We develop a new large-scale hydrological and water resources model, the Community Water Model (CWatM), which can simulate hydrology both globally and regionally at different resolutions from 30 arcmin to 30 arcsec at daily time steps. CWatM is open source in the Python programming environment and has a modular structure. It uses global, freely available data in the netCDF4 file format for reading, storage, and production of data in a compact way. CWatM includes general surface and groundwater hydrological processes but also takes into account human activities, such as water use and reservoir regulation, by calculating water demands, water use, and return flows. Reservoirs and lakes are included in the model scheme. CWatM is used in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which compares global model outputs. The flexible model structure allows for dynamic interaction with hydro-economic and water quality models for the assessment and evaluation of water management options. Furthermore, the novelty of CWatM is its combination of state-of-the-art hydrological modeling, modular programming, an online user manual and automatic source code documentation, global and regional assessments at different spatial resolutions, and a potential community to add to, change, and expand the open-source project. CWatM also strives to build a community learning environment which is able to freely use an open-source hydrological model and flexible coupling possibilities to other sectoral models, such as energy and agriculture.
In recent years, the interactions between natural water systems, climate change, socioeconomic impacts, human management of water resources, and ecosystem management have increasingly been incorporated into the processes of large-scale hydrological models (Wada et al., 2017). Examples of these models are WaterGAP (Alcamo et al., 2003; Flörke et al., 2013), H08 (Hanasaki et al., 2008, 2018), MATSIRO (Pokhrel et al., 2012), LISFLOOD (De Roo et al., 2000; Udias et al., 2016), PCR-GLOBWB (Van Beek et al., 2011; Wada et al., 2014; Sutanudjaja et al., 2018), and SAFRAN-ISBA-MODCOU (Habets et al., 2008; Decharme et al., 2019). Human intervention in hydrology and water resources is becoming essential for the realistic simulation of global and regional hydrological processes. In particular, simulations of human water demands from different sectors such as agriculture, industry, and domestic could have a large impact on estimated hydrological storage (e.g., groundwater) and fluxes (e.g., discharge) (Alcamo et al., 2007; Wada et al., 2016). More efforts have gone into better groundwater representation in large-scale hydrological models to realistically simulate groundwater levels and surface–groundwater interactions (Pokhrel et al., 2015; Wada, 2016; Reinecke et al., 2019; de Graaf et al., 2015, 2017; Decharme et al., 2019).
In recent years, model intercomparison projects such as the WaterMIP (Water and Global Change Water Model Intercomparison Project) (Haddeland et al., 2011), Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) (Warszawski et al., 2014), and the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al., 2016) led to, among other advantages, a systematic overview of models, a consistent database of spatial input data and simulation protocols and scenarios, and a shared database of results, all of which facilitate analysis across different modeling sectors (e.g., water, agriculture, energy, biome, and climate). This has also led to a better understanding of how to assess future changes in land use and climate in relation to water resource constraints under given uncertainties of the forcing drivers such as climate.
Clark et al. (2011) and Bierkens (2015) indicate that model intercomparison efforts have failed to lead to a better understanding of the origins and consequences of systematic model bias and differences and thus to an improved outcome of model components. Bierkens (2015) argues that while there are many catchment hydrological models for specific catchments specializing in their own sophisticated model parameterizations, few global hydrological models – compared with the number of regional hydrological models – interact with these regional models and modeling groups (e.g., Siderius et al., 2018). One way of overcoming this barrier could be to implement multiple modeling or modular approaches into the unifying framework suggested by Clark et al. (2015). Thus, we here develop a new large-scale hydrological and water resources model, the Community Water Model (CWatM), which has a flexible modular structure and unique global and regional spatial representations. Because of complex interactions of hydrology with food, energy, and ecosystems, it is expected that hydrological models can cover these interactions as model components. To advance the move from large-scale hydrological models to better model representations of hydrological processes, we believe that it is also necessary to create a community-driven modeling environment that facilitates the exchange of ideas, components or modules, data, and results in an easily communicable format. In a wider sense, a user-friendly and flexible model structure will enable more active engagement with stakeholders and associated capacity training.
Therefore, CWatM includes the features detailed below:
use of an open-source platform as a way to exchange ideas and develop model
codes that facilitate capacity enhancement, especially in regions with
limited access to high-performance computing facilities and high-resolution data; scalability to allow use of the model at the regional-to-catchment scale and
also at the continental-to-global scale, which facilitates learning between
global and regional hydrological model applications; use of a flexible modular structure to explore the linkages with other
sectoral models such as those relating to land use, agriculture, and energy
so that options and the solution space can be integrated; existing linkages to state-of-the-art models for energy (MESSAGE)
(Sullivan et al., 2013), land use and
ecosystems (GLOBIOM) (Havlík et al., 2013), agriculture (IIASA-EPIC) (Balkovič et al.,
2014), water quality (MARINA) (Strokal et al.,
2016), and the hydro-economy (ECHO) (Kahil
et al., 2018); and linkages to the political economy and stakeholder perspectives
(Tramberend et al., 2020), e.g., social hydrology
(Sivapalan et al., 2012; Seidl and Barthel,
2017). a high-level programming language for easy comprehension of the code and to
facilitate extensibility; an interface to a fast computing language (e.g., C a multiplatform to adjust the model to the users' needs and capacity (e.g.,
Windows, Linux, Mac, and high-performance clusters and supercomputers); a high level of modularity to be extensible for different model options to
solve the same process, e.g., evaporation with Hargreaves, Hamon,
Penman–Monteith, or for a different purpose (e.g., flood forecasting,
water–food nexus, linking to hydro-economic modeling); documentation of the model and the source code in a semiautomatic way to
facilitate immediate documentation and comprehension of the concepts
involved; and a state-of-the-art data structure for reading and writing spatiotemporal data
to allow for efficient management of data storage and facilitate the development
toward high-resolution models.
A model software architecture includes the aspects below:
As described above, the main novelty of CWatM lies not in providing entirely
new concepts for modeling hydrological and socioeconomic processes but in
combining existing good practice in various scientific communities beyond
hydrology itself. CWatM has a modular model structure which is open source
and uses state-of-the-art data storage protocols as input and output data.
Currently, CWatM can use different spatial resolutions from 30 arcmin
(
This paper describes the development of the model, including its structure and modules, and gives some examples of applications. Section 2 of this paper presents a detailed description of the model development of CWatM. Section 3 describes the data used for the model. Section 4 introduces the calibration of the model. Section 5 shows results for several calibrated catchments and two application examples. Section 6 shows how CWatM is linked to other sectoral models. Section 7 discusses the conclusions and the way forward.
Schematic figure of the processes included in the CWatM.
The Community Water Model (CWatM) is an integrated hydrological and channel
routing model developed at the International Institute for Applied Systems
Analysis (IIASA). CWatM quantifies water availability, human water use, and
the effect of water infrastructure, e.g., reservoirs, groundwater
pumping, and irrigation, in regional water resources management. A schematic
view of the processes is given in Fig. 1. CWatM is
a grid-based model, and its recent version has spatial resolutions of
0.5
The CWatM modeling system is written in Python 3.7 with only a few Python
packages (numpy, scipy, gdal, netCDF4) and can be used on different platforms
(Unix, Linux, Windows, Mac). Excessive computational parts can be added via
an interface as C
The target audience of the model is hydrological modelers of varying levels
of programming familiarity. Modelers with no experience in programming
languages like Python can simply use the executable together with the
settings file. Modelers with only limited experience in Python can use the
platform-independent Python version with no need to adapt the source code
itself. Finally, modelers with programming capacity in Python can engage
with the source code and adapt the model to their specific needs. The wide
adoption of Python as a programming language and the open-source approach
will allow for a community of developers to engage with and further develop
CWatM. The code itself comes with a GNU General Public License and is hosted
on GitHub (
The model is accessible and customizable to the needs of different users with varying levels of programming skill, allowing for research questions of varying spatial scales from global to local scales to be answered. This will support and enable different stakeholder groups and scientific communities beyond hydrology and of varying capacities to engage with a hydrological model and support their investigations (see Sect. 6). We hope that we have appropriately represented CWatM and its use of best practices in research software as stated in Wilson et al. (2014) and Jiménez et al. (2017). CWatM was already used in several scientific assessments, including Wang et al. (2019a, b), He et al. (2019), Vinca et al. (2019), and Kahil et al. (2020), and has a small but growing community of users in several countries around the world.
CWatM can use different datasets of daily meteorological forcing as inputs to calculate potential evaporation with Penman–Monteith (Allen et al., 1998) as a default option, as well as other methods such as the Hargreaves (Hargreaves and Samani, 1958) and Hamon (Hamon, 1963) approaches. Elevation data on the subgrid level and temperature are used to split precipitation into rain and snow, while the degree-day factor method (WMO, 1986) calculates snow melt.
CWatM calculates the water balance for six land cover classes separately (forest, irrigated, paddy-irrigated, water covered, sealed area, and “other” land cover class). Soil processes, interception of water, and evaporation of intercepted water are calculated separately for four different land cover classes (forest, irrigated, paddy-irrigated, and other), and the resulting flux and storage per grid cell is aggregated by the fraction of each land cover class in each grid cell. Infiltration into the soil is calculated with the Xinanjiang model approach (Zhao and Liu, 1995; Todini, 1996). The model calculates preferential bypass flow which bypasses the soil layers and percolates directly to groundwater, similar to the approaches of LISFLOOD (Burek et al., 2013), VarKast (Hartmann et al., 2015), and HBV (Lindström et al., 1997). Soil moisture redistribution in three soil layers is calculated using the Van Genuchten simplification (Van Genuchten, 1980) of the Richards equation. The depth of the first soil layer is fixed at 5 cm so that its soil moisture can be compared with products from remote sensing data. The second and third soil layer depths depend on the root zone depth of each land cover class and the total soil depth from data of the Harmonized World Soil Database 1.2 (HWSD) (FAO et al., 2012). Water uptake and transpiration by vegetation are based on an approach by Supit et al. (1994) and Supit and van der Goot (2003), where water stress reduces the maximal transpiration rate. Direct evaporation from the soil surface is calculated separately for two more land cover classes, namely, water and sealed (impermeable) surface; evaporation and runoff are also calculated separately.
Groundwater storage is modeled using a linear reservoir. In the newest version of the model, a MODFLOW coupling is also available, allowing users to include lateral flows between grid cells. Capillary rise from groundwater to the soil layers is included. Runoff concentration in a grid cell is calculated using a triangular weighting function. CWatM applies the kinematic wave approximation of the Saint-Venant equation (Chow et al., 1998) for river routing.
Lakes and reservoirs are included in two different ways: (i) a lake or reservoir has an upstream area beyond the actual grid cell and is part of the grid linking the river routing system and (ii) a lake or reservoir is only a part of the regional river system within a grid cell. Reservoirs are simulated using a simple general reservoir operation scheme as used in LISFLOOD (De Roo et al., 2000; Burek et al., 2013). Lakes are simulated by using the modified Puls approach (Chow et al., 1998; Maniak, 1997).
Water demand and consumptions are estimated for the livestock, industry, and domestic sectors using the approach of Wada et al. (2011). Water demand and consumption for irrigation and paddy irrigation are calculated within CWatM using the crop water requirement methods of Allen et al. (1998). This irrigation scheme can also dynamically link the daily surface and soil water balance with irrigation water.
With these coupled processes, CWatM can facilitate assessment of the changing pattern of water supply and demand across scales under climate change at different spatial resolutions. The modular structure also makes the linking of CWatM with other IIASA models possible, e.g., MESSAGE (Sullivan et al., 2013), GLOBIOM (Havlík et al., 2013), and ECHO (Kahil et al., 2018, 2019), to develop an integrated assessment modeling framework for nexus issues (e.g., water–food–energy) or hydro-economic modeling for quantifying water infrastructure investment options for regional water resources management.
CWatM is able to use different datasets of meteorological forcing for the current
climate – e.g., MSWEP (Beck et al.,
2017), WFDEI (Weedon et al., 2014), PGMFD
(Sheffield et al., 2006), GSWP3 (Kim et al., 2012),
or EWEMBI (Lange, 2018) – or future climate projections from
different general circulation models (GCMs) (e.g., data from ISIMIP
project, Frieler et al., 2016). CWatM can use the netCDF4 repositories of
original meteorological forcing without reformatting. As long as the forcing
data are using the CF 1.6 Convention, CWatM takes care of the different
names of the input variables and divides the dataset for the catchment or global
scale, depending on a mask map or predefined rectangular selection. The forcing data
are automatically regridded to the model grid (e.g.,
Depending on the method used for calculating potential evaporation, e.g., Penman–Monteith method (Allen et al., 1998), Hargreaves method (Hargreaves and Samani, 1958), or Hamon method (Hamon, 1963), different climate data are needed. As a default, the Penman–Monteith needs as inputs precipitation; humidity; long- and short-wave downward surface radiation fluxes; maximum, minimum, and average 2 m temperature; 10 m wind speed; and surface pressure. Temperature data are additionally needed to distinguish between snow and rain.
Potential reference crop evaporation rate (ET
Precipitation is split into rainfall and snow, depending on the temperature.
If the average temperature is below 1
Snow accumulates until it melts or evaporates. The rate of snowmelt is
estimated using a degree-day factor method, which take into account the fact that
snowmelt increases when it is raining (Speers and Versteeg, 1979):
For each time step, the value of
The calculation for interception and evaporation is based on Allen et al. (1998). For each land cover class, a maximum interception storage is defined. Interception storage can be filled by rainfall and depleted by evaporation using potential evaporation from open water. The leftover interception storage is added to the water available for infiltration in the other time step. Evaporation from soil is calculated using the potential reference evapotranspiration rate multiplied by a soil crop factor (default: 0.2). Evaporation from sealed area or open water is calculated using the potential evapotranspiration for the open water rate multiplied by a factor (defaults: 0.2 for sealed, 1.0 for water).
Potential transpiration from plants is calculated using the potential
reference evapotranspiration multiplied by a crop-specific factor available
as a spatially distributed dataset for each land cover type for every 10 d over a year. The crop coefficient is aggregated from MIRCA2000: a
global dataset of monthly irrigated and rainfed crop areas (Portmann
et al., 2010). The actual transpiration rate depends on the available water
and on the ability of the crop type to deal with water stress. The energy
already used up for the evaporation of intercepted water is subtracted here
in order to respect the overall energy balance. The actual transpiration
rate is reduced by a water stress factor which takes into account the
ability of the crop to deal with water stress and an index of water stress
of the soil:
The critical amount of soil moisture is calculated as
The actual transpiration
To estimate the infiltration capacity of the soil, the approach of Xinanjiang
(also known as VIC/ARNO model) (Zhao and Liu, 1995 and Todini,
1996) is used. The saturated fraction of a grid cell that contributes to
surface runoff is related to the overall soil moisture of a grid cell
through a nonlinear distribution function. The saturated fraction
A preferential flow component that lets more water bypass the soil as the soil gets wetter is calculated.
The actual infiltration INF
Unsaturated flow and transport processes can be described with the
1D Richard equation, which requires a high spatiotemporal distribution
of the soil's hydraulic properties and a numerical solver.
In order to apply an analytical and faster solution, Van Genuchten (1980)
hydraulic functions based on the model of Mualem (1976) were adopted. It assumes
a matric potential gradient of zero, which implies a flow that is that is
always in a downward direction at a rate equal to the conductivity of the
soil, and free drainage as the lower boundary condition in the lowest soil
layer. The relationship between hydraulic conductivity and soil moisture
status is described by the Van Genuchten (1980) equation.
The soil hydraulic parameters
Once the unsaturated conductivity for each soil zone is determined, the
water flux to the next zone can be estimated. At a time step of 1 d and
high
Capillary rise occurs only when the groundwater level is close to the surface. CWatM estimates the total fraction of the area with groundwater level of between 0 and 5 m from the surface in discrete steps and calculates the flux from groundwater to the soil layer based on unsaturated conductivity and field capacity (Wada et al., 2014).
Groundwater storage and baseflow are modeled using a linear reservoir
approach as in LISFLOOD (De Roo et al., 2000; Udias et al., 2016). The
groundwater zone is filled by the water percolating from the lower soil zone
and the preferential flow and is emptied by capillary rise and baseflow. The
outflow from the groundwater zone is given by
For considering lateral fluxes among grid cells and the explicit computation
of groundwater levels over finer spatial domains, CWatM has an option to
couple with MODFLOW (McDonald and Harbaugh, 1988,
Harbaugh, 2005) using the FloPy Python package
(Bakker et al., 2016) in a similar way to PCR-GLOBWB (Sutanudjaja et al., 2014). The
CWatM simulates the vertical soil water flow in three soil layers, while MODFLOW simulates lateral groundwater flows. CWatM-MODFLOW is technically coupled (using the Drain package) via capillary rise from groundwater to the soil zones, groundwater recharge from the soil zones, and baseflow outflow from groundwater to the river network system. As the MODFLOW resolution can be smaller than the CWatM resolution, the CWatM mesh is subdivided into two parts: one part where groundwater recharge occurs and one part where capillary rise from groundwater occurs. The area of each part is determined by the percentage of MODFLOW cell, where the water level reaches the lower soil layer inside a CWatM mesh. To distinguish whether the groundwater flow to the surface will be attributed to capillary rise or baseflow, a percentage of rivers is attributed to each MODFLOW cells and calculated based on a 200 m resolution topographic map. Aquifer properties, like transmissivity or aquifer thickness, are estimated using the approach of de Graaf et al. (2015) and Gleeson et al. (2011). The results presented in Sect. 5 of this work are calculated using the simplified linear reservoir approach.
The process between runoff generation and river routing for each grid cell
is called runoff concentration. The runoff generated from each cell is
routed to the corner of each cell. Depending on land cover class, slope, and
runoff group (surface, interflow, or baseflow), a concentration time (peak
time) is determined. The total runoff for a grid cell is then calculated
using a triangular weighting function.
Flow through the river network is simulated using kinematic wave equations.
The basic equations used are the equations of continuity and momentum. The
continuity equation is
The momentum equation can also be expressed as in Chow et al. (1998):
With the coefficients
Solving this for
To calculate
Reservoirs and lakes (RL), based on the HydroLakes database (Messager et
al., 2016; Lehner et al., 2011), are simulated as part of the channel network.
Using the approach of Hanasaki et al. (2018) and
Wisser et al. (2010), we distinguish between global RL
and local RL. Global RL are located in the main channel of a grid cell with
a catchment upstream of this grid cell. Local RL are more or less situated
inside one grid cell at the tributaries of the main channel and not attached
to the main river. Local RL are defined in CWatM depending on the spatial
resolution. All RL with an RL area of less than 200 km
The method of simulating reservoir operations is taken from LISFLOOD
(Burek et al., 2013). A total storage capacity
Another three parameters define how the outflow of a reservoir is regulated.
(a) Each reservoir has a “minimum outflow”
The outflow
Lakes are simulated using the modified Puls approach (Chow et al.,
1998, Maniak, 1997) similar to the approach as in LISFLOOD (Burek
et al., 2013). As lake inflow, the channel flow upstream of the lake location
is used. As lake evaporation, the potential evaporation rate of an open water
surface is taken. The modified Puls approach assumes that lake retention is
a special case of flood retention with horizontal water level and the
equations of river channel routing (see Sect. 2.3.10, “River routing”) can be
written as
The change in storage is inflow minus outflow and open water evaporation.
The equation is solved by calculating the lake storage curve as a function
of sea level,
The assumptions made here to simplify the equation are the following.
A modification of the weir equation by Poleni from Bollrich and
Preißler (1992) is assumed: If the weir does not have a rectangular form but a parabola form, the
equation can be simplified to The lake storage function is simplified to a linear relation:
Irrigation is by far the biggest consumer of water at around 70 % of global gross water demand (Döll et al., 2009). Irrigation water demand is calculated by following the method developed in PCR-GLOBWB (Wada et al., 2011, 2014) using the MIRCA2000 crop calendar of Portmann et al. (2010) and irrigated areas from Siebert et al. (2005) to account for seasonal variability, different crops, and different climatic conditions. MIRCA2000 explicitly considers multiple cropping. The associated crop- and stage-specific crop coefficients are derived from the Global Crop Water Model (Siebert et al., 2010). The crops are then aggregated into paddy and non-paddy and the crop coefficients are similarly aggregated by weighing the area of each crop class. Then, the cell-specific crop coefficient as it changes in time is related to the crops growing in this cell, inclusive of multiple cropping considered in the MIRCA2000 dataset. We refer to Wada et al. (2014) for the detailed descriptions. In brief, irrigation and water withdrawal and consumption are calculated separately for paddy (rice) irrigation and irrigation of other crops. To represent flooding irrigation of paddy fields, a 50 mm surface water depth is maintained until a few weeks before the harvest. Paddy irrigation demand is a function of the storage change of the surface water layer, net precipitation, infiltration to lower soil layers, and open water evaporation from the surface water layer. For non-paddy irrigation, the irrigation demand is calculated using the difference between total and available water in the first two soil layers where total water is equal to the amount of water between field capacity and wilting point and available water is equal to the amount of water between current status and wilting point. Water withdrawal is calculated using the water efficiency rate of FAO (2012) and Frenken and Gillet (2012).
Livestock water demand is assumed to be the same as livestock water consumption and is calculated by the number of livestock in a grid cell with the daily drinking water requirement per individual livestock type (six livestock types in total) and per air temperature for seasonal change in drinking water requirement. The approach is taken from Wada et al. (2011).
Calculation of industrial water demand also follows the method of Shen et al. (2008) and Wada et al. (2011) using the gridded industrial water demand data for 2000 from Shiklomanov (1997) and multiplying it by water use intensity. Water use intensity is a function of gross domestic product (GDP), electricity production, energy consumption, household consumption, and a technological development rate per country. Domestic water demand is calculated by multiplying the population in a grid cell by a country-specific per capita domestic water withdrawal rate taken from FAO (2007) and Gleick et al. (2009). Adjustments for air temperature and for country-based economic and technological development are carried out based on the approach of Wada et al. (2011).
The approach for calculating water withdrawal from different sources, water
consumption, and return flows is based on the work of
de Graaf et al. (2014), Wada
et al. (2014), Sutanudjaja et al. (2018), and Hanasaki et al. (2018). Water demand can be
fulfilled by surface water and groundwater. Based on the work of
Siebert et al. (2010), groundwater for irrigation
can be only used in areas that are equipped for irrigation. Groundwater is,
at first, only abstracted from the renewable groundwater storage. Water
demand that cannot be fulfilled purely from groundwater uses surface water
from rivers, reservoirs, and lakes. An environmental flow cap can be set in
order to sustain environmental needs for rivers, reservoirs, and lakes. If
water demand still cannot be fulfilled, additional water is taken from
nonrenewable groundwater. At
Return flow and associated losses (i.e., conveyance, application) are calculated using the approaches of LPJmL (Rost et al., 2008) and H08 (Hanasaki et al., 2018). Return flow is the flow which is withdrawn from surface water or groundwater but is not consumed. For the return flow rate, we follow the approach of Hanasaki et al. (2018). For irrigation, the return flow is calculated using the irrigation efficiency by Döll and Siebert (2002). For domestic and industrial use, the return rate is based on Shiklomanov (2000) (i.e., 90 % for the industrial sector and 85 % for the domestic sector). Fifty percent of return water from irrigation is lost to evaporation and 50 % is returned to the channel network. This assumption is taken from Hanasaki et al. (2018). Domestic and industrial return flow 100 % is returned to the river channel network.
CWatM can be run globally at 0.5
Various global datasets were used to set the framing conditions for CWatM.
The model provides full global datasets for the
Global dataset, source of dataset and submodule of CWatM.
Most of the global hydrological models are uncalibrated with few exceptions,
e.g., WaterGAP (Müller Schmied et al., 2014). One of the main
reasons for calibrating a model is the uncertainty of its input data,
parameters, model assumptions, and grid cell heterogeneity, especially at
low resolution as, for example, 0.5
As objective function, we used the modified version of the Kling–Gupta
efficiency (Kling et al., 2012), with
The calibration uses a general population size (
Calibration parameters (with flexibility to adjust the number and different parameters).
With a daily time step, a global run of 100 years takes around 12 h, i.e., 7.2 min per year (on a Linux single CPU core – 2400 MHz with Intel Xeon CPU E5-2699A). For the global setting, soil processes are the most time-consuming part, taking 50 % of all computing time, followed by routing with 25 % and runoff concentration with 10 %.
Computational time for a 0.5
A basin run – e.g., for the Rhine basin which is 160 800 km
Computational time for 0.5
The main global water balance components are calculated for the period
1979–2016 with the standard deviation of interannual variation. The spatial
extent is from 90
Global water balance components over the period 1981–2016 simulated by CWatM.
Average global discharge (in m
It is important to note that water withdrawals from the agricultural sector (irrigation and livestock), industry, and domestic sector (households) have been increasing over the years. The range in Table 5 for domestic and industry withdrawals has been rising constantly from 1981 to 2016. Agricultural withdrawals have been increasing over time but achieved their maxima during globally warm years, e.g., 2002, 2009, and 2012. Water withdrawal from either surface water or groundwater is within the range of other models. It has also been affected by the increasing water withdrawal for agriculture, industry, and households.
We used daily discharge simulations (0.5
Performance metrics based on 1366 GRDC stations.
Global map of Kling–Gupta efficiency based on 1366 GRDC stations.
Histograms of Kling–Gupta efficiency and correlation for different basin sizes based on 1366 GRDC stations.
Some model papers (e.g., Döll et al., 2014; and Sutanudjaja et al., 2018) use observed discharge stations or the Gravity Recovery and Climate Experiment (GRACE) (Tapley et al., 2004) to evaluate the global results of their models. As CWatM is a part of the ISIMIP intercomparison project, we think it is best to show the performance of a model in the framework of ISIMIP by comparing it to other models like in Zhang et al. (2017) or Scanlon et al. (2018). An upcoming paper by Pokhrel (2020) on global terrestrial water storage will include a comparison of seven global terrestrial hydrology models (including CWatM) against GRACE data.
For calibration, an evolutionary algorithm with KGE as objective function was applied and WFDEI meteorological data were used as forcing. For all stations, the calibration improved the streamflow simulations compared to the baseline simulation with a default parameter set. During the calibration, human activities (e.g., water abstraction, reservoirs, and changing land cover of time) are included. However, the performance varied depending on the quality of the discharge data and the meteorological forcing, as well as on the processes included in CWatM, as shown in Table 7. Calibration and validation results are shown for each station in the Supplement part 3. Simulating processes such as backflow or large evaporation losses due to swamps in the Nile and Niger basin are still challenging. But this simulation shows the suitability of CWatM for representing the major water balance components and the necessity of calibrating certain basins, especially where water availability is being compared with water withdrawal. A further step in global calibration must be performed by regionalization of model parameters, e.g., by using model parameters from well-performing basins for basins with similar climate and other characteristics (Samaniego et al., 2010, 2017; Beck et al., 2016). A big challenge is the unevenly distributed observed discharge data around the world with big spatial gaps in Africa and Asia. Even if calibration with an objective function based on observed discharge is the best option, the gap might be filled with some sort of Budyko calibration (Greve et al., 2016), where at least the empirical function of actual evapotranspiration against potential evaporation is fitted or satellite-based river levels could replace discharge missing from the observations (Revilla-Romero et al., 2015; Gleason et al., 2018).
Calibration results for some catchments worldwide.
Calibration results for some chosen stations globally.
The essential component of the Water Futures and Solution Initiative of
IIASA (Burek et al., 2016; Wada et al.,
2016) is the assessment of the balance of water supply and demand for the
present and into the future. With the support of the Government of Austria
through the Austrian Development Agency (ADA), we aim to provide a deeper
understanding of critical parameters for achieving water security in East
Africa. This is in the context of competing demands for basic water supply,
sanitation, food security, economic development, and the environment.
UN-Water (2013, p. 1) defines water security as the following: The capacity of a population to safeguard sustainable access to adequate quantities of and acceptable quality water for sustaining livelihoods, human well-being, and socio-economic development, for ensuring protection against waterborne pollution and water-related disasters, and for preserving ecosystems in a
climate of peace and political stability.
Water security is also a key ambition expressed in the “Vision 2050” of the East African Community (EAC, 2016) as rapid growth of the economy and population and a high rate of urbanization are expected for the region and will lead to increased water demand in all sectors as well as further pressure on the water quality status.
The examples of operational areas for CWatM in this paper are not presented with specific results in mind, nor do they reflect results from the project's intensive stakeholder processes. They are there to demonstrate the value of a global hydrological model used in a regional case study that combines the spatiotemporal scale dependencies of water systems produced through a scenario analysis designed to include both the regional and global scales. An “East Africa Regional Vision Scenario” (EA-RVS) was developed (Tramberend et al., 2019, 2020), based on regional visions, and we used available regional scenarios and data that were developed in the context of global studies. As well as regional visions, the study also integrates into the widely applied global scenario development process of the Intergovernmental Panel on Climate Change (IPCC). It is characterized by a Scenario Matrix Architecture (van Vuuren et al., 2014) including the community-developed Shared Socioeconomic Pathways (SSPs) (Jiang and O'Neill, 2017) and the Representative Concentration Pathways (RCPs) (van Vuuren et al., 2011) for the characterization of climate change.
The study area, the extended Lake Victoria basin (eLVB), is a transboundary
basin in the tropics. It comprises the headwaters of the Nile and includes
an area of over 460 000 km
The 61 subbasins of the eLVB and their aggregation into eight major basin regions.
For assessing climate change impact, RCP6.0 was chosen as the most plausible future for East Africa by the “EAC Vision 2050” (EAC, 2016) even though it represents a rather pessimistic outlook of global temperature increases despite being published after the Paris Climate Agreement of 2015. We have chosen the two general circulation models (GCMs) of HadGEM2-ES and MIROC5 out of the four GCMs (see Table 8) used in ISIMIP 2b (Frieler et al., 2016) as being the most feasible for eLVB as the discharge results that were run with CWatM for the historical runs of the GCMs GFDL-ESM2M and IPSL-CM5A-LR showed a large discrepancy from historical results.
General circulation models (GCMs).
Discharge is the variable which incorporates all the meteorological and hydrological processes into a basin and encompasses all the storage components in a basin (i.e., soil, groundwater, lakes, and reservoirs) Especially with the large lakes in the basin, discharge in eLVB has a long memory of past conditions.
Change of seasonal discharge pattern from 2010 to 2040 and for 2050.
The seasonal pattern of discharge in Fig. 7 shows more discharge for 2040 (10-year period 2036–2045) and 2050 (10-year period 2046–2055) in the river system from Lake Victoria, especially for the 2040 period. This is due to a wetter period of weather in the two GCMs from 2038 to 2049 and the strong memory effect of groundwater and the lakes. It also shows the big influence of interannual variability in the eLVB. Even if a general trend of less runoff in the 2050 period can be detected, long-lasting periods of wetter conditions can nevertheless be superimposed over this trend. Because of the strong interannual variability in the lower latitudes, it is difficult to assess the effect of a general climate change impact towards a wetter or drier climate. But under climate change, southwestern Uganda will show generally drier conditions than the western part of the eLVB.
Available water resources per capita, the Water Crowding Index (WCI) (also
called the Falkenmark indicator), is one of the most widely used measures of
water stress (Falkenmark et al., 1989). Based on per capita water
availability, the water conditions in an area can be categorized into
different categories of stress expressed as cubic meters (m
Water Crowding Index and Water Exploitation Index.
The WCI and WEI are mainly shown as annual indicators, but in regions with high intra-annual variability the rainy seasons show a different picture from that of the dry season. An example in Fig. 8 shows the WCI and WEI for the dry season and the most water-scarce month, July, for 61 subbasins of the extended Lake Victoria basin by comparing the situations of 2010 and 2050. The figure shows that there is a clear increase in the WCI. While in the current situation (2010) about half of the subbasins are exposed to some level of water scarcity with some subbasins indicating absolute water scarcity, in 2050 almost all subbasins that are neither directly crossed by the river Nile nor adjacent to a lake experience stress or scarcity and many of them absolute water stress. The water resource availability for the WEI is also based on the RCP6.0 climate scenario and includes the effect of human consumption and effects of land use change up to 2050. Looking at this index for the month of July only, it shows that 9 out of 61 subbasins are likely to experience water scarcity and even severely water-scarce situations by 2050. Such subbasins are mainly located at the south and southeastern shores of Lake Victoria and in densely populated areas of Rwanda and Burundi.
Interestingly, the WEI shows a much lower signal of water scarcity compared to the WCI. The WCI assumes that, regardless of the socioeconomic conditions, every person on the globe has the same “water demand entitlement”. The Water Exploitation Index is based on the in situ situation and on balancing changing water availability and water demand. The fact that both indices show a rather different picture might be interpreted as an indication of economic water scarcity. The situation of low economic development for the extended Lake Victoria basin may still prevail in 2050 (at least compared to the global average). This is the main reason for the relatively low actual water demand compared to global averages and therefore relatively low water scarcity signal for the WEI compared to the WCI.
Water Crowding Index and Water Exploitation Index in July for the extended Lake Victoria basin.
The hydrological model CWatM is intended to be scalable and can be applied over finer spatial scales (e.g., the basin). CWatM has been calibrated for the Zambezi, using six subcatchments and measured discharge provided by the Global Runoff Data Centre (2007). Figure 9 shows two time series of measured vs. simulated river discharge, and the comparison shows good agreement of the modeled discharge with the measured data. The station Matundo-Cais is downstream of the two big reservoirs Kariba Dam and Cahora Bassa, which are included in the model. The reservoir operations are calculated with the approach in Sect. 2.3.11.
By comparing the outputs of the hydrological model ensemble, we see that, especially for sub-Saharan Africa, there is a strong overestimation of river discharge, which indicates an erroneous picture if compared, for example, to water demands for calculating water scarcity. Figure 10 shows a comparison of discharge for the Lukulu in the Zambezi basin of different hydrological models as a violin plot which shows the probability density of the data. While a box plot shows some statistics like mean and quartiles a violin plot shows the full distribution of the data.
The GHMs in Fig. 10 use the WFDEI (Weedon et al., 2014) as forcing meteorological data from 1981 to 2004. Apart from WaterGAP and CWatM (both calibrated), one can see a strong overestimation of discharge for all other models compared to the observed discharge and some models also show a different shape than the observed data.
Calibration results for two stations in the Zambezi basin.
Discharge for Lukulu/Zambezi from 1981 to 2004 for 11 different global hydrological models from the ISIMIP 2a ensemble compared with observed discharge. Each violin plot shows the probability density of the data for the different GHMs. The lines show the average discharge for each model.
Average discharge is overestimated for the noncalibrated models from 2 up to 3 times and maximum discharge up to 7 times. This shows the need to put efforts into calibration of the hydrological model for regional applications to be in line with measured water resources and to minimize the uncertainty from hydrological modeling. Setting up model calibration has been time-consuming but inevitable for the Zambezi case study.
Calibration for the Zambezi basin is performed for six stations (Lukulu, Kongola, Katima, Kafue Hook, Luangwa Road Bridge, Tete – see Fig. 6). The calibration parameters are valid for the subbasin up to the gauging station. The upstream station is calibrated using the best fit of the downstream calibrated subbasins. The parameter set is valid for the subbasin except for the downstream subbasins which have their own parameter sets.
Parameter sets of different hydrological variables.
Subbasins of the Zambezi basin for aggregating data from CWatM.
In a second phase, the CWatM calibrated model is used to assess water
scarcity until 2050 in the Zambezi basin. Water resources at each grid cell
are dependent on climate; water management (e.g., reservoirs); and water use
for irrigation, livestock, domestic, or industry.
For each cell (at 5
Projection of future water resources builds on quantifications of climate
scenarios CMIP5 (Distributed by the Coupled Model Intercomparison Project
(CMIP); see
Water demand for agriculture is taken from calculations within CWatM. Water demand for domestic, livestock, and industry is calculated within CWatM using the approach of Wada et al. (2011). The socioeconomic background needed for this approach uses data and methods for spatial disaggregation for the SSP2 scenario from Jones and O'Neill (2016), Gao (2017), Klein Goldewijk et al. (2017), Kummu et al. (2018), and Gidden et al. (2018).
Water demand projection for scenario SSP2/RCP6.0 to 2050 based on population, GDP, and irrigation area projections.
The WEI is defined in Falkenmark et al. (1989), Falkenmark (1997), and Wada et al. (2011) as comparing blue water availability with net total water demand. A region is considered “severely water stressed” if the WEI exceeds 40 % (Alcamo et al., 2003). The yearly WEI in Fig. 12 shows no water stress for the whole basin in 2010, but water stress will intensify up to 2050 for the business-as-usual (BAU) scenario (composed of the SSP2 and RCP6.0 scenarios), mainly due to agricultural and domestic water demand increasing by a factor of 5; as annual mean river discharge is only increasing by 6 %. August is chosen for monthly comparison as this is the month with the highest rate of water withdrawal (WW) and a mean monthly discharge (MMD) that is only slightly higher than in November. The eastern part of the Zambezi basin, except for the main course of the Zambezi river, was already showing severe water stress in 2010. This will increase in 2050, but the western part is still not suffering from water stress.
Water Exploitation Index for 21 regions of the Zambezi for 2010 and 2050 using the business-as-usual (BAU) scenario (yearly and for the month of August).
The modular structure of CWatM helps to link and integrate with other models. The independent settings files offer possibilities to adapt the input and output to other models. For a lot of applications, no intervention into the code is necessary. If code has to be customized to the linked model, the modular structure of CWatM easily allows users to identify the point of intervention.
To explore potential sustainable pathways for the Zambezi basin, an integrated assessment framework is needed. Therefore CWatM provides data on water availability (runoff and discharge) and water demand (irrigation, domestic, and industrial demands) at subbasin level to the “Extended Continental-scale Hydroeconomic Optimization” (ECHO) model (Kahil et al., 2018) and to the water quality model “Model to Assess River Inputs of Nutrients to seas” (MARINA) (Strokal et al., 2016). Figure 15 gives an overview of the interactions between models and the data flow.
Schematic view of the interaction among CWatM, ECHO, and MARINA.
ECHO is a hydro-economic optimization model. Its objective function minimizes the costs of water management options subject to several resource and management constraints across subbasins within river basins over a long-term planning horizon (e.g., a decade or more). ECHO includes a wide range of supply- and demand-side water management options spanning over the water, energy, and agricultural systems. The supply options are surface water diversion, groundwater pumping, desalination, and wastewater recycling technologies. Other supply options considered in ECHO are surface water reservoirs and interbasin transfer infrastructure. The water demand management options consist of different technologies for irrigation (flood, sprinkler, and drip) and several measures to improve crop water management in irrigation and water use efficiency in the domestic and industrial sectors (Kahil et al., 2018, 2019).
To assess the impacts of human activities on water quality, the MARINA model (Strokal et al., 2016) is used to estimate nitrogen loads and concentrations. MARINA quantifies nutrient (nitrogen and phosphorus) export to rivers and sea at the subbasin scale. It is primarily used for long-term trend analysis and for source attribution, which could guide the identification of effective policy and management measures to reduce water pollution.
Moreover, MARINA uses data from GLOBIOM (Havlík et
al., 2013) for land use and agricultural nitrogen inputs to the basin and
socioeconomic projections (population and GDP) to estimate nitrogen inputs
from human waste. ECHO uses information on existing capacities of various
water management options and the costs of investment and operation of these
options. Nitrogen loads and concentrations calculated by MARINA are compared
with nitrogen standards for different sectors to categorize the suitability
of water use by different users, which can be further used by ECHO to
optimize water allocation and explore economically optimal management
options. The source attribution at the subbasin scale by MARINA
(Fig. 15) provides prior information for ECHO to
prioritize the most relevant nitrogen management options for each subbasin,
such as sewer connections, wastewater treatment, and manure and mineral
fertilizer use in agriculture. Lastly, the coupling of MARINA and ECHO with
CWatM enables analysis of the impacts of climate change and variability on
nutrient export, water allocation, and adaptation costs. CWatM outputs from
different climate forcing could be used in MARINA and ECHO to investigate
the impacts of intrabasin spatial variability and interannual temporal
variability of runoff and discharge. Figure 16 is an
example of MARINA output of total dissolved nitrogen (TDN, in kg km
Increase in river export of total dissolved nitrogen to sea between 2010 and 2050 (business-as-usual scenario).
Figure 17 is an example of ECHO simulation results. It shows the costs for water supply and management in order to satisfy sectoral water demands (irrigation, livestock, domestic, and industrial) and environmental constraints (i.e., minimum environmental flow requirements and groundwater sustainability constraints) in the Zambezi river basin over the 2010–2050 period.
Investment (INV) and operating (O&M) costs for water supply and management in the Zambezi basin between 2010 and 2050 (business-as-usual scenario).
We presented the new global hydrological model CWatM, which can be used globally and regionally at different resolutions with different datasets. The model is open source in the Python environment and has a flexible modular structure. It uses global, freely available data in the state-of-the art format of netCDF4 files to store and produce data in a compact way. It includes major hydrological processes but also takes human water use into account by calculating water demand, water consumption, and return flows. Reservoirs and lakes are included in the model scheme. CWatM is being developed to include a routing scheme related to reservoirs and canals to better simulate water availability in both agricultural and urban contexts.
It is shown that CWatM can be used in the framework of ISIMIP as a global model and also as part of a model integration of hydrological, hydro-economic, and water quality models for assessing and evaluating water management options. This study also presented the need for a hydrological model to be calibrated to be able to estimate a detailed regional balance of water demand and water availability.
An external limitation and a source of uncertainty is the quality of meteorological forcing driving the hydrological models. As shown in Müller Schmied et al. (2014), there are still discrepancies among the CMIP5 datasets and among the datasets and observations. The use of CMIP6 datasets (Eyring et al., 2016) is expected to reduce these uncertainties. Another external model limitation and source of uncertainty is the availability of gauging station data, which is generally globally decreasing, completely unavailable, or difficult to access for some parts of the world. Continuous, consistent, and long-term river discharge data as an integral parameter over the whole basin are essential for basin modeling, water resources management, and flood forecasting. Although the model represents the key hydrological processes, the groundwater model is relatively simple. But groundwater assessments (e.g., Bierkens et al., 2019) are becoming more and more important, as also is the importance of including lateral processes that increase the resolution of the model. Some other hydrological processes representation, e.g., evaporation from swamps, namely, the Sudd in the Nile basin and the Niger river swamps, need to be improved. The main direction of improvement should be better representation of human activities, e.g., management of reservoirs, including intra- and interbasin water transfer, and improving water demand requirements from agricultural sector by including irrigation schemes and plant phenology.
Future work will include (1) intensifying the development of a full dynamic
coupling with a 2D groundwater model, (2) developing a global calibration
scheme that also takes sparse observation of discharge into account, (3) a
finer-resolution setting for 1 km working for the upper Bhima basin in India
as part of the Food–Water–Energy for Urban Sustainable Environments
project (
CWatM is written in Python 3.7 and C
Climate forcing data can be found on the ISI-MIP server (Frieler et al.,
2016) or any other climate forcing dataset stored as netcdf can be used.
Online documentation including documentation on the source code can be found
on
The supplement related to this article is available online at:
PB wrote the original draft, prepared the manuscript and is main developer of the software; YS contributed to the water demand software development; TK contributed to the methodology writing and the results part of linking to hydro-economic modeling and produced Fig. 17; TT contributed to the methodology writing and the results part of linking to water quality and produced Figs. 15 and 16. PG, MS and LG all contributed to software development of the evaporation, water demand and groundwater modules. FZ provided processed daily observation data for the calibration validation, and YW coordinated the funding acquisition and contributed to conceptualization, methodology writing and reviewing.
The author declares that there is no conflict of interest.
The authors acknowledge the Global Environment Facility (GEF) for funding the development of this research and the CWatM model development as a part of the Integrated Solutions for Water, Energy, and Land (ISWEL) project (GEF Contract Agreement: 6993) and the support of the United Nations Industrial Development Organization (UNIDO). The authors also acknowledge the continuous support of the Asian Development Bank (ADB), the Austrian Development Agency (ADA), and the Austrian Federal Ministry of Sustainability and Tourism to the Water Futures and Solutions (WFaS) initiative at Water Program of IIASA. This study and the model development were also conducted as part of the Belmont Forum Sustainable Urbanisation Global Initiative (SUGI)/Food–Water–Energy Nexus theme for which coordination was supported by the US National Science Foundation under grant ICER/EAR-1829999 to Stanford University. The Global Runoff Data Centre (GRDC, Koblenz, Germany) is thanked for providing the observed discharge data. We appreciate all the other open-source projects which we used to collect ideas and which, on the other side, we hope to cross-fertilize with our ideas. We are very grateful to all the freely available datasets. Any opinions, findings, and conclusions or recommendations expressed in this material do not necessarily reflect the views of the funding organizations. This study is also partly supported by financial support from the Austrian Research Promotion Agency (FFG) under the FUSE project funded by the Belmont Forum (grant agreement: 730254), the EUCP (European Climate Prediction System) project funded by the European Union under Horizon 2020 (grant agreement: 776613), and CO-MICC project which is part of ERA4CS, an ERA-NET initiated by JPI Climate with co-funding by the European Union and the Austrian Federal Ministry of Science, Research and Economy (BMWFW).
This study and the model development were also conducted as part of the Belmont Forum Sustainable Urbanisation Global Initiative (SUGI)/Food–Water–Energy Nexus theme for which coordination was supported by the US National Science Foundation under grant ICER/EAR-1829999 to Stanford University. This study is also partly supported by financial support from the Austrian Research Promotion Agency (FFG) under the FUSE project funded by the Belmont Forum (Grant Agreement: 730254), EUCP (European Climate Prediction System) project funded by the European Union under Horizon 2020 (Grant Agreement: 776613), and CO-MICC project which is part of ERA4CS, an ERA-NET initiated by JPI Climate with co-funding by the European Union and the Austrian Federal Ministry of Science, Research and Economy (BMWFW).
This paper was edited by Wolfgang Kurtz and reviewed by three anonymous referees.