Impacts of Climate and Land-Cover Changes on Water Resources: Methodology Review May 6, 2015 Huidae Cho Water Resources Engineer, Dewberry Consultants Part-Time Assistant Professor, Kennesaw State University Joonghyeok Heo, Jaehyung Yu, John R. Giardino, Huidae Cho

Overview Why Is This Study Relevant? What Are The Challenges? SWAT ISPSO Model Calibration Analysis Results Conclusions References

Why Is This Study Relevant? Understand the impact of climate change only on water resources. Most of previous studies Used highly urbanized areas. Used one representative land-cover data for a long-term assessment. This study Uses a watershed with minimum human activities. Uses multiple land-cover sources for a long-term assessment.

What Are The Challenges? Limited historical records No surface run-off No groundwater discharge No soil water content Little or no evapotranspiration What do we have? Data: streamflow, weather data, soil type, land-cover Soil and Water Assessment Tool (SWAT) Curse of dimensionality Multi-modality, equifinality Isolated-Speciation-based Particle Swarm Optimization (ISPSO)

What Are The Challenges? (Cont.) Multi-modality: Global & local solutions Equifinality: Equally good but substantially different X Factor Cost OF

SWAT A watershed-scale, semi-distributed continuous hydrologic model Inputs Topography, land-cover, soil  Hydrologic Response Units (HRUs)  A “LOT” of parameters Precipitation, temperature Simulates Surface run-off Soil water content Groundwater discharge Evapotranspiration

ISPSO Heuristic algorithm using swarm intelligence (Cho et al., 2011) Finding multi-modal solutions while efficiently exploring the search space Derivative-free optimization Successfully applied to Hydrology & Hydraulics Stochastic rainfall generation Storm tracking Uncertainty estimation

Angry Birds in ISPSO!

ISPSO: Himmelblau Function +: True Solutions, X: ISPSO Solutions, o: Particles

Model Calibration Study area: 2,221 km2 Three models: Neches River Basin Study area: 2,221 km2 Three models: Period 1: 1970-1989 Period 2: 1990-1999 Period 3: 2000-2009 Objective function Nash-Sutcliffe coefficient of daily streamflows

Model Calibration (Cont.) Model parameters 12 parameters * 25 subbasins * 1-18 HRUs 300-5,400 parameter values α-rule  12 α values  Still difficult to solve & Equifinality Baseflow parameters Pre-calibrated using a baseflow filter

Model Calibration (Cont.) ISPSO: NS=0.623 AutoCal: NS=0.530 AutoCal built in SWAT did not perform well enough.

Model Calibration (Cont.) NS vs. Model Runs Choose the most realistic model parameters among different solutions. Multi-Modality Equifinality

Model Calibration (Cont.) Model performance Performance rating (Cho & Olivera, 2009) NS >= 0.75: Very good NS >= 0.65: Good Land-cover data NS Period 1 (1970-1989) LULC 0.74 Period 2 (1990-1999) NLCD1992 0.66 Period 3 (2000-2009) NLCD2001 0.75

Temperature Change 5-year moving average H0: Slope=0 or no change Ha: Slope≠0 or change Statistical significance test Increased by 0.7◦C Not significant compared to previous studies Low developed land  Low heat capacity Mean P-value Period 1 (1970–1989) 18.8 0.45 Period 2 (1990–1999) 19.1 0.27 Period 3 (2000–2009) 19.5 0.06 Overall 0.02

Precipitation Change 5-year moving average H0: Slope=0 or no change Ha: Slope≠0 or change Statistical significance test Increased by 16.3% Not much different from previous studies Not affected by urban development Mean P-value Period 1 (1970–1989) 1333.7 0.67 Period 2 (1990–1999) 1495.3 1.00 Period 3 (2000–2009) 1551.6 0.90 Overall 1422.1 0.04

Land-Cover Change Major change: Vegetation (grass, bush/shrub, forest)  Developed land Vegetation Barren land Crop Developed Water Period 1 (1970–1989) 94.9% 0.7% 3.1% 1.1% 0.2% Period 2 (1990–1999) 94.8% 0.6% 3.3% Period 3 (2000–2009) 89.8% 0.1% 3.5% 6.3% 0.3% Change -5.1% -0.6% 0.4% 5.2%

Hydrologic Components Surface run-off  Precipitation (cf. urban watershed) Groundwater discharge  Main source of agricultural and municipal water Soil water content  Decrease in vegetation and increase in developed land Evapotranspiration  Vegetation dominated   Surface run-off Groundwater discharge Soil water content Evapotranspiration Period 1 (1970–1989) 221.5 14.1 279.0 690.3 Period 2 (1990–1999) 243.1 13.2 283.8 770.4 Period 3 (2000–2009) 254.8 12.8 286.5 829.1 Change 15.0% -9.2% 2.7% 20.1%

References Huidae Cho, Dongkyun Kim, Francisco Olivera, Seth D. Guikema, August 16, 2011. Enhanced Speciation in Particle Swarm Optimization for Multi-Modal Problems. European Journal of Operational Research 213 (1), 15-23. doi:10.1016/j.ejor.2011.02.026 Huidae Cho, Francisco Olivera, June 2009. Effect of the Spatial Variability of Land Use, Soil Type, and Precipitation on Streamflows in Small Watersheds. Journal of the American Water Resources Association 45 (3), 673-686. doi:10.1111/j.1752-1688.2009.00315.x Joonghyeok Heo, Jaehyung Yu, John R. Giardino, Huidae Cho, Accepted in June 2014. How Important is Climate Change on the Water Resource in a Humid Subtropical Watershed?: A Case Study from East Texas, USA. Water and Environmental Journal. doi:10.1111/wej.12096

Conclusions We used a semi-distributed model to evaluate the long-term impact of climate change on water resources components. The main challenges were lack of historical records and the high dimensionality of the model. We used a swarm-intelligence based heuristic algorithm to calibrate the model parameters. Surface runoff was mainly affected by precipitation. Groundwater discharge was mostly affected by human activities. Soil water content was more sensitive to land-cover change than to climate change. High evapotranspiration was caused by vegetation-dominated land-cover.