An Assessment of Global Precipitation and Evapotranspiration Products for Regional Applications - MDPI
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remote sensing
Article
An Assessment of Global Precipitation and
Evapotranspiration Products for
Regional Applications
Yan Zhao 1,2 , Zhixiang Lu 2,3 and Yongping Wei 2, *
1 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
Beijing 100101, China; yan.zhao@uq.edu.au
2 School of Earth and Environmental Sciences, The University of Queensland, Brisbane 4067, Australia;
lzhxiang@lzb.ac.cn
3 Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Lanzhou 730000, China
* Correspondence: yongping.wei@uq.edu.au
Received: 18 March 2019; Accepted: 30 April 2019; Published: 7 May 2019
Abstract: Precipitation (P) and evapotranspiration (ET) are the key factors determining water
availability for water resource management activities in river basins. While global P and ET data
products have become more accessible, their performances in river basins with a diverse climate
and landscape remain less discussed. This paper evaluated the performance of four representative
global P (CHIRPSP , GLDASP , TRMMP and PersiannP ) and ET products (CSIROET , GLDASET ,
MODET and TerraClimateET ) against the reference data provided by the Australian Water Availability
Project (AWAP) in the Murray Darling Basin (MDB) of Australia. The disparities among the data
products both in the period from 2001 to 2016 and across the 22 catchments of MDB were related
to a set of catchment characteristics (climate, terrain, etc.) to explore any possible contributors.
The results show that the four global P products presented overall high consistency with AWAPP
across the MDB catchments except in southeastern catchments with abundant rainfalls and large
terrain variations. The Penman–Monteith algorithm based MODET underestimated ET in the MDB,
especially in the arid, less vegetation covered catchments. While the CSIROET , which also estimated
with the Penman–Monteith method, presented overall better estimations, which can be attributed
to the better parameterization of the landscape in the simulation processes. The hydrological
model based TerraClimateET showed overall good consistency with AWAPET except in the arid
catchments, which might be attributed to the simplified water balance model it applied, however
it did not adequately reflect the intensive ground water uses in these catchments. The findings
indicated that basin and catchment characteristics had impacts on the accuracy of global products and
therefore provided important implications for choosing appropriate product and/or conducting field
calibrations for potential users in large basins characterized with diverse rainfall, terrain variations
and land use patterns.
Keywords: precipitation; evapotranspiration; global scale products; regional applications; Google
Earth Engine
1. Introduction
Precipitation (P) and evapotranspiration (ET) are the two basic components of the hydrological
cycle, and the most important variables in river basin managements [1]. P accounts for the major
freshwater input while ET accounts for approximately 70% of P that falls on the Earth’s surface and
transfers the water back to the atmosphere [1–3]. Accurate P and ET estimations are critical to river
Remote Sens. 2019, 11, 1077; doi:10.3390/rs11091077 www.mdpi.com/journal/remotesensing
Remote Sens. 2019, 11, 1077 2 of 18
basin management activities (e.g., water reallocation, land planning, ecosystem restoration). This is
especially true for arid and semiarid regions where the natural ecosystem and the rainfed agricultural
system rely heavily on the available P, and about 94% of P is lost through ET (www.mdba.gov.au).
Therefore, in knowing the spatiotemporal distribution of P and ET, managers will be better placed to
efficiently manage the available water for a sustainable river basin system.
Ground-based P observations provide the most accurate P at plot scale. But it is known that
the P observation networks are not well established across the world, especially in remote regions
with sparse or no distribution of observation stations. This has limited the representativeness
of the ground-based P observation considering the complex climatic and terrain conditions [4].
Recent studies have incorporated the limited ground measurements and satellite observations to
reproduce spatial continuous P products. Such products have become increasingly available in near
real-time with quasi-global to global coverage. However, errors and uncertainties still exist in these P
products [4,5]. Experiences show that this could be associated with the algorithms of transforming
the satellite-measured reflectivity into rainfall rates, or the lack of calibrated ground observations in
remote areas [5–7].
Direct measurements of actual ET are possible only at small scales due to the complexity of the
related physical processes (e.g., landscape characteristics, micrometeorological conditions) and the
requirements of equipment (e.g., using flux towers). ET at a large scale from regional, to continental
and global scales has to be estimated using models. To date, several ET estimation models have been
developed which could be broadly classified into two categories according to their theoretical basis:
Hydro-meteorological models and hydrological models. The hydro-meteorological models deal with
the vertical exchanges of water and heat between the atmosphere and land surfaces and use site and
satellite-based observations to parameterize the processes [8–11]. Hydrological models are developed
based on a water balance approach and focus on spatial distribution of water availability, as well as the
vertical and lateral transfer of water resources [12,13]. Products based on hydro-meteorological models
include, for example, the CSIRO PML ET data collection [9] and the widely used MOD16A2 [8,11],
while the Australian Water Availability Project (AWAP) generated ET [14] and the recently released
TerraClimate ET [13] fall into the second category. Since there are inherent differences among the
different algorithms in relation to, for example, the input data, model parameterization and calibration
procedures, theoretically there exists differences in the performance of these data products.
Experiences have also shown that the performance of these P and ET products could vary from
region to region. For instance, while the widely applied P products from the Tropical Rainfall Measuring
Mission (TRMM) was found to reproduce rainfalls well in wet regions and in warm seasons in East
Asia [15], the product largely overestimated extreme rainfalls in South Asia [16]. Zhao and Yatagai [17]
also found that the TRMM P series tends to overestimate the frequency of heavy rainfall events in
southeastern China but underestimate light to moderate rainfalls in northwestern China. ET estimation
by MODIS (Moderate Resolution Imaging Spectroradiometer) using the Penman–Monteith equation
was found to work equally well as a hydrological model and derived ET estimations in the Sixth
Creek Catchment of South Australia [18], but a similar analysis conducted in the Haihe River Basin of
China revealed that MODIS substantially underestimated ET as assessed by the water balance ET and
tower observed levels [19]. Such discrepancies indicated that features of the site (e.g., climatic, terrain
and land use conditions) might affect the performance of the global scale data products, which has
important implications for precise planning and allocation of water resources in a large river basin.
The aim of this paper is to test the performance of four P (CHIRPSP , GLDASP , TRMMP and
PersiannP ) and ET (CSIROET , GLDASET , MODET and TerraClimateET ) global products developed with
different algorithms in reproducing the P and ET in the 22 catchments of the Murray Darling Basin of
Australia (MDB). The products are accessible through the Google Earth Engine (GEE). It includes three
specific objectives: The first is to understand the overall disparities during the period from 2001 to
2016 at the basin scale, the second is to probe the disparities across the 22 catchments and the third is to
explorer the potential contributions of catchment characteristics. The key findings from this study are
Remote Sens. 2019, 11, 1077 3 of 18
Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 19
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2. Study Area and Methods
2. Study Area and Methods
2.1. The Murray Darling Basin
2.1. The Murray Darling Basin
The study was conducted within the Murray Darling Basin (MDB) located in southeastern
The study was conducted within the Murray Darling Basin (MDB) located in southeastern
Australia (Figure 1). The MDB covers an area of 1.06 ×10 6 km2 , where most of the area is flat and
Australia (Figure 1). The MDB covers an area of 1.06 ×10 6 km2, where most of the area is flat and low-
low-lying land, with mountainous regions primarily focused in the eastern part of the basin (Figure S1).
lying land, with mountainous regions primarily focused in the eastern part of the basin (Figure S1).
The climate of the MDB is sub-tropical in the north, semi-arid in the west and mostly temperate in the
The climate of the MDB is sub-tropical in the north, semi-arid in the west and mostly temperate in
south. A high annual rainfall up to 1500 mm/year is recorded in the eastern side of the MDB while
the south. A high annual rainfall up to 1500 mm/year is recorded in the eastern side of the MDB while
the western side of the MDB is typically hot and dry with an annual rainfall of generally less than
the western side of the MDB is typically hot and dry with an annual rainfall of generally less than 300
300 mm/year (Figure S2). In addition, the MDB is characterised by high ET levels, which account
mm/year (Figure S2). In addition, the MDB is characterised by high ET levels, which account over
over 94% of the rainfall that falls in the basin (www.mdba.gov.au). Thus, water resources are the
94% of the rainfall that falls in the basin (www.mdba.gov.au). Thus, water resources are the critical
critical constraint for development of agriculture and conservation of natural environments in the
constraint for development of agriculture and conservation of natural environments in the MDB. The
MDB. The basin contains 22 catchments, which present substantial differences in terms of the climatic,
basin contains 22 catchments, which present substantial differences in terms of the climatic, terrain
terrain and level of human activities (Table 1). The diversities in climate, landscape characteristics and
and level of human activities (Table 1). The diversities in climate, landscape characteristics and water
water use across the MDB make it an ideal case for carrying out the proposed analysis.
use across the MDB make it an ideal case for carrying out the proposed analysis.
Figure 1. Location of the Murray Darling Basin (MDB). Land use data is extracted from “National scale
Figure 1. Location of the Murray Darling Basin (MDB). Land use data is extracted from “National
land use version 5 (2010–2011)” through http://www.agriculture.gov.au.
scale land use version 5 (2010-2011)” through http://www.agriculture.gov.au.
Remote Sens. 2019, 11, 1077 4 of 18
Table 1. Summary of catchment characteristics in the Murray Darling Basin. Catchment scale precipitation (P) and evapotranspiration (ET) are summarized from the
Australian Water Availability Project (AWAP) data product, and terrain metrics (digital elevation model (DEM) and slope) are calculated with the Shuttle Radar
Topography Mission (SRTM) 90 m digital elevation model.
Production Production Production from
Conservation
P ET DEM from Relatively from Dryland Irrigated Intensive
Catchment Code Lat Lon Slope & Natural Water
(mm/year) (mm/year) (m) Natural Agriculture and Agriculture and Uses
Environments
Environments Plantations Plantations
Moonie MOO −28.0 149.5 522 493 243 0.9 9.35% 11.04% 79.27% 0.22% 0.09% 0.03%
Border Rivers BOR −28.8 150.7 612 579 403 2.3 9.42% 26.98% 60.99% 1.87% 0.57% 0.17%
Warrego WAR −26.8 146.2 462 428 339 1.0 8.86% 63.81% 26.67% 0.12% 0.05% 0.50%
Namoi NAM −30.8 149.9 621 588 401 3.2 13.26% 28.36% 55.06% 2.15% 0.79% 0.38%
Paroo PAR −29.1 144.6 303 287 157 0.7 6.25% 87.75% 3.34% 0.02% 0.01% 2.65%
Condamine-Balonne CON-BAL −27.7 148.4 484 456 281 1.0 5.68% 42.52% 50.41% 0.76% 0.24% 0.39%
Lower Darling L-DAR −32.9 142.7 263 238 93 0.8 6.57% 85.82% 2.91% 0.08% 0.04% 4.59%
Lachlan LAC −33.5 146.8 446 418 277 1.8 10.10% 30.58% 57.70% 0.48% 0.26% 0.88%
Lower Murray L-MUR −34.2 140.4 277 262 119 1.2 24.67% 37.84% 34.40% 1.04% 0.44% 1.61%
Murrumbidgee MUR −35.0 146.9 515 459 337 2.8 12.45% 12.18% 71.20% 2.16% 1.22% 0.78%
Upper Murray U-MUR −36.1 147.9 928 691 750 10.4 43.67% 18.62% 36.88% 0.12% 0.57% 0.13%
Ovens OVE −36.6 146.6 908 658 456 9.2 22.60% 29.03% 43.69% 1.17% 3.14% 0.37%
Mitta Mitta MIT −36.6 147.6 987 700 780 12.4 32.62% 42.10% 22.28% 0.11% 0.87% 2.02%
Wimmera WIM −36.2 142.4 352 334 136 1.2 16.64% 2.76% 77.97% 0.10% 1.09% 1.44%
Lodon-Avoca LON −36.1 143.6 379 353 147 1.2 10.63% 5.51% 73.02% 5.27% 4.22% 1.34%
Goulburn-Broken GOU-BRO −36.8 145.6 651 531 321 5.2 14.59% 19.08% 53.87% 6.95% 4.14% 1.38%
Mid Murray M-MUR −35.6 145.0 390 370 101 0.7 9.38% 4.23% 78.36% 6.87% 0.44% 0.72%
Gwydir GWY −29.8 150.1 640 599 404 2.5 8.02% 27.08% 61.75% 2.26% 0.42% 0.47%
Macquarie-Castlereagh MAC-CAS −31.8 148.2 525 500 330 2.1 6.94% 25.55% 65.82% 0.50% 0.72% 0.47%
Campaspe CAM −36.8 144.6 514 454 287 2.3 8.68% 4.40% 67.84% 4.28% 13.99% 0.82%
Barwon-Darling BAR-DAR −31.6 145.1 333 321 157 0.9 8.29% 87.30% 3.81% 0.08% 0.03% 0.49%
Kiewa KIE −36.5 147.1 1085 704 617 10.8 20.85% 29.64% 35.99% 0.60% 9.74% 3.17%Remote Sens. 2019, 11, 1077 5 of 18
2.2. Data and Processing
2.2.1. Reference Data on P and ET
Ideally, the well-distributed instrument data are a good reference for estimating the performance
of the global productions at regional level. Even if in the river basins where there are few instrumented
data available, this kind of exercise can at least help the river basin managers know the varying range
of performances of the global products, their spatial distribution at the catchment level and temporal
distribution in different hydrological months or years. Fortunately, in the MDB, a dataset estimated
by the Australian Water Availability Project (AWAP), which is an operational data assimilation and
modelling system that monitors the state and trend of the terrestrial water balance of the Australian
continent at a spatial resolution of 5 km, has been developed. The system is relatively well calibrated
and validated using independent datasets. Little bias is observed across the range from dry to wet
catchments at both annual and monthly scales [20]. Therefore, this study adopted the P and ET
datasets developed with the AWAP system as “truth data” to assess the global products. In the
system, P (AWAPP ) performed as a major meteorological forcing, where a gridded daily rainfall dataset
compiled by the Bureau of Meteorological and the Commonwealth Scientific and Industrial Research
Organisation (CSIRO) was used. AWAPP has been used in a number of local studies since it provides a
way to consistently characterize the variation of rainfall over space and time for large catchments across
Australia [21]. Meanwhile, ET in the system (AWAPET ) is the sum of its daily-modelled transpiration
plus soil evaporation integrated to a monthly step. The dynamic water balance model (“WaterDyn”)
forced with P, downward solar irradiance and air temperature was used to simulate the changes in
the shallow (thickness 0–0.7 m) and deep (0.2–1.5 m) soil layers and therefore water fluxes across the
boundaries, with ET included. Previous studies have demonstrated its strength of spatial and temporal
continuity [21–23].
2.2.2. Global P and ET Data Products
Four global P products are evaluated against AWAPP in this study (Table 2). The selected products
include (1) the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPSP ), which
incorporates 0.05o resolution satellite imagery with in situ station data to create quasi-global scale
gridded rainfall time series [24]; (2) the simulated P from the Global Land Data Assimilation System
(GLDASP ), which was forced with National Oceanic and Atmospheric Administration (NOAA)/Global
Data Assimilation System (GDAS) atmospheric analysis fields, the disaggregated Global Precipitation
Climatology Project (GPCP) precipitation fields, and the Air Force Weather Agency’s AGRicultural
METeorological modeling system (AGRMET) radiation fields [25]; (3) the estimated P by the Tropical
Rainfall Measuring Mission (TRMMP ) through algorithmically merging microwave data from multiple
satellites [26,27]; and (4) the Precipitation Estimation from Remotely Sensed Information Using Artificial
Neural Networks (PersiannP ), which integrates gridded satellite infrared data and P observations from
the Global Precipitation Climatology Project [28].
ET estimations from four global ET products were also evaluated. The products are (1) the CSIRO
ET(CSIROET ) datasets estimated using an observation-driven Penman–Monteith–Leuning (PML) model;
(2) the GLDAS simulated ET (GLDASET ) which incorporates satellite and ground-based observations;
(3) MODIS ET (MODET ), which is widely known as the MOD16 data collection, is based on the logic of
the Penman–Monteith equation with model inputs primarily derived from the satellite imagery [8,11];
and (4) TerraClimate ET (TerraClimateET ) estimated using a modified Thornthwaite–Mather climatic
water balance model and extractable soil water storage capacity data [29]. Details of the evaluated
data products are listed in Table 2.Remote Sens. 2019, 11, 1077 6 of 18
Table 2. Details of the collected global precipitation and evapotranspiration data products. One degree is equivalent to about 110 km.
Product Coverage Data Availability Descriptions
Precipitation data products
0.05 degrees CHIRPSP incorporates remotely sensed P from five satellite products and more than 20,000 station records
CHIRPSP Daily from 1981. Available at GEE.
Quasi global to calibrate global Cold Cloud Duration rainfall estimates [24].
0.25 degrees Every 3-hours from 2000. Available GLDASP assimilates satellite based observations from AGRMET and in-situ meteorological observations
GLDASP
globally at GEE. from GDAS and CMAP to produce the refined P [25].
TRMMP merges microwave data from multiple sensors. The multi-satellite data are averaged to the
0.25 degrees Monthly from 1998. Available
TRMMP monthly scale and adjusted to the large-area mean of the monthly surface P gauge analysis by GPCC
globally at GEE.
using an inverse estimated-random-error variance weighting method [26,27].
0.25 degrees PersiannP uses an Artificial Neural Network function and applied to the GridSat-B1 (Gridded Satellite
PersiannP Daily from 1983. Available at GEE.
Quasi global infrared data), along with the GPCP version 2.2 data [28].
Evapotranspiration data products
CSIROET uses an observation-driven Penman-Monteith-Leuning (PML) model, supported with
meteorological forcing including daily P, air temperature, vapor pressure, short- and long-wave
Monthly from 1981–2012. Available
CSIROET 0.5 degrees globally downward radiation and wind speed, along with satellite derived vegetation forcing data, land cover data,
at https://data.csiro.au/
emissivity and albedo. The dataset is validated across 643 unregulated catchments using flux tower
measurements and other surface flux [9].
GLDASET is a land surface model simulation in which the estimation is primarily based on empirical
0.25 degrees Every 3-hours from 2000. Available
GLDASET upscaling of space- and ground-based observations. Inputs in driving GLDAS including P, air temperate,
globally at GEE
downward shortwave and longwave radiation, humidity, surface pressure and wind speed [25].
MODET is the terrestrial ET using a remote sensing-based Penman-Monteith algorithm [8,11]. Inputs
500 m Every 8-days from 2001. Available
MODET include the MODIS derived land cover, LAI, fPAR and albedo products, as well as the meteorological
globally at GEE.
reanalysis dataset from the Global Modelling and Assimilation Office of NASA (GMAO).
TerraClimateET is estimated using a one-dimensional modified Thornthwaite-Mather climatic
Monthly from 1958. Available at
TerraClimateET 2.5 minutes globally water-balance model [13]. Inputs for the water balance calculation include precipitation and reference ET,
GEE.
as well as the plant extractable soil water capacity derived from satellite observations [29].Remote Sens. 2019, 11, 1077 7 of 18
2.2.3. Data Processing
All collected data products were uniformly resampled to the same spatial resolution (1 km) and
temporal resolutions (annual and monthly) to make the data products comparable. The original records
were aggregated into both annual and monthly series. Monthly P and ET could reflect the water
input and consumption dynamics and thus provide vital information for multiple water management
purposes (e.g., reallocation, irrigation), while the annual series could provide additional information for
long term management activities, including land and water resource plans for sustainable developments.
The data series were also confined to the period from 2001 to 2016 (except for the CSIROET which
stopped updating in 2013) to meet the purpose of comparisons between the products. Finally, the basin
scale data was extracted for the 22 catchments within the MDB using the catchment boundaries,
which were digitalized from https://www.mdba.gov.au/discover-basin/catchments. Downloading and
resampling of the global P and ET products (except CSIROET ) were conducted in GEE. Manipulation
of AWAPP , AWAPET and CSIROET were conducted using the ArcGIS platform.
2.3. Methods
The data analysis conducted over the P and ET series include three core components:
(1) Comparison between global products and AWAP estimations at the basin scale to check overall
consistencies; (2) comparison at each catchment to identify the catchments where global products
show low, moderate or high levels of disparities against AWAP; and (3) correlation analysis to test the
possible contributions of catchment characteristics to the identified disparities.
2.3.1. Temporal Disparities at Basin Scale
Both annual and monthly P (and ET) at the basin scale aggregated from global data products were
evaluated against AWAPP (and AWAPET ) estimations using a series of statistical metrics. The selected
metrics include the coefficient determination (R2 ), root mean square errors (RMSE) and Nash Sutcliffe
Efficiency index (NSE). R2 examines the overall consistency (e.g., temporal variation patterns) between
two data products. RMSE measures the average magnitude of the estimation errors, with lower RMSE
indicating greater central tendencies and smaller extreme errors. NSE varies from minus infinity to
one where the negative value means poor quality of the estimated values and values closer to one
indicate better matches between reference and estimated values. The metrics are recommended in
previous literature and can be determined according to the following equations [30].
Pn 2
V i − Vi
i=1 est obs
R2 = 1 − P 2 (1)
n i
i=1 Vest − V est
v
n
t
1 X i i
2
RMSE = Vest − Vobs (2)
n
i=1
Pn 2
i − Vi
Vobs
i=1 est
NSE = 1 − P 2 (3)
n i −V
i=1 Vobs obs
where, Vobs stands for the value derived from the AWAP data collection and Vest stands for the
estimations derived from the studied global data products.
2.3.2. Spatio-Temporal Disparities across the Catchments
Due to the varied terrain and climatic conditions across the MDB, it is possible that the level
of temporal disparities in different catchments will be different as well. To reveal the differences,
the collected datasets were aggregated respectively to generate the annual and monthly time seriesRemote Sens. 2019, 11, 1077 8 of 18
for the 22 catchments in the MDB. The data series were then evaluated against AWAPP and AWAPET
estimations by calculating the selected statistical metrics (R2 , RMSE and NSE) within each catchment.
2.3.3. Impacts of Catchment Characteristics
To interpret the different levels of disparities between global products and AWAP estimations
across the 22 MDB catchments, the calculated RMSE for each catchment was selected and overlaid with
the catchment characteristics (e.g., average P and ET levels, terrain variations and land use compositions
in each catchment as listed in Table 1) and the relationships between RMSE and characteristics were
quantified using a Pearson correlation coefficient. A high correlation relationship might indicate
a possible contribution of the identified catchment characteristic to the data disparities from truth
data. For example, a positive correlation between RMSE (for annual ET between global product and
AWAPET ) and DEM implies that large uncertainties are to be expected with the global ET product in
high elevation areas.
Calculation of the statistic metrics and the Pearson correlation coefficients were conducted in the
R software package (R 3.5.1).
3. Results
3.1. Temporal Disparities at Basin Scale
3.1.1. Precipitations
Overall, annual P for the entire MDB was averaged at 449.4 ± 127.2 mm/year, 448.9 ± 100.9 mm/year,
490.7 ± 135.3 mm/year, 487.2 ± 137.1 mm/year and 468.3 ± 137.9 mm/year estimated with AWAPP ,
CHIRPSP , GLDADP , PersiannP and TRMMP , respectively (Table 3). Similar annual and monthly
changing patterns were captured by different products, including the extremely dry years/months in
2002 and 2006 and wet years/months in 2010, 2011 and 2016 (Figure 2). However, it seems CHIRPSP
tends to record a relative narrow range of monthly P and is less sensitive to high and extreme P events
across the studied period (Figure 2 and Figure S3). The selected statistic metrics, with NSE greater than
0.87, R2 close to 1 and RMSE less than 10% of the annual mean in all cases (Table 3), indicates a good
consistency between the global products and the AWAPP at the annual scale. While at the monthly
scale, the two categories (global and AWAPP ) also showed overall good consistency but with relatively
larger estimation errors as indicated with the RMSE recorded up to 19.4% (for CHIRPSP ) of monthly
mean P levels.
Table 3. Comparison statistics of global precipitation data products against AWAP precipitation at the
basin scale.
Annual Monthly
Mean NSE R2 RMSE Mean NSE R2 RMSE
AWAPP 449.40 - - - 37.45 - - -
CHIRPSP 448.88 0.94 0.98 29.20 37.41 0.90 0.95 7.26
GLDASP 490.68 0.87 0.98 45.22 40.89 0.93 0.96 6.19
PersiannP 487.17 0.89 0.99 41.58 40.60 0.94 0.97 5.52
TRMMP 468.28 0.96 1.00 23.30 39.02 0.96 0.97 4.55Mean NSE R RMSE Mean NSE R RMSE
AWAPP 449.40 - - - 37.45 - - -
CHIRPSP 448.88 0.94 0.98 29.20 37.41 0.90 0.95 7.26
GLDASP 490.68 0.87 0.98 45.22 40.89 0.93 0.96 6.19
PersiannP 487.17 0.89 0.99 41.58 40.60 0.94 0.97 5.52
Remote Sens. 2019, 11, 1077
TRMMP 468.28 0.96 1.00 23.30 39.02 0.96 0.97 4.55 9 of 18
Figure 2. Monthly average precipitation in Murray Darling Basin presented with different precipitation
Figure 2. Monthly average precipitation in Murray Darling Basin presented with different
products.
precipitation products.
3.1.2. Evapotranspiration
3.1.2. Evapotranspiration
Larger differences were observed among the ET products than in the above-obtained P series
Larger differences
comparisons. were
Overall, the observed
average among
annual ET the ET within
levels products
thethan
MDBin are
the 417.0
above-obtained P series
± 75.5 mm/year,
comparisons.
404.0 Overall,451.6
± 67.1 mm/year, the average annual278.5
± 84 mm/year, ET levels
± 55.9within the MDB
mm/year are 417.0
and 410.6 ± 98.0± mm/year,
75.5 mm/year, 404.0
recorded
by AWAPET , CSIROET , GLDASET , MODET , and TerraClimateET products, respectively (Table 4), whereby
± 67.1 mm/year, 451.6 ± 84 mm/year, 278.5 ± 55.9 mm/year and 410.6 ± 98.0 mm/year, recorded
AWAPestimation
MOD ET, CSIROET, GLDASET, MODET, and TerraClimateET products, respectively (Table 4), where
is substantially lower than the other four products. Overall similar annual changing
ET
MOD
patterns were observed with all products (Figure S4), which lead to the high R2 (>similar
ET estimation is substantially lower than the other four products. Overall annual
0.84 for the
4 global products) between global ET products and AWAPET . Only negative (for MODET ) to moderate
positive NSE levels (for CSIROET , GLDASET and TerraClimateET ) were observed due to the substantial
differences in absolute ET levels obtained with different data products. The phenomenon was more
apparent with the monthly profiles, where the temporal fluctuations differed among the data products,
both in terms of the magnitude of absolute monthly ET and timing of peak ET levels of the year
(Figure 3), which contributed to the overall decreased R2 and NSE but increased RMSE levels.
Table 4. Comparison statistics of global evapotranspiration data products against AWAP
evapotranspiration at the basin scale.
Annual Monthly
Mean NSE R2 RMSE Mean NSE R2 RMSE
AWAPET 417.04 - - - 34.99 - - -
CSIROET 404.05 0.83 0.91 33.17 33.97 0.78 0.80 7.06
GLDASET 451.62 0.73 0.97 37.79 37.90 0.77 0.83 6.83
MODET 278.52 −2.75 0.87 141.63 23.32 −0.31 0.38 16.15
TerraClimateET 410.57 0.68 0.84 41.25 34.53 0.35 0.62 11.38Mean NSE R2 RMSE Mean NSE R2 RMSE
AWAPET 417.04 - - - 34.99 - - -
CSIROET 404.05 0.83 0.91 33.17 33.97 0.78 0.80 7.06
GLDASET 451.62 0.73 0.97 37.79 37.90 0.77 0.83 6.83
MODET 278.52 −2.75 0.87 141.63 23.32 −0.31 0.38 16.15
Remote Sens. 2019,TerraClimate
11, 1077 ET 410.57 0.68 0.84 41.25 34.53 0.35 0.62 11.38 10 of 18
Figure3.3. Monthly
Figure Monthly average
average evapotranspiration
evapotranspiration in
in Murray
Murray Darling
Darling Basin
Basinpresented
presentedwith
withdifferent
different
precipitation products.
precipitation products.
3.2. Spatio-Temporal Disparities Across the Catchments
3.2. Spatio-Temporal Disparities Across the Catchments
3.2.1. Precipitations
3.2.1. Precipitations
When it comes to each catchment within the MDB, the P products showed varied performances
When
(Figures 4 andit comes
5). Fromto an
each catchment
annual withinthe
perspective, thefour
MDB, the P products
products showed
present overall varied
high performances
correlations with
(Figure 4 and 2 Figure 5). From an annual perspective, the four products 2 present
AWAPP , with R higher than 0.9 in most cases, except for the relatively low R values observed with overall high
correlations with AWAP P, with R2 higher than 0.9 in most cases, except for the
GLDASP in Gwydir (R = 0.74), with PersiannP in Gwydir (R = 0.78) and Border Rivers (R = 0.81).
2 2 relatively low R
2 2 values
observed
The with GLDAS
catchments P in Gwydir (R2 = 0.74), with PersiannP in Gwydir (R2 = 0.78) and Border Rivers
(e.g., Mitta Mitta, Upper-Murray, Mid-Murray, Goulburn Broken, Wimmera etc.)
(R = 0.81).
2
located in theThe catchmentspart
southeastern (e.g., Mitta
of the Mitta,
basin are Upper-Murray, Mid-Murray,
observed with higher Goulburn
RMSE values Broken,
(Figure 4).
Wimmera etc.) located in the southeastern part of the basin are observed
The catchments showing relative higher RMSE in CHIRPSP are Ovens (RMSE = 295.52 mm), Kiewa with higher RMSE values
(Figure=4).
(RMSE The catchments
199.31 mm) and Mitta showing
(RMSErelative higher
= 140.22 RMSE
mm), in CHIRPS
whereas P are Ovens (RMSE = 295.52
the rest of the catchments have
mm), Kiewa (RMSE = 199.31 mm) and Mitta (RMSE = 140.22
RMSE values well below 70 mm. While for GLDASP , Kiewa (341.04 mm), Mitta mm), whereas the rest of the
Mitta catchments
(198.58 mm),
have RMSE values well below 70 mm. While for GLDAS P, Kiewa (341.04 mm), Mitta Mitta (198.58
Ovens (190.46 mm), Upper-Murray (158.82 mm), Wimmera (128.56 mm), Border Rivers (116.82 mm),
mm), Ovens (116.94
Mid-Murray (190.46 mm)
mm),and Upper-Murray
Namoi (115.9 (158.82
mm) mm), Wimmera
all presented (128.56 high
relatively mm),RMSE
Bordervalues.
Rivers As
(116.82
for
mm), Mid-Murray (116.94 mm) and Namoi (115.9 mm) all presented relatively
PersiannP , the above listed high GLDASP RMSE catchments showed a similar high RMSE as well. high RMSE values.
TRMMP performs better with lower RMSE levels compared to the other three products, a high RMSE
with TRMMP was only observed in Ovens (341.15 mm) and Kiewa (133.69 mm). NSE further captured
the variations within GLDASP and PersiannP , especially in the southern catchments, where GLDASP
and PersiannP NSE values are substantially lower than those for CHIRPSP and TRMMP . Specifically,
NSE values for GLDASP are lower than 0.5 in 10 out of the 22 catchments, typically in Border Rivers
(−0.03), Wimmera (−0.69) and Kiewa (−0.41) where negative NSE values are observed. Negative NSE
also observed with PersiannP in Wimmera (−0.4) and Kiewa (−0.65).well. TRMMP performs better with lower RMSE levels compared to the other three products, a high
RMSE with TRMMP was only observed in Ovens (341.15 mm) and Kiewa (133.69 mm). NSE further
captured the variations within GLDASP and PersiannP, especially in the southern catchments, where
GLDASP and PersiannP NSE values are substantially lower than those for CHIRPSP and TRMMP.
Specifically, NSE values for GLDASP are lower than 0.5 in 10 out of the 22 catchments, typically in
Remote Sens. 2019, 11, 1077 11 of 18
Border Rivers (−0.03), Wimmera (−0.69) and Kiewa (−0.41) where negative NSE values are observed.
Negative NSE also observed with PersiannP in Wimmera (−0.4) and Kiewa (−0.65).
Bar plots
plots show
show the
the statistics
statistics (coefficient
(coefficient determination
determination (R 2 root mean square errors (RMSE)
Figure
Figure 4. 4. Bar (R2),), root mean square errors (RMSE)
and
and Nash
Nash Sutcliffe
Sutcliffe Efficiency
Efficiency index
index (NSE)).
(NSE)). Ofofannual
annualPPestimated
estimatedwith
withCHIRPS
CHIRPS P P, ,GLDAS
GLDASPP,, Persiann
PersiannPP
and TRMM products within the 22 catchments of the MDB when compared
and TRMMPP products within the 22 catchments of the MDB when compared with the annual with the annual AWAPP
AWAPP
values.
values.
Remote The embedded
The
Sens. 2019, embedded barREVIEW
bar
11, x FOR PEER plots with
plots with numbers
numbers at the upper-left
at the upper-left corners
corners areare the
the scales
scales forfor interpreting
interpreting
12 of 19
the bars associated with each catchment.
the bars associated with each catchment.
Figure 5 presented the comparisons of the detailed monthly trends of the four P products with
the monthly AWAPP series. Overall, the comparison between monthly values presented lower R2 and
NSE values compared to the annual results, which might indicate a different capability of the
products in capturing seasonal P variations. Specifically, CHIRPSP and TRMMP showed high
consistency with monthly AWAPP, with R2 values higher than 0.9 in most of the comparisons. While
for GLDASP and PersiannP, most of the R2 values are lower than 0.9, RMSE results again showed that
southeastern catchments showed relatively higher RMSE values, especially for GLDASP (in Upper-
Murray, Ovens, Mitta Mitta and Kiewa) and PersiannP (in Upper Murray, Ovens, Mitta Mitta and
Kiewa, as well). NSE values for CHIRPSP ranged from 0.75 to 1, with relative fewer variations across
the catchments. NSE for GLDASP ranged from 0.37 to 0.92, which is much lower than the GLDASP
NSE values estimated with annual results. The lowest GLDASP NSE values were observed in Kiewa
(0.37), Mitta Mitta (0.54) and Wimmera (0.59). Monthly PersiannP also received much lower NSE
values with lowest values observed in Kiewa (0.37), Mitta Mitta (0.58) and Upper Murray (0.58).
Figure
Persiann 5. Bar plots showing the statistics (R2 , RMSE and NSE) of monthly P estimated with CHIRPS ,
P showed higher performance from2 the NSE aspect with NSE values greater than 0.86Pin the
Figure 5. Bar plots showing the statistics (R , RMSE and NSE) of monthly P estimated with CHIRPSP,
GLDASP , PersiannP and TRMMP products within the 22 catchments of the MDB when compared with
22 catchments.
GLDASP, PersiannP and TRMMP products within the 22 catchments of the MDB when compared with
the monthly AWAPP values. The embedded bar plots with numbers at the upper-left corners are the
the monthly AWAPP values. The embedded bar plots with numbers at the upper-left corners are the
scales for interpreting the bars associated with each catchment.
scales for interpreting the bars associated with each catchment.
Figure 5 presented the comparisons of the detailed monthly trends of the four P products with the
3.2.2. Evapotranspiration
monthly AWAPP series. Overall, the comparison between monthly values presented lower R2 and
Figurecompared
NSE values 6 shows the comparison
to the statistics
annual results, when
which decomposing
might indicate athe basin scale
different annual
capability ofET
theinto the
products
incatchment
capturing scale. Most
seasonal comparisons
P variations. between the
Specifically, four ET
CHIRPS P products
and TRMM andP AWAP
showed ET obtained
high R 2 values
consistency with
greater than 0.8, which 2 indicated that the products presented overall similar
monthly AWAPP , with R values higher than 0.9 in most of the comparisons. While for GLDASP and annual variation
patterns.
Persiann Mitta Mitta is observed
2 to have the lowest R2 values for CSIROET (0.39), GLDASET (0.52) and
P , most of the R values are lower than 0.9, RMSE results again showed that southeastern
MODET (0.43).
catchments showedRMSE for thehigher
relatively four products in different
RMSE values, catchments
especially for GLDAS showed that MODET has the
P (in Upper-Murray, Ovens,
Mitta Mitta and Kiewa) and PersiannP (in Upper Murray, Ovens, Mitta Mitta andBorder
highest RMSE levels, especially in the northern catchments (including Moonie, Kiewa,Rivers,
as well).
Warrego, Namoi, Paroo and Gwydir) where RMSEs are greater than 200
NSE values for CHIRPSP ranged from 0.75 to 1, with relative fewer variations across ET mm. GLDAS theis catchments.
observed
to have
NSE the second largest RMSE levels, with relatively higher values in southern catchments (e.g.,
for GLDAS P ranged from 0.37 to 0.92, which is much lower than the GLDASP NSE values estimated
Wimmera, Mid-Murray and Campaspe). Similarly, NSE values for MODET are significantly lower
with annual results. The lowest GLDASP NSE values were observed in Kiewa (0.37), Mitta Mitta
than the other three products, where negative NSE levels were observed in 16 out of the 22
(0.54) and Wimmera (0.59). Monthly PersiannP also received much lower NSE values with lowest
catchments. Even though fewer negative NSE values are observed with CSIROET (2), GLDASET (8)
values observed in Kiewa (0.37), Mitta Mitta (0.58) and Upper Murray (0.58). PersiannP showed higher
and TerraClimateET (3), the NSE values for most of the comparisons are relatively low (e.g., lower
performance from the NSE aspect with NSE values greater than 0.86 in the 22 catchments.
than 0.5, although some of them have high R2), through which we can infer that large variations exist
within the ET products in the catchments which might be explained by their capabilities in capturing
extremely high or low ET levels.Figure 5. Bar plots showing the statistics (R2, RMSE and NSE) of monthly P estimated with CHIRPSP,
GLDASP, PersiannP and TRMMP products within the 22 catchments of the MDB when compared with
the monthly AWAPP values. The embedded bar plots with numbers at the upper-left corners are the
Remote Sens. 2019, 11, 1077 12 of 18
scales for interpreting the bars associated with each catchment.
3.2.2. Evapotranspiration
3.2.2. Evapotranspiration
Figure 6 shows the comparison statistics when decomposing the basin scale annual ET into the
Figure 6 shows the comparison statistics when decomposing the basin scale annual ET into the
catchment scale. Most comparisons between the four ET products and AWAPET obtained R2 values
catchment scale. Most comparisons between the four ET products and AWAPET obtained R2 values
greater than 0.8, which indicated that the products presented overall similar annual variation
greater than 0.8, which indicated that the products presented overall similar annual variation patterns.
patterns. Mitta Mitta is observed to have the lowest R2 values for CSIROET (0.39), GLDASET (0.52) and
Mitta Mitta is observed to have the lowest R2 values for CSIROET (0.39), GLDASET (0.52) and MODET
MODET (0.43). RMSE for the four products in different catchments showed that MODET has the
(0.43). RMSE for the four products in different catchments showed that MODET has the highest RMSE
highest RMSE levels, especially in the northern catchments (including Moonie, Border Rivers,
levels, especially in the northern catchments (including Moonie, Border Rivers, Warrego, Namoi, Paroo
Warrego, Namoi, Paroo and Gwydir) where RMSEs are greater than 200 mm. GLDASET is observed
and Gwydir) where RMSEs are greater than 200 mm. GLDASET is observed to have the second largest
to have the second largest RMSE levels, with relatively higher values in southern catchments (e.g.,
RMSE levels, with relatively higher values in southern catchments (e.g., Wimmera, Mid-Murray and
Wimmera, Mid-Murray and Campaspe). Similarly, NSE values for MODET are significantly lower
Campaspe). Similarly, NSE values for MODET are significantly lower than the other three products,
than the other three products, where negative NSE levels were observed in 16 out of the 22
where negative NSE levels were observed in 16 out of the 22 catchments. Even though fewer negative
catchments. Even though fewer negative NSE values are observed with CSIROET (2), GLDASET (8)
NSE
and values are observed
TerraClimate with CSIROET (2),
ET (3), the NSE values for GLDAS
most ofETthe(8)comparisons
and TerraClimate ET (3), the
are relatively NSE
low values
(e.g., for
lower
most R 2
than 0.5, although some of them have high R ), through which we can infer that large variations exist ),
of the comparisons are relatively low (e.g.,
2 lower than 0.5, although some of them have high
through which
within the we can infer
ET products that
in the large variations
catchments which exist
mightwithin the ET products
be explained in the catchments
by their capabilities which
in capturing
might be explained
extremely by their
high or low capabilities in capturing extremely high or low ET levels.
ET levels.
Figure 6. Bar plots showing the statistics (R2 , RMSE and NSE) of annual ET estimated with CSIROET ,
GLDASET , MODET and TerraClimateET products within the 22 catchments of the Murray Darling
Basin when compared with the annual AWAPET levels. The embedded bar plots with numbers at the
upper-left corners are the scales for interpreting the bars associated with each catchment.
Monthly comparisons between the ET products provided further insights as displayed in Figure 7.
Overall, CSIROET and GLDASET presented better correlations with AWAPET evidenced with higher
R2 values across all 22 catchments. Lower R2 values with MODET and TerraClimateET are typically
observed in northern (e.g., Border Rivers, Moonie and Gwydir) and western (e.g., Barwon-Darling,
Lower Darling and Lower Murray) catchments where R2 range from about 0.3 to 0.6. RMSEs associated
with comparisons of CSIROET and GLDASET to AWAPET are relatively uniform across the catchments
(around 10 mm) while larger RMSEs are observed with MODET and TerraClimateET , especially for
northern catchments including Warrego, Condamine-Balonne, Moonie, Border Rivers, Gwydir and
Namoi. Overall lower NSE values are also observed with the monthly ET profiles estimated with
the four products, but significant lower values are obtained by MODET and TerraClimateET in most
catchments except several southeastern catchments (including Upper Murray, Mitta Mitta, Goulburn
Broken, Ovens and Campaspe).catchments (around 10 mm) while larger RMSEs are observed with MODET and TerraClimateET,
especially for northern catchments including Warrego, Condamine-Balonne, Moonie, Border Rivers,
Gwydir and Namoi. Overall lower NSE values are also observed with the monthly ET profiles
estimated with the four products, but significant lower values are obtained by MODET and
TerraClimate
Remote Sens. 2019,ET11,in most catchments except several southeastern catchments (including Upper
1077 13 of 18
Murray, Mitta Mitta, Goulburn Broken, Ovens and Campaspe).
Figure 7. Bar plots showing the statistics (R2 , RMSE and NSE) of monthly ET estimated with CSIROET ,
Figure 7. Bar plots showing the statistics (R2, RMSE and NSE) of monthly ET estimated with CSIROET,
GLDASET , MODET and TerraClimateET products within the 22 catchments of the Murray Darling
GLDASET, MODET and TerraClimateET products within the 22 catchments of the Murray Darling Basin
Basin when compared with the monthly AWAPET levels. The embedded bar plots with numbers at the
when compared with the monthly AWAPET levels. The embedded bar plots with numbers at the
upper-left corners are the scales for interpreting the bars associated with each catchment.
upper-left corners are the scales for interpreting the bars associated with each catchment.
3.3. Impacts of Catchment Characteristics
3.3. Impacts of Catchment Characteristics
Correlations of RMSE for each pair of global P products and AWAPP at both annual and monthly
scalesCorrelations of RMSE
with catchment for each pairare
characteristics of summarized
global P products and AWAP
in Figure 8. TheP at both annual
annual and monthly
result indicated that
data products showing higher RMSE values are generally associated with catchments with athat
scales with catchment characteristics are summarized in Figure 8. The annual result indicated high
data products
annual P level, showing
and RMSE higher RMSE
is highly values are
correlated withgenerally associated
the greater with catchments
terrain fluctuations with a high
characterized with
annual P level, and RMSE is highly correlated with the greater terrain fluctuations characterized
a high DEM as well as larger DEM variations (indicated with average slope levels). This is especially with
a high
true forDEM
GLDASas well as larger DEM variations (indicated with average slope levels). This is especially
P and PersiannP , which indicated that these two data products are more sensitive to
true for GLDAS P and PersiannP, which indicated that these two data products are more sensitive to
changes in elevation. While most land use types do not show high correlations with the statistics,
changes
the in elevation.
“1 conservation While
and most
natural land use typesand
environments” do “5
notIntensive
show high correlations
uses” presentedwith the statistics,
a consistent the
moderate
“1 conservation and natural environments” and “5 Intensive uses” presented a consistent
positive correlation with RMSEs associated with the four P products. The above phenomenon is more moderate
positive correlation with RMSEs associated with the four P products. The above phenomenon is more
apparently indicated with the correlation analysis between RMSE derived from the monthly P series
apparently indicated with the correlation analysis between RMSE derived from the monthly P series
and catchment characteristics. Catchments characterized by relatively high P levels, high altitude and
and catchment characteristics. Catchments characterized by relatively high P levels, high altitude and
large terrain variations and more distribution of land use type 1 and 5 tend to deliver high RMSEs
large terrain variations and more distribution of land use type 1 and 5 tend to deliver high RMSEs
according to the correlation coefficients.
according to the correlation coefficients.
The situation is more complex and revealed with the correlation analysis between statistics of
ET products and catchments’ characteristics (Figure 9). From an annual perspective, RMSE levels
for CSIROET , MODET and TerraClimateET are observed to positively correlate with the location of
the catchments (latitude and longitude), more eastern and northern catchments tend to have higher
RMSE levels, while GLDASET presented the opposite trend. Additionally, RMSE values are positively
correlated with P and ET levels for CSIROET and TerraCliamteET , which indicated that catchments
with higher P, and thus higher ET, would yield higher uncertainties for the ET products. High altitude
located catchments also tends to have higher uncertainties supported with the positive correlation
coefficients between RMSE and DEM (and Slope). The high positive correlation between MODET
RMSE levels with latitude and longitude, well represented in the north eastern catchments, showing
significant higher RMSE values as previously identified. Similar impacts of land use composites
on CSIROET and TerraClimateET are observed where catchments with more water (land use type
6) distributions tend to have lower RMSE levels. For GLDASET , RMSE in annual ET is negatively
correlated with land use type 2 but positively correlated with land use type 3 and 4 areas, whereas for
MODET , RMSE in annual ET is negatively correlated with land use type 1, 4, 5 and 6 areas. Similar
correlations between RMSE and catchment characteristics are also observed with the monthly ET series.Remote Sens. 2019, 11, 1077 14 of 18
Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 19
Figure 8. Heatmap
Figure 8. Heatmap ofof
pairwise
pairwisecorrelation
correlation(Pearson)
(Pearson) values RMSEofofprecipitation
betweenRMSE
values between precipitation (columns)
(columns)
andand
the the
catchment
catchment characteristics (rows) for the 22 catchments in the Murray Darling Basin. Numbers
Remote Sens. 2019, 11, x FORcharacteristics
PEER REVIEW (rows) for the 22 catchments in the Murray Darling Basin. Numbers 15 of 19
in the grids show the correlation coefficients.
in the grids show the correlation coefficients.
The situation is more complex and revealed with the correlation analysis between statistics of
ET products and catchments’ characteristics (Figure 9). From an annual perspective, RMSE levels for
CSIROET, MODET and TerraClimateET are observed to positively correlate with the location of the
catchments (latitude and longitude), more eastern and northern catchments tend to have higher
RMSE levels, while GLDASET presented the opposite trend. Additionally, RMSE values are positively
correlated with P and ET levels for CSIROET and TerraCliamteET, which indicated that catchments
with higher P, and thus higher ET, would yield higher uncertainties for the ET products. High
altitude located catchments also tends to have higher uncertainties supported with the positive
correlation coefficients between RMSE and DEM (and Slope). The high positive correlation between
MODET RMSE levels with latitude and longitude, well represented in the north eastern catchments,
showing significant higher RMSE values as previously identified. Similar impacts of land use
composites on CSIROET and TerraClimateET are observed where catchments with more water (land
use type 6) distributions tend to have lower RMSE levels. For GLDASET, RMSE in annual ET is
negatively correlated with land use type 2 but positively correlated with land use type 3 and 4 areas,
whereas for MODET, RMSE in annual ET is negatively correlated with land use type 1, 4, 5 and 6 areas.
Similar correlations between RMSE and catchment characteristics are also observed with the monthly
ET series.
Figure Heatmap
9. 9.
Figure Heatmapofofpairwise
pairwisecorrelation
correlation (Pearson) values between
(Pearson) values RMSEofofevapotranspiration
betweenRMSE evapotranspiration
(columns) and the catchment characteristics (rows) for the 22 catchments in the Murray Darling
(columns) and the catchment characteristics (rows) for the 22 catchments in the Murray Darling Basin.
Basin.
Numbers in the grids show the correlation coefficients.
Numbers in the grids show the correlation coefficients.
4. Discussion
4. Discussion
4.1. Evaluation of Global P Products
4.1. Evaluation of Global P Products
Comparison of the four P products indicated broadly similar temporal variations and spatial
Comparison of the four P products indicated broadly similar temporal variations and spatial
distribution
distributionof of
rainfall within
rainfall withinthe
theMDB.
MDB.Results
Results show
show that CHIRPSPPand
that CHIRPS andTRMM
TRMM P presented
P presented
overall
overall
better consistency with 2
R2, ,lower RMSEand NSE
better consistency withAWAP
AWAP P Pas
asindicated
indicated with
with higher
higher R lowerRMSE andhigh
highNSE associated
associated
with both annual and monthly data series. Both products (CHIRPS and
with both annual and monthly data series. Both products (CHIRPSP and TRMMP) are generated
P TRMM P ) are generated
with
with intensive information derived from microwave P sensors which seems
intensive information derived from microwave P sensors which seems to reproduce rainfall betterto reproduce rainfall
better across
across the MDB.
the MDB. Microwave
Microwave sensors sensors
estimate estimate rainfall
rainfall from from microwave
microwave radiation
radiation which which is
is recognized
recognized
as a moreasrobust
a moreway
robust way of estimating
of estimating rainfall
rainfall [31]. This [31].
might This
alsomight also contribute
contribute to the fewer to the fewer
spatial
disparities
spatial associated
disparities with thewith
associated two products observed across
the two products the catchments.
observed across the Conversely,
catchments.theConversely,
infrared
thesensor information,
infrared on which Persiann
sensor information, on which P is based, shows
Persiann the most
P is based, apparent
shows the differences
most apparent (Figure 4 and
differences
Figure 5). Infrared sensors relate surface P to the brightness and temperature of the cloud tops,
however there are complex processes and high uncertainties from the cloud information into rainfall
especially for the regions with high cloudiness and abundant rainfall, which might cause these
disparities. This is supported with the evidence that most catchments showing high RMSE and low
NSE values with PersiannP are located in the south eastern part of the MDB, where the regions areRemote Sens. 2019, 11, 1077 15 of 18
(Figures 4 and 5). Infrared sensors relate surface P to the brightness and temperature of the cloud
tops, however there are complex processes and high uncertainties from the cloud information into
rainfall especially for the regions with high cloudiness and abundant rainfall, which might cause these
disparities. This is supported with the evidence that most catchments showing high RMSE and low
NSE values with PersiannP are located in the south eastern part of the MDB, where the regions are
subjected to favourable rainfall topography with greater elevation changes and very likely anomalies
exist at the cloud tops across the MDB. This statement is further supported with the overall high
correlation between RMSE and terrain characteristics (DEM and slope, Figure 8) (which implies the
challenge of capturing orographic precipitations for all products) where PersiannP presented relative
higher correlations (i.e., more susceptible to terrain variations). Similar findings are also available in
some recent publications where the authors concluded that microwave-based precipitation estimation
outperform infrared-based estimations [32]. GLDASP , which incorporates both microwave and infrared
sensors derived P estimations (https://ldas.gsfc.nasa.gov/gldas/), has a performance located between
those of CHIRPSP , TRMMP and PersiannP .
It is interesting to note that the land use type 1 (conservation and natural environment) tends to
impact the performance of the P products. A possible explanation to this is the landscape patches,
which are normally covered with moderate to dense forests distributed discontinuously across the
catchments, impact local climate. It is also worth mentioning that, in remote catchments in the
western part of the MDB (low-lying, less terrain variation and few vegetation cover, Table 1), all P
products presented consistently high performances, which indicates the effectiveness of satellite-based
P estimations in reproducing low surface P with few field observations.
4.2. Evaluation of ET Products
Relatively higher disparities in the dry months during the studied period and relatively
good consistency between products in catchments with abundant P (e.g., Kiewa, Upper Murray,
Mitta Mitta in Figure 7) but poor consistency in less humid catchments (e.g., Warrego, Barwon-Darling,
Condamine-Balonne and other catchments in the northern part of the MDB in Figure 7) were observed.
These findings indicate that the products with different capabilities in capturing ET values might be
more sensitive in arid situations. Both annual and monthly MODET underestimated ET levels in almost
every catchment. This is in agreement with several previous studies assessing the performance of
MODET under various climatic conditions [19]. MODET gives priority to vegetation covered landscapes.
This might also partly explain the positive correlation between R2 (and NSE) derived with monthly
MODET and percentage of natural vegetation coverage (land use type 1) within the catchments, where
MODET captured water losses better in the surfaces that were well covered by vegetation.
As we discussed above, the ET products chosen in this study utilize two different methods:
The hydrological method (TerraClimateET and AWAPET ) and the hydro-meteorological method
(MODET and CSIROET ). There are substantial disparities in ET estimations between these two
groups, especially evidenced by the monthly series across the 22 catchments (Figure 7), which indicated
the importance of appropriately parameterizing the involved processes. Apparent disparities also
exist between products created using the same method. For instance, TerraClimateET used a similar
water balance method as AWAP for deriving water budget related components. The difference
between the two is; TerraClimateET used a simplified one-dimensional Thornthwaite–Mather climatic
water-balance model [13] while AWAP employed a two-layer model to better represent the intensive
exchange of surface and deep water within the MDB [14]. This could partly explain the higher
disparities between TerraClimateET and AWAPET (indicated with higher RMSE and negative NSE
values) in the northeastern catchments (Figure 7) where ground water plays an important role in
supporting local dry land agricultural activities (Land use type 3 in Figure 1). For the two products
based on the Penman–Monteith algorithm, CSIROET outperforms MODET in almost all catchments at
both the annual and monthly scale (Figures 6 and 7). This might be attributed to the input datasets
applied in CSIROET which better reflected the characteristics of the Australian territory, especially inYou can also read
