Stochastic hydro-financial watershed modeling for environmental impact bonds
Cite this dataset
Brand, Matthew (2020). Stochastic hydro-financial watershed modeling for environmental impact bonds [Dataset]. Dryad. https://doi.org/10.7280/D1M38Z
Stream erosion, poor water quality, and degraded ecosystems impose major cost burdens and challenges for stormwater managers. We present a stochastic hydro-financial watershed modeling framework for designing an Environmental Impact Bond (EIB) - a new form of financing for comprehensive, watershed scale interventions. EIB's provide capital for interventions that is repaid over time with interest by stakeholders who experience reduced costs (savings). The framework not only estimates cost savings from interventions, but aleatory and epistemic uncertainty in costs - precisely the information needed by investors for interest rates and payment periods. This work links the probability distributions of watershed states and fluxes with financial paramters and thus overcomes a barrier to the widespread adoptation of EIBs. The framework is applied to a trans-national pollution and sedimentation problem on the U.S. - Mexico border, and has broad applicability for a wide range of environmental problems.
The dataset for raingague data was collected in Los Laureles Canyon in Mexico using a tipping bucket raingauge and is found in San_Diego_Rainfall_Data_v2.xlsx. The dataset for the Lindbergh airfield rainfall is collected from the NOAA National Centers for Environmental Information (Station #USW00023188) and is again found in San_Diego_Rainfall_Data_v2.xlsx.
The MCMC generator for turning the San Diego Rainfield data (dat.mat) into future potential values of rainfall for LLC is located in the MATLAB script <MCMC_Generator_v3_04022020_parmsmooth.mat>. This MCMC data of future rainfall was then run through the AnnAGNPS model (http://go.usa.gov/KFO) to develop future watershed sediment yeilds. These yields were then fit to a statisical model and and used to generate 500,000 MC realizations of future sediment loads over a 10-year period for LLC under the different sediment management scenarios using the MATLAB script <MCMC_PVsed_01102020>. Finally, these generated sediment load data (J_CC.mat, J_BMP.mat, J_PV.mat, and J_BMP_PV.mat) were used in the financial model in the MATLAB script <financial_modelv12_shtrll_gul_removed_04302020.mat> to generate Figure 9 and Table 2.
National Oceanic and Atmospheric Administration, Award: NA16NOS4780206
National Science Foundation, Award: Graduate Research Fellowship Program