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Proc. IAHS, 383, 261–266,
2020https://doi.org/10.5194/piahs-383-261-2020© Author(s) 2020.
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Open Access
Hydrologicalprocesses
andw
atersecurityin
achanging
world
A review of the adaptability of hydrological models fordrought
forecasting
Zikang Xing1,2,3, Miaomiao Ma2, Zhicheng Su2, Juan Lv2, Peng
Yi1,3, and Wenlong Song21State Key Laboratory of Hydrology-Water
Resources and Hydraulic Engineering,
Hohai University, Nanjing, China2Research center on Flood &
Drought Disaster Reduction of the Ministry of Water Resources,
China Institute of Water Resources and Hydropower Research,
Beijing, China3College of Hydrology and Water Resources, Hohai
University, Nanjing, China
Correspondence: Zikang Xing ([email protected])
Published: 16 September 2020
Abstract. Drought intensity and frequency are increasing in
recent years in multiple regions across the worlddue to global
climate change and consequently drought forecasting research has
received more and more atten-tion. Previous studies on drought
forecasting mostly focus on meteorological drought based on
precipitation andtemperature. However, the trend of predicting
agriculture and hydrological drought, which consider soil
moistureand runoff, have developed rapidly in recent years.
Hydrological drought forecasting is based on the hydrolog-ical
models and the model structure plays a role to improve predictions.
This study scrutinized more than 50hydrological models, including
lumped models, semi-distributed models, distributed models, surface
water andgroundwater coupled models, to explore the adaptability of
hydrological models in drought simulation and fore-casting. The
advantages and disadvantages of typical models, such as DTVGM,
GWAVA, and HEC-HMS modelswere analyzed to provide valuable
reference for drought forecasting model development. Future work
aims atimproving the hydrological models to simulate the drought
processes and make better prediction.
1 Introduction
Drought intensity and frequency are increasing in past andrecent
years in multiple across the globe due to global climatechange
(Wilhite and Glantz, 1985). In this context, meth-ods to predict
drought has received more and more atten-tion (Edwards and McKee,
1997). Previous study of droughtforecasting mostly focused on
meteorological drought basedon precipitation and temperature, while
the trend predic-tion of agriculture and hydrological drought,
which considersoil moisture and runoff, have developed rapidly in
recentyears (Wilhite, 2000; Wanders et al., 2019). There are
twomain types of methods involved in the hydrological
droughtforecasting. One is the statistical method that tries to
de-velop construct the relationship between hydrological
char-acteristics and drought events, such as the gray
forecastingmethod (Vishnu and Syamala, 2012), Markov chain
method(Paulo and Pereira, 2007), Error back Propagation neural
net-
work (Raju et al., 2011), and correlation analysis. The sec-ond
method is the methods based on hydrological modelsand coupled
atmospheric-hydrological models (Mishra andSingh, 2011). For the
latter method, the model structure playsa significant role to
improve predictions.
Most hydrological models have been developed for floodsimulation
and prediction (Huggins and Monke, 1970). Gen-erally, flood
generation tends to focus on fast processes andpeaks can form in a
few days or even hours, while the pro-cess of drought development
is much slower covering sev-eral months or years. Therefore, flood
forecasting is a short-term high flow prediction, but the drought
forecasting fo-cuses on medium and long-term low flow prediction
(Mishraand Singh, 2010). Most hydrological models could improveon
drought prediction in humid watersheds as they put em-phasis on
peak flow simulation rather than low flow (Yu etal., 1999). If
these models are used for the drought forecast-ing, they might
suffer from the limitations of structure. Two
Published by Copernicus Publications on behalf of the
International Association of Hydrological Sciences.
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262 Z. Xing et al.: A review of the adaptability of hydrological
models for drought forecasting
scientific questions are addressed: what are the limitations
ofexisting hydrological models when used for the drought
fore-casting, and how to improve the structure in order to makethe
drought prediction better? In this study, we scrutinizedmore than
50 hydrological models, including lumped mod-els, semi-distributed
models, distributed models, integratedsurface water and groundwater
models, to explore the adapt-ability of hydrological models in
drought simulation andforecasting. Afterwards, improvements of the
model struc-ture are proposed.
2 Methods
2.1 Model selection and classification
More than 50 hydrological models were involved in thisstudy. The
main source was from the model review paperby Singh and Woolhiser
(2002), some seldom used modelswere abandoned, and other recent
representative hydrolog-ical models were also included (20 models).
The baselineof model selection is that the model has been widely
usedand is easy to get the manual. According to the evolution
ofhydrological models, these selected models were classifiedinto
four types: lumped model, semi-distributed model, dis-tributed
model and integrated surface water and groundwa-ter model. The
selected models include: 13 lumped models(Fig. 1), 6
semi-distributed models (Fig. 2), 29 distributedmodels (Fig. 3) and
10 integrated surface water and ground-water models (Fig. 4).
2.2 Evaluation method for assessment of theadaptability of
hydrological models for droughtforecasting
2.2.1 Evaluation criteria
Considering the differences between flood and drought
fore-casting, their corresponding model structure need to be
dis-tinct. For drought forecasting, emphasis on low flows,
there-fore, the simulation of evapotranspiration, soil water
mois-ture and the recession process need to be well developed.Snow
melting, vegetation interception, groundwater and sur-face water
exchange cannot be neglect any more. The influ-ences of crops and
soil water dynamics should be consid-ered in the low flow
simulation and prediction. In addition,human impacts, including
water storage and drainage fromreservoirs, water transfer,
agricultural irrigation, groundwa-ter pumping or artificial
recharging, water consumption, alsoneed to be included in drought
forecasting, because they havesignificant effect on drought in some
areas (Van Loon et al.,2016). Given that the drought forecasting
needs longer leadtimes for prediction, hydrological models should
be linkeddirectly to the climate forecasting results in order to
extendthe lead times. Since drought forecasting deals with low
flow,the energy balance should be considered to a certain extentto
supplement the water balance calculation. To sum up, 15
Table 1. Summary of evaluation indicators.
No. Evaluation indicators
1 Special human impact module2 Water storage and drainage from
reservoirs3 Water transfer4 Irrigation simulation5 Groundwater
pumping and artificial recharging6 Water consumption7
Evapotranspiration simulation8 Canopy interception9 Snowmelt
simulation10 Soil water moisture simulation11 Low flow recession
process12 Surface water and groundwater exchange13 Connection to
climate forecasts14 Energy balance15 Crop growth interaction with
soil moisture
evaluation criteria (Table 1) were used to explore the
adapt-ability of hydrological models for drought forecasting in
thisstudy.
2.2.2 Evaluation method
A rank evaluation method was used in this study. Threegrades
were distinguished: 0, 1 and 2. “0” stands for no con-sideration of
the function or module; “1” stands for simplecalculation of the
function or module; “2” stands for matureor powerful calculation of
the function or module.
3 Results
3.1 Lumped model evaluation
The adaptability of lumped hydrological models for
droughtforecasting is shown in Fig. 1. Most of selected
lumpedmodels simulate evaporation calculation except CLS
model,whereas the XAJ and VM show the powerful function with athree
layers evaporation calculation method. 70 % of the se-lected models
simulate soil water moisture with simple equa-tions, while the
Sacramento model divides the soil into differ-ent layers and
calculate soil moisture in each layer. Five mod-els include
simulation of canopy interception and snowmelt.Three models (i.e.
EPIC, ARM and SVAT) consider the en-ergy balance together with the
water balance. Only the EPICmodel has irrigation module, while the
EPIC and ARM con-sider the influences of crop growth.
3.2 Semi-distributed model evaluation
The evaluation results of six selected semi-distributed mod-els
are shown in Fig. 2. Results exhibit that all the
selectedsemi-distributed models simulate evaporation and soil
water
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Z. Xing et al.: A review of the adaptability of hydrological
models for drought forecasting 263
Figure 1. Evaluation of the adaptability of lumped hydrological
models for drought forecasting. Where white color stands for grade
0 (noconsideration of the function or module); gray color stands
for grade 1 (simple simulation of the function or module); black
color stands forgrade 2 (powerful simulation of the function or
module).
moisture, whereas the LASCAM and ARNO appear pow-erful soil
water moisture simulation. The ARNO model in-volves the spatial
probability distribution of soil moistureto dynamically change the
saturation contribution area. TheSWRRB and SLURP consider the
effects of human activi-ties, such as water storage and drainage
from reservoirs. Inparticular, the SLURP model includes simulation
of severalhuman activities, including water transfers, irrigation
andgroundwater pumping and artificial recharging. The ARNOmodel
simulates water consumption, while the Top modeltakes the exchange
of groundwater and surface water intoconsideration. All
semi-distributed models only simulate wa-ter balance without taking
the energy balance into account.None of the selected models
consider low flow recession andcrop growth influences on soil water
moisture and evapotran-spiration.
3.3 Distributed model evaluation
The evaluation results of the 29 selected distributed mod-els
are shown in Fig. 3. All the models simulate evapo-transpiration
and soil water moisture, where SWAT, WIS-TOO, CASC2D,
PARCHED-THIRST, VIC and PRMS ap-proach the soil water moisture
simulation in different ways.SWAT, GWAVA, MIKE-SHE and DTVGM
consider mostof the human activities that impact the hydrological
sys-tem. The four human activity modules we put forward, areranked
as follows: water storage and drainage from reser-voirs, water
transfer, irrigation, groundwater pumping and
artificial recharging, water consumption. The impact of
thereservoirs is most frequently addressed by model
developers.Nearly half of the selected models consider canopy
intercep-tion and snowmelt modules, which often can be explainedfor
which catchment the model originally has been devel-oped. Half of
selected models consider groundwater and sur-face water exchange,
among which SWIM, SHETRAN andHEC-HMS containing specialized
groundwater simulationmodules. Five models consider the simulation
of low flowconditions, while night models (i.e. HYDROTEL,
CREST,HSPF, GWAVA, VIC, Wasim-ETH, MIKE-SHE, DHSVMand GBHM) can be
connected to weather forecasts well, be-cause of their grid-based
structure. Four models (i.e. SWAT,PARCHED-THIRST, SWIM, and
Mike-SHE) investigatecrop growth influences on the hydrological
system. Com-pared with lumped and semi-distributed models, many
dis-tributed models start to consider the energy balance to
sup-plement the water balance simulation.
3.4 Integrated surface water and groundwater modelevaluation
The evaluation results of 10 selected integrated surface
waterand groundwater models are shown in Fig. 4. All the
modelstudied consider evaporation, canopy interception, soil wa-ter
moisture and the exchange between surface water andgroundwater. The
ability to simulate groundwater flow andgroundwater pumping and
artificial recharging makes thistype of models more powerful than
the other types of models.
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264 Z. Xing et al.: A review of the adaptability of hydrological
models for drought forecasting
Figure 2. Evaluation of the adaptability of semi-distributed
hydrological models for drought forecasting. Where white color
stands for grade0 (no consideration of the function or module);
gray color stands for grade 1 (simple simulation of the function or
module); black colorstands for grade 2 (powerful simulation of the
function or module).
Figure 3. Evaluation of the adaptability of distributed
hydrological models for drought forecasting. Where white color
stands for grade 0(no consideration of the function or module);
gray color stands for grade 1 (simple simulation of the function or
module); black color standsfor grade 2 (powerful simulation of the
function or module).
MODHMS, HydroGeoSphere and Parflow are fully coupledmodels of
groundwater and surface water. They use partialdifferential
equations to describe surface water and ground-water flow
processes. SWATMOD, and MODHMS performwell in simulating human
activities. The Parflow model is ahydrological model for parallel
computing that takes the en-
ergy balance into account and can be efficiently linked to
cli-mate models. The SWATMOD as the coupled model betweenSWAT and
Modflow consider the influence of crop growth onthe hydrological
system.
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Z. Xing et al.: A review of the adaptability of hydrological
models for drought forecasting 265
Figure 4. Evaluation of the adaptability of integrated surface
water and groundwater models for drought forecasting. Where white
colorstands for grade 0 (no consideration of the function or
module); gray color stands for grade 1 (simple simulation of the
function or module);black color stands for grade 2 (powerful
simulation of the function or module).
4 Discussion
The model structure of lumped hydrological model seemsa bit
simpler compared with the other types of models,and they seldom
consider human impact (Bergstrom, 1995).While distributed models
investigate more human impactin the hydrological cycle, such as the
water storage anddrainage from reservoirs, water transferring,
water consump-tion, irrigation. The integrated surface water and
ground-water model couple the advantages of surface water
andgroundwater simulations. They consider the exchange be-tween
surface water and groundwater, and enable to simulatethe
groundwater pumping and artificial recharging. However,they are too
sophisticated to be used for drought forecast-ing, because they
need too many data to run (Beven and Bin-ley, 1992). In summary,
distributed models are more suitablefor drought forecasting
compared with lumped and semi-distributed models. Results indicate
that SWAT, GWAVA,MIKE-SHE and DTVGM are the most powerful in the
cate-gory of the distributed models because they simulate the
hu-man impact on the hydrological system, and they solve theenergy
balance together with water balance.
The MIKE-SHE model covers ranges of physical pro-cesses with
high requirements on parameters and data, andhence its operation
seems more sophisticated than the otherthree above-mentioned
distributed models (Refsgaard andStorm, 1995). The SWAT model is an
open source modeland it is updated by world researchers, which have
developedseveral modules for human impacts and crop growth. TheSWAT
model has been widely used in non-point source pol-
lution simulation (Jayakrishnan et al., 2005), but seldom
hasbeen used for drought forecasting. When applied to
droughtforecasting, it is better to improve the low flow
simulationsand the connection to climate forecasting. The advantage
ofGWAVA model is the human impact module that can simu-late water
demand, water transfer, agricultural irrigation andpopulation
distribution as a driver for human activities. How-ever, the
simulation modules for human impacts seems a bitsimple. In
contrast, the DTVGM model was developed basedon the characteristics
of Chinese Yellow River basin, withemphasis on water conservation,
water consumption, reser-voirs and low flow simulations (Ning et
al., 2016). The high-light of HEC-HMS is reflected in the
flexibility of its modelstructure, which can be adapted to the
catchments for dif-ferent natural conditions (Hydrologic
Engineering Center,2000). To sum up, combination of the model
structures ofSWAT, GWAVA and DTVGM might be a good solution
fordrought forecasting, using the flexible structure of the HEC-HMS
model.
5 Conclusions
This study scrutinized more than 50 hydrological models,
in-cluding lumped models, semi-distributed models,
distributedmodels, surface water and groundwater coupled models,
toexplore the adaptability of hydrological models for
droughtsimulation and forecasting. In this paper, we discuss the
lim-itations of the wide range of selected hydrological modelsto be
used for the drought forecasting and to propose im-provement of
model structures to make better fit for drought
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261–266, 2020
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266 Z. Xing et al.: A review of the adaptability of hydrological
models for drought forecasting
prediction. Results indicate that distributed models are
moresuitable for drought forecasting compared with the othertypes
of models. A combination of SWAT, GWAVA andDTVGM might be a good
solution for drought forecastingusing the flexible structure of the
HEC-HMS model.
Data availability. All relevant data are within the paper.
Author contributions. PY and WS designed the research; ZS andJL
collected the data; ZX and MM analyzed the data and wrote
thepaper.
Competing interests. The authors declare that they have no
con-flict of interest.
Special issue statement. This article is part of the special
issue“Hydrological processes and water security in a changing
world”.It is a result of the 8th Global FRIEND–Water Conference:
Hydro-logical Processes and Water Security in a Changing World,
Beijing,China, 6–9 November 2018.
Financial support. This research has been supported bythe IWHR
Research & Development Support Program (grantno.
JZ0145B582017), the the National Natural Science Founda-tion of
China (grant no. 51609257), the the Hydraulic scienceand technology
in Hunan Province (grant no. [2017]230-36),and the the National Key
R&D Program of China (grantno. 2017YFC1502406).
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Proc. IAHS, 383, 261–266, 2020
https://doi.org/10.5194/piahs-383-261-2020
https://doi.org/10.1155/2011/686258https://doi.org/10.1038/ngeo2646https://doaj.org/article/806ab2d8b3134e98a512ddf3409ca26fhttps://doaj.org/article/806ab2d8b3134e98a512ddf3409ca26fhttps://doi.org/10.1175/JHM-D-18-0040.1
AbstractIntroductionMethodsModel selection and
classificationEvaluation method for assessment of the adaptability
of hydrological models for drought forecastingEvaluation
criteriaEvaluation method
ResultsLumped model evaluationSemi-distributed model
evaluationDistributed model evaluationIntegrated surface water and
groundwater model evaluation
DiscussionConclusionsData availabilityAuthor
contributionsCompeting interestsSpecial issue statementFinancial
supportReferences