4.1. The water balance conceptual method for assessing basin storage-discharge relationship

In this study, the water balance method is used to discuss the relationship between discharge and storage in six catchments in southern Taiwan using three low flow models from Brutsaert [21], Vogel and Kroll [14], and Kirchner [16] to select streamflow data. However, the groundwater storage cannot be measured directly at the catchment; thus, the streamflow sensitivity function (Eq. (6)) is used to assess the sensitivity of discharge to changes in storage.

First, the storage-discharge relationship in southern Taiwan according to the Brutsaert model is discussed. The results show that the most sensitivity is at the Chungde Bridge Station, and the least is at Chaozhou Station. The order of sensitivity is as follows: Chungde Bridge at the Erren River, Changpan Bridge at the Bazhang River, Laonong at the Gaoping River, Xinbei at the Linbian River, and Chaozhou at the Donggang River. However, the Xinshi catchment cannot exhibit the relationship between storage-discharge because the process of selecting streamflow data using the Brutsaert model is more complex than other alternatives. Thus, the streamflow data for Xinshi Station, which cannot reveal the storage-discharge relationship, are less than that for the other stations (Figure 2(a)). According to Eq. (8), when the discharge sensitivity to changes in groundwater storage is high as indicated by the slope of the storage-discharge relationship, the recession time is shorter. The time of the order of recession is Chaozhou at the Donggang River, Xinbei at the Linbian River, Laonong at the Gaoping River, Changpan Bridge at the Bazhang River, and Chungde Bridge at the Erren River as shown in Figure 3(a). However, Xinshi Station cannot reveal the storage-discharge relationship, which causes the relationship between discharge and time to be inaccurate as well.

The results for the Vogel and Kroll model show that the Chungde Bridge station cannot display the storage-discharge relationship because the Q<sup>t</sup> ≥ 0.7Qt-1 selection criteria resulted in discontinuous selected data. Figure 2 shows that for the same changes in discharge, the Kirchner model has higher storage variations and shorter recession periods.

## 4.2. Low flow analysis

In this study, low flow analyses of the Brutsaert [21], Vogel and Kroll [14], and Kirchner [16] models are fitted by lower envelope, linear regression, and binning to parameterize streamflow recession curve and discuss the relationship between parameters (e.g., hydrological characteristic constants and recession time) and topography factors. Stream order, average elevation, length of main stream, average slope, slope of main streamflow, and basin area are selected and shown in Table 1 to analyze the correlation between hydrological characteristic constants and recession time (K).

In order to ensure that low flow is a period without precipitation, thus, the Brutsaert [21], Vogel and Kroll [14], and Kirchner [16] models have their own selection procedure criteria for streamflow data. Figure 4 shows that different models will affect the selection of streamflow

Figure 2. Water balance conceptual method for storage-discharge relationship in six catchments in southern Taiwan.

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(a) Brutsaert (2008), (b) Vogel and Kroll (1992) and (c) Kirchner (2009).

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4. Results and discussions

relationship

64 Aquifers - Matrix and Fluids

4.2. Low flow analysis

constants and recession time (K).

4.1. The water balance conceptual method for assessing basin storage-discharge

function (Eq. (6)) is used to assess the sensitivity of discharge to changes in storage.

relationship between discharge and time to be inaccurate as well.

Kirchner model has higher storage variations and shorter recession periods.

In this study, the water balance method is used to discuss the relationship between discharge and storage in six catchments in southern Taiwan using three low flow models from Brutsaert [21], Vogel and Kroll [14], and Kirchner [16] to select streamflow data. However, the groundwater storage cannot be measured directly at the catchment; thus, the streamflow sensitivity

First, the storage-discharge relationship in southern Taiwan according to the Brutsaert model is discussed. The results show that the most sensitivity is at the Chungde Bridge Station, and the least is at Chaozhou Station. The order of sensitivity is as follows: Chungde Bridge at the Erren River, Changpan Bridge at the Bazhang River, Laonong at the Gaoping River, Xinbei at the Linbian River, and Chaozhou at the Donggang River. However, the Xinshi catchment cannot exhibit the relationship between storage-discharge because the process of selecting streamflow data using the Brutsaert model is more complex than other alternatives. Thus, the streamflow data for Xinshi Station, which cannot reveal the storage-discharge relationship, are less than that for the other stations (Figure 2(a)). According to Eq. (8), when the discharge sensitivity to changes in groundwater storage is high as indicated by the slope of the storage-discharge relationship, the recession time is shorter. The time of the order of recession is Chaozhou at the Donggang River, Xinbei at the Linbian River, Laonong at the Gaoping River, Changpan Bridge at the Bazhang River, and Chungde Bridge at the Erren River as shown in Figure 3(a). However, Xinshi Station cannot reveal the storage-discharge relationship, which causes the

The results for the Vogel and Kroll model show that the Chungde Bridge station cannot display the storage-discharge relationship because the Q<sup>t</sup> ≥ 0.7Qt-1 selection criteria resulted in discontinuous selected data. Figure 2 shows that for the same changes in discharge, the

In this study, low flow analyses of the Brutsaert [21], Vogel and Kroll [14], and Kirchner [16] models are fitted by lower envelope, linear regression, and binning to parameterize streamflow recession curve and discuss the relationship between parameters (e.g., hydrological characteristic constants and recession time) and topography factors. Stream order, average elevation, length of main stream, average slope, slope of main streamflow, and basin area are selected and shown in Table 1 to analyze the correlation between hydrological characteristic

In order to ensure that low flow is a period without precipitation, thus, the Brutsaert [21], Vogel and Kroll [14], and Kirchner [16] models have their own selection procedure criteria for streamflow data. Figure 4 shows that different models will affect the selection of streamflow

Figure 2. Water balance conceptual method for storage-discharge relationship in six catchments in southern Taiwan. (a) Brutsaert (2008), (b) Vogel and Kroll (1992) and (c) Kirchner (2009).

data points; thus, the streamflow data points of the Kirchner model are approximately 40 times than the Brutsaert model. Those results in assessing the basin hydrology characteristics that

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Table 2 shows that the characteristic constant a order is binning > linear regression > lower envelope. Generally, the characteristic constant a using the binning and linear regression has higher values due to the fact that the regression line is moved upward when compared to the lower envelope fit line. Table 3 shows that the recession time order is lower envelope > linear regression > binning. This indicates the inverse relationship between the characteristic constant a and recession time. The six basins with the distribution of recession time show that the binning fitting method exhibits the most quickly recessions, with ranges from 3 to 22 days. The lower envelope fitting method had the longest recessions, with ranges from 32 to 127 days. Because the average value will be influenced by the outlier median is more suitable for representing recession time. It can be noted in Figure 5 that lower envelope fitting with the

Figure 4. Fitted method by linear regression in Xinshi station. (a) Brutsaert (2008) and (b) Kirchner (2009).

Lower envelope Linear Binning Basin BRU VOG KIR BRU VOG KIR BRU VOG KIR Changpan Bridge 0.016 0.018 0.026 0.064 0.099 0.218 0.067 0.077 0.321 Xinshi 0.026 0.016 0.022 0.062 0.099 0.204 0.068 0.115 0.334 Chungde Bridge 0.029 0.013 0.031 0.084 0.110 0.244 0.085 0.096 0.355 Laonong 0.008 0.010 0.022 0.033 0.011 0.079 0.047 0.068 0.065 Chaozhou 0.012 0.018 0.012 0.044 0.034 0.071 0.046 0.060 0.082 Xinbei 0.020 0.019 0.017 0.057 0.054 0.180 0.061 0.076 0.248

Table 2. Characteristic constant a for three extraction procedures (BRU, VOG, and KIR) and three different fitted models

are more subjective.

(lower envelope, linear, and binning).

Figure 3. Discharge and recession time relationship in six catchments in southern Taiwan. (a) Brutsaert (2008), (b) Vogel and Kroll (1992) and (c) Kirchner (2009).

data points; thus, the streamflow data points of the Kirchner model are approximately 40 times than the Brutsaert model. Those results in assessing the basin hydrology characteristics that are more subjective.

Table 2 shows that the characteristic constant a order is binning > linear regression > lower envelope. Generally, the characteristic constant a using the binning and linear regression has higher values due to the fact that the regression line is moved upward when compared to the lower envelope fit line. Table 3 shows that the recession time order is lower envelope > linear regression > binning. This indicates the inverse relationship between the characteristic constant a and recession time. The six basins with the distribution of recession time show that the binning fitting method exhibits the most quickly recessions, with ranges from 3 to 22 days. The lower envelope fitting method had the longest recessions, with ranges from 32 to 127 days. Because the average value will be influenced by the outlier median is more suitable for representing recession time. It can be noted in Figure 5 that lower envelope fitting with the

Figure 4. Fitted method by linear regression in Xinshi station. (a) Brutsaert (2008) and (b) Kirchner (2009).


Table 2. Characteristic constant a for three extraction procedures (BRU, VOG, and KIR) and three different fitted models (lower envelope, linear, and binning).

Figure 3. Discharge and recession time relationship in six catchments in southern Taiwan. (a) Brutsaert (2008), (b) Vogel

and Kroll (1992) and (c) Kirchner (2009).

66 Aquifers - Matrix and Fluids


Table 3. Recession time for three extraction procedures (BRU, VOG, and KIR) and three different fitted models (lower envelope, linear, and binning) (day).

Brutsaert model obtained a median of 55.5 days; the Vogel and Kroll model obtained a median of 60 days, and the Kirchner model obtained a median of 45.5. Figure 6 shows a characteristic constant a range from 0.008 up to 0.355. Figure 7 shows that the characteristic constant b varies from 0.678 to 1.432 without outliers.

In the results of the correlation between recession curve and topography factors, the Vogel and Kroll model, which is fitted using a linear regression, is the most suitable result for characterizing southern Taiwan basin hydrological behavior. The recession time is highly correlated with average elevation, length of main stream, and basin area as shown in Table 4. According to Tague and Grant [27], a higher characteristic hydrology constant a represents rapid recession. In this study, only the characteristic hydrology constant a from the Vogel and Kroll model as fitted using a linear regression is found to be highly correlated with the slope of the main stream as shown in Table 5. There is a high correlation the between characteristic hydrology constant b and average slope according to the Vogel and Kroll model, which is fitted using a linear regression, as shown in Table 6. According to above results indicating that the Vogel and Kroll model as fitted using a linear regression is the most optimal result to represent

southern Taiwan basin characteristics, the Vogel and Kroll model is used to quantify the lowest

Figure 7. The distribution of characteristic constant b for three extraction procedures and three different fitted models.

Figure 6. The distribution of characteristic constant a for three extraction procedures and three different fitted models.

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The optimal results of the Vogel and Kroll model as fitted using a linear regression is shown in Section 4.2. It is used to quantify the lowest groundwater storage in southern Taiwan. In the trend test results shown in Table 7, Changpan Bridge, Chungde Bridge, and Xinbei Station exhibit the most significant increasing trend. Chaozhou Station exhibits a significant decreasing trend. Xinshi Station has an increasing trend. Laonong Station has a decreasing trend. Figure 8 shows the perennial basin slope test indicating that the increasing trend range for

groundwater storage in southern Taiwan.

4.3. The trend test of lowest groundwater storage

Figure 5. The distribution of recession time for three extraction procedures and three different fitted models.

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Figure 6. The distribution of characteristic constant a for three extraction procedures and three different fitted models.

Figure 7. The distribution of characteristic constant b for three extraction procedures and three different fitted models.

southern Taiwan basin characteristics, the Vogel and Kroll model is used to quantify the lowest groundwater storage in southern Taiwan.

#### 4.3. The trend test of lowest groundwater storage

Brutsaert model obtained a median of 55.5 days; the Vogel and Kroll model obtained a median of 60 days, and the Kirchner model obtained a median of 45.5. Figure 6 shows a characteristic constant a range from 0.008 up to 0.355. Figure 7 shows that the characteristic constant b varies

Table 3. Recession time for three extraction procedures (BRU, VOG, and KIR) and three different fitted models (lower

Lower envelope Linear Binning Basin BRU VOG KIR BRU VOG KIR BRU VOG KIR Changpan Bridge 61 57 39 16 10 5 15 13 3 Xinshi 39 63 46 16 10 5 15 9 3 Chungde Bridge 35 75 32 12 9 4 12 10 3 Laonong 127 102 45 30 90 13 21 15 14 Chaozhou 85 57 83 23 29 14 22 17 12 Xinbei 50 53 60 17 19 6 16 13 4

In the results of the correlation between recession curve and topography factors, the Vogel and Kroll model, which is fitted using a linear regression, is the most suitable result for characterizing southern Taiwan basin hydrological behavior. The recession time is highly correlated with average elevation, length of main stream, and basin area as shown in Table 4. According to Tague and Grant [27], a higher characteristic hydrology constant a represents rapid recession. In this study, only the characteristic hydrology constant a from the Vogel and Kroll model as fitted using a linear regression is found to be highly correlated with the slope of the main stream as shown in Table 5. There is a high correlation the between characteristic hydrology constant b and average slope according to the Vogel and Kroll model, which is fitted using a linear regression, as shown in Table 6. According to above results indicating that the Vogel and Kroll model as fitted using a linear regression is the most optimal result to represent

Figure 5. The distribution of recession time for three extraction procedures and three different fitted models.

from 0.678 to 1.432 without outliers.

envelope, linear, and binning) (day).

68 Aquifers - Matrix and Fluids

The optimal results of the Vogel and Kroll model as fitted using a linear regression is shown in Section 4.2. It is used to quantify the lowest groundwater storage in southern Taiwan. In the trend test results shown in Table 7, Changpan Bridge, Chungde Bridge, and Xinbei Station exhibit the most significant increasing trend. Chaozhou Station exhibits a significant decreasing trend. Xinshi Station has an increasing trend. Laonong Station has a decreasing trend. Figure 8 shows the perennial basin slope test indicating that the increasing trend range for


Table 4. The correlation between recession time and topography factors.


Table 5. The correlation between characteristic constant a and topography factors.

changes in the lowest groundwater storage ranges from 33.5% to 2.7 times, and the decreasing trend ranges from 16.3% to 96.9%.

1980s, the average global temperature increased rapidly, resulting in abnormal weather phenomena. Additionally, Lu et al. [29] pointed out that after the 1980s, the temperature increased significantly by about twice than the century before. These studies show that there was a change in Taiwan's climate in the 1980s, thus suggesting that the impact of climate change on Taiwan's hydrological conditions caused a significant change point to occur in the 1980s. Fan et al. [30] indicated that the frequency of extreme rainfall events related to typhoons increased from 2000 to 2009, occurring once on an average 3–4 years between 1970 and 1999 and an average once every year after 2000. According to the research of Tsuang et al. [31], an annual average of 3.3 typhoons occurred in the twentieth century. However, in the research of Tu et al. [32], typhoon frequency increased up to an annual average of 5.7, causing significant increases

In this study, the Brutsaert [21], Vogel and Kroll [14], and Kirchner [16] models are fitted using lower envelope, linear regression, and binning methods to parameterize the recession curve.

in rainfall and affecting changes in groundwater storage.

Fitting method Model Stream

Basin Gaging station Trend

Donggang River

order

Table 6. The correlation between characteristic constant b and topography factors.

Table 7. The lowest groundwater storage trend test in the southern basins of Taiwan.

Slope estimator

test

Average elevation (m)

Lower envelope Brutsaert 0.202 0.471 0.403 0.677 0.294 0.364

Linear regression Brutsaert 0.431 0.649 0.547 0.784 0.116 0.549

Binning Brutsaert 0.360 0.592 0.431 0.737 0.033 0.457

Bazhang River Changpan Bridge 3.40 0.046 192.4 1989 0.826 2.767 235.0 Yanshui River Xinshi 0.92 0.007 33.5 — — —— Erren River Chungde Bridge 2.25 0.022 158.0 2004 0.481 1.176 144.55 Gaoping River Laonong 0.64 0.28 16.3 — — ——

Linbian River Xinbei 5.34 0.057 272.6 1989 0.647 4.069 528.9

Length of main stream (m)

The Discharge-Storage Relationship and the Long-Term Storage Changes of Southern Taiwan

Vogel and Kroll 0.492 0.817 0.580 0.698 0.553 0.701 Kirchner 0.385 0.086 0.344 0.091 0.166 0.225

Vogel and Kroll 0.779 0.855 0.633 0.741 0.529 0.773 Kirchner 0.583 0.268 0.224 0.038 0.534 0.287

Vogel and Kroll 0.014 0.316 0.067 0.130 0.757 0.165 Kirchner 0.691 0.652 0.677 0.483 0.251 0.694

> The lowest groundwater storage variable (%)

Chaozhou 3.92 0.457 96.9 2000 26.322 11.345 56.9

Average slope (m/m)

Change point

The lowest groundwater storage average

Before After

Slope of main stream (m/m)

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Area (km<sup>2</sup> ) 71

Variable of change point (%)

In this study, the change point is primarily set from 1983 to 2004 as shown in Figure 9. The lowest groundwater storage is smaller than before the change point at Chaozhou Station and greater than before the change point at Changpan Station, Chungde Station, and Xinbei Station. The lowest groundwater storage before the change point is 0.65 mm, and after the change point, it is 4.07 mm. The difference between before and after is up to 5.3 times than that of the Xinbei Station. The change point differences before and after are approximately 2.4, 1.4 and 56.9% at Changpan Station, Chungde Station, and Chaozhou Station, respectively. Xinshi Station and Laonong have no change point.

In the southern Taiwan basin, the lowest groundwater storage test results show that a significant change point occurred in the 1980s and after 2000. According to Hsu et al. [28], after the The Discharge-Storage Relationship and the Long-Term Storage Changes of Southern Taiwan http://dx.doi.org/10.5772/intechopen.73163 71


Table 6. The correlation between characteristic constant b and topography factors.


Table 7. The lowest groundwater storage trend test in the southern basins of Taiwan.

changes in the lowest groundwater storage ranges from 33.5% to 2.7 times, and the decreasing

In this study, the change point is primarily set from 1983 to 2004 as shown in Figure 9. The lowest groundwater storage is smaller than before the change point at Chaozhou Station and greater than before the change point at Changpan Station, Chungde Station, and Xinbei Station. The lowest groundwater storage before the change point is 0.65 mm, and after the change point, it is 4.07 mm. The difference between before and after is up to 5.3 times than that of the Xinbei Station. The change point differences before and after are approximately 2.4, 1.4 and 56.9% at Changpan Station, Chungde Station, and Chaozhou Station, respectively.

In the southern Taiwan basin, the lowest groundwater storage test results show that a significant change point occurred in the 1980s and after 2000. According to Hsu et al. [28], after the

trend ranges from 16.3% to 96.9%.

Fitting method Model Stream

70 Aquifers - Matrix and Fluids

Fitting method Model Stream

order

Table 4. The correlation between recession time and topography factors.

order

Table 5. The correlation between characteristic constant a and topography factors.

Average elevation (m)

Average elevation (m)

Lower envelope Brutsaert 0.666 0.841 0.784 0.662 0.381 0.828

Linear regression Brutsaert 0.738 0.797 0.707 0.546 0.571 0.799

Binning Brutsaert 0.802 0.549 0.456 0.325 0.569 0.561

Lower envelope Brutsaert 0.712 0.708 0.542 0.528 0.594 0.646

Linear regression Brutsaert 0.785 0.723 0.601 0.462 0.655 0.723

Binning Brutsaert 0.817 0.571 0.410 0.330 0.709 0.556

Length of main stream (m)

Vogel and Kroll 0.168 0.753 0.927 0.685 0.259 0.818 Kirchner 0.734 0.302 0.272 0.161 0.342 0.337

Vogel and Kroll 0.633 0.942 0.948 0.794 0.181 0.965 Kirchner 0.780 0.551 0.520 0.370 0.384 0.583

Vogel and Kroll 0.793 0.461 0.290 0.392 0.436 0.403 Kirchner 0.753 0.682 0.685 0.514 0.281 0.721

> Length of main stream (m)

Vogel and Kroll 0.032 0.626 0.852 0.573 0.367 0.710 Kirchner 0.761 0.107 0.067 0.052 0.621 0.116

Vogel and Kroll 0.719 0.569 0.385 0.279 0.821 0.555 Kirchner 0.835 0.591 0.533 0.392 0.456 0.622

Vogel and Kroll 0.760 0.498 0.300 0.519 0.303 0.408 Kirchner 0.867 0.671 0.605 0.515 0.378 0.689

Average slope (m/m)

> Average slope (m/m)

Slope of main stream (m/m)

> Slope of main stream (m/m)

Area (km<sup>2</sup> )

Area (km<sup>2</sup> )

Xinshi Station and Laonong have no change point.

1980s, the average global temperature increased rapidly, resulting in abnormal weather phenomena. Additionally, Lu et al. [29] pointed out that after the 1980s, the temperature increased significantly by about twice than the century before. These studies show that there was a change in Taiwan's climate in the 1980s, thus suggesting that the impact of climate change on Taiwan's hydrological conditions caused a significant change point to occur in the 1980s. Fan et al. [30] indicated that the frequency of extreme rainfall events related to typhoons increased from 2000 to 2009, occurring once on an average 3–4 years between 1970 and 1999 and an average once every year after 2000. According to the research of Tsuang et al. [31], an annual average of 3.3 typhoons occurred in the twentieth century. However, in the research of Tu et al. [32], typhoon frequency increased up to an annual average of 5.7, causing significant increases in rainfall and affecting changes in groundwater storage.

In this study, the Brutsaert [21], Vogel and Kroll [14], and Kirchner [16] models are fitted using lower envelope, linear regression, and binning methods to parameterize the recession curve.

The results show that the Vogel and Kroll model is the most optimal model to describe hydrological behavior in southern Taiwan. Thus, it is selected to quantify the lowest groundwater storage in the six basins. According to the trend, slope, and change point tests, the lowest groundwater storage exhibits a decreasing trend at the Laonong and Chaozhou stations. Most notably, Chaozhou Station exhibits a significant decreasing trend. In the future, shortage problem of Chaozhou station has a greater probability of facing low groundwater storage than the other basins under consideration. Moreover, in the study, the storage-discharge relationships in southern Taiwan are assessed using the water balance concept. All of the above hydrological studies in the long-term in southern Taiwan can be provided to water resources

Figure 9. The lowest groundwater change point in the southern Taiwan. (a) Changpan Bridge, (b) Chungde Bridge, (c)

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This study examined the discharge-storage relationship and the changes in the lowest groundwater storage in six selected southern Taiwan basins over the last 40 years using observed daily

authority as a reference for water resource management planning.

5. Conclusions

Chaozhou, and (d) Xinbei.

Figure 8. Annual trends in the minimum groundwater storage value for each study basin. (a) Changpan Bridge, (b) Xinshi, (c) Chungde Bridge, (d) Laonong, (e) Chaozhou, and (f) Xinbei.

The Discharge-Storage Relationship and the Long-Term Storage Changes of Southern Taiwan http://dx.doi.org/10.5772/intechopen.73163 73

Figure 9. The lowest groundwater change point in the southern Taiwan. (a) Changpan Bridge, (b) Chungde Bridge, (c) Chaozhou, and (d) Xinbei.

The results show that the Vogel and Kroll model is the most optimal model to describe hydrological behavior in southern Taiwan. Thus, it is selected to quantify the lowest groundwater storage in the six basins. According to the trend, slope, and change point tests, the lowest groundwater storage exhibits a decreasing trend at the Laonong and Chaozhou stations. Most notably, Chaozhou Station exhibits a significant decreasing trend. In the future, shortage problem of Chaozhou station has a greater probability of facing low groundwater storage than the other basins under consideration. Moreover, in the study, the storage-discharge relationships in southern Taiwan are assessed using the water balance concept. All of the above hydrological studies in the long-term in southern Taiwan can be provided to water resources authority as a reference for water resource management planning.

### 5. Conclusions

Figure 8. Annual trends in the minimum groundwater storage value for each study basin. (a) Changpan Bridge, (b)

Xinshi, (c) Chungde Bridge, (d) Laonong, (e) Chaozhou, and (f) Xinbei.

72 Aquifers - Matrix and Fluids

This study examined the discharge-storage relationship and the changes in the lowest groundwater storage in six selected southern Taiwan basins over the last 40 years using observed daily streamflow. The method is based on the water balance concept and low flow analysis, where precipitation and evapotranspiration in the natural river system are ignored, causing the base flow to be directly controlled by groundwater storage. Therefore, the observed streamflow can be used to assess the discharge-storage relationship and quantify the catchment groundwater storage. The results from this study show that the methods are valid for evaluating the discharge-storage relationship and long-term groundwater storage trends.

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The water balance concept method indicated that different low flow analysis models will affect the sensitivity of discharge and groundwater storage. The Kirchner model is the most sensitive model. Thus, the recession time in this model was shown to be significantly shorter than that of the others under consideration. To summarize, the Vogel and Kroll model as fitted using a linear regression is most the optimal model by which to represent the hydrological characteristics in southern Taiwan. Overall, the lowest groundwater storage exhibited a significant decreasing trend at Chaozhou Station. The assessment results can be provided to water resource agencies to assist with water resource management plans.
