An assessment of uncalibrated CLIGEN in Australia

Agricultural and Forest Meteorology 119 (2003) 131–148

An assessment of uncalibrated CLIGEN in Australia B. Yu∗ Faculty of Environmental Sciences, Griffith University, Nathan, Qld 4111, Australia Accepted 26 May 2003

Abstract Daily weather data are required as climate input to many models that continuously simulate natural resources systems. CLIGEN is a stochastic weather generator to produce 10 daily weather variables to meet this need. CLIGEN has primarily been used to provide climate input for the process-based runoff and soil erosion prediction model WEPP (water erosion prediction project). Runoff and erosion prediction is particularly sensitive to the four precipitation variables generated by CLIGEN. Weather data for 43 sites representing all major climate zones around Australia were used to prepare input parameter files for CLIGEN to generate 100 years climate data for each of these sites. The quality of generated precipitation variables was assessed in terms of (1) simulated runoff and soil loss for each of three soils under bare fallow conditions with WEPP, (2) climate inputs for the revised universal soil loss equation (RUSLE), and (3) a comparison with published rainfall intensity maps for fixed average recurrence interval and duration. This paper shows that uncalibrated CLIGEN can generate the required climate data for WEPP for these sites. Model efficiency between predicted runoff and soil loss using CLIGEN-generated and observed precipitation data is in excess of 0.95. Generated rainfall erosivity for RUSLE is systematically higher than (about 50% for the R-factor and 25% for the 10-year storm erosivity index) and closely related to the measured erosivity for these sites (r 2 = 0.88–0.94), a trend consistent with what is observed for sites in the United States. CLIGEN can also be used to predict the seasonal distribution of rainfall erosivity quite well for all climate zones in Australia. Detailed analysis of the observed 6 min intensity data for these sites shows that over-prediction of rainfall erosivity and rainfall intensity at short time scales in general is an outcome of the particular storm pattern adopted in CLIGEN for WEPP, not an intrinsic deficiency of CLIGEN per se. © 2003 Elsevier B.V. All rights reserved. Keywords: WEPP; RUSLE; Runoff; Erosion; Weather generator

1. Introduction Daily weather data are commonly required as climate input to simulation models in agriculture and forestry. Historical observations are often short, incomplete, or simply unavailable. Thus there is a need to generate synthetic weather sequences that statistically preserve the mean and variations found in historical observations. CLIGEN is such a stochastic weather generator to produce long-term daily climate files for ∗ Tel.: +61-7-3875-5258; fax: +61-7-3875-7459. E-mail address: [email protected] (B. Yu).

hydrologic models. In particular, CLIGEN was developed, inter alia, for WEPP (water erosion prediction project) to predict runoff, soil erosion and sediment delivery at the hillslope and watershed scales (Nicks et al., 1995; Flanagan and Nearing, 1995; Laflen et al., 1997). WEPP is a useful tool for natural resources inventory and land use assessment in agriculture and forestry (Laflen et al., 1997; Elliot, 2002). CLIGEN was based on weather generators used in EPIC and SWRRB models (Williams et al., 1984; Arnold et al., 1990; Nicks et al., 1995). To meet WEPP’s climate data requirements, the capacity to simulate intensity patterns within a storm was introduced (Nicks et al.,

0168-1923/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0168-1923(03)00141-2

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B. Yu / Agricultural and Forest Meteorology 119 (2003) 131–148

1995). A critical coding error in generating peak intensity was uncovered, subsequently the algorithm for storm generation was modified, and relevant parameters re-calibrated (Yu, 2000). The entire CLIGEN program has been re-coded recently with its structure simplified and readability improved (Meyer, 2001). At the same time CLIGEN parameter database was checked for its quality, errors corrected, its size expanded to include about 2600 sites in the United States (Flanagan et al., 2001). Unlike several other weather generators such as WGEN, USCLIMATE, and WM2 (Richardson and Wright, 1984; Hanson et al., 1994; Hansen and Mavromatis, 2001), unique to CLIGEN is its ability to generate storm duration, peak rain intensity, and time to peak. These are required for predicting runoff amount and peak runoff rate which in turn determine the amount of soil erosion during runoff events. In CLIGEN, 10 weather variables are generated for each day of the simulation period, namely precipitation depth and duration, peak storm intensity and time to peak, minimum and maximum temperatures, dew-point temperature, solar radiation, wind speed and direction. Of these, the four precipitation-related variables are of particular importance because previous studies have shown that predicted runoff and soil loss are most sensitive to these precipitation variables (Nearing et al., 1990; Chaves and Nearing, 1991). The quality of CLIGEN-generated daily precipitation amount, minimum and maximum temperatures, and solar radiation has been assessed, and CLIGEN has been found to perform reasonably well in comparison to other stochastic generators such as USCLIMATE (Johnson et al., 1996). The assessment of the quality of generated storm duration, peak intensity and time to peak has been limited because there are not any suitable alternative generators that would allow meaningful comparisons. Nicks and Gander (1994) reported that an isoerodent map calculated by CLIGEN for the eastern United States was broadly similar to the published isoerodent map in Agriculture Handbook no. 537 on the universal soil loss equation (USLE; Wischmeier and Smith, 1978), although no details were given as to how this was achieved. Baffaut et al. (1996) tested the sensitivity of CLIGEN parameters and the effect of simulation periods on predicted runoff and soil loss at a number of sites in the United States, and found that the intensity parameter had no effect on predicted runoff

and soil loss. Headrick and Wilson (1997) evaluated CLIGEN for a few sites in Minnesota in the United States, and noted that CLIGEN performed badly for time intervals less than 24 h. Arnold and Elliot (1996) and Elliot and Arnold (2001) tested the CLIGEN with rainfall data for two sites in Uganda, Africa, and observed that the average storm duration was consistently around 3 h (2.96–3.28 h) for all months in spite of the large variation in the observed mean storm duration (1.64–5.20 h). Some of these strange observations and conclusions can be explained by the coding error in CLIGEN (Yu, 2000). Yu (2000) tested the modified CLIGEN for 14 sites in the United States and found that CLIGEN was able to reproduce statistically similar precipitation variables in terms of predicted runoff and soil loss. Yu (2002) predicted the R-factor and other climate inputs for the revised universal soil loss equation (RUSLE, Renard et al., 1997) using CLIGEN-generated climate files and found that while the measured and generated R-factor were highly correlated, the generated R-factor values were systematically higher than the corresponding measured R-factor values. CLIGEN has not been widely tested outside the United States, although CLIGEN has been used to generate climate input files for WEPP at two sites in Australia (Yu et al., 2000; Yu and Rosewell, 2001), four sites in Brazil (Favis-Mortlock and Guerra, 1999; De Maria et al., 2001), and about two sites in the UK (Favis-Mortlock, 1994; Brazier et al., 2001; Mintae Choi, personal communication). It is easier to use CLIGEN in the US because CLIGEN input parameter files have been prepared for about 2600 sites (Flanagan et al., 2001). Elsewhere in the world, processing historical weather data is required to prepare these input files. Once the parameter files are prepared, CLIGEN can be used without calibration to generate climate files to predict runoff and soil loss with WEPP and provide necessary climate inputs for RUSLE. Alternative approaches are available to generate sub-daily rainfall intensity sequences. Srikanthan and McMahon (1985) developed a model using 6 min rain data and calibrated the model for 15 sites in Australia. The 6 min model was based on a daily model and an hourly model. The total number of parameters required for this approach was huge (5000– 6000). Hershenhorn and Woolhiser (1987) developed a model to disaggregate daily rain into any number of storms with varying rain amount and duration.

B. Yu / Agricultural and Forest Meteorology 119 (2003) 131–148

Intensity pattern within a storm, however, was not considered. Connolly et al. (1998) developed and calibrated a daily disaggregation model having about 70 parameters for four sites in Australia. They considered intensity pattern within individual storms. Elsewhere in Australia, Gyasi-Agyei and Willgoose (1997, 1999) developed a hybrid model using 15 min data for one site in central Queensland. Menabde and Sivapalan (2000) used bounded random cascades and Levy-stable distributions to generate 6 min data for Melbourne. Heneker et al. (2001) developed a disaggregation model that includes a component for intensity patterns within a storm and calibrated the model using 6 min data for three sites in Australia. All these models require a large number of parameters. Probability distributions used to describe rainfall characteristics typically have up to four parameters. Sophisticated method of calibration is also needed. In addition, none of the models has the capacity to generate concurrently daily temperature and radiation variables needed to simulate biomass production. The net result is that application of such models is usually limited to a few sites, and models that have large number of parameters and require calibration rarely end up being used beyond the research group in which such models have been developed. Uncalibrated models are essential for predictive use in areas external to the environment from which the models originate. For this paper, CLIGEN input files were prepared for 43 sites in Australia. CLIGEN-generated climate files were then used as inputs to other runoff and soil erosion models. No calibration of any of the parameters for CLIGEN was attempted to match observations. The primary objective of this paper is to assess the strength and weakness of CLIGEN in an environment that is distinct from the one in which CLIGEN was developed, and quantify the likely errors when CLIGEN is used to generate climate inputs for runoff and erosion predictions.

2. A brief description of the algorithm and functionality of CLIGEN and the storm pattern generated for WEPP Nicks et al. (1995), Yu (2000) and Meyer (2001) provide the algorithm currently used in CLIGEN in great detail. Only a brief summary is presented here.

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Precipitation occurrence is modeled by a two-state, first order Markov process. On wet days, precipitation amount was simulated using a skewed normal probability distribution. Storm duration and peak intensity are simulated using a one-parameter Gamma distribution for the ratio of peak 30 min rain to total rain. Time to peak is simulated using an empirical distribution based on historical data. Minimum and maximum temperatures and solar radiation are simulated independently assuming normal distributions. Solar radiation has a lower bound of 5% and an upper bound of 90% of the maximum radiation possible for the day. Input to CLIGEN are the mean, standard deviation, coefficient of skewness of wet–day rain amount and ‘wet following wet’ and ‘wet following dry’ probabilities for each month. Mean maximum 30 min rainfall intensity is used to generate peak rainfall intensity and storm duration. Empirical cumulative distributions of the time to peak are represented by 12 discrete values. CLIGEN input also includes monthly mean and standard deviation of the minimum, maximum temperatures and solar radiation. Most of the input parameters for CLIGEN are dimensional and similar to a climate summary table because they are mostly low-order statistics of daily weather variables. At present CLIGEN contains a number of options in terms of how CLIGEN is initiated, how random numbers are generated, what schemes to use for interpolating monthly statistics of weather variables. For most recent updates, visit http://www//horizon.nserl.purdue. edu/Cligen/. Output from CLIGEN can be used for WEPP to predict runoff and erosion, transport and deposition of sediments. CLIGEN can also be used to generate input data for CREAMS/GLEAMS and for RUSLE (Nicks and Gander, 1994; Nicks et al., 1995; Yu, 2002). CLIGEN generates four precipitation-related variables for each wet day (Nicks et al., 1995): precipitation amount P (mm), storm duration D (h), time to peak as a fraction of storm duration, tp , and the ratio of peak intensity over the average intensity, ip . In CLIGEN, time is normalized by storm duration, D, and rainfall intensity is normalized by the average intensity, P/D. Therefore both tp and ip are dimensionless variables and they can be regarded as normalized time to peak and normalized peak intensity, respectively.

134

100

100

Intensity (mm/h)

80

60

60

40

40

20

20

0 10

12

14

16

18

20

Time since 9:00 am, 10 Feb 1992 (h)

22

24

0 10

12

14

16

18

20

Time (h)

Fig. 1. A sample storm recorded at Brisbane Regional Office (station no. 040214) to illustrate the storm pattern adopted for CLIGEN and WEPP.

22

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WEPP rain pattern

Observed rain pattern 80

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In WEPP, a double exponential function is used to describe the normalized intensity pattern as
ip eb(t−tp ) , 0 < t < tp , i(t) = (1) −bt (t−t )/(1−t ) p p p ip e , tp < t < 1

It can be seen that WEPP preserves the total rain and peak intensity, but loses much of the variation in intensity within the event. We will re-visit this illustration when discussing the results.

where b is a parameter implicitly depending on tp and ip :

3. Data and methods

ip (1 − e−btp ) − btp = 0

For this study, 43 sites were selected to represent all major climate zones in Australia (Fig. 2). Location, long-term mean annual rainfall (MAR), and a rainfall seasonality index (SI) for these 43 sites are presented in Table 1. The SI is defined as follows:

(2)

Thus WEPP storm pattern is uniquely defined by the four variables generated by CLIGEN. Based on this storm pattern, WEPP uses the Green–Ampt equation to predict runoff amount and peak runoff rate (Stone et al., 1995). To illustrate the rain pattern generated by CLIGEN for WEPP, 6 min data recorded on 10 February 1992 at Brisbane Regional Office (station no. 040214) are shown in Fig. 1 along with the assumed storm pattern for WEPP. This was the 52nd largest event in terms of daily rain total for the period 1908–1994. Rain duration and amount were 8.7 h and 107 mm, respectively. Peak 6 min intensity was 97.9 mm/h, hence ip = 7.96 for the event. Time to peak was 3.55 h with tp = 0.408.

SI =

S−W S+W

(3)

where S is the average rainfall for the summer half of the year and W is that for the winter half. The convention that the summer half of the year is the period from November to April was adopted (Bureau of Meteorology, 1989). An SI value >0.13 indicates a marked wet season in summer, while SI < −0.13 indicates a marked wet season in winter (Bureau of Meteorology, 1989). From Fig. 2 and Table 1, it can be

Fig. 2. Location map for the 43 sites in Australia. Boundaries for climate zones were delineated after Bureau of Meteorology (1989) (St: summer rainfall, tropical; Ss: summer rainfall, subtropical; U: uniform rainfall, temperate; W: winter rainfall, temperate; A: arid).

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Table 1 Location, period of record, long-term MAR, and the SI for 43 Australian sites Station no. 2012 3003 4032 6011 7045 8051 9021 9741 9789 12038 13017 14015 14508 15135 15590 16001 17043 18012 23034 26021 27006 27022 29041 29127 31011 31083 32040 33119 36031 39083 40223 44021 48027 55024 59040 66037 70014 72150 76031 85072 86282 91104 94008

Station name

Longitude

Latitude

Elevation (m)

Year start

Year end

MAR (mm)

Halls Creek Airport Broome Airport Port Hedland Airport Carnarvon Airport Meekatharra Airport Geraldton Airport Perth Airport Albany Airport Esperance Kalgoorlie-Boulder Airport Giles Meteorological Office Darwin Airport Gove Airport Tennant Creek Airport Alice Springs Airport Woomera Aerodrome Oodnadatta Airport Ceduna Amo Adelaide Airport Mount Gambier Aero Coen Airport Thursday Island MO Normanton Post Office Mount Isa Aero Cairns Aero Koombooloomba Dam Townsville Aero Mackay MO Longreach Aero Rockhampton Aero Brisbane Aero Charleville Aero Cobar MO Gunnadah Coffs Harbour MO Sydney Airport Amo Canberra Airport Wagga Wagga Amo Mildura Airport East Sale Airport Melbourne Airport Launceston Airport Hobart Airport

127◦ 40

18◦ 14

410 17 9 4 522 38 20 71 25 360 580 31 54 375 537 165 113 24 4 63 162 60 8 343 3 732 4 6 192 10 4 306 221 307 5 6 571 221 51 5 132 171 4

1955 1948 1953 1956 1953 1953 1961 1965 1971 1939 1956 1953 1966 1969 1951 1955 1961 1954 1967 1942 1967 1961 1964 1967 1942 1960 1953 1959 1966 1939 1949 1953 1962 1946 1960 1962 1937 1947 1953 1953 1970 1938 1960

2000 2000 1998 1998 1998 2000 1998 1998 1998 1999 1998 2000 1998 1996 1999 1999 1985 2000 2000 2000 1997 1993 1999 1998 1998 1997 1999 1997 1997 1999 1998 1999 2000 1997 2000 2000 2000 1996 1998 2000 2000 2000 2000

522 546 303 230 190 466 795 810 617 264 260 1654 1385 423 274 194 171 449 449 714 1166 1728 912 409 2000 2660 1125 1620 449 835 1175 481 405 635 1653 1109 634 586 298 630 573 680 516

122◦ 14 118◦ 37 113◦ 40 118◦ 33 114◦ 42 115◦ 58 117◦ 48 121◦ 54 121◦ 28 128◦ 18 130◦ 52 136◦ 49 134◦ 11 133◦ 54 136◦ 49 135◦ 27 133◦ 43 138◦ 32 140◦ 47 143◦ 07 142◦ 13 141◦ 04 139◦ 29 145◦ 45 145◦ 36 146◦ 46 149◦ 13 144◦ 17 150◦ 29 153◦ 07 146◦ 16 145◦ 50 150◦ 16 153◦ 07 151◦ 10 149◦ 12 147◦ 18 142◦ 05 147◦ 09 144◦ 51 147◦ 13 147◦ 30

17◦ 57 20◦ 22 24◦ 53 26◦ 37 28◦ 48 31◦ 56 34◦ 57 33◦ 50 30◦ 47 25◦ 02 12◦ 25 12◦ 17 19◦ 38 23◦ 49 31◦ 09 27◦ 34 32◦ 08 34◦ 57 37◦ 45 13◦ 46 10◦ 35 17◦ 40 20◦ 40 16◦ 53 17◦ 50 19◦ 15 21◦ 07 23◦ 26 23◦ 23 27◦ 23 26◦ 25 31◦ 29 31◦ 02 30◦ 19 33◦ 56 35◦ 19 35◦ 08 34◦ 14 38◦ 06 37◦ 41 41◦ 33 42◦ 50

seen that these 43 sites cover all the five major climate zones in Australia. The MAR varies from 170 mm at Oodnadatta in the arid region of South Australia to 2660 mm at Koomboomla in the wet tropical region of Queensland. SI values for the 43 sites vary from −0.71 at Perth in the southwest of Australia with a Mediterranean climate to 0.96 at Coen on Cape York

SI 0.84 0.79 0.56 −0.39 0.07 −0.67 −0.71 −0.50 −0.46 −0.04 0.40 0.86 0.79 0.78 0.34 −0.04 0.22 −0.36 −0.36 −0.35 0.96 0.90 0.93 0.71 0.72 0.41 0.78 0.63 0.50 0.48 0.35 0.35 0.13 0.18 0.30 0.09 0.08 −0.14 −0.13 0.00 0.00 −0.18 0.02

Peninsular in far north Queensland where rain occurs almost exclusively in summer months. This data set covers the spatial variations in rainfall experienced for most part of Australia. Raw weather data for these sites were available from three different sources. Pluviograph data at 6 min intervals were extracted directly from Bureau of

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Meteorology archives. Daily rainfall, minimum and maximum temperatures, and solar radiation data were extracted using MetAccess from a separate weather database. Because solar radiation data are not widely measured, daily solar radiation data estimated using geo-stationary meteorological satellites (GMS) were prepared for the 43 sites with NCCSOL version 2.205 (Gautier et al., 1980; Weymouth and Le Marshall, 1994). These weather data were then analyzed to prepare CLIGEN parameter files to generate long-term climate files for runoff and soil loss prediction using WEPP. The weather data were also processed to prepare observed climate files for WEPP and calculate climate inputs for RUSLE. To produce parameter files for CLIGEN, daily rainfall and temperature data were used to calculate the monthly statistics for each site. Estimated solar radiation data using GMS data were used to calculate the monthly statistics for daily radiation amount. Pluviograph data were processed to compute the monthly mean maximum 30 min rain intensity, and an empirical distribution for the time to peak. Parameters in relation to wind speed and direction, and dew-point temperature were not prepared because we have adopted the option of using the Priestley–Taylor equation to determine the potential evapotranspiration in WEPP, and snowdrift is of little concern at these sites. Generated wind and dew-point data thus were not used in predicting runoff and soil loss with WEPP. Generators of uniform and normal random numbers in CLIGEN version 5.0 were replaced with those from Numerical Recipes (Press et al., 1992; Yu, 2002) to produce a new version called version 5.2. This is because the quality of random number generators used in version 5.0 and prior versions were poor and failed some standard statistical tests (Meyer, 2001; Yu, 2002). Although linear or Fourier interpolation of monthly parameter values are available in the latest versions of CLIGEN, these schemes were not used in this study. Random seeds drawn from 1 to 231 − 1 were randomly and independently selected to initiate CLIGEN. CLIGEN (version 5.2) was used to generate a climate sequence of 100 years for each of the 43 sites. These CLIGEN-generated climate sequences were used to simulate runoff and soil loss with WEPP, and they were also used to determine R-factor and other climate inputs for RUSLE.

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To prepare climate input data using observed rain data for WEPP, the 6 min pluviograph data were processed to determine the following daily storm characteristics: rain amount (P), rain duration (D), time to peak rain intensity as a fraction of rain duration (tp ), and the ratio of peak rain intensity over average rain intensity (ip ). Rain amount is the total rain (mm) for a 24 h period ending at 9:00 am. Rain duration is the total number of wet intervals (h). All the intervening dry intervals, if there is any, were discarded in calculating the rain duration. Average rain intensity was defined as rain total divided by rain duration, and ip was calculated as the maximum 6 min rainfall intensity divided by this average intensity. Time to peak is the time lapsed between first wet interval and the time interval when peak intensity occurred. Again any intervening dry intervals were discarded. tp is calculated as time to peak divided by rain duration. To assess the quality of CLIGEN-generated rain data, the four CLIGEN-generated rainfall variables were replaced with the observed rainfall data for the observation period (Table 1). Temperature and solar radiation data were not replaced so that the effect of CLIGEN-generated rainfall data on runoff and soil loss predictions can be isolated. The record length for the observed rainfall data is highly variable for the 43 sites, ranging from 24 to 62 years with an average of 42 years. This is in contrast to 100 years for all the CLIGEN-generated climate sequences. More importantly, the MAR based on the pluviograph data was 654 mm on average for the 43 sites. This was 113 mm, or 15%, less than the long-term MAR based on more reliable daily data for these sites. This discrepancy occurs largely because the 6 min pluviograph data are incomplete to various degrees for all sites. To take into account the discrepancy due to record length and MAR, the simulated runoff and soil loss using observed climate rainfall data were multiplied by the ratio of CLIGEN-generated MAR to that based on pluviograph data. Both adjusted and unadjusted runoff and soil loss predictions will be considered. For runoff and soil loss predictions, a standard USLE slope profile (22 m long with a 9% slope) was used with fallow treatment for all sites. Three soils (Caribou, Providence and Tifton) were used to simulate a range of model responses in this climate environment because these three soils have the highest (Tifton), the median (Caribou) and the lowest

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Table 2 Soil properties, and WEPP runoff and erosion parameters for the three soils considered in this paper Soils (%)a

Sand Clay (%)a Organic matter (%)a CEC (meq/100 g soil)a,b Rock (%)a Interill erodibility (kg s/m4 ) Rill erodibility (s/m) Critical sheer (Pa) Effective hydraulic conductivity (mm/h) a b

Caribou

Providence

Tifton

38.8 13.7 3.76 13.2 32.9 4.78 × 106 0.0052 2.94 4.66

2.0 19.8 0.81 9.3 0.0 4.96 × 106 0.0095 3.5 0.47

87.0 5.7 0.7 4.1 9.8 5.17 × 106 0.017 2.3 20.69

For the top 20 cm. Cation exchange capacity.

(Providence) baseline hydraulic conductivity (Risse et al., 1995; Table 2). WEPP as a Windows application released in April 2001 (beta version 4.0) was used for all runoff and soil loss predictions. The four precipitation variables generated by CLIGEN were used to calculate storm energy and peak 30 min rainfall intensity. The unit energy equation recommended for RUSLE is given by e(i) = e0 (1 − α e−i/I0 )

(4)

where e0 = 0.29 MJ ha−1 mm−1 , α = 0.72, and I0 = 20 mm h−1 (Brown and Foster, 1987; Renard et al., 1997). The total storm energy, E, can be derived by integrating the unit energy over the double exponential storm pattern:   αip I0 −(Ip /I0 ) e−btp −Ip /I0 E = Pe0 1 − (e −e ) (5) btp Ip where Ip is the peak intensity (mm h−1 ). For each day when precipitation occurs and when mean air temperature is greater than 0 ◦ C, peak 30 min intensity is calculated as follows. If D is less than or equal to 30 min, I30 = 2P (mm h−1 ) by definition. If D is greater than 30, 30 min peak intensity is given by I30 =

2Pip (1 − e−btp /2D ) btp

(6)

The daily storm erosion index, EI, is the product of Eqs. (5) and (6). These are accumulated for each month, and the R-factor, by definition, is the sum of mean monthly EI values. The 10-year storm EI value is determined from an annual series of maximum

storm EI values. Each value in the annual series is assigned an average recurrence interval using Weibull’s formula (Maidment, 1993). The 10-year storm EI value can be determined either directly or using the linear interpolation technique. The 6 min pluviograph data were used to compute the R-factor for the RUSLE with program RECS (Yu and Rosewell, 1998). CLIGEN-generated climate data can also be used to produce the R-factor (Yu, 2002). A comparison of the measured and generated R-factor for these 43 sites will indicate the quality of generated precipitation-related variables produced by CLIGEN. The metric unit for the R-factor is MJ mm/(ha h per year), and MJ mm/(ha h) for 10-year storm EI. Throughout this paper, wherever appropriate ‘R-factor in SI units’ instead of ‘R-factor in MJ mm/(ha h per year)’ is used for simplicity. The same also applies to 10-year storm EI. To obtain the R-factor in US customary units of hundreds of foot tonf inch acre−1 h−1 per year, the R-factor in SI units needs to be divided by a factor of 17.02 (Foster et al., 1981; Renard et al., 1997). Recent development in rainfall modeling shows that extreme rainfall statistics and rainfall intensity at a range of time scales are important considerations when evaluating stochastic weather generators (Cameron et al., 2000; Heneker et al., 2001) For this reason, we extract from published maps on rainfall intensity at fixed duration and average recurrence intervals from Australian Rainfall and Runoff, vol. 2, commonly known as AR&R (Canterford, 1987). These maps have been indispensable for engineering design, and have long been regarded as an authoritative

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data source. Rainfall intensities for 2-year 1 h, 2-year 12 h, 50-year 1 h, and 50-year 12 h were calculated using the 100-year CLIGEN-generated climate data for each of the 43 sites. Weibull plotting position was used to assign the average recurrence intervals. These simulated intensities were compared to those read off the map at the same duration and recurrence intervals for these sites. Linear regression technique was used to quantify relationships between observed and predicted variables. When appropriate, model efficiency, Ec , was also used (Nash and Sutcliffe, 1970). This model efficiency measure has become a standard indicator of WEPP’s performance in previous studies (Zhang et al., 1996; Tiwari et al., 2000; Yu et al., 2000; Yu and Rosewell, 2001).

4. Results 4.1. Runoff and soil loss predictions with WEPP Predictions by WEPP using CLIGEN-generated climate files are termed simulated runoff and soil loss, while those using observed rainfall data are termed reference runoff and soil loss. The reference runoff and soil loss were adjusted using the ratio of long-term MAR to that based on pluviograph data because the latter is often incomplete, hence tends to be systematically less than the long-term MAR. Both adjusted and unadjusted results will be presented. Table 3 summarizes the comparison between simulated and reference runoff and soil loss in terms of model efficiency, Ec , for the three soils tested. It can be seen that the Ec values are quite high overall; model efficiency is higher for runoff than soil loss. Adjusted runoff and soil loss have higher model efficiency than unadjusted ones. Fig. 3 shows that reference runoff Table 3 A comparison in terms of model efficiency between simulated and reference runoff and soil loss for three different soils and 43 sites Soils

Unadjusted runoff

Adjusted runoff

Unadjusted soil loss

Adjusted soil loss

Caribou Providence Tifton Overall

0.93 0.93 0.90 0.93

0.98 0.99 0.96 0.98

0.87 0.82 0.90 0.86

0.91 0.88 0.95 0.91

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and simulated runoff are in good agreement for the three soils and these 43 sites in Australia. The best-fit line is almost identical to the 1:1 line, indicating there is essentially no bias between simulated and reference runoff. The standard error is about 16% of the mean for the 43 sites. Fig. 3 and a high Ec value of 0.98 show that uncalibrated CLIGEN can generate the required rainfall data that is statistically similar to the observed rainfall data in terms of predicted runoff with WEPP. In comparison to runoff, the agreement between simulated and reference soil loss is not as good with Ec equal to 0.91 (Table 3, Fig. 4). Soil loss tends to have a greater variability than runoff. The standard error is about 35% of the mean for predicted soil loss for the 43 sites. It is important to note there are no systematical errors in simulated soil loss. The slope for the best fit in Fig. 4 is 0.989, which is not significantly different from unity. We further examined the effect of adjusting the reference runoff and soil loss on the results by considering the average sediment concentration predicted by WEPP. Average sediment concentration, being a ratio of soil loss to runoff, is not sensitive to the total rain amount. The average sediment concentration using CLIGEN-generated climate files was 32.2 kg/m3 for the 129 soil-site combinations. The average sediment concentration predicted by WEPP using observed pluviograph data was 30.8 kg/m3 without any adjustment. The discrepancy in the average sediment concentration was only 4.5% for the 43 sites in Australia. 4.2. Climate inputs for RUSLE To use RUSLE to predict the long-term mean annual soil loss, R-factor, its monthly distribution, and 10-year storm EI are needed as climate inputs (Renard et al., 1997). CLIGEN-generated climate files for WEPP were processed to calculate the R-factor and its monthly distribution for the 43 Australian sites. Fig. 5 shows a scatter plot for the measured R-factor using 6 min pluviograph data and CLIGEN-generated R-factor for these sites. The relationship for 75 sites in the US is also presented to the upper limit of the observed R-factor values for the US sites. Note that the maximum measured R-factor for the US sites (10,212 SI units) was slightly less than that among the 43 Australian sites (13,900 SI units). The measured R-factor is systematically larger than the generated R-factor for

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Simulated runoff (mm/yr)

1500 Caribou Providence Tifton 1:1 line best fit 1000

500

0 0

500

1000

1500

Reference runoff (mm/yr) Fig. 3. Comparison between simulated runoff using CLIGEN-generated climate data and the reference runoff using observed precipitation data. The mean annual runoff was calculated with WEPP for three different soils and for each of the 43 sites.

the Australian sites, although they are well correlated: R = 0.668Rgen ,

r = 0.94 2

(7)

and the relationship is quite similar to that for sites in the United States (Fig. 5). The consistent relationship shows the robustness of CLIGEN in reproducing rainfall characteristics relevant to soil erosion predictions across a range of climate environments. The cause of the systematic over-estimation of the R-factor for both Australian and the US sites is discussed later in this paper. For 10-year storm EI, again CLIGEN-generated values are systematically larger than measured values for the 43 sites in Australia. The linear relationship between the two is EI = 0.808EIgen ,

r2 = 0.88

(8)

This is also broadly similar to the corresponding relationship for the US sites (Fig. 6). The maximum measured 10-year EI value for the US sites (3404 SI units) was considerably smaller than the maximum

observed 10-year value for the Australian sites (8038.7 SI units). To investigate the likely cause of this over-estimation of rainfall erosivity assuming the double exponential distribution for storm pattern, wet day storm energy, E, and peak 30 min intensity, I30 , were calculated using the measured 6 min pluviograph data. E and I30 were also determined using P, D, tp , and ip and Eqs. (5) and (6). Linear regression technique with zero intercept was used to relate measured E, I30 and the product EI30 to those calculated assuming the exponential storm pattern. The number of storms analyzed for each site was about 2800 on average. It is clear from Table 4 that the assumption of a double exponential distribution has led to the systematic over-estimation of both E and I30 . Table 4 presents the regression results for these 43 sites. It can be seen that the over-estimation is greater for I30 (about 20%) than E (about 7.5%) and the relationship for E is better than that for I30 as the r2 values indicate. The combined over-estimation of E and I30 has led to an over-estimation of EI30 (about 37%) which in

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turn resulted in the generated R-factor being much higher than the measured R-factor for the 43 sites in Australia, as for sites in the United States (Yu, 2002). Go back to the 10 February 1992 storm in Brisbane (Fig. 1). For the WEPP storm pattern, the peak intensity and total rain amount are preserved. So is the effective storm duration. The actual storm energy based on measured 6 min data was 25.1 MJ/ha for this storm, the calculated E assuming the WEPP storm pattern was 26.5 MJ/ha, an over-estimation of 5.6%. Calculated I30 assuming the WEPP storm pattern was 78.6 mm/h, which was 21% more than the observed 30 min peak Table 4 Average regression results for daily storm energy, peak 30 min intensity and storm EIa

Storm energy, E Peak 30 min intensity, I30 EI30

b

r2

0.93 ± 0.02 0.83 ± 0.06 0.73 ± 0.09

0.99 ± 0.00 0.91 ± 0.02 0.92 ± 0.03

a A linear model with zero intercept was used for all 43 sites in Australia (b: average slope for the regression between calculated (X) and measured (Y) values as in Y = bX; r2 : correlation coefficient squared; the error represents one standard deviation among the 43 sites).

intensity of 64.9 mm/h for the storm. The net result is that although the rain amount and peak intensity are the same, there is an increase of 28% in the storm EI if the WEPP storm pattern is used. From Fig. 1, it can be seen that this increase in I30 is largely because the WEPP storm pattern fails to recognize multiple peaks within the storm. I30 is, by definition, the maximum rain intensity for any 30 min period. I30 calculated assuming the WEPP storm pattern is essentially the average of the five largest observed intensities, each at 6 min interval. Fig. 7 shows the monthly distribution of rainfall and rainfall erosivity for 3 of the 43 sites selected to represent contrasting seasonal patterns. It can be seen that the CLIGEN-generated climate data can be used to reproduce the seasonal pattern quite well. The average absolute discrepancy between simulated and long-term mean monthly rainfall was 0.5% for the three sites. The average absolute discrepancy with respect to rainfall erosivity is 1.9% for the three sites. It is interesting to note that the discrepancy was the greatest (0.9% for rainfall and 2.4% for rainfall erosivity) for Melbourne with an essentially uniform rainfall distribution over the year. In April 1978, a storm occurred in Melbourne with an EI30 value of 1053.6 SI

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Fig. 7. A comparison of measured and simulated seasonal distribution of rainfall and rainfall erosivity for three selected sites in Australia.

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units, the highest ever recorded for the site. Because of this, the total EI30 value for the month was more than twice the R-factor for Melbourne (643.9 SI units). The mean monthly value for April would have been decreased by a factor of nearly 2 had this April 1978 storm not occurred. This illustrates the highly variable nature of rainfall erosivity and partially explains the discrepancy in the distribution of rainfall erosivity for Melbourne.

4.3. Intensity–frequency–duration Fig. 8 compares the simulated and AR&R intensities for the average recurrence interval and duration selected. A summary of the average intensities and regression relations with a zero intercept is presented in Table 5. The standard error is expressed as percent of the mean AR&R intensity to indicate the spread around the line of best fit. It can be seen that the

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Table 5 Relationship between generated and AR&R intensity for two average recurrence intervals and two durations (b: the regression coefficient between generated and AR&R intensities with zero intercept; r2 : correlation coefficient squared; S.E.%: standard error expressed as percent of the mean intensity for the 43 sites) Frequency and duration

b

r2

S.E.%

Average AR&R (mm/h)

2-Year, 1 h 2-Year, 12 h 50-Year, 1 h 50-Year, 12 h

0.782 1.08 0.753 0.853

0.84 0.95 0.70 0.85

24 13 26 21

33.5 6.1 66.1 13.1

relationship between generated and AR&R intensity is better for small average recurrence intervals as expected. The generated intensity is systematically larger than the AR&R intensity for short duration (1 h). This observation is consistent with the over-estimation of storm energy, peak 30 min intensity, and the R-factor reported above because EI30 is particularly sensitive to any bias in the peak intensity at short duration.

± ± ± ±

19.0 3.4 30.7 6.9

Average generated (mm/h) 44.2 5.6 89.5 14.9

± ± ± ±

sary climate inputs for RUSLE as well, this section discusses two additional issues in an attempt to explain why this is likely the case. In the late 1980s, CLIGEN contains what is known as the B-factor to interconnect precipitation, temperature and radiation variables (Nicks and Gander, 1994). Cross-correlation among precipitation, temperature and radiation variables is also considered in WGEN and USCLIMATE (Richardson and Wright, 1984; Hanson et al., 1994). The B-factor depends on the transition probabilities between wet and dry days (Nicks and Gander, 1994). These cross-connections among daily variables were deliberately removed in the mid 1990s (cf. Nicks and Lane, 1989; Nicks and Gander, 1994, versus Nicks et al., 1995). It is unknown what would happen to

5. Discussion While this paper shows that CLIGEN is able to generate rainfall variables needed to drive WEPP for runoff and soil predictions and provide the neces-

7 ∆ - Australian sites ∆ - U.S. sites CV - Australian sites CV - U.S. sites

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Fig. 9. Seasonal variation of two CLIGEN parameters for sites in Australia and the United States.

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CLIGEN’s performance in Australia had the correlation structure is retained to this day. This, however, may not be a critical issue because predicted runoff and soil loss are known to be insensitive to simulated temperatures and solar radiation. Another issue about CLIGEN is related to the two parameters set internally to generate storm patterns in CLIGEN. One of the parameters is the average ratio of peak intensity to average intensity (∆) and the other is the coefficient of variation (CV) of the ratio of the maximum 30 min rainfall to daily rainfall (Yu, 2000). Fig. 9 shows the mean monthly values of the two dimensionless parameters for the 43 Australian sites. Also shown in this diagram are the corresponding parameter values for the 14 US sites used to calibrate CLIGEN (Yu, 2000). It is clear that the seasonal distribution is essentially opposite because the two groups of sites are located in different hemispheres. Apart from these the magnitude of the two parameters are essentially the same. The mean of ∆ is 3.76 and the mean of CV is 0.378 for Australian sites. These compare favorably with 3.99 for ∆ and 0.37 for CV set internally in CLIGEN (Yu, 2000). Had a more sophisticated parameterization scheme been used for storm generation in CLIGEN, it would be necessary to adjust these parameters and their seasonal variation for sites in the Southern Hemisphere. In the end, it is always a compromise between a superior fit at a single site and a wider applicability in diverse climate environments.

6. Conclusion In terms of the range of weather variables generated and, especially, in terms of the size of parameter database for stochastic weather generators, CLIGEN is unsurpassed anywhere in the world. CLIGEN input parameters are nearly all low-order statistics such as the mean and standard deviation of daily weather variables, which can be routinely calculated for large number of sites. As a tool to generate continuous daily weather sequences to drive runoff and soil loss prediction model WEPP or to provide necessary climate inputs for RUSLE, this paper shows that CLIGEN does its job reasonably well for the 43 Australian sites tested, considering no calibration was attempted. CLIGEN can serve as a benchmark against which more sophisticated models can be gauged. While

CLIGEN generates statistically similar precipitation variables needed to define WEPP storm patterns, the paper also finds that CLIGEN over-predicts the rainfall intensity at short time intervals, which leads to a systematic over-prediction of rainfall erosivity, a trend consistent with what is noted for sites in the United States. Because of the particular storm pattern assumed in WEPP, this over-prediction of rainfall intensity at short duration occurs regardless whether observed or CLIGEN-generated precipitation data is used. Thus over-prediction of rainfall erosivity and rainfall intensity in general is not a reflection on the quality of CLIGEN per se, but a result of the particular storm pattern adopted in WEPP.

Acknowledgements This work on CLIGEN validation is in part supported through the Collaborative Research Centre for Catchment Hydrology Project 2.2 ‘Managing pollutant delivery from dryland upland catchment’. Ms. R. Trevithick of Griffith University assisted in analyzing weather data and running WEPP for these sites. References Arnold, C.D., Elliot, W.J., 1996. CLIGEN weather generator predictions of seasonal wet and dry spells in Uganda. Trans. ASAE 39, 969–972. Arnold, J.G., Williams, J.R., Nicks, A.D., Sammons, N.B., 1990. SWRRB, A Basin Scale Simulation for Soil and Water Resources Management. Texas A&M Press, College Station, TX, 142 pp. Baffaut, C., Nearing, M.A., Nicks, A.D., 1996. Impact of CLIGEN parameters on WEPP-predicted average annual soil loss. Trans. ASAE 39, 447–457. Brazier, R.E., Beven, K.J., Anthony, S.G., Rowan, J.S., 2001. Implications of model uncertainty for the mapping of hillslope-scale soil erosion predictions. Earth Surf. Process. Land. 26, 1333–1352. Brown, L.C., Foster, G.R., 1987. Storm erosivity using idealized intensity distributions. Trans. ASAE 30, 379–386. Bureau of Meteorology, 1989. Climate of Australia. AGPS Press, Canberra, 49 pp. Cameron, D., Beven, K., Tawn, J., 2000. An evaluation of three stochastic rainfall models. J. Hydrol. 238, 130–149. Canterford, R.P. (Ed.), 1987. Australian Rainfall and Runoff: A Guide to Flood Estimation, vol. 2. Institution of Engineers, Canberra, Australia. Chaves, H.M.L., Nearing, M.A., 1991. Uncertainty analysis of the WEPP soil erosion model. Trans. ASAE 34, 2437–2444.

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