Meteorological and Hydrological Drought Assessment for Dong Nai River Basin, Vietnam under Climate Change

Abstract

The main goal of this study is to examine future changes in meteorological, hydrological drought under the impact of climate change in Dong Nai River Basin, using Standardized Precipitation Index (SPI) and Stream flow Drought Index (SDI). The Soil and Water Assessment Tool (SWAT) is used as a simulated tool to estimate the streamflow in baseline (1980–2005) and climate change (RCP 4.5, 2016–2035) scenarios for meteorological, hydrological calculation. The results show that both types of drought tend to occur in the dry season. The area affected by meteorological and hydrological drought expand in both baseline and RCP 4.5 scenarios. In addition, meteorological drought duration is also significantly increased, especially severely drought months. Although it was detected slightly decreasing in the duration of hydrological drought, the number of months which is occurred moderately drought in sub-basins still goes up in the climate change scenario. These findings could be useful for water shortage assessment and allocation planning in this area in the climate change context in the Dong Nai River Basin.

Appendix

1.1 SWAT

SWAT integrates many of ARS’s models, developed from the Simulator for Water Resources in Rural Basin (SWRRB) [3]. This model, which is a water and soil assessment tool, developed by Dr. Jeff Arnold of the Agricultural Research Service (ARS) Agricultural Research Service of the United States Department of Agriculture and Prof. Srinivasan of Texas A & M University, USA. SWAT allows a number of different physical processes to be simulated in a watershed. The hydrologic cycle as simulated by SWAT is based on the water balance equation:

$$ {SW}_t={SW}_0+\sum \limits_{i=1}^t\left({R}_{day}-{Q}_{surf}-{E}_a-{w}_{seep}-{Q}_{gw}\right) $$

(3)

where SW_t is the final soil water content (mm H₂O), SW₀ is the initial soil water content on day i (mm H₂O), t is the time (days), R_day is the amount of precipitation on day i (mm H₂O), Q_surf is the amount of surface runoff on day i (mm H₂O), E_a is the amount of evapotranspiration on day i (mm H₂O), w_seep is the amount of water entering the vadose zone from the soil profile on day i (mm H₂O), and Q_gw is the amount of return flow on day i (mm H₂O) (Fig. 12).

Fig. 12

Create SWAT–SUFI2 Input file [3]

1.2 SWATCUP

Research using SWAT-CUP tool to calibrate and test the model. The SWAT-CUP consists of five calculation methods (SUF12, GLUE, PARASOL, MCMC, PSO) [1]. The SUFI-2 algorithm [2] in the SWAT-CUP software package was used for model calibration, validation, sensitivity, and uncertainty analysis. Model correction is conducted in two phases: phase one is the flow correction, after the flow is well adjusted, the second stage is the TSS correction. Input data for SWAT-CUP should include:

• Simulation results of the SWAT model.

• Daily monitoring data for flow and TSS.

• Model calibration kit.

Steps to program with:

• Step 1: Determine the sensitivity parameters for the model.

• Step 2: The program calibrates the input data with the upper limit, below each parameter, the actual comparison data.

• Step 3: Edit the input file with the new value of the parameters.

• Step 4: Simulate and relaunch the SWAT model.

• Step 5: Compare the reliability test results to the value of the optimal parameters.

1.3 Model performance evaluation

The coefficient of determination (R²) [11] and Nash–Sutcliffe Index (NSI) [18] and PBIAS [7] were used to evaluate the model performance. Those coefficients are calculated as following equations:

$$ {R}^2={\left(\frac{\sum_{i=1}^n\left({Q}_i^{obs}-{\overline{Q}}^{obs}\right)\left({Q}_i^{sim}-{\overline{Q}}^{sim}\right)}{\sqrt{\sum_{i=1}^n{\left({Q}_i^{obs}-{\overline{Q}}^{obs}\right)}^2}\sqrt{\sum_{i=1}^n{\left({Q}_i^{sim}-{\overline{Q}}^{sim}\right)}^2}}\right)}^2 $$

(4)

$$ NSI=1-\frac{\sum_{i=1}^n{\left({Q}_i^{obs}-{Q}_i^{sim}\right)}^2}{\sum_{i=1}^n{\left({Q}_i^{obs}-{\overline{Q}}^{obs}\right)}^2} $$

(5)

$$ PBIAS=\left[\frac{\sum_{i=1}^n\left({Q}^{obs}-{O}_i^{sim}\right)\times 100}{\sum_{i=1}^n\left({O}_i^{obs}\right)}\right] $$

(6)

Where, ${Q}_i^{obs}$ is the observed discharge at time i, ${\overline{Q}}^{obs}$ is the average observed discharge, ${Q}_i^{sim}$ is the simulated discharge at time i, ${\overline{Q}}^{sim}$ is the average simulated discharge, and n is the number of registered discharge data.