Estimation of bathymetric depth William Fung Foundation Limited, Betty and Gordon Moore and slope from data assimilation of swath altimetry into a Foundation, the Borrego Foundation, Flora Family hydrodynamic model. Geophysical Research Letters,35 Foundation and Starwood Foundation. (20). doi:10.1029/2008GL034150 Fekete, B.M., et al., 2010. Millennium ecosystem assessment scenario drivers (1970–2050): climate and hydrological ORCID alterations. Global Biogeochemical Cycles, 24, 4. doi:10.1029/2009GB003593 Kashif Shaad http://orcid.org/0000-0002-9954-6323 Getirana, A.C.V., et al., 2012. The hydrological modeling and analysis platform (HyMAP): evaluation in the Amazon basin. Journal of Hydrometeorology, 13(6), 1641–1665. 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