fixed italics

docs/source/index.rst

@@ -21,6 +21,8 @@ Design and Functionality
 .. figure:: images/figure_1.png
   :alt: **statemodify** workflow
 
+Figure 1: **statemodify** workflow  
+
 
 The steps of the workflow are outlined below: 
 
@@ -32,27 +34,27 @@ The steps of the workflow are outlined below:
 
     <ul>
 
-    <li><i>modify_eva( ):<i> Modifies StateMod’s <i>.eva<i> file, or net reservoir annual evaporation data file. Users can either impose a specific change to the evaporation rate or use a Latin hypercube sample to create many scenarios of evaporation rate changes (typically between -15.24 and 30.46 cm/month (-0.5 to +1 ft); Hadjimichael et al., 2020) across specific reservoirs or all larger reservoirs across the West Slope basins.</li>
-    <li><i>modify_ddm( ):<i> Modifies StateMod’s <i>.ddm<i> file, or monthly water demand data file. Users can either impose a specific change to monthly demands for a list of stakeholders or use a Latin hypercube sample to multiply the current demand time series for the users by a value between 0.5 to 1.5. </li>
-   <li><i>modify_ddr( ):<i> Modifies StateMod’s <i>.ddr<i> file, or water rights data file.  In this function, the user can specify a list of the IDs of the stakeholders and (1) sample from a range and apply a multiplier to how much water is decreed to a stakeholder (2) hard code in a new decree for a stakeholder or (3) adjust the rank of the stakeholder to be more or less senior. </li>
-   <li><i>modify_res( ):<i> Modifies StateMod’s <i>.res<i> file, or the reservoir account data file. This file lists the accounts associated with each reservoir and the amount of water allocated to specific stakeholders. In this function, we sample losses of up to 20% of the capacity of the reservoirs (informed by Graf et al. (2010)) which can be due to erosion and sedimentation of reservoirs resulting in reduced storage. The accounts associated with the reservoirs are also reduced equally to accommodate the new storage level. Users can change the reservoir storage for a specific set of reservoirs by specifying the reservoir IDs or can decrease storage at all reservoirs in the basin.  </li>
-   <li><i>• modify_xbm_iwr( ):<i> Modifies StateMod’s <i>.xbm<i> (monthly streamflow) and <i>.iwr<i> (irrigation demand) data files. The <i>.xbm<i> file is overwritten with synthetic streamflow that is simultaneously generated across the outlet nodes of the five West Slope basins using a multi-basin Hidden Markov Model-based approach that effectively maintains the spatial correlation in the generated streamflow (Gold et al., in prep). A statistical disaggregation approach is used to partition aggregated flow at the outlet nodes to StateMod gauge nodes upstream (Nowak et al., 2010). The HMM-based approach is fit to observed annual streamflow but can create synthetic traces characterized by more flood and drought events (Gold et al., in prep). The irrigation demands (in the <i>.iwr<i> file) are also inherently tied to the generation of the streamflow, as irrigation demands will increase in dry years. Thus, a regression is fit across historical irrigation anomalies and historical annual flow anomalies and the appropriate irrigation anomaly is determined from this regression for every synthetically generated flow anomaly. More information on this method can be found in Hadjimichael et al. (2020).</li> 
+    <li>*modify_eva( ):* Modifies StateMod’s *.eva* file, or net reservoir annual evaporation data file. Users can either impose a specific change to the evaporation rate or use a Latin hypercube sample to create many scenarios of evaporation rate changes (typically between -15.24 and 30.46 cm/month (-0.5 to +1 ft); Hadjimichael et al., 2020) across specific reservoirs or all larger reservoirs across the West Slope basins.</li>
+    <li>*modify_ddm( ):* Modifies StateMod’s *.ddm* file, or monthly water demand data file. Users can either impose a specific change to monthly demands for a list of stakeholders or use a Latin hypercube sample to multiply the current demand time series for the users by a value between 0.5 to 1.5. </li>
+   <li>*modify_ddr( ):* Modifies StateMod’s *.ddr* file, or water rights data file.  In this function, the user can specify a list of the IDs of the stakeholders and (1) sample from a range and apply a multiplier to how much water is decreed to a stakeholder (2) hard code in a new decree for a stakeholder or (3) adjust the rank of the stakeholder to be more or less senior. </li>
+   <li>*modify_res( ):* Modifies StateMod’s *.res* file, or the reservoir account data file. This file lists the accounts associated with each reservoir and the amount of water allocated to specific stakeholders. In this function, we sample losses of up to 20% of the capacity of the reservoirs (informed by Graf et al. (2010)) which can be due to erosion and sedimentation of reservoirs resulting in reduced storage. The accounts associated with the reservoirs are also reduced equally to accommodate the new storage level. Users can change the reservoir storage for a specific set of reservoirs by specifying the reservoir IDs or can decrease storage at all reservoirs in the basin.  </li>
+   <li>*• modify_xbm_iwr( ):* Modifies StateMod’s *.xbm* (monthly streamflow) and *.iwr* (irrigation demand) data files. The *.xbm* file is overwritten with synthetic streamflow that is simultaneously generated across the outlet nodes of the five West Slope basins using a multi-basin Hidden Markov Model-based approach that effectively maintains the spatial correlation in the generated streamflow (Gold et al., in prep). A statistical disaggregation approach is used to partition aggregated flow at the outlet nodes to StateMod gauge nodes upstream (Nowak et al., 2010). The HMM-based approach is fit to observed annual streamflow but can create synthetic traces characterized by more flood and drought events (Gold et al., in prep). The irrigation demands (in the *.iwr* file) are also inherently tied to the generation of the streamflow, as irrigation demands will increase in dry years. Thus, a regression is fit across historical irrigation anomalies and historical annual flow anomalies and the appropriate irrigation anomaly is determined from this regression for every synthetically generated flow anomaly. More information on this method can be found in Hadjimichael et al. (2020).</li> 
   </ul>
 
 
   <li><p>Extracting output of interest: Users can extract time series of either individual stakeholder shortages or reservoir storage levels for any simulation using the following functions:  </p>
   <ul>
 
-    <li><i>convert_xdd( ):<i> Stakeholder shortages resulting from a StateMod simulation are aggregated in the <i>.xdd<i> output file. Users can specify a list of stakeholder IDs to extract shortages for and results are stored in a highly compressed and columnar <i>.parquet<i> file which easily interoperates with Pandas data frames.</li>
-    <li><i>extract_xre_data( ):<i> Reservoir storage resulting from a StateMod simulation is aggregated in the <i>.xre<i> output file. Users can specify the list of reservoir IDs or the name of the reservoir that storage needs to be extracted for and the resulting storage can be stored in <i>.parquet<i> or <i>.csv<i> files.</li>
+    <li>*convert_xdd( ):* Stakeholder shortages resulting from a StateMod simulation are aggregated in the *.xdd* output file. Users can specify a list of stakeholder IDs to extract shortages for and results are stored in a highly compressed and columnar *.parquet* file which easily interoperates with Pandas data frames.</li>
+    <li>*extract_xre_data( ):* Reservoir storage resulting from a StateMod simulation is aggregated in the *.xre* output file. Users can specify the list of reservoir IDs or the name of the reservoir that storage needs to be extracted for and the resulting storage can be stored in *.parquet* or *.csv* files.</li>
 
   </ul>
 
-  <p>Visualizations: Within the Jupyter notebooks are examples of simple functions that can be used to visualized changes in shortages and reservoir storage relative to a historic baseline. However, within <b>statemodify<b>, there are more complex visualization functions that summarize reservoir storage across many simulations. These functions can be further adapted by the user to explore stakeholder shortages. </p></li>
+  <p>Visualizations: Within the Jupyter notebooks are examples of simple functions that can be used to visualized changes in shortages and reservoir storage relative to a historic baseline. However, within **statemodify**, there are more complex visualization functions that summarize reservoir storage across many simulations. These functions can be further adapted by the user to explore stakeholder shortages. </p></li>
 
 
-    <li><i>plot_reservoir_boxes( ):<i> This function allows the user to create boxplots to compare the range of monthly historical reservoir storage experienced at a given reservoir vs. the range of reservoir storage that can result from synthetically generated streamflow. </li>
-    <li><i>plot_res_quantiles( ):<i> This function allows the user to visualize the historical monthly distribution of reservoir levels at a given location versus the distribution that results under an array of simulated scenarios.</li>
+    <li>*plot_reservoir_boxes( ):* This function allows the user to create boxplots to compare the range of monthly historical reservoir storage experienced at a given reservoir vs. the range of reservoir storage that can result from synthetically generated streamflow. </li>
+    <li>*plot_res_quantiles( ):* This function allows the user to visualize the historical monthly distribution of reservoir levels at a given location versus the distribution that results under an array of simulated scenarios.</li>
 
   </ol>