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  • View in gallery

    Map showing the watershed (light gray) of the inner Gulf of Honduras. Locations with measurements are Corozal (precipitation), Belize Airport (precipitation, temperature), Central Farms (precipitation), Punta Gorda (precipitation), La Ceiba (precipitation), Guatemala City (precipitation, temperature), and Kingston (precipitation, temperature)

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    Isohyets of mean annual precipitation (mm) in the watersheds of the inner Gulf of Honduras

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    Isotherms of mean annual temperature (°C) in the watersheds of the inner Gulf of Honduras

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    (a) Comparison of mean monthly precipitation (mm) for Corozal (plus sign, 30 yr), Belize City (star, 51 yr), Central Farms (diamond, 32 yr), Punta Gorda (square, 39 yr), all in Belize; La Ceiba, Honduras (circle, 36 yr), Kingston, Jamaica (triangle, 48 yr), and Guatemala City, Guatemala (inverted triangle, 35 yr). (b) Comparison of mean monthly temperatures (°C) for Belize City (star, 56 yr), Central Farms (diamond, 11 yr), Punta Gorda (square, 6 yr), La Ceiba, (circle, 6 yr), Kingston, (triangle, 48 yr), and Guatemala City, (inverted triangle, 35 yr)

  • View in gallery

    Land cover map of the inner Gulf of Honduras. The land cover types shown include crop land (dot), forest (open diamond), irrigated land (wavy line), nonirrigated land (back-slash), grazing land (inverted v), sand (white space), wetland (circles and dots), and forested wetland (gray shade)

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    (top) Sediment load (t yr−1) as a function of discharge (m3 s−1) and (bottom) sediment yield (t km−2 yr−1) as a function of basin area (km2) from applying the RUSLE model to each of the 12 watersheds in Table 3 for three cases of model parameter C. Open diamonds denote results from C values varying by land use, as given in section 3c, while solid triangles and solid squares, respectively, represent universal use of C = 0.3 (high erosion potential) and C = 0.003 (low erosion potential). The linear best-fit line is shown for each C-value case, associated with a correlation of (top) 0.9 and (bottom) 0.7

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Hydrometeorology and Variability of Water Discharge and Sediment Load in the Inner Gulf of Honduras, Western Caribbean

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  • 1 Department of Geological Sciences, University of South Carolina, Columbia, South Carolina
  • | 2 Department of Geological Sciences and Marine Science Program, University of South Carolina, Columbia, South Carolina
  • | 3 The Nature Conservancy, Punta Gorda, Belize
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Abstract

The hydrological and meteorological characteristics of the watersheds of the inner Gulf of Honduras in the western Caribbean, including runoff, sediment load and yield, and the effects of the El Niño–La Niña cycle, are examined using available data. The inner Gulf of Honduras, bordered by the second-longest coral reef complex in the world, the MesoAmerican Barrier Reef, receives runoff from the watersheds of 12 rivers with a total simulated annual discharge of 1232 m3 s−1. Expanding agricultural and industrial activities contribute to the influx of sediments, nutrients, and pollutants from these rivers, leading to increased threats to the health of the reef ecosystem. The watersheds of the Moho, Sarstún, and Polochic-Dulce Rivers receive more than 4000 mm of rainfall annually and are major sources of discharge and sediment load, along with the Motagua and Ulua, farther to the east. The drainage basins are characterized by runoff ratios of 0.30–0.55 and simulated sediment yields as high as 869 t km−2 yr−1. The results from two different sediment load/yield models agree to within ±2.3% at the 95% confidence level. Sediment load estimates increase by as much as 5 times on model comparisons of present land use to increased land use. Time series of precipitation for the inner Gulf of Honduras exhibit bimodal distribution with maxima in May–June and in September–October. Analysis of long-term climatic data reveals only a weak but measurable correlation with El Niño–La Niña. The Southern Oscillation index explains on average 7%–15% of the precipitation and temperature variability for the inner Gulf of Honduras.

Corresponding author address: Dr. Björn Kjerfve, Marine Science Program, University of South Carolina, Columbia, SC 29208. Email: bjorn@msci.sc.edu

Abstract

The hydrological and meteorological characteristics of the watersheds of the inner Gulf of Honduras in the western Caribbean, including runoff, sediment load and yield, and the effects of the El Niño–La Niña cycle, are examined using available data. The inner Gulf of Honduras, bordered by the second-longest coral reef complex in the world, the MesoAmerican Barrier Reef, receives runoff from the watersheds of 12 rivers with a total simulated annual discharge of 1232 m3 s−1. Expanding agricultural and industrial activities contribute to the influx of sediments, nutrients, and pollutants from these rivers, leading to increased threats to the health of the reef ecosystem. The watersheds of the Moho, Sarstún, and Polochic-Dulce Rivers receive more than 4000 mm of rainfall annually and are major sources of discharge and sediment load, along with the Motagua and Ulua, farther to the east. The drainage basins are characterized by runoff ratios of 0.30–0.55 and simulated sediment yields as high as 869 t km−2 yr−1. The results from two different sediment load/yield models agree to within ±2.3% at the 95% confidence level. Sediment load estimates increase by as much as 5 times on model comparisons of present land use to increased land use. Time series of precipitation for the inner Gulf of Honduras exhibit bimodal distribution with maxima in May–June and in September–October. Analysis of long-term climatic data reveals only a weak but measurable correlation with El Niño–La Niña. The Southern Oscillation index explains on average 7%–15% of the precipitation and temperature variability for the inner Gulf of Honduras.

Corresponding author address: Dr. Björn Kjerfve, Marine Science Program, University of South Carolina, Columbia, SC 29208. Email: bjorn@msci.sc.edu

1. Introduction

Large-scale atmospheric circulation and local processes influence climate, which is the state of weather averaged over months or years (Rasmusson et al. 1992). Local controls on climate include topography, vegetation, land–ocean interaction, land use, and catchment geology. Climatic processes are characterized by temperature and rainfall variability and affect humans directly through influence on soil, agriculture, hydrological processes, and water availability. At least several cycles of extreme weather phenomena, such as tropical storms, droughts, floods, and El Niño–La Niña, must be averaged to establish the long-term climate (Kundzewicz et al. 1993; Kousky et al. 1984; Ropelewski and Halpert 1987; Rogers 1988; Glantz 1997). Regional and local climates vary with time, on scales of centuries and longer, whereas human influences on decadal scales or shorter have significantly accelerated changes in the local hydrology during the last five centuries (Kaczmarek et al. 1996).

The objective of this paper is to assess the hydrological and meteorological characteristics of the watersheds of the inner Gulf of Honduras (iGoH) in the western Caribbean. Runoff mean and variability, sediment load and yield, and the effect of the El Niño-La Niña cycle are examined using available data. The iGoH is of particular interest, because it borders the most extensive coral reef complex in the Atlantic Province, the MesoAmerican Barrier Reef System (MBRS; Heyman and Kjerfve 2001). The iGoH has more than a dozen designated, protected, coastal marine conservation areas. These are among the premier ecotourism destinations in the Western Hemisphere (Perkins and Carr 1985).

Watershed boundaries and regional weather systems do not respect man-made sociopolitical boundaries. Hence, any impacts in the interior reaches of a watershed in one country are bound to have repercussions in neighboring countries and receiving water bodies. The MBRS, which receives runoff from Belize, Guatemala, Honduras, and Mexico, is thus dependent on the watershed processes in these other countries. Runoff comes from regions with extensive banana plantations, aquaculture ponds, new roads, and otherwise altered land use (Gibson et al. 1998; López and Scoseria 1996), yielding high levels of sediment and pollutant discharges into the coastal fringes of the iGoH, which impact the coral reef and its biota (Gibson et al. 1998; Katz 1989). Maintenance of healthy conditions along the MBRS complex is important for biodiversity conservation and local economic development. Thus, the MBRS is a focal area for local and international conservation organizations (e.g., The Nature Conservancy and the World Wildlife Fund) and has recently attracted several multimillion-dollar bank projects to the region. Knowledge of the underlying hydrometeorological variability is an important component in assessing reef viability and impacts on flora and fauna; and also in understanding oceanic circulation, transport of sediments, pollutants, and nutrients in the reef-associated waters; and in predicting the impacts of climate change and global warming on the reef ecosystem.

2. Geographical setting

The iGoH, measuring 10 000 km2, is situated in the northwestern Caribbean Sea (Fig. 1). It is bordered by Belize, Guatemala, and Honduras, and extends from Dangriga (Stann Creek), Belize, to La Ceiba, Honduras. The 42 408 km2 watersheds that border the iGoH are principally drained by the Moho, Sarstún, Polochic-Dulce, Motagua, and Ulua Rivers (Fig. 2). The iGoH includes the southern portion of the MBRS complex. The principal coastal cities are Belize City (population 56 000), Belize, just north of the defined area; Livingston (population 40 000) and Puerto Barrios (population 100 000), Guatemala; and Puerto Cortés (population 100 000), Honduras. Belize City, Big Creek (Belize), Puerto Barrios, and Puerto Cortés are the major commercial ports.

The watersheds of the iGoH extend from high mountains to the coast. The smaller rivers are confined to an elevation no more than 100 m above sea level, whereas the larger rivers originate at 2500–3000-m elevation and have average elevations above 500 m. The rapid expansion of agricultural operations in Belize is restructuring the watershed dynamics (Stednick et al. 1995). The expansion of citrus and shrimp aquaculture (in Belize) and banana plantations (in Guatemala and Honduras), and the accompanied road building, have led to increased erosion of land, and consequently, more sediment is carried by rivers to the coast. Other sources for sediment accumulation are the destruction of mangroves, mining, and dredging activities (Gibson et al. 1998). The rivers also transport chemicals, herbicides/pesticides, and other pollutants as they flow through agricultural lands, industrial zones (sugar refineries, citrus processing plants, and metal industries), and zones of high population. These pollutants eventually enter the MBRS, and threaten the lagoon and reef systems and impact fishing resources (Yáñez-Arancibia et al. 1999). An excess of nutrients leads to increased algal growth, which eventually kills corals. Rapid sedimentation likewise leads to coral die-off (McField et al. 1996).

Hurricanes are a constant threat in the Caribbean, although only 12 hurricanes have made landfall in the iGoH during the past 50 yr. Analysis of data (Rappaport and Fernandez-Partagas, 1997; Neumann et al. 1993) reveals that this is less than 5% of the total number of Atlantic hurricanes in the same period. Most storms pass well to the north of the iGoH. Still, the effects of hurricanes are considerable. The most severe hurricane to have impacted both the Caribbean and iGoH is Mitch (28 October–5 November 1998, category 5), which damaged parts of Honduras, Nicaragua, El Salvador, and Guatemala, and resulted in more than 9000 fatalities. The second-worst hurricane in terms of damage was Fifi (14–22 September 1974, category 2). The main damage from Mitch and Fifi was due to land slumping caused by heavy rains. Fifi traveled west across the Caribbean and made landfall near Placencia, Belize, after passing between the Bay Islands and the northern coast of Honduras. Hurricane Hattie (26–31 October 1961, category 5) destroyed Belize City (Heyman and Kjerfve 2001), and Hurricane Iris (5–9 October 2001, category 4) devastated several coastal towns in central and southern Belize. Hurricane-caused damages to the coast, reefs, and mangrove cays usually persist for more than a decade. Many shallow reefs were severely degraded as a result of Mitch (1998), whereas the battering waves and storm surge accompanying Iris (2001) resulted in severe damage to coastal infrastructure and vegetation (Bood 2001).

3. Data and analysis

a. Meteorological data

Our analysis of the hydrometeorology of the iGoH is based on the analysis and synthesis of available temperature and precipitation data. The temperature and precipitation data were analyzed for seven stations in/near the iGoH: Corozal, Belize International Airport, Central Farms, Punta Gorda (all in Belize), La Ceiba (Honduras), Guatemala City (Guatemala), and Kingston (Jamaica) (Fig. 1). Long-term (30 yr or more) data were obtained from 1) the World WeatherDisc CD (Spangler and Jenne 1990), which is part of the World Monthly Surface Station Climatology (WMSSC), for data until 1990; and 2) the University Corporation for Atmospheric Research (http://dss.ucar.edu/datasets/ds570.0/) for data from 1991 to 1998, also part of the WMSSC. The data for the seven stations span different periods; the precipitation data overlap for at least 28 yr (1952–79), but the temperature data is sparser with La Ceiba and Punta Gorda having only 6 yr of data and Central Farms 11 yr. The other stations which overlap for at least 31 yr (1954–85) lie outside the iGoH watershed.

The Southern Oscillation index (SOI) data were obtained from the U.S. National Weather Service's Climate Prediction Center Web site (http://www.cpc.ncep.noaa.gov/data/indices/). The land cover data was obtained from the Digital Atlas of Central America CDs prepared by the Center for the Integration of Natural Disaster Information (CINDI 1999). In exploring the regional relationship between precipitation/temperature and the SOI in the iGoH, the monthly precipitation and temperature data from the seven stations mentioned earlier were used. The precipitation and temperature anomalies were calculated as the difference between the precipitation/temperature of each month and the monthly mean over the length of each dataset. The time series of anomalies were then filtered (low pass) using a Butterworth filter (Middleton 2000) to remove seasonal trends. The same procedure was followed for SOI and correlation coefficients were calculated between precipitation and SOI, and temperature and SOI. The results are summarized in Table 1.

b. Discharge modeling

There is no consistent gauging of the rivers discharging into the iGoH. Also, lack of long-term temperature and precipitation data from stations within each of the many watersheds makes it impossible to calculate the temporal variability of discharge and sediment load within a year and between years. Hence the calculation of discharges for the regional rivers was limited to an empirical water balance model (Schreiber 1904; Kjerfve 1990), using annual precipitation and temperature data to estimate a mean or annual runoff ratio:
i1525-7541-4-6-985-e1
where Δf is runoff (mm yr−1), e0 is potential evapotranspiration (mm yr−1), and r is precipitation (mm yr−1). The local annual potential evapotranspiration is a function of the mean temperature, and thus the latitude and elevation; and is expressed empirically as
i1525-7541-4-6-985-e2
(Holland 1978), where T is surface temperature (K). The discharge q (m3 s−1) of each river was then calculated as
i1525-7541-4-6-985-e3
where a (m2) is the subarea of a basin represented by a meteorological station and n is the total number of polygons for each basin (Kjerfve et al. 1997). Similar estimates have previously been simulated for southern Belize by Heyman (1996) and Heyman and Kjerfve (1999) and the model has been verified with good success for Rio Grande, Belize, based on a comparison to limited gauging data.

c. Sediment load modeling I

The annual average erosion was first calculated using the Revised Universal Soil Loss Equation (RUSLE) (Renard et al. 1997, chapter 1, p. 15). The RUSLE model has had a number of successful validations and predictions within the United States, and is increasingly being applied to tropical regions outside the United States as more and more data become available in these regions (El-Swaify and Dangler 1982; Millward and Mersey 1999; Kinnell 2000). RUSLE calculates the erosion A (ton ha−1 yr−1) (ha: hectare) expected on field slopes as
ARKLSCP,
where R is the rainfall-runoff erosivity factor (MJ mm ha−1 h−1 yr−1), K is the soil erodibility factor (t ha h ha−1 MJ−1 mm−1; Table 2). Only A, R, and K in the equation have units. The rainfall erosivity factor R is computed as a product of the total storm energy and the maximum 30-min intensity, summed over the storms occurring through the year. The rainfall intensity data for the Belize watersheds comes from daily measurements provided by the Belize Meteorological Service. Average intensity values for the Guatemalan and Honduran watersheds were used to calculate R (Mikhailova et al. 1997; Portig 1976). The soil erodibility factor, K, is a measure of the soil properties. The soil data needed to estimate the K values were obtained from the CINDI dataset (CINDI 1999). The slope-length factor is given by L = (λ/22.1)m, where λ is the horizontal projection of the slope length (m). The value m is a variable slope-length exponent. The value of m depends on the slope angle. The slope steepness factor S is a measure of the effect of the slope on erosion. The values λ, m, and S for each grid cell of the watershed were calculated from the Digital Elevation Model (DEM; GLOBE Task Team et al. 1999), using ArcView routines.

The cover-management factor, C, reflects the effects of cropping and management practices on erosion rates. Simulations have been run with C values of 0.003, 0.10, 0.13, and 0.30 corresponding to forested land, grazing land, irrigated land, and plantations, respectively (Shen and Julien 1992).

The support practice factor, P, is the ratio of soil loss with implemented support practice, such as contouring, strip cropping, and terracing, to straight-row farming along a slope. A value of 1 for P has been used in cases where there is no information available on conservation practices, or when the model is used on larger watershed basins rather than smaller agricultural plots similar to the standard RUSLE plot. The value P is also assumed to be one when the model is applied to nonagricultural areas, since it is a ratio of soil loss before and after conservation practices on agricultural lands (Boggs et al. 2001). The main agricultural industries in the iGoH watershed (citrus and banana farming) do not apply any conservation strategy. Thus, in this study, P has been assumed to be one throughout (Millward and Mersey 1999).

The average annual sediment yield (t km−2 yr−1) is obtained by multiplying the annual average erosion values by the sediment delivery ratio (SDR) since only a portion of the eroded material ultimately reaches the river mouth. The SDR is computed from the equation
A−0.3t
where At is the drainage area of the basin (km2) (Shen and Julien 1992). The annual average sediment load (t yr−1) is obtained by multiplying the sediment yield with the drainage basin area.

d. Sediment load modeling II

Since there are no actual sediment load measurements available, we used a second model to determine sediment loads for comparative purposes. This model is based on calculating the sediment load using the basin area, relief, and mean annual temperature (Morehead et al. 2003). The long-term mean sediment load Qs (kg s−1) is given as
Qs−5R3/2A1/20.0578 × T
where R is basin relief (m), A is basin area (m2), and T is mean annual basin temperature (°C). This model is much easier to apply as compared to the RUSLE model and does not require the detailed polygon-based data of the RUSLE approach. The results from the two models were compared.

4. Results and discussion

a. Temperature and rainfall

The climate of the iGoH is controlled by the easterly trade winds and their interactions with the central mountain ridge and the intertropical convergence zone (ITCZ; Nieuwolt 1977; Martyn 1992). Along the coast of the iGoH, the climate is tropical with an average June–August air temperature of 27°C, and an average January–March air temperature of 24°C. The annual average temperatures in the iGoH basins are shown in Fig. 3. The precipitation follows a north–south gradient, increasing towards the south. Easterly waves bring intense rains to the coast of southern Belize, where the annual rainfall exceeds 4000 mm. However, rainfall is reduced along the Honduran coast of the iGoH, because it trends east–west, almost parallel to the prevailing wind direction. The isohyets of mean annual precipitation (Fig. 2) over the watershed of the iGoH indicate regions of pronounced maxima along the coast in southern Belize and Guatemala (adapted from Portig 1976). On the other hand, the interior of the Motagua River valley is semiarid and lies in a rain shadow.

The monthly precipitation and temperature data for the regional meteorological stations (Figs. 4a,b) are mostly similar. The monthly temperature variability is small for all six stations with the temperature in Guatemala City being significantly lower because of the higher elevation (1500 m). Precipitation exhibits maxima in May–June and in September–October, with a pronounced minimum in July–August. Tropical storms cause most of the rainfall in October and November. This bimodal distribution of precipitation, referred to as the midsummer drought (Magaña et al. 1999), is prevalent in Mexico and along the Pacific coast of Central America. An absence of bimodal precipitation distribution in Punta Gorda and La Ceiba is a result of the local orography and is not representative of the entire western Caribbean. Based on our analysis, the precipitation data clearly indicate that most of the iGoH and the western Caribbean exhibit a bimodal precipitation pattern, for example, Corozal, Belize City, Central Farms, Guatemala City, and Kingston (Fig. 4a).

b. Estimated discharge and sediment load

The major rivers Moho, Sarstún, Dulce, Motagua, and Ulua, in addition to many smaller streams, empty into the iGoH. The calculated average discharges for the regional rivers are presented in Table 3. The cumulative area of the drainage basins bordering the iGoH measures 42 408 km2 and yields a mean discharge of 1232 m3 s−1. The largest drainage basin is that of Ulua, which measures 16 880 km2 with a discharge of 370 m3 s−1. Even though Sarstún, Dulce and Moho have significantly smaller drainage basins compared to Motagua and Ulua, they have proportionally higher discharges because of greater rainfall. The isohyets of precipitation (Fig. 2) over the watersheds indicate that the drainage basins of the larger rivers experience significantly less precipitation. The watershed of Polochic-Dulce measures 5832 km2, exhibits 3150 mm annual rainfall, has a calculated average runoff of 313 m3 s−1, and transports fertilizers, pollutants, and nutrients from the Verapaz districts of Guatemala into the Caribbean Sea. The proportionally higher discharge rates for Sarstún, Dulce, and Moho are reflected in the high runoff ratios for these watersheds (Table 3).

Wide flood plains and meanders characterize the lower flood plain of the Motagua River (Yáñez-Arancibia et al. 1999). The northeast–southwest-trending Cayman Trench strikes through the Motagua basin as a depression. The Ulua is the largest river in Honduras, and its drainage area is characterized by a series of vegas, or discontinuous terraces, 10–15 m above sea level (Schortman and Urban 1995). The watersheds of both the Motagua and the Ulua contain extensive agriculture fields and banana plantations (Fig. 5).

A sensitivity analysis was carried out to assess the dependence of discharge on temperature and rainfall. An increase or decrease in the temperature [Eq. (2)] by 1°C results in a decrease or increase, respectively, in the discharge by 5%. A change in the annual rainfall rate by +100 and −100 mm results in a change of +8.6% and −8.4% in discharge, respectively. The use of the discharge model seems reasonable for the smaller watersheds; whereas the larger watersheds would benefit from being subdivided into additional polygons based on elevation (Kjerfve et al. 1997). The need for river discharge simulations is necessitated by the lack of local river gauging.

Sediment load for the regional drainage basins was calculated based on the two empirical models (Table 3). The total sediment load calculated from the RUSLE model is 21 × 106 t yr−1, and from the Morehead et al. model is 25 × 106 t yr−1. The Morehead et al. equation results in slightly greater sediment loads on average as compared to the RUSLE model, but the difference could be reduced by adjusting the values of the parameters in the RUSLE equation. The Morehead et al. model does not explicitly include land use, which is represented by the C factor in the RUSLE model. The sediment loads from the two models were compared using a paired t test, and there was no significant difference between the means (t = 1.46) with 9 degrees of freedom at the 95% level of significance. The results from the two models agreed to within ±2.3%. The availability of more comprehensive data on precipitation intensity and land use for the iGoH watershed would presumably improve the estimation using the RUSLE model.

The sediment yields for the 12 watersheds range from 8 to 1224 t km−2 yr−1 according to the RUSLE estimates, and from 5 to 869 t km−2 yr−1 according to the Morehead et al. equation. The Polochic-Dulce watershed has the highest calculated sediment yield (sediment load normalized by basin area) among all the basins, even though its sediment load is not the highest. The high value results from higher elevation and precipitation compared to the other larger watersheds. The Camélocon-Ulua and Motagua-San Francisco watersheds have similar sediment loads (8.9 × 106 and 9.1 × 106 t yr−1) and large drainage areas, and the rivers flow across flat lands. Sediment load has a high correlation with discharge (R2 = 0.9) and sediment yield also correlates well with basin area (R2 = 0.7) (Figs. 6a,b).

Changes in land practices, including clearing of forests, agriculture, and implementation of conservation practices have been included in the simulations of sediment load by changing the C value in RUSLE. This allows the assessment of the importance of land cover on sediment loss. Two additional simulations were run with C values of 0.3 (less land cover, representing high erosion) and 0.003 (high land cover, representing very little erosion) for the entire watershed, which were the extreme limits used in the model. The additional simulations indicate that increased cover plays a great role in conserving sediment and lowering sediment load in the rivers (Figs. 6a,b). The increase in sediment load when C = 0.3 is a maximum of 5 times greater for Motagua, and averages 3 times the present best estimate. However, the decrease in sediment load when C = 0.003 is much greater; by as much as a factor of 100 times smaller for Deep River, and on average smaller by a factor of 41. Hence, implementation of proper land use and conservation measures in the iGoH watersheds assumes urgent importance because a large area of the region is now subject to both commercial and slash-and-burn (milpa) agriculture, which accelerate sediment loss. The effects of varying the C factor on the sediment load and yield for the rivers in the iGoH are shown in Figs. 6a,b.

The average sediment yield for the iGoH rivers is 593 t km−2 yr−1, with the total sediment load being 25 × 106 t yr−1. The sediment yield is very similar to a set of rivers in Colombia, located in the Caribbean basin (541 t km−2 yr−1; Restrepo and Kjerfve 2000). For large rivers, a considerable portion of the sediment load may be trapped in the delta. This is not applicable to the iGoH rivers, and the entire sediment load will mostly be discharged into the lagoon or the Gulf of Honduras. Increased sediment loads could potentially affect the growth, or worse, the very existence, of the reefs (Woolfe and Larcombe 1999). It is a chronic threat that delays reef recovery. Edinger et al. (1998) found that stresses from land-based sources of pollution, including sediments, resulted in 40%–70% reduction in coral species diversity in Indonesia. The destructive effects of sedimentation on the reefs of the Bay Islands in Honduras have been documented (Harborne et al. 2001). The sediment load carried by the Monkey, Sarstún, Dulce, Motagua, and Ulua Rivers is a continuing threat to the reefs in the southern region of the MBRS, including the Sapodillas. This situation can be improved only with better land management practices in the watersheds. Certainly, nutrients, pesticides, herbicides, and chemicals often adhere to sediment particles and constitute an additional threat (Stednick et al. 1995), although not treated here.

c. El Niño–La Niña variability

The El Niño–La Niña cycle has been correlated with weather patterns worldwide (Kousky et al. 1984; Ropelewski and Halpert 1987; Rogers 1988). Ropelewski and Halpert (1987) found a weak relationship between precipitation and the El Niño–Southern Oscillation (ENSO), or El Niño–La Niña, in southern Mexico, Guatemala, southward into Panama, and eastward into the Caribbean. Rogers (1988) examined 300 stations to suggest that significant large-scale precipitation variability occurs over the Caribbean and tropical Americas during the extremes of the El Niño–La Niña cycle. Based on a canonical correlation analysis between the Caribbean rainfall and sea level pressures (SLP) and SST over the eastern Pacific and Atlantic Oceans (1951–80), 30% of the Caribbean rainfall is explained by the interannual climatic variability of the eastern Pacific or Atlantic Oceans and their effects on the tropical Atlantic SST (Giannini et al. 2000).

The occurrence of El Niño and La Niña is one of the most important modulators of the Atlantic tropical storm activity (Fitzpatrick 1999). An average of five hurricanes occurred annually between 1851 and 2000, and there were an additional three tropical storms each year. Examination of the data shows that during El Niño years, hurricanes occur four or less times, and during La Niña years, they occur six or more times. The reduced frequency of tropical storms during El Niño years is more consistent than the increased frequency during La Niña years.

The regional meteorological stations exhibited higher and more variable precipitation between July and December, whereas the first half of the year is relatively dry. The yearly data was split into four sets of three months each: January–February–March (JFM), April–May–June (AMJ), July–August–September (JAS), October–November–December (OND). There is no specific pattern seen in the correlations, but temperature shows higher correlation with SOI than precipitation. Guatemala City has 5%–15% of its precipitation correlated with SOI throughout the year, whereas 8%–22% of Kingston's rainfall during nine months is from the influence of SOI. A majority of the temperature–SOI correlations lie between 4% and 23%; 22%–32% of Kingston's temperature and 5%–27% of Guatemala City's temperature are influenced by SOI, whereas only the JFM temperature of Punta Gorda is strongly influenced by SOI (41%). La Ceiba's temperature correlates well over six months (January–June, 29%–86%). The iGoH lies between the Gulf of Mexico, which shows positive rainfall correlations with the El Niño phase, and northern South America, which shows negative rainfall correlations with El Niño (Ropelewski and Halpert 1987). It thus exhibits the characteristics of both the regions and is also affected by the climatic variabilities of the Atlantic and eastern Pacific (Giannini et al. 2000; Enfield and Alvaro 1999). Our results confirm that the variability in the western Caribbean does not strongly depend on ENSO events. Local or regional orographic and climatic conditions influence the temperature and precipitation patterns more than global weather cycles.

5. Conclusions

The inner Gulf of Honduras (iGoH) receives on average 1232 m3 s−1 of freshwater discharge from 42 408 km2 of drainage area, including from seven rivers with a mean discharge in excess of 100 m3 s−1. The drainage basins are characterized by calculated runoff ratios of 0.30–0.55. The composite sediment load from the watersheds is 25 × 106 t yr−1, and the sediment yield for the 12 main watersheds ranges from 8 to 1224 t km−2 yr−1. The sediment load and yield are sensitive to the degree of land cover as estimated through the C factor in the Revised Universal Soil Loss Equation (RUSLE). Applying C = 0.003 corresponds to natural land cover (less erosion). It decreases sediment load by as much as a factor of 100 as compared to the actual situation. Applying C = 0.3 corresponds to well-developed agriculture (high erosion) with an increase in the sediment load by as much as a factor of 5 as compared to the actual situation. The present best estimates are thus closer to the high-erosion scenario, stressing the need for implementation of conservation strategies to reduce erosion. The high sediment load is a potential threat to the MesoAmerican Barrier Reef (MBRS) which extends through the iGoH. The MBRS is the site of a number of marine conservation areas, and is also one of the most important tourism destinations in Central America.

Time series of precipitation for the iGoH exhibits bimodal distribution with maxima in May–June and in September–October, similar to the distributions in the rest of the Caribbean and eastern Pacific regions. Local orography, however, destroys the bimodal pattern in the southeastern coast of Belize and the northern coasts of Guatemala and Honduras. The SOI explains only 7%–15% of the precipitation and temperature variability for the iGoH. Less than 5% of all tropical storms that pass through the western Caribbean impact the iGoH.

Acknowledgments

This work was supported by a grant from the Nature Conservancy's Ecosystem Grant/Mellon Foundation (“Physical–biological coupling in a tropical bay with implications for conservation and environmental management: Inner Gulf of Honduras” to Kjerfve and Heyman). Thanks are due to Dr. Venkat Lakshmi for comments on the manuscript, Mr. Francisco Agreñal, UTOH/SERNA, for the isotherms of Honduras, Mr. Justin Hulse, National Meteorological Service, Belize, for the isohyets and isotherms of Belize, E. Majzlik, K. Sattison, and B. Glett for help with map graphics, and to Sahadeb De for help with the L factor calculation in the RUSLE model. The base maps for the figures have been adapted from ESRI online data (http://www.esri.com/data/online/index.html).

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Fig. 1.
Fig. 1.

Map showing the watershed (light gray) of the inner Gulf of Honduras. Locations with measurements are Corozal (precipitation), Belize Airport (precipitation, temperature), Central Farms (precipitation), Punta Gorda (precipitation), La Ceiba (precipitation), Guatemala City (precipitation, temperature), and Kingston (precipitation, temperature)

Citation: Journal of Hydrometeorology 4, 6; 10.1175/1525-7541(2003)004<0985:HAVOWD>2.0.CO;2

Fig. 2.
Fig. 2.

Isohyets of mean annual precipitation (mm) in the watersheds of the inner Gulf of Honduras

Citation: Journal of Hydrometeorology 4, 6; 10.1175/1525-7541(2003)004<0985:HAVOWD>2.0.CO;2

Fig. 3.
Fig. 3.

Isotherms of mean annual temperature (°C) in the watersheds of the inner Gulf of Honduras

Citation: Journal of Hydrometeorology 4, 6; 10.1175/1525-7541(2003)004<0985:HAVOWD>2.0.CO;2

Fig. 4.
Fig. 4.

(a) Comparison of mean monthly precipitation (mm) for Corozal (plus sign, 30 yr), Belize City (star, 51 yr), Central Farms (diamond, 32 yr), Punta Gorda (square, 39 yr), all in Belize; La Ceiba, Honduras (circle, 36 yr), Kingston, Jamaica (triangle, 48 yr), and Guatemala City, Guatemala (inverted triangle, 35 yr). (b) Comparison of mean monthly temperatures (°C) for Belize City (star, 56 yr), Central Farms (diamond, 11 yr), Punta Gorda (square, 6 yr), La Ceiba, (circle, 6 yr), Kingston, (triangle, 48 yr), and Guatemala City, (inverted triangle, 35 yr)

Citation: Journal of Hydrometeorology 4, 6; 10.1175/1525-7541(2003)004<0985:HAVOWD>2.0.CO;2

Fig. 5.
Fig. 5.

Land cover map of the inner Gulf of Honduras. The land cover types shown include crop land (dot), forest (open diamond), irrigated land (wavy line), nonirrigated land (back-slash), grazing land (inverted v), sand (white space), wetland (circles and dots), and forested wetland (gray shade)

Citation: Journal of Hydrometeorology 4, 6; 10.1175/1525-7541(2003)004<0985:HAVOWD>2.0.CO;2

Fig. 6.
Fig. 6.

(top) Sediment load (t yr−1) as a function of discharge (m3 s−1) and (bottom) sediment yield (t km−2 yr−1) as a function of basin area (km2) from applying the RUSLE model to each of the 12 watersheds in Table 3 for three cases of model parameter C. Open diamonds denote results from C values varying by land use, as given in section 3c, while solid triangles and solid squares, respectively, represent universal use of C = 0.3 (high erosion potential) and C = 0.003 (low erosion potential). The linear best-fit line is shown for each C-value case, associated with a correlation of (top) 0.9 and (bottom) 0.7

Citation: Journal of Hydrometeorology 4, 6; 10.1175/1525-7541(2003)004<0985:HAVOWD>2.0.CO;2

Table 1.

Calculated correlation coefficients between precipitation and the Southern Oscillation Index (SOI) and temperature and SOI based on mean monthly data. Punta Gorda, La Ceiba, and Guatemala City lie within the drainage basin. Low rainfall is associated with the warm phase (El Niño) and high rainfall is associated with the cold phase (La Niña)

Table 1.
Table 2.

Explanation of the variables used in the RUSLE equation and their units. The values are assigned based on an assessment of the individual polygons. The slope-length and slope steepness factors vary for each polygon and are specified in the GIS grid

Table 2.
Table 3.

Characteristics of the inner Gulf of Honduras watersheds as calculated from (i) the runoff-ratio-based discharge equation, (ii) the RUSLE-based sediment load equation (SL—R), and (iii) the sediment load equation by Morehead et al. (SL—M )

Table 3.
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