Analysis of Climate Change Impacts on Stream Discharges using STREAM STEP 1: Hydrological Model

Transcription

1 Analysis of Climate Change Impacts on Stream Discharges using STREAM STEP 1: Hydrological Model

2 Table of Contents 1. Introduction Climate Specific Objectives Project Team 3 2. Materials and Methodology Study Area MOSAICC Climate Downscaling Principal Component Analysis Preliminary Interpolation Kriging Interpolation STREAM Data Requirements Model Calibration Model Evaluation Results and Discussion Model Validation Discharge Projection Future Trends Observed Conclusion and Recommendations References Appendix 25 1

3 1. Introduction 1.1. Climate Change in the Philippines Climate change poses a serious threat on agricultural production systems and areas due to increased incidence and intensity of droughts, floods and storms. Developing countries are especially vulnerable, as they have limited resources to cope with the negative effects of climate change. Recently, a Global Climate Risk Index was published ranking the country s vulnerability to climatic change wherein the Philippines ranked fifth among the countries that are most affected due to its archipelagic geography (Harmeling and Eckstein, 2012). For the Philippines, projected changes in climate include drier summer months, excessive rainfall during the rainy season and an increase in the average temperature. This is predicted to negatively affect crop yields and therefore poses a great risk for the country s economy, as rice and corn alone contribute to almost half of gross value added to the country s economy in 2007 (PCARRDSTA, 2012). Water availability is a major concern here, as rice production takes up 95% of the water volume dedicated to the agricultural sector (Greenpeace, 2007). Accurate predictions for water availability are therefore required in order to cope with the negative effects of climate change. A range of different tools and models is available to study the effect of climate change. One of these tools is the Modeling System for Agricultural Impacts on Climate Change (MOSAICC), which was developed to assess the impacts of climate change on agriculture and food security. The toolbox is based on a generic methodology - defined to assess the impact of climate change on climate, river runoff, crop yields and the economy. This framework was used in the Analysis and Mapping of Impacts under Climate Change for Adaptation and Food Security (AMICAF) Project of the Food and Agriculture Organization of United Nations. Due to its high vulnerability to climate change, The Philippines is one of the study areas of the project. The hydrological model that was incorporated to MOSAICC is the Spatial Tools for River basins and Environment and Analysis of Management options (STREAM). It is a gridbased spatial water balance model which describes the hydrological cycle of a catchment as a series of storage compartments and flows (Aerts et al., 1999). In this project, calibrations and simulations were done using a stand-alone version of STREAM as there are still components yet to be installed in the MOSAICC version. The goal of this study is to use the hydrological module of the MOSAICC toolbox to estimate river runoff projections under different scenarios of climate change for the major river basins in the Philippines. Historical datasets on climate and river runoff are used for model calibration. 2

4 Figure 1. Overall flow of data in MOSAICC Specific Objectives As stipulated in the Letter of Agreement between the Food and Agriculture Organization and UP National Institute of Geological Sciences (UP NIGS), the following are the tasks of the team from UP NIGS. A summary of the various outputs from this component is also included: * Collect available national input data on historical water use, historical discharge, soil and land use, and dam statistics and format it for MOSAICC. 3

5 - The three volumes of historical stream discharge data (20 years) were acquired from the Bureau of Research Standards (BRS-DPWH). The entire data were digitized to produce soft copies of the measurements. - The watersheds were delineated by the Environment Monitoring Laboratory in UP NIGS using the ArcHydro software. - The dam data were acquired from the National Water Resources Board. * Participate in the trainings for MOSAICC system and its hydrological component. - Al trainings for STEP 1 Partners were attended: a. Statistical Climate Downscaling Workshop for PAGASA b. WABAL Workshop for PhilRice c. STREAM Workshop for UP NIGS d. PAM Model Workshop for NEDA - Training in Wageningen University and Research Centre, Netherlands for STREAM with Ate Poortinga. * Participate in the design of an integrated climate change impact study. - Interpolation of the downscaled climate data was also done since the climate data output will be used as input for the STREAM model. * Calibrate the hydrological component of MOSAICC against historical data. -The stream discharges produced by STREAM for each river were calibrated using the measured data from the BRS. * Run the hydrological component of MOSAICC for the study to simulate future projections. Analyze the results of the simulations, making sure the links between upstream/downstream models within MOSAICC, and that the results contribute to the overall study. -The downscaled climate data has been run for three climate change scenarios in 8 river basins in the Philippines. 4

6 1.3. Project Team Composition The hydrological modeling component was performed by scientists from Environment Monitoring Laboratory of the University of the Philippines National Institute of Geological Sciences: Name Carlos Primo C. David, Ph.D. Pamela Louise Tolentino Justine Perry Domingo Maria Teresa Lorenzo Peter A. Cayton (Statistician) Designation Project Leader Principal Researcher Researcher Researcher Statistician 5

7 2. Materials and Methodology 2.1 Study Area The Philippines is an archipelago in Southeast Asia. It is composed of 7,107 islands which has a total area of 299,764 square kilometers (sq. km) (Porcil, 2009). There are three main island groups, Luzon (141,000 sq. km), Visayas (57,000 sq. km) and Mindanao (102,000 sq. km). The country is divided into 80 provinces, 143 cities and 1491municipalities (NCSB, 2012). The Philippines has 421 principal river basins, 18 of which are considered as major river basins with at least 1400 sq. km watershed area (Figure 2) (RBCO, 2013). These river basins are important freshwater resources for agriculture, commercial and domestic demands. Three of the major river basins are included in the current study. Selection of the rivers to be calibrated is based on their geographic location so that various parts of the country are represented and the availability of the discharge data of the stream. Fourteen rivers were selected for the study. Table 1 shows information of each river and Figure 2 shows the location of the rivers. The discharge locations are based on the established gauging stations of the Bureau of Research Standards (BRS). The basin maps are generated from STREAM as one of the base maps for the discharge simulations. Table 1. List of rivers used in STREAM. Location of the River Year Record No. Irrigated Province River River Basin N E Barangay City Province Start End Status 1 Ilocos Norte Laoag Laoag Poblacion Laoag City Ilocos Norte Fair 2 Cagayan Pared Cagayan Baybayog Alcala Cagayan Good 3 Nueva Vizcaya Magat Cagayan Baretbet Bagabag Nueva Vizcaya Good 4 Isabela Ganano Cagayan Ipil Echague Isabela Good 5 Pampanga Gumain Gumain Sta. Cruz Lubao Pampanga Fair 6 Nueva Ecija Rio Chico River Pampanga Sto. Rosario Zaragosa Nueva Ecija Poor 7 Oriental Mindoro Pangalaan Pangalaan Pangalaan Pinamalayan Oriental Mindoro Good 8 Camarines Sur Yabo Yabo Yabo Sipocot Camarines Sur Fair 9 Capiz Panay Panay Poblacion Panitan Capiz Fair 10 Leyte Das-ay Das-ay Sto. Nino II Hinunangan Southern Leyte Fair 11 Zamboanga del Sur Tukuran Tukuran Tinotongan Tukuran Zamboanga del Sur Fair 12 Bukidnon Agusan Canyon Agusan Canyon Damilag Manolo Fortich Bukidnon Fair 13 Sultan Kudarat Allah Cotabato Impao Isulan Sultan Kudarat NA 14 Agusan del Sur Wawa Agusan Wawa Bayugan Agusan del Sur Poor 6

8 Figure 2. Upstream maps of the discharge points and their locations in the Philippines. 7

9 2.2 MOSAICC The MOSAICC toolbox is based on a generic methodology defined to assess the impact of climate change on agriculture, including statistical downscaling of climate data, crop yield projections, water resources estimations and economic modeling. The primary input of the model is low resolution climate data, which serves as an input for the whole model structure. This climate data is used to produce the downscaled climate scenarios, which in turn serves as input data for hydrological modeling and crop growth simulation modules along with other basic information on elevation, land cover and soil. The outputs of the hydrological and crop model may then be used as input for the economic model, which calculates the economic impact. Details of data transformation and analysis for the hydrological component of MOSAICC are detailed in the succeeding sections Climate Downscaling FAO-MOSAICC utilizes low-resolution projections generated from General Circulation Models (GCMs) for future climates, similar with most climate change impact assessment methods. Due to the coarse resolution of GCMs, downscaling is necessary in a regional or country scale. Projections are statistically downscaled onto particular weather stations for a specific time period in order to obtain a more precise resolution. The Santander Meteorology Group of the University of Cantabria, Spain has developed and incorporated into MOSAICC a statistical downscaling tool for coarse climate grids generated by GCMs. This tool has been designed to downscale weather variables (i.e. precipitation, minimum temperature, and maximum temperature) simultaneously for a weather station network. Three GCMs were used in downscaling climate data, the BCCR- BCM2.0 (BCM2), the CNRM-CM3 (CNCM3) and the ECHAM5/MPI-OM (MPEH5). Information on these GCMs are listed in Table 2. Three scenarios were used for the discharge simulations, (1) 20C3M which is the past climate data ( ), (2) SRES scenario A1B, which assumes very rapid economic growth, a global population that peaks in mid-century and rapid introduction of new and more efficient technologies and (3) SRES scenario A2 which represents the negative extremes, high population growth, slow economic development and slow technological change. No likelihood has been attached to any of the SRES scenarios. A1B and A2 are future climate emission scenarios ( ). 8

10 Table 2.Information on each GCM used for simulation (Randall et al., 2007). Model ID, Vintage Sponsor(s), Country Atmosphere Ocean Sea Ice Coupling Land Top Resolution(b) Dynamics, Leads Flux Adjustments Soil, Plants, Routing Resolution(a) Z Coord., Top BC References References References References References 1 BCCR-BCM2.0 Bjerknes Centre for Climate top = 10 hpa x 1.5 L35 rheology, leads no adjustments Layers, canopy, routing 2005 Research, Norway T63 (1.9 x 1.9 ) L31 density, free surface Hibler, 1979; Harder, 1996 Furevik et al., 2003 Mahfouf et al., 1995; Déqué et al., 1994 Bleck et al., 1992 Douville et al., 1995; Oki and Sud, CNRM-CM3 Météo-France/Centre top = 0.05 hpa x 2 L31 rheology, leads no adjustments layers, canopy,routing 2004 National de Recherches T63 (~1.9 x 1.9 ) L45 depth, rigid lid Hunke-Dukowicz, 1997; Terray et al., 1998 Mahfouf et al., 1995; Météorologiques, France Déqué et al., 1994 Madec et al., 1998 Salas-Mélia, 2002 Douville et al., 1995; Oki and Sud, ECHAM5/MPI-OM Max Planck Institute for top = 10 hpa 1.5 x 1.5 L40 rheology, leads no adjustments bucket, canopy, routing 2005 Meteorology, Germany T63 (~1.9 x 1.9 ) L31 depth, free surface Hibler, 1979; Jungclaus et al., 2005 Hagemann, 2002; Roeckner et al., 2003 Marsland et al., 2003 Semtner, 1976 Hagemann and Notes: a. Horizontal resolution is expressed either as degrees latitude by longitude or as a triangular (T) spectral truncation with a rough translation to degrees latitude and longitude. Vertical resolution (L) is the number of vertical levels. b. Horizontal resolution is expressed as degrees latitude by longitude, while vertical resolution (L) is the number of vertical levels. Dümenil-Gates, 2001 Spatial consistency can be maintained through different methods incorporated into the system (e.g. regression, weather typing). A weather generator is included to derive time series of the weather variables needed. Using both historical weather data and GCMs, the tool is able to construct climate scenario predictions. The downscaled climate data are subsequently subjected to different interpolation methods, in order to obtain an appropriate spatial resolution for hydrological and crop modeling Principal Component Analysis After climate downscaling, the Principal Component Analysis (PCA) is the next step in order to prepare supporting data. The interpolation tools in MOSAICC have been designed to process large amounts of data with minimum user input. All interpolation methods integrated in the system require the following data: 1) digital elevation model (DEM) of the area of interest, 2) polygon shapefile of the interpolation area, and 3) point shapefile with the locations of the input weather stations and the input time series. The main objective is to derive all the maps necessary in subsequent interpolation procedures. Using the grid data of the input files (DEM and administrative 9

11 boundaries/river basins), the area of analysis is selected as shown in Figure 3. The resolution of the interpolation grid can be manipulated through the step value. PCA analysis will be performed and produce the following grids: interpolation grid, mask of the interpolation area, grid of the distance to the sea, grids of the first principal components. These outputs will be the basis for all interpolation methods. Figure 3.Principal Component Analysis in MOSAICC Preliminary Data Interpolation In order to determine the regression models, Preliminary Data Interpolation (Prelim), as shown in Figure 4, is performed prior to other interpolation methods. This function provides additional processes to assist in the determination of the interpolation parameters. The elements considered for the analysis include geographical and temporal experiment specifications, stations, predictors and variogram model parameters. Using these data as input, Prelim executes the following analyses: 1) logarithmic transformation of the data; 2) stepwise selection of predicting variables for regression model and diagnosis; and 3) variogram fitting on median values of the regression residuals. Based on the initial results, the most relevant predictive parameters are selected and the variogram model values are adjusted for the next interpolation process. 10

12 Figure 4.Preliminary data interpolation Kriging Interpolation After performing preliminary interpolation, Kriging method would be used for automatic interpolation of data on selected time series, shown in Figure 5. The supporting data produced by PCA and the output from the Preliminary Data Interpolation are selected. The data type and time frame are both set accordingly. Variogram parameters are modified as necessary. 11

13 Figure 5. Data Interpolation through Kriging Method 2.3 STREAM Calculation of forecasted stream discharge as affected by climate change is calculated through MOSAICC s hydrological model called STREAM. The Spatial Tools for River basins and Environment and Analysis of Management options (STREAM) is a gridbased spatial water balance model which describes the hydrological cycle of a catchment as a series of storage compartments and flows (Aerts et al., 1999). The model was developed to study the processes that impact water availability within a river basin with a specific focus on the effects of climate change and land use. STREAM has been extensively used for scenario analysis for a range of different spatial and temporal scales. STREAM requires spatially explicit input on precipitation, temperature, land-cover and soil type (Figure 6). From the temperature the evapotranspiration is estimated using the Thornthwaite equation (Thornthwaite et al., 1957). Based on these parameters, the water balance is calculated for each grid cell. The former STREAM model uses a flow routing algorithm to calculate the total runoff, whereas in the new MOSAICC-STREAM it uses an upstream mask to sum the accumulated runoff. The latter significantly decreases calculation time and makes it easier to include dams/reservoirs and other compartments in the calculation. 12

14 Precipitation Temperature Snow Soil type Land-cover Soil Evapotranspiration Σ Runoff Groundwater Figure 6. Flowchart of how STREAM model works Data requirements The inputs used for the STREAM model are shown in Table 3. Table 3: Inputs for the STREAM model Input Digital Elevation Model (DEM) and basin delineation Climate Data: precipitation, evaporation or temperature. Land use map Soil data Source MOSAICC Output (PAGASA/UP NIGS) BSWM BSWM Model Calibration The hydrological model output also includes a time range from 1979 to 2011 wherein the results can be compared to actual BRS discharge measurements which spans the years The datasets comparison represents the calibration procedure of the hydrological model. In the current model calibration scheme, three calibration parameters are used. These are shown in Table 4. 13

15 Table 4.Calibration parameters of STREAM. Parameter Description Value C slow flow >1 WATERH water holding capacity TOGW To groundwater fraction 0 1 Model calibration is done in two steps. The first step is done by visually comparing the actual hydrograph with the predicted hydrograph output, in order to get an estimate for the different parameters. The actual data is from the stream discharge data from the Bureau of Research Standards (BRS-DPWH) dataset. The data is presented in a tabulated form with daily and monthly discharges. The median of the daily measurements were calculated and compared with the modeled data. Figure 7 shows an example of a page containing the discharge data. The upper section contains information about the river and the lower section contains the daily discharge data. Figure 7.Stream discharge data from the Bureau of Research Standards. The second step is optimizing the parameters with Model-Independent Parameter Estimation software (PEST). PEST is a software for model calibration, parameter estimation and predictive uncertainty analysis. PEST runs the model as many times as needed adjusting the parameters until the difference between the model output and measured values approaches a minimum in terms of weighted squares (Doherty, 2004). 14

16 Output (discharge timeseries) PEST Instruction file Hydrological module PEST Control file PEST Model input file ( groundwater and Channel velocity) PEST Template file Figure 8: The PEST optimization scheme. The PEST optimization routine consists of a control file, template file, module input file and instruction file as shown in Figure 8. The control file holds all the important information on parameters, parameter groups, settings and observed values. From a template file where the parameters are listed a model input file is created. This model input file can be read by the hydrological module after which the model is run. The output of the model, a time series of discharge data in this case, is read by PEST following an instruction file. The predicted data of the model is compared to the observer data, after which the parameters are adjusted. This process repeats itself until the Gauss-Marquardt-Levenberg algorithm has found an optimum. The theoretical description of PEST and the Marquardt- Levenberg algorithm can be found in the manual (Doherty, 2004). 15

17 2.4 Model Evaluation To evaluate the model performance in quantitative terms, the Nash and Sutcliffe Efficiency (NSE) criteria is used. This method is typically developed for hydrological models. An efficiency of 1 indicates a perfect relation between the predicted and observed discharge. Negative values indicate a relation which is worse than when the predicted values would be replaced by the mean observed value. The NSE is defined below: where, omean pi oi T 2 Σ t 0(pi o i) Efficiency 1 T 2 (1) Σt 0(o i omean) mean observed value predicted (modeled) values observed values The median of actual discharge data from BRS are compared with the output discharge of the model using ERAINT using NSE. The ERAINT were then compared to the 20C3M of each GCM. The NSE between ERAINT and 20C3Ms may be used to determine the better GCM. Criss and Winston (2008) discussed the disadvantages on using NSE as a measure of the model s efficiency. They reported that NSE tends to overemphasize large flows relative to other measurements due to squaring of various deviations. According to the them, it can be a problem since the flows that are more represented in the goodness-of-fit calculations are the least accurately measured. Large negative values may falsely indicate poor performance of the model, these values may be due to steady observed streamflow. Due to these problems, another equation was used to determine the efficiency of the model, the volumetric efficiency (VE). The VE (Equation no. 2) is claimed to be more useful for the comparison of similarly scaled, rainfall-runoff transfer functions and it treats every cubic metre of water the same as any other cubic meter, whether it be delivered during slow recession or during peak flow (Criss and Winston, 2008). VE values are shown in Table 7. (2) 16

18 To show the general trend of future discharge projections, the slope and p-value were computed. Positive value of slope indicates increase in discharge while a negative value corresponds to decrease in discharge. The p-value determines the significance of this trend, a value less than or equal to 0.1 indicates that the trend is significant while a value more than 0.1 indicates that the trend depicted by the slope is not significant. 3. Results and Discussion 3.1 Model Validation The efficiency of the model was evaluated from the monthly discharges of the selected river produced by STREAM indicated by ERAINT(converted from cubic meters per month to cubic meters per second)and the observed monthly median (derived from the observed daily discharge), using the NSE equation. The ERAINT were also compared with the 20C3M scenario of each GCM. Table 6 shows the list of the NSE value for each province for different GCM comparison. Table 6. NSE measures for each province. GCM - 20C3M Ilocos Cagayan Nueva Vizcaya Isabela Pampanga Nueva Ecija Bicol Observed vs ERAINT ERAINT vs BCM ERAINT vs CNCM ERAINT vs MPEH GCM - 20C3M Mindoro Capiz Leyte Zamboanga Bukidnon Sultan Kudarat Agusan del Sur Observed vs ERAINT ERAINT vs BCM ERAINT vs CNCM ERAINT vs MPEH

19 Table 7. VE values for each province. GCM - 20C3M Ilocos Cagayan Nueva Vizcaya Isabela Pampanga Nueva Ecija Bicol Observed vs ERAINT ERAINT vs BCM ERAINT vs CNCM ERAINT vs MPEH GCM - 20C3M Mindoro Capiz Leyte Zamboanga Bukidnon Sultan Kudarat Agusan del Sur Observed vs ERAINT ERAINT vs BCM ERAINT vs CNCM ERAINT vs MPEH Most of the rivers used in the study for calibration showed negative NSE values but high VE values. The low to negative values of NSE indicate poor performance of the model to reproduce the discharge of rivers using historical climate data. Low efficiency values may be caused by one of the following reasons 1) the model was not able to capture the actual river setting due to coarse interpolation of climate data, or 2) errors in calibration due to incorrect measurement of the observed discharge data. The comparison of ERAINT with the 20C3M scenario of the GCMs also showed poor efficiency. 3.2 Discharge Projection Ten hydrographs were produced for each river in this study as shown in Figure 8. The upper graph shows the precipitation (top), temperature (middle) and potential evapotranspiration (bottom), while the lower graph shows the hydrographs produced for each GCM. Figure 8a shows the hydrographs of the simulated past discharge using ERAINT (black) and the hydrograph of the median monthly observed data (blue). Figures 8b, 8c and 8d show the hydrographs of the simulations using ERAINT (green), 20C3M (blue), A1B (black) and A2 (red). Based from the hydrographs of the simulated and actual discharges, the model generally underestimated the peak discharges. Nonetheless, the model was able to capture the periods of peak discharges and simulate the relative changes in river discharges over specific time periods. 18

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21 3.3 Future Trends Observed Figures 8a-d.Graphs of STREAM output. The maximum, average and minimum discharges were computed for the years for both climate scenarios, A1B and A2.The general trends of the discharges were examined through slope computation. Figure 9 shows the discharge output of the model for Nueva Ecija BCM2-A1B. The trendlines of the graphs show that the discharge is generally increasing, as also implied by the positive slope. The observed increasing trend in the discharges is significant, as indicated by p-values less than 0.1. The results suggest that the long-term trend of increasing mean discharge will continue throughout the twenty-first century. The discharge steadily increases towards year 2050 and there is also an observed increasing variation in the min/max discharge. This is in accordance with a previous climate change vulnerability assessment by Jose and Cruz (1999), which suggested that the expected rise in temperature in the future may not be a significant factor in runoff variability in particular major reservoirs in the Philippines. 20

22 Millions Millions (a) Nueva Ecija BCM2-A1B Max Average Min (b) Nueva Ecija BCM2-A1B StDev Figure 9.Plots of the (a) maximum, average, minimum discharges and the (b) standard deviation of Nueva Ecija BCM2-A1B. Table 6.Computation of the general trend of projection of river discharge for Nueva Ecija using BCM2-A1B for years NUEVA ECIJA slope se(slope) T stat p-value BCM2 A1B

23 4. Conclusion and Recommendations This study used STREAM to simulate projections of river discharge. The model was able to capture the trend of the peak discharges but the efficiency of the model was found to be below satisfactory. This suggests that the model can be useful in determining the discharge patterns for future climate projections. However, further adjustments should be made, i.e. modification of STREAM with respect to the Philippines setting,in order to increase the robustness of the model and capture the actual discharge better. It could also be worth trying to calibrate STREAM using the 20C3Mscenarios of the GCMs. A better projection of the variations of monthly and future river discharges will be useful for the planning and management of agriculture, irrigation and hydropower production in the country. 22

24 5. References Aerts, J.C.J.H, Kriek, M. and Schepel, M STREAM (Spatial Tools for River Basins and Environment and Analysis of Management Options): Set Up and Requirements. Phys. Chem. Earth (B), Vol. 24, No. 26, pp Dayrit, Hector Formulation of a Water Vision. NWRB.In The FAO-ESCAP Pilot Project on National Water Visions - From Vision to Action - A Synthesis of Experiences in Southeast Asia. FAO/ESCAP Reference source: (Accessed on June 8, 2013) Doherty, J., PEST: Model Independent Parameter Estimation. Fifth edition of user manual. Watermark Numerical Computing, Brisbane, Australia. Greenpeace The State of Water Resource in the Philippineshttp:// (Accessed on June 8, 2013) Jose, A.M. and Cruz, N.A Climate Change Impacts and Responses in the Philippines: Water Resources. Clim Res. Vol. 12. pp Harmeling, S. and Eckstein, D Global Climate Risk Index Reference source: on June 8, 2013) Malig, J. 2013, April 6. Quirino temperature soars to 40.1C. ABS-CBNnews.com Reference source: (Accessed on June 9, 2013) National Statistical Coordination Board (NCSB) Philippine Standard Geographic Code. Reference source: (Accessed on June 12, 2013) PAGASA Climate in the Philippines. Reference source: (Accessed on June 8, 2013) Porcil, J. T Philippines Country Profile. Reference source: on June 11, 2013) 23

25 Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Cilmate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA River Basin Control Office Major River Basins. (Accessed on June 12, 2013) 24

26 6. Appendix Future Trend Statistical Analysis BCM2 CNCM3 MPEH5 ILOCOS slope STEYX se(slope) T stat p-value A1B Minimum E Average Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation A1B Minimum E Average Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation A1B Minimum E Average Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation

27 BCM2 CNCM3 MPEH5 CAGAYAN slope STEYX se(slope) T stat p-value A1B Minimum E Average E Maximum E Standard Deviation E A2 Minimum E Average E Maximum E Standard Deviation E A1B Minimum E Average E Maximum E Standard Deviation E A2 Minimum E Average E E-05 Maximum E Standard Deviation E A1B Minimum E Average E Maximum E Standard Deviation E A2 Minimum E Average E Maximum E Standard Deviation E

28 BCM2 CNCM3 MPEH5 ISABELA slope STEYX se(slope) T stat p-value A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average E-05 Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation

29 BCM2 CNCM3 MPEH5 NUEVA VIZCAYA slope STEYX se(slope) T stat p-value A1B Minimum E Average E-05 Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation A1B Minimum E Average Maximum Standard Deviation A2 Minimum E E-05 Average E-06 Maximum Standard Deviation E A1B Minimum E Average E-05 Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation

30 BCM2 CNCM3 MPEH5 PAMPANGA slope STEYX se(slope) T stat p-value A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average E-05 Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation

31 BCM2 CNCM3 MPEH5 NUEVA ECIJA slope STEYX se(slope) T stat p-value A1B Minimum E Average Maximum Standard Deviation A2 Minimum Average E-05 Maximum E-05 Standard Deviation A1B Minimum E-05 Average E-08 Maximum E-07 Standard Deviation A2 Minimum E Average E-05 Maximum E-06 Standard Deviation A1B Minimum E Average Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation

32 BCM2 CNCM3 MPEH5 BICOL slope STEYX se(slope) T stat p-value A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation

33 BCM2 CNCM3 MPEH5 MINDORO slope STEYX se(slope) T stat p-value A1B Minimum Average E-05 Maximum Standard Deviation A2 Minimum E Average E-05 Maximum Standard Deviation A1B Minimum E Average Maximum Standard Deviation A2 Minimum E Average E-05 Maximum Standard Deviation A1B Minimum Average E-05 Maximum Standard Deviation A2 Minimum E Average E-08 Maximum Standard Deviation

34 BCM2 CNCM3 MPEH5 CAPIZ slope STEYX se(slope) T stat p-value A1B Minimum Average Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation A1B Minimum E Average Maximum Standard Deviation A2 Minimum E Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation

35 BCM2 CNCM3 MPEH5 LEYTE slope STEYX se(slope) T stat p-value A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average ; Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation

36 BCM2 CNCM3 MPEH5 ZAMBOANGA slope STEYX se(slope) T stat p-value A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation

37 BCM2 CNCM3 MPEH5 BUKIDNON slope STEYX se(slope) T stat p-value A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation

38 BCM2 CNCM3 MPEH5 SULTAN KUDARAT slope STEYX se(slope) T stat p-value A1B Minimum Average E-05 Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average E-10 Maximum Standard Deviation

39 BCM2 CNCM3 MPEH5 AGUSAN DEL SUR slope STEYX se(slope) T stat p-value A1B Minimum Average Maximum Standard Deviation A2 Minimum Average E-06 Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation A1B Minimum Average Maximum Standard Deviation A2 Minimum Average Maximum Standard Deviation This report comes with a CD containing the following documents as part of the deliverables: a. Observed streamflow data of the 14 river basins. b. Discharge values from STREAM for the 14 river basins. - ERAINT - BCM2 (20C3M, A1B, A2) - CNCM3 (20C3M, A1B, A2) - MPEH5 (20C3M, A1B, A2) c. Hydrographs of the discharge outputs. 38