Following results by Crow et al. (2017) [Geophys. Res. Lett. 44, 5495–5503] on the impact of antecedent soil moisture on runoff production, we investigate total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vege- tation cover for catchments with drainage areas ranging from 80-1000 km2in the state of Iowa, USA. For our study, we use Enhanced SMAP soil moisture estimates, the MODIS enhanced vegetation index (EVI), gauge-corrected Stage IV radar rainfall, and USGS streamflow data. We analyze the event runoff ratio as a function of event-scale rainfall, antecedent SMAP soil moisture and soil- moisture-deficit-normalized rainfall for the events in a period from March 31, 2015 to October 31, 2018. Our goal is to confirm the relationships identified by Crow et al. (2017) in heavily managed agricultural landscapes and to refine some of their methodological steps to quantify the role of additional variables controlling runoff production. To this end, we define three different strategies to identify rainfall-runoff events and add a baseflow separation step to better insu- late event scale stormflow runoff. We test the effects of antecedent soil moisture, rainfall, and vegetation on the event-scale runoff ratio. The antecedent SMAP soil moisture and event-scale rainfall are found to have significant predictive power in estimating event runoff ratio. Soil moisture deficit-normalized rainfall, introduced as the ratio of event-scale rainfall to available space in top soil be- fore initiation of the event, exhibited a more distinct relationship with runoff Corresponding author Email address: navid-jadidoleslam@uiowa.edu (Navid Jadidoleslam) Preprint submitted to Journal of Hydrology August 16, 2019 ratio. Thelong-termanalysisofrunoffratio, rainfall, andMODISEVIindicated that, in an agricultural region, vegetation plays a significant role in determin- ing event-scale runoff ratios. The methodology and outcome of our study have direct implications on real-time flood forecasting and long-term hydrologic as- sessments. Keywords: soil moisture, runoff ratio, satellite, rainfall, hydrology, vegetation 1. Introduction1 A recent study by Crow et al. (2017) revealed a significant nonlinear rela- 2 tionship between antecedent soil moisture at the watershed scale and event-scale3 runoff ratios for 16 basins in the Southern United States. They showed that soil4 moisture estimations from data assimilation of SMAP (Soil Moisture Active5 Passive) brightness temperature observations along with meteorological data6 into CLSM (Catchment Land Surface Model) (Koster et al., 2000) improves7 representation of prestorm soil moisture conditions for streamflow forecasting.8 In this study, we follow-up and further expand the methodology used by 9 Crow et al. (2017) using remote sensing observations of antecedent soil moisture,10 rainfall, and vegetation to understand the relationships with estimated runoff11 ratios. Our study is motivated by three issues described as follows.12 First, the accuracy of satellite-based soil moisture retrievals depends on 13 biomass or vegetation water content (e.g. Jackson and Schmugge (1991), Njoku14 and Entekhabi (1996), Calvet et al. (2011)). The study region in Crow et al.15 (2017) had low vegetation water content. We assess the validity and significance16 of the relationship between antecedent SMAP satellite soil moisture and event-17 scale runoff ratio found by Crow et al. (2017) in a different region, dominated18 by cropland, such as Iowa.19 Second, in contrast to antecedent soil moisture, Crow et al. (2017) were not 20 able to find a significant relationship between event total rainfall and event-21 scale runoff ratio. We test if event total rainfall has a significant predictive22 power for estimating event runoff ratio and we examine the dependence of these23 relationships on storm event definition and/or baseflow separation.24 Finally, apart from antecedent soil moisture and rainfall, previous studies 25 have shown that vegetation is another significant factor in controlling runoff26 2 production (e.g. Kozak et al. (2007), Elhakeem and Papanicolaou (2009)). The27 effect of vegetation can become more prominent in heavily agricultural regions.28 Wetestthisaspectbyanalyzingannualcyclesofrunoffratio, eventtotalrainfall,29 and vegetation.30 For addressing the objectives of our study, we use SMAP satellite soil mois- 31 ture and radar rainfall to investigate the role of antecedent soil moisture and32 the event total rainfall on event runoff ratio by introducing three different types33 of rainfall-runoff event definition. We estimate runoff ratio before and after34 baseflow separation in basins that are less than 1000 km2in the state of Iowa, 35 USA. For more practical flood forecasting purposes, we introduce soil moisture36 deficit-normalized rainfall to evaluate the potential added-value of using both37 SMAP satellite soil moisture and S-band radar rainfall data. Furthermore, we38 explore the long-term annual variation of runoff ratio and we try to explain39 its potential controls by event total rainfall and MODIS (Moderate Resolution40 Imaging Spectroradiometer) vegetation index.41 We begin with description of the study basins, methods and data used for 42 this study. Then, we present our main findings from data analysis with interpre-43 tations, followed by discussion of results and their relevance to previous studies.44 We conclude with the main implications that can be drawn from our study for45 hydrologic applications and flood forecasting.46 2. Study region and Methodology47 2.1. Study basins48 In this study, we consider basins located in Iowa that are not affected by up- 49 stream flow regulation and have drainage areas in a range from 80 to 1000 km2. 50 IowaislocatedatU.S.Cornbeltwithawarmandhumidclimate. USGS(United51 States Geological Survey) stream gage locations of the 38 basins considered in52 this study are shown in Fig. 1.53 3 Figure 1: Location and outlet (green square) of the basins in Iowa used in this study. The selection of basins with drainage areas less than 1000 km2is mainly 54 based on three reasons: (1) streamflow for larger basins exhibit longer memory55 thanbasinswithsmallerdrainageareas(Hirpaetal.,2010). Therefore, attribut-56 ing streamflow to a specific rainfall event becomes more challenging due to the57 merging effect of two consecutive rainfall responses in streamflow measured at58 the outlet of larger basins; (2) the calculation of runoff ratio from streamflow59 becomes more prone to errors because larger basins are less sensitive to rain-60 fall intermittency (Ayalew et al., 2015); (3) finally, due to spatial variability of61 rainfall and soil moisture, the spatial average of these two quantities may not62 be representative for the basins with larger drainage areas.63 2.2. Data64 For this study, we use radar rainfall, SMAP satellite soil moisture and 65 streamflow. We obtain these three datasets for April 1, 2015 to October 31,66 2018 for 38 basins shown in Fig. 1. To avoid the probable impacts of frozen sur-67 face on estimation of soil moisture from SMAP, we exclude January, February,68 March, November and December months.69 4 Furthermore, for understanding the runoff ratio variability over time, we 70 obtain radar rainfall, streamflow and basin-averaged MODIS EVI for Jan 1,71 2002 to Dec 31, 2018. Specific details of each dataset is provided as follows,72 Soil moisture: We use Enhanced SMAP Level 3 Version 2 (L3_P_E) soil 73 moisture generated by using Backus-Gilbert optimum interpolation method74 (Chaubell et al., 2016). Enhanced SMAP L3 soil moisture is based on Single75 ChannelAlgorithm(O’neilletal.,2016)andbrightnesstemperaturecorrespond-76 ing to Vertical Polarization (SCA-VPOL). Enahnced SMAP data is provided on77 EASE-Grid version 2 (Brodzik et al., 2012) with resolution of 9 km in space.78 We obtain SMAP soil moisture data for March 31, 2015 to October 31, 2018.79 Descending and ascending orbits of SMAP correspond to approximately 6 AM80 and 6 PM local time of the satellite footprint, respectively. For our study, at any81 timestamp, SMAP data should cover at least 60 percent of the basin’s drainage82 area to be considered as a representative average antecedent soil moisture of the83 basin.84 Radarrainfall: Weuseraingaugebias-correctedStageIVhourlyrainfall(Lin 85 and Mitchell, 2005) that is distributed by National Centers for Environmental86 Prediction (NCEP) and posted on a grid with approximate spatial resolution87 of 4km(Reed and Maidment, 1999). We calculate daily total rainfall volume 88 for each basin by temporal summation of hourly rainfall volume for each basin89 and then we normalize it by basin drainage area for finding the basin-averaged90 rainfall depth.91 Streamflow: We obtain daily mean streamflow provided by U.S. Geological 92 Survey on National Water Information System (U.S. Geological Survey, 2016).93 Enhanced Vegetation Index (EVI): We use MODIS satellite EVI (MCD43A4 94 version 6) dataset (Schaaf and Wang, 2015) which is daily product driven by95 using 16-day retrieval algorithm with an approximate spatial resolution of 50096 (m). We calculate basin-averaged MODIS EVI for each rainfall-runoff event. 97 For this purpose, we use Google Earth Engine (Gorelick et al., 2017) which is a98 cloud-based platform for planetary geospatial analysis.99 2.3. Methods100 For understanding the dependence of runoff ratio on definition of rainfall- 101 runoff event, we consider three different methods to define a runoff event,102 5 whereas for each method, we calculate the event total runoff with and with-103 out base flow separation.104 Event-scale runoff ratio ( RC) is defined as 105 RC=VQ VP(1) whereas VQ(m3)istotalvolumeofstreamflowand VP(m3)istotalprecipitation 106 volume over a given basin for the duration of a rainfall-runoff event.107 2.3.1. Baseflow separation108 Streamflow, during a rainfall-runoff event, is composed of stormflow and 109 baseflow. Stormflow, which is due to surface runoff, is defined as110 Qs=QQb (2) where Q(m3=s) is daily mean streamflow and Qb(m3=s) is daily mean baseflow 111 at the outlet of the basin. For estimation of baseflow ( Qb), we use the local 112 minimum method of HYSEP, which is a tool developed by USGS for hydrograph113 separation (Sloto and Crouse, 1996). In HYSEP, the duration of surface runoff114 for calculation of stormflow is defined by an empirical relationship given as115 N= 0:8A0:2(3) where Nis the number of days after which surface runoff ceases and A(km2) 116 is the drainage area of the basin.117 We calculate runoff ratio using streamflow and stormflow to understand the 118 effect of baseflow separation on the relationship of antecedent soil moisture,119 event total rainfall and deficit-normalized rainfall with runoff ratio. We use120 three types of rainfall-runoff event definition whereas we discuss the details of121 each method in following sections (Sections 2.3.2-2.3.4).122 2.3.2. Method 1 – Constant event duration (M1)123 For this event definition, we follow the rainfall-runoff event definition by 124 Crow et al. (2017) that used a constant duration. They defined the event as125 a 6-day period following a triggering threshold rainfall depth. They selected126 rainfall-runoff events based on the threshold rainfall depth. They used two127 criteria for selecting a rainfall-runoff event: (1) an event should not have more128 6 than one basin-averaged rainfall equal or greater than threshold depth ( Pth) 129 during the storm event; (2) the rainfall of the day prior to initiation of a storm130 event should not exceed the threshold depth.131 We calculate daily average SMAP soil moisture of the basin. Then, we 132 select the antecedent soil moisture of the event as the minimum of the daily133 average SMAP soil moisture for a two-day period prior to the initiation of134 event. Crow et al. (2017) found that using different event durations does not135 effectsignificanceoftherelationshipbetweenantecedentsoilmoistureandevent-136 scale runoff ratio. Therefore, considering the basin drainage areas of our study,137 we use a three-day period following a triggering threshold rainfall for definition138 of rainfall-runoff event. A schematic of the event definition for this method is139 shown in Fig. 2.140 Figure 2: Schematic of rainfall-runoff event (highlighted gray) definition after Crow et al. (2017) and pre-storm soil moisture (red circles). Top panel: Basin-averaged rainfall depth (blue bars) and daily average soil moisture (black circles); Bottom panel: streamflow (black line) and stormflow (red line). 2.3.3. Method 2 – Stormflow based (M2)141 We use stormflow time series for identifying the beginning and end times- 142 tamps of the rainfall-runoff events by finding local minima of the daily mean143 stormflow calculated by Eq. (2). We calculate event-scale runoff ratio by ac-144 cumulating the streamflow/stormflow over the duration of an event. We use145 both ascending and descending SMAP orbits’ soil moisture estimations. For146 7 consistency with M1, the antecedent soil moisture of an event is defined as the147 minimum basin-averaged soil moisture of two days prior to initiation of the148 event. A schematic for the definition of rainfall-runoff event using stormflow is149 shown in Fig. 3.150 Figure 3: Rainfall-runoff event definition by using local minima of the stormflow time series. Top panel: Daily streamflow (black line) and stormflow (red line); Bottom panel: Rainfall (blue bars), ascending (upward triangles) and descending (downward triangles) orbit’s SMAP soil moisture, antecedent soil moisture of events (red triangles). 2.3.4. Method 3 – Event definition based on Model Routed Rainfall (M3)151 We used the HLM (Hillslope Link Model) implemented at the Iowa Flood 152 Center (e.g. Krajewski et al. (2017); Quintero et al. (2016)), with the assump-153 tion of impervious surface (i.e. a constant runoff ratio of 100%). The main154 motivation for using a model for translating rainfall into streamflow is to iden-155 tify the minimum travel time of the event rainfall, including the effect of rainfall156 variability in space and time during a rainfall-runoff event.157 In HLM, each basin is decomposed of hillslopes and links (i.e. river segment) 158 whereas the median area of hillslopes is 0.3 km2. The change of channel flux 159 at a given link could be calculated by solving two coupled nonlinear ordinary160 differential equations given as161 dq dt=q1 [q+qpcAh+qin(t)] (4) 162 dsp dt=p(t)RCqpc (5) 8 where q(m3=s) is the channel discharge at a given link in the basin’s river 163 network, qin(t)is the discharge entering the channel from the parent link(s) 164 (upstream channel), Ah(m2) is the area of the hillslope, 1is the exponent 165 of channel discharge and qpc(m=s) is the flux from ponded surface storage to 166 channel which is defined as167 qpc=vhL Ahsp (6) where vh(m=s) is overland flow velocity, L(m) is channel length. In Eq. (4), 168 is the residence time of flow in a given link formulated as 169 1 =vr L(11)Aup Ar2 (7) Aupistheupstreamdrainageareaofthelink, vristhechannelreferencevelocity 170 and2is the exponent of channel’s upstream contributing area. 171 A schematic of the event definition using routed rainfall is shown in Fig. 4 172 where events are highlighted as gray.173 We solve in Eq. (4) and Eq. (5), implemented in HLM model to identify 174 the rainfall-runoff events. As shown in Fig. 4, first, we find the local minimums175 of the routed rainfall time series. Then, we truncate the identified time to176 the beginning of the day (00:00 UTC-6) to be consistent with the daily scale177 streamflow data. Thereafter, we calculate the runoff ratio for the corresponding178 rainfall-runoff event. Finally, we select the minimum basin-averaged SMAP soil179 moisture of the two-day period prior to initiation of the rainfall-runoff event.180 Red triangles, in top panel of Fig. 4, are the soil moisture values considered181 as the antecedent soil moisture of the following rainfall-runoff event. Similar182 to Fig. 3, upward and downward triangles represent the basin-averaged SMAP183 satellite soil moisture.184 9 Figure 4: Event definition by using routed rainfall at the outlet of the basin. Top panel: Rainfall (blue bars), ascending (upward triangles) and descending (downward triangles) orbit’s SMAP soil moisture, antecedent soil moisture of events (red triangles); Middle panel: Routed rainfall (black line), Local minima of the routed rainfall timeseries; Bottom panel: Daily streamflow (black line) and stormflow (red line). 2.4. Correlation analysis185 Forthethreeeventdefinitions, weinvestigatetherelationshipbetweenevent- 186 scale runoff ratios with antecedent soil moisture, rainfall and soil moisture187 deficit-normalized rainfall. Deficit-normalized rainfall is the ratio of event-scale188 rainfall to available space in soil prior to initiation of the event and it is defined189 as190 Pdef=PP Ds( SM i)(8) wherePP(mm) is the event accumulated rainfall depth calculated by dividing 191 the total rainfall volume for a given basin by its drainage area, (cm3=cm3) is 192 soil porosity and SM i(cm3=cm3) is antecedent basin-averaged soil moisture of 193 the event and Ds(mm) is normalization length assumed as a constant depth of 194 50 (mm). 195 We explore the relationship between runoff ratio and antecedent soil mois- 196 ture, rainfall and deficit-normalized rainfall by using Spearman correlation co-197 10 efficient that is an indicator of the monotonicity of the nonlinear relationship198 between two variables. Spearman correlation coefficient ( s) is defined as 199 s=cov(rgX; rgY) rgXrgY(9) where rgXandrgYare ranks of the two variables, cov(rgX; rgY)is the covari- 200 ance and rgXandrgYare standard deviations of the ranks. 201 Due to short time period of SMAP mission, we combine the data for all 202 of the basins that are used in this study. We test the significance of the non-203 linear relationship of runoff ratio before and after baseflow separation for the204 three variables and for each method. We compare the correlations between our205 proposed event definitions (M2 and M3) with the event definition method M1206 adopted from Crow et al. (2017) by event-scale basin-averaged rainfall depth.207 2.5. Annual cycles of runoff ratio, rainfall and vegetation208 We investigate temporal variability of runoff ratio for 17 years (2002-2018) 209 by using event definition Method 2 (M2). We calculate event-scale runoff ratio210 using stormflow (baseflow separated flow) and event-scale rainfall for each event.211 Furthermore, we extract basin-averaged MODIS (MODerate resolution Imaging212 Spectroradiometer) Enhanced Vegetation Index (EVI) to investigate the impact213 of vegetation on temporal variability of runoff ratio. EVI is defined as214 EV I =GNIRred NIR +C1redC2blue+L(10) In Eq. (10), NIR,redandblueare atmospherically adjusted surface re- flectance values corresponding to Near-Infrared (NIR), red, and blue bands re- spectively, Lis the canopy background adjustment that accounts for nonlinear differential NIR and red radiant transfer through canopy, C1andC2are aerosol resistance coefficients that use blue band to correct reflectance for aerosol in- fluences in red band and G is gain factor (Huete et al., 2002). Following Huete et al. (1994, 1997), the coefficients used in EVI algorithm are L= 1; C1= 6; C2= 7:5; G= 2:5 We use Wilcoxon rank sum test for comparing medians of event-scale rainfall 215 and runoff ratio distributions for each month. Wilcoxon test is a nonparametric216 test that examines if samples from two distributions have equal medians versus217 11 alternative hypothesis that the medians of two distributions are not equal. For218 statistical tests, we set a 5% significance level.219 3. Results220 We divide our results section in three parts, first we present results on the 221 variations of runoff ratio with antecedent soil moisture, rainfall and deficit-222 normalized rainfall for 4 years (2015-2018), respectively. Thereafter, we in-223 vestigate the impact of event-scale rainfall, event-definition methodology and224 baseflow separation on the prominence of these relationships using Spearman225 correlation. Finally, we explore event-scale rainfall, and basin-averaged MODIS226 EVI to describe temporal variability of runoff ratio for 17 years (2002-2018).227 3.1. Antecedent soil moisture and event-scale runoff ratio228 Variation of runoff ratio with respect to the antecedent SMAP L3 soil mois- 229 ture for rainfall-runoff events are shown in Fig. 5 as violinplots (Hintze and230 Nelson, 1998). In this figure, the shape of the violinplot represents estimated231 kernel density of the data points. The inner boxplot shows median (white cir-232 cles), interquartile range from 25 percentile ( q1) to 75 percentile ( q3) and high 233 and low whiskers that are calculated as q3+ 1:5(q3q1)andq11:5(q3q1), respec- 234 tively. Each row, in this figure, corresponds to event definition method (denoted235 as M1, M2 and M3). The numbers shown at the top of each subplot, from top236 to bottom, refer to the number of rainfall-runoff events with runoff ratio values237 greater than 1 and less than 1, for a given antecedent SMAP soil moisture range,238 respectively.239 12 Figure 5: Violinplots of the event-scale runoff ratio (left column: streamflow, right column: stormflow) versus basin-averaged antecedent soil moisture from SMAP L3 for rainfall-runoff events with rainfall depth equal or greater than 10 (mm). The number of rainfall-runoff events with runoff ratio greater than 1 and less than 1 are annotated at the first row and second row at the top of each subplot for each corresponding antecedent soil moisture. Subplots in each row correspond to rainfall-runoff event definition (M1-M3). The number of events detected by each method for event-rainfall depths 240 larger than 10 (mm) are more than 2000, 2600 and 3200 by using methods241 M1 to M3, respectively. Runoff ratios and the number of events with runoff242 ratios greater than one decrease significantly by removing baseflow signal from243 streamflow (right column in Fig. 5).244 Events with lower antecedent soil moisture tend to have lower runoff ra- 245 tios while runoff ratio increases for higher antecedent soil moisture conditions246 whereas the range of variability for runoff ratio is higher compared to dry an-247 tecedent conditions.248 As shown in Fig. 5, regardless of accounting for baseflow in runoff ratio 249 calculation, median runoff ratio shows a positive trend with basin-averaged an-250 13 tecedent SMAP soil moisture for all event definition methods. However, runoff251 ratios calculated by using stormflow show less variability and smaller interquar-252 tile range for lower antecedent soil moistures (i.e. dry conditions). On the other253 hand, runoffratioscalculatedbystormflowonlyexhibitsmallervaluescompared254 to using total streamflow.255 3.2. Rainfall and event-scale runoff ratio256 Fig. 6 illustrates the variations of runoff ratio with respect to event-scale 257 rainfall. There is a positive trend between event rainfall and runoff ratio, and258 this trend is more pronounced in case of runoff ratios calculated using stormflow259 only. On the other hand, the probability of higher runoff ratios increases as the260 event scale rainfall increases.261 Figure 6: Violinplots of the event-scale runoff ratio (left column: streamflow, right column: stormflow) versus basin-averaged event scale rainfall for events with rainfall depth equal or greater than 10 (mm). The number of rainfall-runoff events with runoff ratio greater than 1 and less than 1 are annotated at the first row and second row at the top of each subplot. As shown in Fig. 6 the relationship between event runoff ratio and event 262 14 scale rainfall becomes clear by using stormflow. Furthermore, event definition263 method M1 shows a slight decrease in median runoff ratio for events with larger264 basin-averaged rainfall magnitudes. While the positive trend of median runoff265 ratio is more distinct and monotonic for event definition methods M2 and M3.266 Given the differences in definitions of events, overall, all three event defini- 267 tion methods exhibit a positive association in median runoff ratios with respect268 to basin-averaged event scale rainfall. It should be noted that for smaller mag-269 nitudes of event-scale rainfall, runoff ratios calculated by M2 method exhibit270 least variability among other methods.271 3.3. Soil moisture deficit-normalized rainfall and runoff ratio272 Distributions of runoff ratio with deficit-normalized rainfall for events with 273 event-scale rainfall equal or greater than 10 (mm) are shown in Fig. 7.274 Figure 7: Violinplots of the event-scale runoff ratio (left column: streamflow, right column: stormflow) versus deficit-normalized rainfall for events with rainfall depth equal or greater than 10 (mm). The number of rainfall-runoff events with runoff ratio greater than 1 and less than 1 are annotated at the first row and second row at the top of each subplot. 15 The runoff ratio shows an asymptotic increase with an increasing deficit- 275 normalized rainfall, whereas for large deficit-normalized rainfall, median runoff276 ratio tends to become a constant value. In contrast to runoff ratios calculated277 by streamflow (left column in Fig. 7), the interquartile range is smaller by using278 stormflow (right column in Fig. 7). The median of the runoff ratio distribution279 shift toward larger runoff ratios by approaching to larger deficit-normalized280 rainfall.281 3.4. Summary of correlation and significance282 A summary of Spearman correlation coefficients between event-scale runoff 283 ratio with antecedent soil moisture, event-scale rainfall, and deficit-normalized284 rainfall calculated by using the three event definitions is shown in Fig. 8. Cor-285 relations correspond to events with event-scale basin-averaged rainfall (PP) 286 greater or equal to values ranging from 5 to 30 (mm).287 Figure 8: Spearman correlation coefficient for different methods and event-scale rainfall depths. Runoff ratio calculation methods are denoted in left hand side. The correlation coefficient between antecedent soil moisture and runoff ratio 288 increases as the event-scale rainfall depth increases. The highest Spearman cor-289 relations correspond to constant event duration method (M1). Right tail (pos-290 16 itive correlation) Spearman correlation test with 95% confidence level suggests291 that correlations are statistically significant for both runoff ratio calculation292 methods (streamflow/stormflow).293 Thecorrelationsforevent-scalerainfallandrunoffratiocalculatedbystream- 294 flow indicate weak or no relationship. For this case, the statistical test was not295 able to reject the non-existence of significant positive association between these296 two variables. In contrast, the correlations between runoff ratio and event-scale297 rainfall are higher and statistically significant by using stormflow only for runoff298 ratio calculations especially for stormflow-based event definition (M2). This in-299 dicates that baseflow separation is a critical step in calculation of runoff ratio.300 Deficit-normalized rainfall yields higher Spearman correlation coefficients 301 compared to antecedent soil moisture and rainfall. Exclusion of events by larger302 rainfall thresholds in Fig. 8, increases the monotonicity of the relationship be-303 tween deficit-normalized rainfall and runoff ratio for M1 and M3, while M2304 shows less sensitivity to event-scale rainfall depth. With this respect, event305 definition M2 has the highest correlation coefficient whereas the interquartile306 ranges of runoff ratio are smaller than other two methods (see Fig. 7).307 3.5. Annual cycles of runoff ratio, rainfall and vegetation308 Fig. 9a and Fig. 9c show variation of event scale runoff ratio and event scale 309 rainfall for each month and Fig. 9b shows basin-averaged MODIS EVI at the310 beginning of each rainfall-runoff event. The number of events at each month and311 events with runoff ratio greater than 1 are annotated at the top of this figure.312 Note that data shown in Fig. 9 correspond to more than 16000 rainfall-runoff313 events for the period of 17 years (2002-2018).314 17 Figure 9: Temporal variation of (a) runoff ratio, (b) MODIS basin-averaged Enhanced Vege- tation Index ( EV I < 0not shown) and (c) event-scale rainfall over 17 years (2002-2018). The number of rainfall-runoff events with runoff ratio greater than 1 and less than 1 are annotated at the first row and second row at the top. There is a positive trend in median runoff ratio from January to May while 315 median and interquartile range of runoff ratios decrease significantly from June316 to September (Fig. 9a) whereas in August and September the median runoff317 ratio is lower compared to May and June.318 Basin-averaged EVI calculated from MODIS satellite data for each rainfall- 319 runoff event during 2002-2018 (Fig. 9b) suggests that, for majority of the basins,320 peak EVI occurs in a period starting from mid-July to mid-August. Higher321 variability of EVI during some time periods (e.g. April-June) is due to year to322 18 year change and variability of EVI from one basin to another.323 As shownin Fig. 9c, there is seasonalityin event-scalerainfall whereas lowest 324 and highest median event-scale rainfall depths correspond to January and June,325 respectively.326 Using 95 % confidence level, two-tailed Wilcoxon statistical test was not able 327 to reject the null hypothesis of equal medians for event-scale rainfall distribution328 in August and April. However, the test result on distributions of runoff ratio329 suggested that there is statistically significant decrease in median value of the330 runoff ratio in August compared to April.331 4. Discussion332 Our analysis of the relation between runoff coefficients and antecedent soil 333 moisture conditions using Enhanced SMAP L3 soil moisture data (Fig. 5) show334 agreement with findings from Crow et al. (2017).335 It is important to account for SMAP soil moisture uncertainty in inter- 336 pretation of our results for relationship between antecedent soil moisture and337 event-scale runoff ratio (Fig. 5). SMAP satellite soil moisture has an average338 unbiased root mean square error (ubRMSE) in a range from 0.04-0.055 ( m3=m3) 339 (Chan et al., 2018; Colliander et al., 2018; Zhang et al., 2019) which is close to340 SMAP mission’s ubRMSE requirement. While SMAP soil moisture has shown341 a better performance compared to other satellite soil moisture products (Still-342 man and Zeng, 2018), however, it is also found that SMOS and SMAP soil343 moisture dry-downs are more rapid than in-situ observations following rainfall344 events (Rondinelli et al., 2015; Shellito et al., 2016).345 In contrast to results from Crow et al. (2017), the relationship between 346 event-scale rainfall and runoff ratio (right column in Fig. 6) is more distinct af-347 terbaseflowseparation. Moreover, resultsfrommethodsM3andM2havehigher348 correlations than M1 for event-scale rainfall and runoff ratio (Fig. 8). Consecu-349 tive rainfall storms, which increases cumulative rainfall, during a rainfall-runoff350 event eventually increase the soil moisture that consequently lead to a poten-351 tially higher runoff ratio. Previous studies used antecedent precipitation index352 (API) as a proxy to soil moisture conditions (e.g. Brocca et al. (2008); Crow353 19 et al. (2005); Fedora and Beschta (1989)) for estimation of antecedent condi-354 tions.355 Deficit-normalized rainfall, introduced as a combination of event total rain- 356 fall with antecedent soil moisture of the event, decreased the interquartile range357 of probable runoff ratios compared to rainfall or antecedent soil moisture re-358 lationships with runoff ratio (Fig. 7). To be consistent with SMAP satellite359 soil moisture sensing depth, we used a constant depth of Ds= 50(mm) for 360 normalization of rainfall by soil moisture deficit. Previous studies have found361 that representative sensing depth of L-band microwave emission depends on the362 soil moisture and temperature profiles Escorihuela et al. (2010); O’Neill (1982).363 Also, Akbar et al. (2018) have estimated the hydrologic storage length scales364 for Contiguous United States (CONUS) by minimizing the difference between365 reconstructed time series of soil moisture from rainfall observations and En-366 hanced SMAP L3 soil moistures. Their estimation of hydrologic depth for Iowa,367 suggests values in ranges of 30 to 210 (mm).368 Iowa is in a heavily agricultural region, where more than 75 percent of its 369 area is cultivated cropland (Homer et al., 2007) and it is planted mainly by corn370 and soybean, respectively. Previous results from field experiments by Frasson371 and Krajewski (2011), conducted during full development of maize plant (July372 21 to August 26), have shown that maize canopy modifies the rainfall intensity373 and decreases accumulated rainfall depth underneath the maize plant. Indeed,374 our results for annual cycle of runoff ratio (Fig. 9a) confirms that median runoff375 ratio is significantly lower after full development of vegetation. During this376 time period, median runoff ratio decreases significantly favoring lower runoff377 ratios. On the other hand, event-scale runoff ratio (Fig. 9a) from October to378 December indicate an increasing trend which corresponds to time period after379 active harvesting season for corn in Iowa, between October 5th to November380 9th (U.S. Department of Agriculture, 2010).381 According to our results shown in Fig. 9c June was identified as the peak 382 frequencyfordistributionofannualmaximumdailyrainfallforIowa. Wefurther383 found that median event scale rainfall values are equal for April and August.384 Thus, based on previous literature and our data analysis on long-term temporal385 variation of runoff ratio and MODIS EVI, the statistically significant decrease in386 20 median runoff ratio, from April to August, can be attributed to fully developed387 vegetation in August compared to lower vegetation cover or bare ground in388 April. Similar reduction in runoff ratio is expected in similar regions such as389 Midwest United States where corn and soybean are dominant. We highlight390 that other than soil moisture, vegetation can significantly decrease the runoff391 production.392 First limitation of our study is the coarse spatial resolution of radar rainfall 393 (4 km) and SMAP satellite-based soil moisture ( 9 km). Also, we have used 394 aggregated quantities (e.g. spatial average) for simplification of our analysis395 whileeachquantityhasspatialvariabilityandassociatedinherenterrorsinspace396 and time. We have used three different methods to define a rainfall-runoff event397 based on daily streamflow observations. There are differences in approximations398 of runoff ratio by each rainfall-runoff event definition. Therefore each method399 could introduce errors to estimated runoff ratio. We note that associating a400 portion of streamflow at a given time to a specific rainfall event is challenging.401 We have used an empirical method for baseflow separation which may introduce402 errorsincalculationofrunoffratios. Therefore, anaccuratebaseflowseparation,403 event definition, and runoff ratio estimation demands more field observations404 such as application of isotopes.405 5. Conclusions406 In this study, we investigated the role of antecedent SMAP satellite soil 407 moisture, rainfall, and soil moisture deficit-normalized rainfall on runoff ratio.408 We used 4 years (2015-2018) of soil moisture, rainfall and streamflow data for409 38 basins with areas ranging from 80 to 1000 km2in Iowa, USA. We examined 410 the dependence of these relationships on the methodology, such as impact of411 event-scale rainfall depth, baseflow separation and rainfall-runoff event defini-412 tion in the calculation of runoff ratio. We applied three different methods for413 event definition such as the one proposed by Crow et al. (2017). We summarized414 our results for the relationship of soil moisture, rainfall and deficit-normalized415 rainfall with event-scale runoff ratio using Spearman correlation coefficient. We416 further explored temporal variability of runoff ratio based on 17 years of data417 and tried to explain its physical controls by MODIS EVI and event-scale rain-418 21 fall. Considering all above-mentioned points, the following conclusions could be419 drawn:420 • The positive trend of runoff ratio (with and without baseflow separation) 421 with respect to antecedent soil moisture implies that SMAP satellite soil 422 moisture, without data assimilation, is by itself skillful for estimation of 423 event-scale runoff ratio. Therefore, our study shows that the relationship 424 shown by Crow et al. (2017) exists in Iowa, which more than 75% of it is 425 covered by cropland (corn and soybean). 426 • Althoughbaseflowseparationdecreasesthe runoffratio estimates, wehave 427 found that the significance of the relationship between antecedent soil 428 moisture and runoff ratio is independent of the methodology. 429 • In contrast to results by Crow et al. (2017), event-scale rainfall has a 430 significant relationship with runoff ratio and it is revealed after removing 431 the baseflow signal from streamflow hydrograph. 432 • We showed that deficit-normalized rainfall has more predictive power for 433 estimation of basin-scale event runoff ratios compared using SMAP an- 434 tecedent soil moisture and event-scale rainfall individually. 435 • Long-term analysis of enhanced vegetation index (EVI) indicated that 436 vegetation peak occurs in a time period from mid-July to mid-August 437 which correspond to time period of full development of crops, after which 438 runoff ratio decreases significantly. 439 • In addition to antecedent soil moisture and event-scale rainfall, vegeta- 440 tion also plays a significant role in determination of runoff ratio in an 441 agricultural region. 442 We have demonstrated a potential application of remote sensing data for un-443 derstanding the rainfall-runoff partitioning and main physical controls on runoff444 ratio in basin scale. We used MODIS satellite data along with L-band radio-445 metric SMAP satellite soil moisture, and S-band radar rainfall that complement446 the essential information for understanding hydrologic processes. An analysis447 of runoff production and its controls with a similar spatial coverage and time448 period as used in this study could be challenging without remote sensing data.449 22 Our findings are useful for regions that have similar climate and cropland 450 conditions. Such similarities are found in regions within Upper Mississippi and451 Ohio River basins (Schilling et al., 2015).452 For practical flood forecasting purposes, we introduced deficit-normalized 453 rainfall, as a dimensionless index, that could be used for assessment of the454 severity of forecast rainfall with respect to available space in top soil and esti-455 mation of runoff. In this case, daily total rainfall forecast and antecedent soil456 moisture could be used for estimating runoff production and local flash flood457 warnings.458 Our approach could be used for evaluating the degree of coupling between 459 antecedent soil moisture, rainfall and impact of vegetation on runoff ratio in460 hydrologic models, similar to study done by Crow et al. (2018), in agricultural461 regions.462 6. Acknowledgements463 This study is funded by NASA SUSMAP (Science Utilization of the Soil 464 Moisture Active-Passive Mission) program with grant No. “15-0104” and Iowa465 Flood Center at the University of Iowa.466 The dataset used in this study is available from the corresponding author.467 468 7.