DETERMINING AND APPLYING THE RESPONSE OF EPHEMERAL LAKES TO EXTREME EVENTS USING EO

Dr Robert G. Bryant and Michael P. Rainey SCEOS and Department of Geography, University of Sheffield, Winter Street, Sheffield S10 2TN, UK, Phone Number: +44 (0)114 222 7966, Fax Number: +44 (0) 114 222 7912 Email address: [email protected]

Abstract It is known that closed lake-volumes fluctuate in response to changes in evaporation and precipitation rates within their catchment basin. Measurement of lake-volume changes are, therefore, not only important for hydrological and economic purposes, but can also provide a climate record. By integrating the precipitation over the catchment basins, they can provide a fuller picture of precipitation changes than the more localised in situ measurements. Playas are common features of closed, arid basins, and are the most sensitive of closed systems to regional climate changes. However, as playa systems operate under a high-evaporation/low-rainfall regime (often > 20:1), hydrological changes are not generally expressed in terms of simple volumetric changes within a lake. Instead, playas record changes in terms of variations in the timing, magnitude, frequency and residence time of specific ephemeral flooding events; generally a much more complex response, that is less well understood Using a time-series of NOAA-AVHRR data (1979-present), and associated climate data for playas in southern , appropriate methods are presented for the accurate monitoring and detection of ephemeral flooding within specific catchments. Results have led to a greater understanding of playa response to of these often-extreme events, and further analyses of specific large have shown that values of P-E (or the hydrologically effective precipitation, PE) can be estimated for specific dryland catchments by inverting a simple water-balance model allied to an EO-derived curve of basin volume against area. These observations may make an important contribution to the wider monitoring and understanding of precipitation/evaporation changes within drylands using closed-lake systems.

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Introduction General Circulation Models (GCMs) predict significant 21st climate changes that are likely to increase drought frequencies and intensities in subtropical and temperate drylands (e.g. Hulme, 1992). Irrespective of the nature of forcing mechanism, empirical data suggest that hydrological and ecological system responses to these changes are likely to be rapid and un-buffered. Most perennial lakes within drylands respond in a simple and well-understood fashion to changes in the balance of precipitation and evaporation (P-E) over the lake and catchment by changing in volume; and hence predictably in both area and level (Street- Perrott and Harrison, 1985). For closed lakes, the area of the perennial lake represents equilibrium between run-off from the catchment and the water deficit over the lake (Khofield and Harrison, 2000). Although changes in lakes can be caused by local, non- climatic factors, regionally synchronous changes are reasonably assumed to be climatically determined (Mason et al., 1994). Measurements of lake-area/volume changes are, therefore, not only important for hydrological and economic purposes, but can also provide a proxy climate record (Birkett, 2000). In more arid dryland regions, however, perennial lakes are rare. In particular, playas (arid, ephemeral lakes without surface outflow) are more common. These systems have been recognized as being the most sensitive of closed systems to regional changes in rainfall patterns, and are also commonly unaffected by contemporary human interference or management (Bryant et al., 1994ab; Bryant, 1996). However, as playa systems operate under a high-evaporation/low-rainfall regime (often > 20:1), hydrological changes resulting from changes in P-E are not generally a simple volume/area response. Instead, playas demonstrate changes in the timing, magnitude, frequency and residence time of specific ephemeral flooding events; generally a much more complex response, that is less well understood (Bryant, 1999). Nevertheless, these environments, if monitored properly, may have a crucial role in providing contemporary and past (i.e. last 20-30 years) information relating to regionally synchronous changes in P-E for specific dryland regions for which climate data are either sparse or unreliable. These data may then be helpful in both validating GCM predictions for specific drylands, and helping to understand past sedimentary records from playas basins (e.g. Sweezey et al., 1999) The aim of this research was to examine the application of a time-series of EO data (NOAA-AVHRR) to the study of climatically sensitive (e.g. Richards and Vita-Finzie, 1982; Rognon, 1987) playas in southern Tunisia and eastern Algeria (principally the el ). Specific objectives of the study were to: (i) monitor monthly changes in lake areas and seasonal water balance, (ii) determine the detailed hydrologic response of the playa to rainfall events, and (iii) briefly examine the extent to which RS of playa flooding may be used to give important information regarding contemporary changes in regional rainfall patterns within other parts of North Africa. A summary of the principal findings of this work are presented here. More detailed results will be available shortly (Bryant and Rainey, in prep; Bryant et al., in prep)

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Study Area This closed basin forms part of the Zone of , a large lowland depression that stretches from southern Tunisia to central Algeria. The Chott el Djerid basin forms the northeastern and one of the lowest extremities of the Bas Saharan Basin. This artesian basin covers most of the Algerian and Tunisian and extends to Morocco and Libya, enclosing the whole of the (Roberts and Mitchell, 1987). The Chott el Djerid is an ephemeral salt playa situated in an arid-zone closed basin which has a catchment area of 10,500km2 The Chott el Djerid itself has a surface area of approximately 5360 km2, and is situated at a latitude of around 340N (Bryant et al., 1994). The climate in the Chott el Djerid basin is Pre-Saharan. A dry period occurs for 7-8 months between April and November. Average temperature recorded at Kibili in January is 9.4 0C. For the same station, the average July temperatures are 32 0C. The mean annual temperature for this region is 20.9 0C. Mean annual rainfall in Kibili is 89 mm rising to approximately 150mm in Gafsa. The region of North Africa studied is characterized by a wet-and-dry season regime; the winters being cool and generally wet (dominated by secondary depressions), and the summers dry and hot (dominated by the expanded north Atlantic anticyclone). Extreme rainfall events in this region (e.g. the winters of 1969 and 1990) have generally been associated with occasional desert depressions, which intensify as they move eastward along the Atlas Mountains. Most of the rainfall therefore occurs as inland orographic or convectional thunderstorms (Berndtsson, R., 1989). Evaporation at Kibili is at a maximum in the dry season, reaching between 2520 and 2550 mm a-1 (almost 30 times the mean annual rainfall). Alternations of wetter and drier periods throughout the Pleistocene and Holocene in this part of Tunisia have been recognised for some time (e.g. Richards and Vita-Finzi 1982; Rognon, 1987; Fontes and Gasse, 1989; Causse et al., 1989; Swezey et al., 1999).

Figure 1. AVHRR (1,2,1) image from January 1990 showing the location of the main closed ephemeral basins within the "Zone of Chotts". This image was collected just after a severe rainfall event (1:20) which affect the whole region, and resulted in the flooding of the ephemeral lakes.

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Data Procurement The Institut National de la Meteorologie, Tunis provided detailed climate data for Tunisia, covering the period 1979 to 1998. Annual climate almanacs from the same institution were also used to provide decadal (10-day) precipitation and evaporation figures for selected years. Latterly, climate data for Algeria were acquired via the NOAA National Climate Data Center. AVHRR data (Appendix) covering the same period were obtained from the NERC satellite receiving station at Dundee University. Suitable cloud-free HRPT images of the ephemeral lakes were identified over the Internet and subsequently ordered from the archive in level 1B - NOAA format, with associated calibration files. In all, a total of 154 images representing monthly intervals spanning the period 15th October 1983 to 23rd March 1996 were obtained. Each image was geo-rectified (1st order), with sun-angle and radiometric corrections also being applied. Following this, the data were re-projected to a latitude/longitude co-ordinate system and spatially sub-set to the regions needed for further analyses. The image sub- sets then underwent an image-to-image geo-correction, which defined a consistent pixel- size for the data set (1.1km; Wu and Liu, 1997). Landsat MSS imagery from 1990 of Southern Tunisia was supplied to this project by NERC. Using this higher spatial resolution data, the objective was to test the accuracy of the AVHRR lake area detection technique (see Section 3.2.3).

180 600 El Ninio La Ninia 160 500 140 120 400 100 300 80 60 200 Evaporation (mm) Precipitation (mm) 40 100 20 0 0

O-83 A-84 A-84 A-85 A-85 J-86 M-86 S-86 F-87 J-87 O-87 F-88 J-88 M-89 J-89 D-89 A-90 S-90 J-91 A-91 J-92

Figure 2. Climate data for southern Tunisia. Evaporation (yellow), Precipitation (blue), ENSO events are also labeled.

Results

Determining Flooding Styles within the Zone of Chotts using EO. Flooding within a closed ephemeral system is principally a function of either direct precipitation (often strongly seasonal, with high inter-annual variation), or groundwater inflow (which can be a direct function of seasonal evaporation rates) (7). Flooding from either mechanism takes place in four key flooding stages (1 = Flooding, 2 = Evaporative Concentration, 3 = Brine pool, 4 = Desiccation; Bryant et al., 1994); each of which invoke major changes on the surface reflectance properties of the lake-bed (Bryant, 1999). Using such knowledge, this study has demonstrated that graphs of temporal reflectance variability within a time-series of AVHRR band-2 data can be used to define the flooding style of a particular ephemeral system (Drake and Bryant, 1994, Bryant,

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1999). In this study, temporal reflectance profiles were extracted from areas of high coefficient of variation (CV) within the image time series (Figure 2). Importantly the simple technique developed here can be applied to ephemeral lakes with areas of > 10km2 in order to both identify when flooding occurs, and to determine the main hydrological controls on flooding (Bryant, 1999). For this study this approach allows ephemeral systems that are predominantly rainwater-fed (e.g. Chot el Djerid, Chott el Rharsa) to be differentiated from those with a groundwater-dominated or mixed regime (e.g. Chott el Melrir).

60

50 )

40

30

20 Surface Reflectance (DN

10

0 Oct-91 Oct-90 Oct-89 Oct-88 Oct-87 Oct-86 Oct-85 Oct-84 Oct-83 Jun-92 Jun-91 Jun-90 Jun-89 Jun-88 Jun-87 Jun-86 Jun-85 Jun-84 Feb-92 Feb-91 Feb-90 Feb-89 Feb-88 Feb-87 Feb-86 Feb-85 Feb-84

Figure 3. Contrasting lake flooding regimes under similar climatic conditions. (a) Chott el Melrir (blue) demonstrates a strongly seasonal variability in reflectance, largely resulting from groundwater and river inflow into the basin. (b) In contrast, Chott el Djerid (red) is primarily affected by the extreme rainfall events of 1990, and 1991, but also shows some seasonal dips in reflectance, related to a combination of smaller seasonal rainfall inputs and higher groundwater levels. (c) Similar to the Chott el Djerid, Chott el Rharsa (black), is predominantly affected by the extreme rainfall event of January 1990 and December 1991, but not by smaller, seasonal events.

Improved Lake Area Detection for Ephemeral Lakes. Generally, the relatively poor spatial and spectral resolution of the AVHRR scanner makes its use in the mapping and detecting the areas and boundaries of most water bodies rather difficult (Cracknell, 1997). However, it has been shown that large closed basins (>100km2), whose areas vary greatly, can be successfully monitored using time series of AVHRR data (Bryant, 1999). In these cases, it is the temporal resolution of the sensor, and the existence of complete/accessible archives of data (e.g. Verdin, 1996) that make it a viable proposition. To facilitate its use, a number of techniques have been developed in order to derive lake areas from AVHRR (see Bryant, 1999). The approach used here is a simple histogram manipulation technique (Harris and Mason, 1989; Harris, 1994) , and several innovations have been made to it during the course of this research in order to allow its application to ephemeral systems:

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Variable Area of Interest (AOI). This technique relies on an even distribution of land and water pixels within each scene in order to extract accurate lake areas. In contrast to perennial lakes, which have relatively stable areas, ephemeral lake areas commonly fluctuate greatly on an annual and inter-annual basis. As a result methods were developed to extract histogram values from close fitting AOI’s that vary in size as the lake area changes over time. This has greatly increased the accuracy of histogram extraction from image data (Figure 3b).

Figure 4. AVHRR band-2 image (left), and Landsat MSS band -3 image (right), of Chott el Rharsa (both acquired 23-02-90) Accurate Curve Fitting. In order to optimize the separation of land peaks from water peaks within image histograms, methods were devised for (a) clipping histograms, (b) curve fitting the data, and (b) using the 1st differential to determine the boundary between peaks. This proved to be an accurate, fast generic approach that greatly improved the accuracy and reliability of lake extraction from image data (Figure 3a).

175 Lake Land 125

75 Frequency (f)

25

-25 13579111315171921 Bin

Figure 5. Clipped histogram (blue) showing a water peak on the left, and a land peak on the right. A polynomial is fitted between the peaks (green), and the differential of this curve (red). Validation of Lake Areas. A number of workers have also used a similar histogram clipping routine on a range of lake types. For Loch Neagh (Harris and Mason, 1989), AVHRR band-2, and the night-time thermal bands 4 and 5 gave areas within 1% of true. For ephemeral lakes in Niger (Verdin, 1996) errors in lake area ranging from 4% (for large lakes > 100 ha) to over 100% (for small lakes < 20 ha) were observed.

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Figure 6. AVHRR and MSS images of Chott el Rharsa (23-02-90). Both demonstrate the extent of detected lake (blue) produced after the lake-area clipping. In this study AVHRR data were validated against co-incident Landsat MSS data for three dates in 1990, when flooding was known to occur; one of which involved an algal bloom (which had a specific correction applied to it). Results of this process (see Appendix for full data) revealed accuracy's of 1-3% for large lakes (>400 km2), 2-6% for intermediate lakes (100 - 400km2), and 5-13% for small lakes (<100 km2). Given the coarse resolution of the sensor used (1 pixel = 1.1km), these results are significant, and underline both the viability and reliability of this approach to the monitoring of any large ephemeral system (> 100km2) that responds primarily to changes in the precipitation/evaporation balance within its basin.

Image Date AL AL Area Diff. Difference AVHRR (km2) MSS (km2) (km2) (%) a) Chott el Djerid 23.02.90 842.72 850.69 -19.53 0.94 29.03.90 619.22 632.71 -13.49 2.13 09.02.90 953.17 928.74 +24.43 2.63 Algae Correction 953.17 930.98 +22.19 2.34 b) Chott el Rharsa 23.02.90 346.43 342.54 +3.89 1.11 29.03.90 346.28 331.07 +15.20 4.59 09.02.90 375.45 310.75 +64.70 20.81 Algae Correction 375.45 352.27 +23.18 6.17 c) Chott el Geuttar 23.02.90 42.14 44.48 -2.34 5.27 29.03.90 38.47 44.39 -5.92 13.33 09.02.90 47.63 44.61 +3.02 6.78 Table.1 Summary of lake area validation data from 1990.

Development of a simple Hydrological Model for Ephemeral Lakes using EO. Dynamic changes within a closed lake basin generally result from the interaction of surface water, sediments, and groundwater (Bryant et al., 1994b). The hydrological component of such change for most closed systems can be defined simply in terms of changes to the catchment hydrological balance (Bryant et al., 1994a). Using time- averaged data, this can be expressed as follows (Mason et al., 1994; Bryant, 1999):

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dV/dT = R - A (E - P) - D + Gi - Go (eq.1) If we take a typical scenario for a flood event within a playa basin, the general water balance can be simplified, and expressed as follows: dV/dT = -A.dE/dT (eq.2)

which by substituting Vo = B.P can be re-written as: dA/dT = -A .dA/dV (eq.3) Importantly, this shows that if we can determine A (or V) from eq.3, we can then: (i) estimate P from observations of Ao, and (ii) estimate E(t) from observations of A(t). For the Chott el Djerid (a large playa currently dominated by P inflow: see Section 3.1) the time-series of image data depicting A for a range of flooding events (1982-1996) showed a very strong correlation with both P and P-E (Figure 4). Consequently, for this and similar playas, both P and P-E can be estimated from EO through simple inversion of this relationship.

1400 y = 0.021x2 + 7.4651x + 669.85 1200 2 1000

) R = 0.9421 2 800 y = 4.5204x + 672.25 600 R2 = 0.8087 400 200 Lake Area (km 0 -250 -200 -150 -100 -50-200 0 50 100 -400 Effective Precipitation (mm)

Figure 7. The relationships (both linear and polynomial) between P-E (effective precipitation) and lake areas extracted from EO data for individual flooding events the Chott el Djerid (1982-1996) To estimate E from these data, however, a clear relationship needs to exist between the lake area (A) at any given time, and total volume of water contained within the lake (V). Most playas lack any surface height or bathymetric data that would allow V to be estimated from A (e.g. Prata 1990) . However, for the duration of any individual flood event, and in particular the during evaporation/desiccation phase, we can use EO data to gain necessary information on the geometric/bathymetric nature of the basin. For example, we can relate the changes in A observed by EO over the course of the drying of an ephemeral lake to the geometric shape of the basin via the following relationship: b A/A0 = (H/H0) (eq.4) where b is simple a shape-factor with values of b > 0 (important values of b are: 0 = cylinder, 1 = sphere, 2 = cone). From this we can derive: -1/b dA/dV ∝ A (eq.5) which can also be written as: -1-1/b dA/dE ∝ - A (eq.6).

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Importantly, eq.6 is in a form that can be calibrated using the sequential EO data outlined in Section 5, and any available ground E(t) information for at least one flood - and then independently applied to estimate E for any subsequent floods. To test this assumption, two separate flood-desiccation events (1990 and 1991; Bryant et al., 1994b) were studied on the Chott el Djerid; in this case using AVHRR data collected with a 10- day time-interval over to maximise data confidence. The results (Figure 4b) show a relatively smooth and predictable contraction in lake area with increasing evaporation. As a result of solving eq.6 using data extracted for each of these floods (Figure 4b), a consistent shape factor of ≈1.9 was generated, suggesting a basin architecture approximating that of a flattened cone. Consequently, V/A curves were then fitted to the data (for varying water depths) in order estimate the volume of water on the playa for any given lake area. Significantly, these curves can also be used to allow the simple estimation of E(t) to be made for any subsequent flooding event (Prata, 1990).

1200

1000 ) 2 800

600

400 Lake Area (km 200

0 0 500 1000 1500 Cumulative Evaporation (mm)

Figure 8. The relationship between contracting lake areas (from EO) and measured cumulative evaporation on the Chott el Djerid from a large flood event in 1990 (Jan- March).

SUMMARY AND CONCLUSIONS This project has sought to develop EO methods for understanding the short-term changes in the flooding regimes of ephemeral lakes, using north African lake systems as a test bed. Such an understanding is important for either estimating ephemeral system response to regionally synchronous climatic events/changes, or deriving simple climatic information from drylands regions for which in situ climate data are either sparse or unreliable. From the work undertaken here, the key scientific and practical achievements are as follows: (i) Development of simple methods for measurement and assessment of the flooding regime of individual playas - allowing the hydrological response to either groundwater or precipitation inflows to be determined, (ii) The development of improved generic methods for the reliable extraction of ephemeral lake areas from coarse-resolution data, (iii) The successful extraction of a long time series of lake areas from EO of selected playas on a weekly/monthly time-scale, and

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(iv) The development of simple hydrological models for key ephemeral lakes (e.g. those whose hydrologic response is directly linked to changes in P) allowing the prediction of both P and E from image data over large areas given minimal climate data. The results of this project are generic (limited only by EO availability), and therefore have important implications for both contemporary and past study of climate and hydrologic changes within dryland regions that include ephemeral lake systems as important terminal hydrologic components.

Acknowledgements This work was undertaken as part of a NERC funded research program(GR8/03696). RGB wishes to thank: Rob Ferguson for advice concerning hydro-modelling, Chris Clark for useful comments and suggestions throughout, Nick Drake for initial inspiration on many entertaining trips to Tunisia, Kevin White for help with climate data, and general encouragement, Jane Wellens for starting me on the AVHRR road, Jeff Settle for sharing thoughts on atmospheric correction, Neil Lonie at the Dundee Satellite Receiving Station, Judith Fox at the Geography Map Library in Reading, and the Royal Society for help with funding to attend this conference.

References Birkett CM , 2000. Synergistic remote sensing of Lake Chad: Variability of basin inundation, Remote Sensing of the Environment, 72: (2) 218-236 Bryant, R. G., Drake, N. A., Millington, A. C. and Sellwood, B. W., 1994a, The chemical evolution of the brines of Chott el Djerid, Southern Tunisia, after an exceptional rainfall event in January 1990., in: Renaut, R. W. and Last, W. M. (eds): The Sedimentology and Geochemistry of Modern and Ancient Saline Lakes., SEPM SP#50, p. 3-12 Bryant, R. G., Sellwood, B. W., Millington, A. C., and Drake, N. A., 1994b, Marine-like potash evaporite formation on a continental playa: case study Chott el Djerid, southern Tunisia. Sedimentary Geology, 90(3-4), 269-291 Bryant, R. G., 1996, Validated linear mixture modelling of LANDSAT TM data for mapping evaporite minerals on a playa surface. International Journal of Remote Sensing. 17(2), p 315-330. Bryant, R. G., 1999, Monitoring climatically sensitive playas using AVHRR data. Earth Surface Process and Landforms, 24, 283-302 Berndtsson, R., 1989, Topographical and coastal influence on spatial precipitation patterns in Tunisia. International Journal of Climatology, 9, 357-369. Cracknell, A., 1997, The Advanced Very High Resolution Radiometer (London: Taylor and Francis) Drake, N.A. and Bryant, R.G., 1994, Monitoring the flooding ratio of Tunisian playas using advanced very high-resolution radiometer (AVHRR) imagery. Environmental Change in Drylands Biogeographical and Geomorphological Perspectives, Millington, A.C. and Pye, K. (Eds.). John Wiley & Sons: London.

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Harris, A.R., 1994, Time series remote sensing of a climatically sensitive lake. Remote Sensing of the Environment, 50, 83-94. Harris, A. R., and Mason, I. M., 1989, Lake area measurement using the AVHRR. International Journal of Remote Sensing, 10, 885-895 Hulme, M., (1992) Rainfall Changes in Africa: 1931-1960 to 1960-1990. International Journal of Climatology, 12, 685-699 Kohfeld, K. E., and Harrison, S. P., 2000, How can we simulate past climates? Evaluating the models using global palaeoenvironmental datasets. Quaternary Science Reviews, 19, 321-346. Mason I.M., Guzkowska, M.A.J., Rapley, C.G. and Street-Perrot, F.A., 1994, The response of lake levels and areas to climatic change. Climatic Change, 27, 161-197. Prata, A.J., 1990, Satellite-derived evaporation from Lake Eyre, South Australia. International Journal of Remote Sensing, 11, 11, 2051-2068. Richards, G. W. and Vita-Finzi, C., 1982, Marine deposits 35000-25000 years old in the Chott el Djerid, southern Tunisia. Nature 295: p.54-55. Roberts, C. R. and Mitchell, C. W., 1987, Spring mounds in southern Tunisia, in: Frostick, L. and Reid, I. ed: Desert Sediments: Ancient and Modern, Geological Society Special Publication 35, London. p.321-334. Rognon, P., 1987, Late Quaternary climatic reconstruction for the Maghreb (North Africa).” Palaeogeography, Palaeoclimatology, Palaeoecology, 58: p.11-34. Street-Perrot, F. A., and Harrison, S. P., 1985, Lake levels and climatic reconstruction, in Hecht, A. D., (ed.) Palaeoclimate Analysis and Modelling. (New York: Wiley) Swezey, C., Lancaster, N., Kocurek, G., Deynoux, M., Blum, M., Price, D., Pion, J. C., 1999, Response of aeolian systems to Holocene climatic and hydrologic changes on the northern margin of the Sahara: a high-resolution record from the Chott Rharsa basin, Tunisia Holocene 9: (2) 141-147 Verdin, J. P. 1996, Remote sensing of ephemeral water bodies in western Niger. . International Journal of Remote Sensing, 17, 4, 733-748. Wu, B.F., and Liu, H.Y., 1997, A simplified method of accurate geometric correction for NOAA AVHRR 1B data. International Journal of Remote Sensing 18: (8) 1795- 1808

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