Facies Analysis and Biostratigraphy of the Miocene Sequence, Cairo-Suez District, Egypt

Abdelbaset El-Sorogy; Mohamed Abd-Elmoneim; Ahmed Mowafi; Khaled Al-Kahtany; Hisham Gahlan

doi:10.1007/s12583-016-0906-2

Volume 28 Issue 1

Feb 2017

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Article Contents

Abstract

0. INTRODUCTION

1. METHODS

2. RESULTS AND DISCUSSION

3. DISCUSSION

4. CONCLUSIONS

References

Journal of Earth Science > 2017 > 28(1): 1-8.

Mingming Luo, Zhihua Chen, Dechao Yin, Hamza Jakada, He Huang, Hong Zhou, Tao Wang. Surface flood and underground flood in Xiangxi River Karst Basin: Characteristics, models, and comparisons. Journal of Earth Science, 2016, 27(1): 15-21. doi: 10.1007/s12583-016-0624-5

Citation:

Abdelbaset El-Sorogy, Mohamed Abd-Elmoneim, Ahmed Mowafi, Khaled Al-Kahtany, Hisham Gahlan. Facies Analysis and Biostratigraphy of the Miocene Sequence, Cairo-Suez District, Egypt. Journal of Earth Science, 2017, 28(1): 1-8. doi: 10.1007/s12583-016-0906-2

Citation:

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Facies Analysis and Biostratigraphy of the Miocene Sequence, Cairo-Suez District, Egypt

doi: 10.1007/s12583-016-0906-2

1.
Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
2.
Geology Department, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

More Information

Author Bio:
Abdelbaset El-Sorogy: http://orcid-org/0000-0003-0283-1433
Corresponding author: Abdelbaset El-Sorogy, elsorogyabd@yahoo.com
Received Date: 09 Jul 2014
Accepted Date: 10 Dec 2014
Publish Date: 01 Feb 2017

Abstract

Abstract

Miocene siliciclastic-carbonate deposits are widely exposed in Cairo-Suez District, Egypt. These deposits are underlain and overlain by continental sediments of Oligocene and post Miocene, respectively. Three stratigraphic sections were investigated at Gabals Geneife, Homeira and Gharra. Lithostrtigraphically, the Miocene sequence could be differentiated into two main rock units representing shallow deposits with relatively intermittent deep marine incursions. These are from base to top, Gharra Formation and Genefe Formation. Detailed macrofossils investigations led to the recognition of four macrofossil zones, namely Alectryonella plicatula-Crassostrea frondosa Range Zone, Echinolampas amplus-Scutella ammonis Range Zone, Chlamys(Macrochlamys)sardoa-Chlamys(Argopecten)submalvinae Range Zone, and Chlamys gentoni-Pecten (Oppenheimopecten)benedictus-Pecten(P.)ziziniae Assemblage Zone. Microfacies analysis and identified taxa indicated that the Miocene sequence was deposited in transgressive-regressive cycles ranged from near shore, warm shallow inner to middle shelf marine environments with storm influence during the accumulation of the oyster banks.
- facies analysis,
- biostratigraphy,
- Miocene,
- Cairo-Suez District,
- Egypt

Supplementary materials (Tables S1-S2) are available in the online version of this article at http://dx.doi.org/10.1007/s12583-016-0906-2.

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0. INTRODUCTION

According to the European Union Floods Directive, a traditional flood is an overflow of water that submerges land which is usually dry; this is generally referred to as a surface flood. However, floods can also occur in underground rivers or karst springs when the flow of water exceeds the capacity of the underground river channel or karst conduit, leading to a rapid increase in the water level. Hydrological studies have focused on surface floods for obvious reasons, while underground floods have received only cursory treatment.

Carbonate rocks are widely distributed in Southwest China where they cover an area of 469 000 km²; there are at least 2 836 underground rivers with an estimated total flow of 1 482 m³/s (Yuan, 2000). These underground rivers are an essential component of the groundwater systems and dominate the base flow of the surface streams into which they discharge.

Water resources are essential to the economy and environmental health of this region, thus accurate assessment of water availability and quality is very important (Guo and Chen, 2006; Yuan, 2000). However, the heterogeneity and complexity of karst media makes such assessment difficult. Flood analysis can be a useful approach to studying water flow media structure and characteristics. Productive and sustainable water resource management and planning is highly dependent on accurate water resource assessment and water budgeting.

The methods of water resource assessment have experienced several stages. Statistical methods were commonly used before 1980, when water balance methods became more popular. In the last decade distributed hydrological models (e.g., Wang et al., 2010; Jia et al., 2006), numerical models, and GIS and remote sensing have become widely utilized. The application of stochastic models or artificial neural networks (ANN) is now gaining interest for karst environments (Wang et al., 2010).

1. METHODS

1.1 Field Site Description

Xiangxi River is a tributary of the Yangtze River situated at Xingshan County, western Hubei Province. The river is 97 km long and has a catchment of 3 190 km². Xiangxi River has 3 branches: Gaolan River in the east, Gufu River in the middle, and Nangyang River in the west (Fig. 1). The region has a subtropical monsoon climate with four seasons and abundant rainfall. Average annual precipitation is 900–1 200 mm, with that delivered during the April–September rainy season representing 68% of the total. Due to the undulating topography and high hills, the climate varies significantly, with large inter-annual variability, spatially heterogeneous rainfall. Forest cover is 60.3%.

Figure 1. Drainage system of Xiangxi River Basin, including selected gauging stations (Menjiahe, Kongzixia, Xingshan) and large karst springs (Wulongdong, Xiangshuidong, Bailong).

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Tectonic erosion is well developed in this steep area of middle-low mountains and deep ravines, with great relief and complex karst landforms. Thick units of limestone and dolostone are widespread, mainly of Sinian, Cambrian, Ordovician, Permian and Triassic age. Non-carbonate rocks are also present, mostly clastic and metamorphic rocks of Archean, Lower Cambrian, Silurian and Jurassic age. Xiangxi River Basin is characterized by a well-developed karst system that includes steep karst depressions with numerous sinkholes, caves, grooves, and karst springs, but underground river systems are not as well developed as in the highly karstified areas of South China. Rainfall is the main recharge source of groundwater which easily percolates through sinkholes and fissures to recharge the aquifers. In some areas the groundwater flows into karst fissures or conduits, ultimately to be discharged through springs or directly into steams.

1.2 Data Collection and Processing

Many diversion hydropower plants have been recently developed in the Xiangxi River Basin. There currently are over 70 power plants on the tributaries and main streams of the Xiangxi River, but these infrastructures cannot control flood levels due to the limited capacity of the penstocks that divert the water for power generation.

The power plants maintain hourly logs of power generation, which provides a method to calculate discharge that is especially valuable in unmonitored areas (Wang, 2012). Such discharge records are available for surface streams, underground rivers and springs that are all used to generate power; examples are the springs at Wulongdong and Xiangshuidong (Fig. 1). We have collected these power plant data records for more than 5 years. The equation for gravitational potential energy can be recast to calculate discharge from power plant data

$Q = \frac{N}{{9.8H\eta }}$

(1)

where Q is the discharge (m³/s); N is the generated power (kW); H is the water head for the power plant (m); and η is the transfer efficiency (<1).

Xingshan is a gauging station, located at the confluence of the Gufu and Nanyang rivers. This station has a large repository of hydrological data that is used by the water department of Xingshan County. The flow (Q) has a quadratic relation to the water level of the reservoir (H) for this station

$Q=7.424(H-20.5)^{2}$

(2)

Similar relationships were found for the Menjiahe and Kongzixia gauging stations located on the Gaolan River, which have channel cross sections and flood processes much like those at Xingshan. Water level data were collected at these sites by an online automatic ultrasonic water level gauge (CJ800) from July 2013. Menjiahe and Kongzixia are similar to Xingshan and have similar correlations between the water level and flow. The relationship at the Menjiahe gauging station is

$Q=56.769 H^{2}+2.756 H+0.735$

(3)

and for the Kongzixia gauging station

$Q=78.909 H^{2}-3.845 H$

(4)

1.3 Darcian Model Method

A dimensionless theoretical hydrograph based on Darcy's Law describes groundwater discharge following sharp precipitation events (Criss, 2010; Criss and Winston, 2008a)

$\frac{Q}{{{Q_{\max }}}} = {\left({\frac{{2{\rm{e}}b}}{{3t}}} \right)^{\frac{3}{2}}}{{\rm{e}}^{ - \frac{b}{t}}}$

(5)

${t_p} = \frac{2}{3}b$

(6)

where Q is flow at any time; Q_p is the peak flow; t is the time elapsed since the perturbation of head; e is Euler's number, and b is the basin time constant, representing the characteristic response time of the watershed. Also, t_p is the theoretical lag time between the head perturbation and the flow peak; this value is shorter than the interval between rainfall delivery and peak flow, because of the time necessary for the rain to percolate to the saturated zone.

The dimensionless ratio Q/Q_p varies from 0 to 1, with peak flow occurring at time 2b/3 after the head perturbation (Eq. 6). This function embodies the mathematical characteristics of natural hydrographs, and accurately simulates the shape of hydrographs for many springs, creeks and small rivers throughout the world. This model also provides a method for comparative studies of basin response in a regional context (Criss and Winston, 2008b). We confirm that karst springs and gauging stations in Xiangxi River Basin have small catchments that are suitable for such modeling (Fig. 2).

Figure 2. (a) Hydrograph for Wulongdong Spring in 2012, along with two terms of the Darcian model (Eq. 5) using a time constant of 0.68 d. (b) Rhodamine tracer test of Bailong Spring in 2013, along with a fit also calculated using Eq. 5, but with b=4.5 d, and with dye concentration substituted for flow (see text).

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1.4 Exponential Model Method

Boussinesq (1904) and Maillet (1905) were the first to describe discharge recession by an exponential function; over the years this model has been widely used to study surface water and groundwater recession. Jadro Spring in Yugoslavia is a typical example where the recession stages and components of water quantity in aquifers are calculated (Karst Geology Study Group in China National Administration of Geology, 1978). This model can describe a relatively closed, independent and thick aquifer whose only water source is precipitation and only loss is discharge (Miao and Miao, 1984). The traditional calculation can describe the depletion of the aquifer network reserves over time (Civita, 2008). The exponential recession equation is

$Q_{t}=Q_{0} \mathrm{e}^{-\alpha t}$

(7)

which can be rewritten as

$\alpha = \frac{{\lg {Q_0} - \lg {Q_t}}}{{0.434\, 3t}}$

(8)

where Q₀ is the initial flow; Q_t is the flow at any moment; and α is the recession coefficient.

On a log scale, recession curves are traditionally divided into one or several broken lines which are alleged to reflect different classes of stored water, including cave water, karst fissure water, fissure water and micro fissure water. For heterogeneous aquifers, on a small scale, especially in areas with highly karstified media, the recession curve is subdivided into 3 to 4 broken lines. Examples are Bangbangdong underground river (Luota, Hunan, China) and Fouxdelavis Spring (France), which have several types of transportation media in the aquifers. In contrast, if the recession curve is described by a single line, as for Dumanli Spring (Turkey) and Niangziguan Spring (Shanxi, China), the aquifer is homogeneous, has a long recharge period over a large area, has a response to precipitation and a slow recession. Alternatively, if the recession curve shows two broken lines, the aquifers are interpreted to have dual water storage; an example is Litno Spring (Yugoslavia). Hatipoglu-Bagci and Sazan (2014) used spring recession curves to determine the storage properties and parameters that characterize karst.

For a feature with different flow components, the exponential equation is extended to accommodate the different recession coefficients and recession periods (Huang, 1982)

${\alpha _i} = \frac{{\lg {Q_{i - 1}} - \lg {Q_i}}}{{0.434{\kern 1pt} \, 3({t_i} - {t_{i - 1}})}}$

(9)

${Q_t} = \left\{ {\begin{array}{*{20}{c}} {{Q_1}{{\rm{e}}^{ - {\alpha _1}t}}(0 \leqslant t \leqslant {t_1})} \\ {{Q_2}{{\rm{e}}^{ - {\alpha _2}t}}({t_1} \leqslant t \leqslant {t_2})} \\ {{Q_3}{{\rm{e}}^{ - {\alpha _3}t}}({t_2} \leqslant t \leqslant {t_3})} \end{array}} \right.$

(10)

This recession equation has been widely used to calculate hydrogeological parameters such as minimum flow during drought, water storage capacity in aquifers, aquifer parameters, effective infiltration coefficients, etc. (Miao and Miao, 1984).

Water storage capacity during different recession periods can be calculated from this model for the different recession coefficients and their times, using the following equations

$\left\{ {\begin{array}{*{20}{l}} {{V_1} = \int_0^{{t_1}} {\left({{Q_1}{{\rm{e}}^{ - {\alpha _1}t}} - {Q_2}{{\rm{e}}^{ - {\alpha _2}t}}} \right)dt} } \\ {{V_2} = \int_0^{{t_2}} {\left({{Q_2}{{\rm{e}}^{ - {\alpha _2}t}} - {Q_3}{{\rm{e}}^{ - {\alpha _3}t}}} \right)dt} } \\ {{V_3} = \int_0^\infty {{Q_3}{{\rm{e}}^{ - {\alpha _3}t}}dt} } \\ {{V_0} = \mathop \sum \limits_{i = 1}^3 {V_i}} \end{array}} \right.$

(11)

The percentage of water storage capacity (V_i, i=1, 2, 3) to total water storage capacity (V₀) can be calculated as below

${K_i} = \frac{{{V_i}}}{{{V_0}}} \times 100\%$

(12)

For karst basins, the surface runoff is made up of overland flow and groundwater, and groundwater in karst areas is usually contained in different classes of karst conduits and fissured zones. The recession equation is also used to analyze surface floods in karst mountain areas (Lao et al., 2009).

2. RESULTS AND DISCUSSION

2.1 The Characteristics of Surface Flood and Underground Flood Process

During April 2014, the hydrographs for Menjiahe and Kongzixia had similar shapes and the same number of flood peaks (Fig. 3). The floods were characterized by a steep rise and more gradual fall (Fig. 3) as they returned to base flow. The average lag time, duration time, and peak heights increase downstream (Table 1). Kongzixia has a larger catchment area than Menjiahe, and has a longer response time and more runoff; the flow is larger but it has a slower response (Fig. 3).

Figure 3. Stage hydrographs of Menjiahe (black curve) and Kongzixia (gray curve) compared to precipitation, from April 1 to May 6, 2014.

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Table 1. Hydrograph comparison between surface flood and underground flood

Gauging stations	Types	b (day)	Watershed size (km²)	Average response time (h)	Average duration time (h)	Average increase of peak water level (m)	Water resources utilization
Menjiahe	Surface water	0.17	292	17.4	39.7	0.464	Diversion hydropower
Kongzixia	Surface water	0.18	408	20.7	47.4	0.516	Diversion hydropower
Wulongdong	Groundwater	0.68	15	36.0	272.5	-	Diversion hydropower
Xiangshuidong	Groundwater	0.45	70	26.5	407.0	-	Diversion hydropower
Response time is from the beginning of a rainfall to the beginning of water level increase because of the rainfall, duration time is from the beginning of water level increase to the time when water level back to base flow, increase of peak water level is the difference value between peak water level and base flow level. All of the averages are calculated from the floods during April 2014; "-" no data.

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Compared to Menjiahe and Kongzixia, Xingshan has a larger catchment (Table 1); the flood hydrographs still rise and fall rapidly following precipitation, but this station has a longer duration. The larger the catchment, the longer the lag and other delays; also, superposition effects due to tributaries become more obvious.

The basin time constant (b) is calculated from Eqs. (5) and (6) (Table 1). For surface floods, b increases with watershed size. However, there is a great contrast with underground floods, with b and related parameters being much longer than for surface catchments of comparable size.

Groundwater recharge is sensitive to precipitation in highly karstified environments, as exemplified at Xiangxi River Basin. Just as for surface flow, the underground flow rises rapidly which indicates the existence of a developed karst network. However, surface floods regress more rapidly than underground floods due to groundwater being stored in karst fissures and pores. The difference in lag time represents different levels of karst conduits; it can be thus inferred that the interconnectivity of karst conduits is better developed in Xianglongdong than in Wulongdong. However, the average duration time is longer in Xiangshuidong than Wulongdong because of its larger recharge area.

2.2 The Recession Process of Surface Floods and Underground Floods

In the Xiangxi River Karst Basin, regardless of the type of flood, 3 to 4 line segments are needed to fit the exponential model to the observed recession curve, which is traditionally interpreted to be indicative of the complexity of the aquifer media. The recession coefficient is larger in small catchments, indicating a short recession period. For example, the recession coefficient (α) is clearly larger for Menjiahe and Kongzixia than that for Xingshan during the same recession period. According to the 3 recession curves for Xingshan, the recession coefficient increases for greater peak flows. The proportion of overland flow is higher for the larger peaks, which leads to rapid regression.

Interestingly, the first recession stage for underground floods is similar to the second recession stage for surface floods, where α is of the order of magnitude of 10^-2, interpreted as representing turbulent flow from karst conduits or caves. In the second recession stage of underground floods, α is nearly an order of magnitude lower than that of the first stage, interpreted to represent flow form tensile fractures or dissolution gaps. After the second recession stage, α is likely of the order of magnitude of 10^-4, representing laminar flow from tiny fractures or pores. In the later recession stage, ground water becomes slow-flow, which responds slowly to input; such as the tracer test in Bailong Spring. In this case, a simple fit of Eq. 5, using dye concentrations instead of discharge (cf., Winston and Criss, 2004), indicates that the time constant b for rhodamine is 4.5 d, likely approximating fissure flow recession (Fig. 2). Consequently, after the first recession stage that represents overland flow in surface floods, the succeeding recession stages for surface floods are the same as the recession stages for underground floods.

2.3 The Components of Surface Water and Groundwater Resources Based on Flood Peak

Hydrograph analysis has a long history in karst hydrology (Burdon and Papakis, 1963), successfully used in Yugoslavia, Serbo-Croatian and Mendip hills of southwest England (Milanovic, 1981; Atkinson, 1977). Recession analysis is a traditional methodology for the assessment and quantification of different aquifer media components and analysis of surface water recession in karst basins (Lao et al., 2009). Atkinson concluded from an analysis of spring hydrographs that 50% of the spring discharge was by quick-flow and 50% by slow-flow (or diffuse flow). Atkinson also concluded that 92% of the storage was in the fracture permeability (White, 2002). While the recession coefficient is less than 0.02, it is regarded as karst fissure water from the medium whose maximum diameter is less than a centimeter or millimeter, which is considered to represent most of the stored water in the Luota Karst Basin (Huang, 1982).

Kongzixia and Wulongdong are respectively chosen to exemplify surface flood recession and underground flood recession, using Eqs. 9 and 10. After the peak of April 21, 2014, the curve for Kongzixia has four recession stages. Overland flow only exists in the first stage, which represents the largest component of water released at 46%. In the second stage, cave water and karst fissure water represent the largest components. Karst-fissure water and micro fissure water account for the major proportion in the third and fourth stages. In total, micro fissure water is the dominant volumetric component, with the other three types being smaller, subequal fractions.

Wulongdong Spring also has four line segments on its recession curve, according to the exponential model. The first stage is dominated by cave water and karst fissure water, but these components are less important in following stages. Fissure water and micro fissure water dominate the water sources during the second stage, with the latter becoming even more important in stages Ⅲ and Ⅳ. Overall, micro fissure water represents the dominant water component at 71%, followed by fissure water and karst fissure water that represent smaller fractions that are similar to those in surface floods. Cave water represents only a small component, 4.8%. Both surface and underground floods are dominated by micro fissure water and fissure water that constitutes more than 75% of the total flow (Table 3).

Table 2. Calculation of recession coefficients and recession stages, according to the exponential model

Stages	Ⅰ		Ⅱ		Ⅲ		Ⅳ		Peak time	Peakflow(m³)
Stations	α (1/h)	T (h)	α(1/h)	T (h)	α(1/h)	T (h)	α(1/h)	T(h)	Peak time	Peakflow(m³)
Menjiahe	0.059 8	22	0.034 0	33	0.005 1	> 51	-	-	2014.04.19	22.12
Menjiahe	0.060 3	23	0.027 8	43	0.004 5	165	0.000 7	8 000	2014.04.21	41.17
Kongzixia	0.050 5	24	0.029 9	31	0.004 1	> 51	-	-	2014.04.12	27.95
Kongzixia	0.073 2	26	0.025 1	46	0.004 8	222	0.000 8	7 000	2014.04.21	92.96
Xingshan	0.004 1	48	0.003 0	72	0.000 9	168	0.000 5	> 384	1963.10.05	112
	0.013 0	48	0.004 2	72	0.001 1	264	0.000 2	25 000	1963.11.08	257
	0.021 4	48	0.004 7	72	0.001 9	> 144	-	-	1986.06.15	415
Wulongdong	0.020 9	45	0.006 8	135	0.002 2	350	0.000 8	7 000	2012.05.29	1.11
	Overflow	Overflow	0.017 8	22	0.006 8	> 25	-	-	2014.04.12	0.53
	Overflow	Overflow	0.008 5	61	0.001 6	> 377	-	-	2012.04.21	0.59
Xiangshuidong	Overflow	Overflow	0.010 1	> 19	-	-	-	-	2014.04.12	2.50
	Overflow	Overflow	0.005 0	> 52	-	-	-	-	2014.04.21	2.59
	0.164 0	20	0.003 0	> 52	-	-	-	-	2014.05.06	1.20
"Overflow" is the curve where is flat top, which cannot get the recession coefficient; "-" no data.

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Table 3. Components of the flood hydrograph (Gaolan River at Kongzixia) and Wulongdong Spring, according to the exponential model

Kongzixia	α and time		0.073 2	(0, 26]	0.025 1	(26, 72]	0.004 8	(72, 294]	0.000 8	(294, ∞)
Stages	Total		Ⅰ		Ⅱ		Ⅲ		Ⅳ
Types of water	V (10⁴ m³)	%	V (10⁴ m³)	%	V (10⁴ m³)	%	V (10⁴ m³)	%	V (10⁴ m³)	%
Overland flow	169.13	11.23	169.13	45.85
Cave water	193.77	12.86	139.19	37.73	54.58	38.63
Karst fissure water	183.91	12.21	40.95	11.10	54.25	38.39	88.71	38.52
Micro fissure water	959.61	63.70	19.60	5.31	32.48	22.98	141.59	61.48	765.95	100.00
Total	1506.42	100.00	368.87	100.00	141.31	100.00	230.30	100.00	765.95	100.00

Wulongdong	α and time		0.020 9	(0, 45]	0.006 8	(45, 170]	0.002 2	(170, 520]	0.000 8	(520, ∞)
Stages	Total		Ⅰ		Ⅱ		Ⅲ		Ⅳ
Types of water	V (10⁴ m³)	%	V (10⁴ m³)	%	V (10⁴ m³)	%	V (10⁴ m³)	%	V (10⁴ m³)	%
Cave water	3.53	4.76	3.53	29.65
Karst fissure water	7.57	10.22	4.10	34.51	3.47	26.53
Fissure water	10.26	13.84	2.16	18.14	4.27	32.60	3.84	23.58
Micro fissure water	52.77	71.19	2.11	17.70	5.35	40.87	12.44	76.42	32.88	100.00
Total	74.13	100.00	11.89	100.00	13.09	100.00	16.28	100.00	32.88	100.00

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2.4 The Similarity between Surface Flood and Underground Flood

Conduit flow often has more in common with surface water than with ground water. Karst hydrology requires a mix of surface water concepts and ground water concepts (White, 2002). The recession characteristics of surface floods were found to be similar to underground floods in the Luota Karst Basin, Hunan Province (Lao et al., 2009), suggesting similarities in the structure of the karst basin and flow system. Both types of catchments have variable areas, and the boundaries of underground systems tend to be consistent with the surface water basin. Atmospheric precipitation is the main source of recharge. Groundwater in the karst conduits can be turbulent so classic Darcy flow theory is arguably not suitable to describe it; however, the hydrograph is dominated by slower flow components, and the shape of both surface water and spring hydrographs is well described by Darcian model represented by Eq. 5.

If the response time of the aquifer is fast with respect to the mean spacing between the storms, the individual storm pulses will appear in the spring hydrograph. These pulses have the same shape as for surface stream hydrographs, and feature a steep rising limb, a crest and a slower recession limb (White, 2002). The exponential model (Eqs. 7–10) cannot describe the lag time or the rising limb, and it presumes a linear relationship between discharge and storage. In contrast, the Darcian model (Eq. 5) presumes a relationship between flow and head gradient.

Figure 4 shows the hydrograph of Xingshan in 1987 and Wulongdong Spring in 2012. Atmospheric precipitation was similar in these two years. Both cases show obvious flood crests with steep rising limbs and more gradual recession limbs, with a relatively rapid response to storm events following a short lag time. Scale effect can be seen for both surface and underground floods; the larger the areal scale, the slower the recession.

Figure 4. Hydrograph of Wulongdong Spring (2012) and Xingshan (1987).

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This analysis of flood recession indicates that surface and underground floods are similar in the Xiangxi River Karst Basin. Interestingly, micro fissure is considered to be the dominant water component, which may explain why the Darcian model (Eq. 5) fits peak shapes well. However, overland flow appears to be only important in surface floods, but the exponential model suggests that this component tends to be small in this area.

3. DISCUSSION

The above results show that surface flood and underground flood have many similarities in their resources, flow processes and water compositions. Groundwater and surface water have a close relationship between recharge and discharge, and in most cases, groundwater sustains surface water flows in the Xiangxi River Karst Basin. Guo and Chen (2006) reported analogous results from the formation and distribution of water resource in karst underground river system indicating striking similarity to those of surface water. These considerations provide the prerequisites for applying the methods and theory of modern hydrology to resource assessment of karst water systems.

Besides the recession equation for calculating karst water resource quantity, distributed watershed hydrological model is another choice for assessing, estimating and predicting water resources in karst areas. In karst mountainous areas where there may be inadequate data, emphasis should be laid on obtaining a variety of data including topographical, geomorphological, vegetation, land-use etc.. In addition, remotely sensed imagery can be used to derive digital elevation models, while lidar and airbone hyper-spectral imaging can provide relative water depths as well as information on water quality and sediment transport.

An integrated study is required to make high precision, comprehensive assessments on a basin scale. It is necessary to select several typical sub-basins with different scales and types to study a given area. For example, while Menjiahe and Kongzixia are surface water sites with different catchment areas, and Wulongdong Spring and Xiangshuidong Spring are karst flow systems with different recharge areas, these sites are insufficient in number to characterize an area as large as the Xiangxi River Basin, so extra gauging stations are being built. We plan to exploit existing historic observation data including additional power plant records to estimate relevant hydrologic parameters.

We will also apply modern system analysis theory to establish a suitable stochastic model for the quantitative assessment of corresponding study sites. Our future work will attempt to use such an integrated approach to further assess the karst water resources of the Xiangxi River Karst Basin.

4. CONCLUSIONS

Hydrographs for surface and underground floods in the Xiangxi River Karst Basin have similar shapes and the same number of flood peaks, but show different responses to precipitation. All the hydrographs feature a steep rising limb, a crest and a slower recession limb. Underground floods usually have longer average response times and longer flow duration times than surface floods. These times tend to increase with basin scale but involve many other factors.

The Darcian model and the traditional exponential model were both used to fit hydrographs in the study area. The Darcian model presumes a relationship between flow and head gradient; we find that it can fit both the rising limb and the entire recession limb with a single parameter. This model is easy to apply, and shows that underground flood hydrographs tend to have longer time constants (b values) and lag times than surface floods, even when the latter catchments are much larger.

The exponential model presumes a linear relationship between discharge and storage, and cannot describe the rising limb or flat peak of natural hydrographs. However, this model is commonly used to infer water resource components from the slopes of the recession limb. Use of this model requires 3 to 4 line segments to fit the recession curves for surface and underground floods observed in this area. The recession coefficients are clearly largest for small surface catchments, except for the earliest recession stage. Also, with the exception of the first recession stage for surface floods, we find that the hydrograph components of surface and underground floods are rather similar. We suggest that this is because overland flow is only important in the first recession stage for surface floods, while water components in subsequent recession stages for both surface and underground floods are dominated by cave water, karst fissure water, fissure water and micro fissure water. Overall, micro fissure water is considered to be the dominant volumetric component. We recommend use of modern theory and 3S technology to characterize water resources in karst basins with high anisotropy and heterogeneity.

ACKNOWLEDGMENTS: This study was supported by the China Geological Survey (No. 12120113103800). We express our thanks to the staff of the hydropower plants who assisted with collecting and providing data, and thank our colleagues for their valuable discussions and advice, especially Robert E. Criss and JES editors for their kindly reviewing and editing our paper. The final publication is available at Springer via http://dx.doi.org/10.1007/s12583-016-0624-5.

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