# Comparison of Satellite Rainfall Estimates and Rain Gauge Measurements in Italy, and Impact on Landslide Modeling

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods and Results

#### 2.1. Climatic and Morphological Framework

#### 2.1.1. Climate Variability

#### 2.1.2. Morphological Subdivision

#### 2.2. Rainfall Data

#### 2.2.1. Rain Gauge Measurements (VRF)

#### 2.2.2. Satellite Rainfall Estimates

#### 2.3. Analysis of Cumulated Rainfall

#### 2.3.1. Correlation Analysis

^{2}, measuring the goodness of fit), and the confidence and prediction intervals. The analysis was performed for the whole of Italy and for the individual morpho-climatic subdivisions, using the four different TMPA products. Figure 4 shows an example of the results for the TMPA-V6 product in the Alps (Table 2 and Figure 1), using a minimum threshold of cumulated rainfall of 16 mm.

#### 2.3.2. Statistical Distribution Estimation

#### 2.4. Analysis of Rainfall Events

#### 2.4.1. Identification of Rainfall Events

#### 2.4.2. Comparison of Rainfall Events

## 3. Discussion

^{2}squared estimated for both fitting models vary for different rainfall threshold values in the entire territory and in each morphological zones. In all cases, the determination coefficients estimated for the power-law model are higher than those calculated for the linear models. Overall, the power-law regression models provided the best fitting performance compared to the linear regression models but at the expense of larger prediction intervals (i.e., the estimate of the intervals in which future observations will fall; [70,71] in particular for high rainfall values (see the example given in Figure 4a,b). The coefficients for the power-law models exhibit an increasing trend, stabilizing quickly towards high values, but in general for threshold values from 5 (TMPA-V6 and TMPA-V6-RT) to 10 mm (TMPA-V7 and TMPA-V7-RT). The linear determination coefficients are more sensitive to the threshold values along their entire range with general increasing trends except for: APEL, POPL for TMPA-V6 (Figure 5e,h), SARD for TMPA-V6-RT (Figure 6i), SARD, TYRR for TMPA-V7 (Figure 7i,k), and ALPS, APEL, POPL, SARD, SICI for TMPA-V7-RT (Figure 8c,e,h–j). An increasing trend along the threshold values range means that the more extreme the rainfall considered, the higher the correlation between VRF and TMPA data. However, for a few cases (e.g., 12 mm for SICI for TMPA-V7-RT show in Figure 8j), the higher correlation corresponds to specific threshold rainfall values. On average the lowest determination coefficient values and the largest differences between the linear and the power-law models are observed for the TMPA-V7 and TMPA-V7-RT products (Figure 7 and Figure 8). These two satellite products also show a peculiar sudden increase of the linear determination coefficients towards high threshold values for APEU, LANG, SICI with TMPA-V7 (Figure 7f,g,j), and APEC, APEL, APEU, LANG, SARD, TYRR with TMPA-V7-RT (Figure 8d–g,i,k), indicating a much better correlation for higher rainfall values.

^{2}estimates for different rainfall thresholds in Italy and in each Italian morphological zones. For both fitting models (linear and power-law) the regression parameters change in different morphological zones, indicating a complex correlation between TMPA and VRF data. In general, results show that satellite data underestimates the ground data (as shown by the coefficient greater than 1 in Figure 9a,e,i,m,c,g,k,o) particularly in high elevation areas such as the Alps (ALPS) and the northern Apennines (APEU), rather than in low altitude areas (SICI, ADRI, SARD). Results for some morphological subdivisions (POPL, APEC, LANG) are not consistent with this finding. This is probably due to the generalized aggregation procedure that does not directly consider some important territory characteristics such as relief orientation (aspect) and climatic differences (see Figure 1). The differences of the linear (Figure 9a,e,i,m) and power-law coefficients (Figure 9c,g,k,o) obtained for the four TRMM products also show that the range of the degree of underestimation is less pronounced for TMPA-V7-RT (Figure 9m,o), followed by TMPA-V7 (Figure 9i,k), TMPA-V6-RT (Figure 9e,g), and finally by TMPA-V6 (Figure 9a,c). Moreover, the variability of the linear determination coefficients estimated for the TMPA-V7 (Figure 9j) and TMPA-V7-RT (Figure 9n), in the different morphological subdivisions, is larger than the variability for the other two products TMPA-V6 (Figure 9c) and TMPA-V6-RT (Figure 9f) and in particular they are inversely correlated with the linear model coefficients (Figure 9i,m). For all the satellite products, the power-law regression models provided the best fitting performance (i.e., the power-law correlation values in Figure 9d,h,i,p are higher than the linear correlation ones in Figure 9b,f,j,n).

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A

## References

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**Figure 1.**(

**a**) Digital Elevation Model (DEM) at 230 m × 230 m pixel resolution; (

**b**) Climatic subdivision of the Italian territory (see details in Table 1) derived from [44,45]; (

**c**) Mean annual rainfall map [46,47,48]; (

**d**) Morphological subdivision of the Italian territory (see details in Table 2) derived by Guzzetti and Reichenbach [49].

**Figure 2.**(

**a**) Location of the rain gauges along the Italian territory (available through the Experience Platform of the Italian Civil Protection Department [54]); (

**b**) Location of TRMM cell centroids (0.25° × 0.25° resolution) covering the Italian territory.

**Figure 3.**Minimum distance criteria used to couple, each TMPA centroid (blue circles) with one or more VRF rain gauge stations (red triangles). The example refers to the coupling in the Umbria region (Central Italy).

**Figure 4.**Linear (

**a**) and power-law (

**b**) fitting between TMPA-V6 rainfall estimates T and rain gauge measures R. The two regression examples are for the 72-h cumulative rainfall in the Alpine region (Alps, see Table 2 and Figure 1) filtered using a rainfall threshold of 16 mm. Solid black lines is the best fitting, dotted red and dot-dashed blue lines are the confidence and the prediction intervals respectively.

**Figure 5.**Linear (grey dots) and power-law (red dots) fitting (between TMPA-V6 rainfall estimates and VRF ground data) determination coefficients for 72-h cumulative rainfall filtered using a peak over threshold approach taking values above different thresholds: 0, 2, 4, 6, 8, 10, 12, 14, 16, 18 and 20 mm. The different plots show results for different morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr.

**Figure 6.**Linear (grey dots) and power-law (red dots) fitting (between TMPA-V6-RT rainfall estimates and VRF ground data) determination coefficients for the 72-h cumulative rainfall filtered using a peak over threshold approach taking values above different thresholds: 0, 2, 4, 6, 8, 10, 12, 14, 16, 18 and 20 mm. The different plots show results for different Italian morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr.

**Figure 7.**Linear (grey dots) and power-law (red dots) fitting (between TMPA-V7 rainfall estimates and VRF ground data) determination coefficients for the 72-h cumulative rainfall filtered using a peak over threshold approach taking values above different thresholds: 0, 2, 4, 6, 8, 10, 12, 14, 16, 18 and 20 mm. The different plots show results for different Italian morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr.

**Figure 8.**Linear (grey dots) and power-law (red dots) fitting (between TMPA-V7-RT rainfall estimates and VRF ground data) determination coefficients for the 72-h cumulative rainfall filtered using a peak over threshold approach taking values above different thresholds: 0, 2, 4, 6, 8, 10, 12, 14, 16, 18 and 20 mm. The different plots show results for different Italian morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr.

**Figure 9.**Variability of the linear coefficients (

**a**,

**e**,

**i**,

**m**) and the relative determination coefficients (

**b**,

**f**,

**j**,

**n**), and the power-law coefficients (

**c**,

**g**,

**k**,

**o**) and the relative determination coefficients (

**d**,

**h**,

**l**,

**p**) for different rainfall thresholds values (see Figure 5, Figure 6, Figure 7 and Figure 8) in the entire Italian territory and in the different morphological subdivisions (see Table 2 and Figure 1).

**Figure 10.**Comparison of the probability density (see Table 5 for Kolmogorov-Smirnov test results) of the logarithm of 72-h cumulative rainfall estimated using HDE (points) and KDE (lines) method for VRF data and for the different satellite rainfall estimates (TMPA-V6, TMPA-V6-RT, TMPA-V7, TMPA-V7-RT) thresholds in the entire Italian territory and in the different morphological subdivisions (see Table 2 and Figure 1).

**Figure 11.**Exponential distribution estimated for 72-h cumulative rainfall using the MLE method for VRF data. The different plots show results for different Italian morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr. The insets show QQ-plot measuring the fitting performance of the exponential model. KS D and p-value are two-sided bootstrapped Kolmogorov-Smirnov test statistics (see Table 6).

**Figure 12.**Exponential distribution estimated for 72-h cumulative rainfall using the MLE method for satellite estimates (TMPA-V6). The different plots show results for different Italian morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr. The insets show QQ-plot measuring the fitting performance of the exponential model. KS D and p-value are two-sided bootstrapped Kolmogorov-Smirnov test statistics (see Table 6).

**Figure 13.**Exponential distribution estimated for 72-h cumulative rainfall using the MLE method satellite estimates (TMPA-V6-RT). The different plots show results for different Italian morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr. The insets show QQ-plot measuring the fitting performance of the exponential model. KS D and p-value are two-sided bootstrapped Kolmogorov-Smirnov test statistics (see Table 6).

**Figure 14.**Exponential distribution estimated for 72-h cumulative rainfall using the MLE method for satellite estimates (TMPA-V7). The different plots show results for different Italian morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr. The insets show QQ-plot measuring the fitting performance of the exponential model. KS D and p-value are two-sided bootstrapped Kolmogorov-Smirnov test statistics (see Table 6).

**Figure 15.**Exponential distribution estimated for 72-h cumulative rainfall using the MLE method for satellite estimates (TMPA-V7-RT). The different plots show results for different Italian morphological subdivisions: (

**a**) entire Italian territory; (

**b**) Adri; (

**c**) Alps; (

**d**) ApeC; (

**e**) ApeL; (

**f**) ApeU; (

**g**) Lang; (

**h**) Popl; (

**i**) Sard; (

**j**) Sici; (

**k**) Tyrr. The insets show QQ-plot measuring the fitting performance of the exponential model. KS D and p-value are two-sided bootstrapped Kolmogorov-Smirnov test statistics (see Table 6).

**Figure 16.**Events separation criteria implemented in the automatic procedure for the detection rainfall events. The procedure main parameters are the separation period (in hours) and the rainfall threshold values (in mm).

**Figure 17.**Duration and cumulative rainfall of the events detected by the automatic procedure in the entire Italian territory in the period September 2009–August 2010 for the VRF data (

**a**) and for the TMPA-V6 (

**b**), TMPA-V6-RT (

**c**), TMPA-V7 (

**d**), TMPA-V7-RT (

**e**) satellite rainfall estimates. (

**f**) Rainfall events associated with landslides in the Italian territory. The boxplots and the ECDF plots (lines) describe the empirical distributions of the duration and cumulative rainfall of the events. Overlapped quantile grids (drawn from the marginal boxplot values) highlight the bivariate median (dotted lines), the bivariate interquartile ranges (dashed lines) and bivariate notches ranges (solid lines) of the rainfall events.

**Figure 18.**Maps of mean cumulative rainfall (1st row), mean rainfall event duration (2nd row), and mean number of events (3rd row) detected by the automatic procedure in the entire Italian territory in the period September 2009–August 2010 for the VRF data (

**a**,

**f**,

**k**) and for the TMPA-V6 (

**b**,

**g**,

**l**), TMPA-V6-RT (

**c**,

**h**,

**m**), TMPA-V7 (

**d**,

**i**,

**n**), TMPA-V7-RT (

**e**,

**j**,

**o**) satellite rainfall estimates.

Köppen-Geiger Class | Class Description | Italian Regions |
---|---|---|

CS | Temperate subtropical | Western coastal Liguria, and in the Tyrrhenian and the Ionian parts |

Csa | Warm temperate | Along the Tyrrhenian coast from Liguria to Calabria, along the southern end of the Adriatic coast, and along the Ionian zone |

Csb-Cfb | Temperate sub-littoral | Hilly areas of Tuscany, foothills of the Umbria-Marche Apennines, and southern Apennines |

Cfsa | Temperate sub-continental | Parts of the Veneto and Friuli plain, northern Adriatic coastline and internal peninsular part |

Cfa | Temperate continental | Po valley and Veneto region |

Cfc | Cool temperate | Alpine foothills and mostly of the Apennines axial part, sometimes also with sub-continental characteristics |

Dw | Temperate cold | Parts of the higher elevation areas in the Alps and the Apennines |

H | The cold altitude | Alpine areas above 2000 m |

EF | Snow levels | Area of the Alps above 3500 m with perpetual snow |

**Table 2.**Morphological parameters associated with morpho-climatological subdivisions. Values taken from Table 2 in [49].

Subdivision | Parameter | Min Value | Max Value | Lowland (%) | Upland (%) | Highland (%) |
---|---|---|---|---|---|---|

Tyrr Central Tyrrhenian coast | Elevation (m a.s.l.) | s.l. | 1738 | 55.4 | 40.0 | 4.6 |

Slope (°) | 0 | 41 | ||||

Elevation relief ratio | 0 | 0.98 | ||||

Slope reversal (1/km^{2}) | 0.14 | 12.07 | ||||

Curvature (1/m) | −1.82 | 0.86 | ||||

Sici Southern/Western Sicily | Elevation (m a.s.l.) | s.l. | 3340 | 49.3 | 45.6 | 5.1 |

Slope (°) | 0 | 43 | ||||

Elevation relief ratio | 0.01 | 0.91 | ||||

Slope reversal (1/km^{2}) | 0.14 | 10.07 | ||||

Curvature (1/m) | −2.57 | 1.65 | ||||

Sard Sardinia | Elevation (m a.s.l.) | s.l. | 1786 | 37.7 | 46.1 | 16.2 |

Slope (°) | 0 | 48 | ||||

Elevation relief ratio | 0.01 | 0.93 | ||||

Slope reversal (1/km^{2}) | 0.14 | 9.43 | ||||

Curvature (1/m) | −5.25 | 2.71 | ||||

Popl Po plain and Alpine foothills | Elevation (m a.s.l.) | s.l. | 842 | 92.9 | 7.1 | 0.0 |

Slope (°) | 0 | 38 | ||||

Elevation relief ratio | 0 | 0.99 | ||||

Slope reversal (1/km^{2}) | 0.14 | 9.14 | ||||

Curvature (1/m) | −1.39 | 4.02 | ||||

Lang Liguria/Piedmont hills | Elevation (m a.s.l.) | s.l. | 1287 | 31.3 | 61.9 | 6.8 |

Slope (°) | 0 | 36 | ||||

Elevation relief ratio | 0.06 | 0.88 | ||||

Slope reversal (1/km^{2}) | 0.14 | 10.79 | ||||

Curvature (1/m) | −0.79 | 0.71 | ||||

ApeU Northern Apennines | Elevation (m a.s.l.) | s.l. | 2121 | 3.4 | 60.0 | 36.6 |

Slope (°) | 0 | 49 | ||||

Elevation relief ratio | 0.01 | 0.86 | ||||

Slope reversal (1/km^{2}) | 0.14 | 10.79 | ||||

Curvature (1/m) | −3.84 | 1.23 | ||||

ApeL Southern Apennines | Elevation (m a.s.l.) | s.l. | 2267 | 8.7 | 55.4 | 35.9 |

Slope (°) | 0 | 48 | ||||

Elevation relief ratio | 0.03 | 0.98 | ||||

Slope reversal (1/km^{2}) | 0.14 | 9.64 | ||||

Curvature (1/m) | −1.94 | 1.33 | ||||

ApeC Central Apennines | Elevation (m a.s.l.) | 27 | 2914 | 1.5 | 63.0 | 35.5 |

Slope (°) | 0 | 57 | ||||

Elevation relief ratio | 0.02 | 0.86 | ||||

Slope reversal (1/km^{2}) | 0.14 | 10.86 | ||||

Curvature (1/m) | −5.59 | 3.46 | ||||

Alps Northern alpine area | Elevation (m a.s.l.) | s.l. | 4810 | 13.8 | 32.0 | 54.1 |

Slope (°) | 0 | 72 | ||||

Elevation relief ratio | 0.02 | 0.96 | ||||

Slope reversal (1/km^{2}) | 0.14 | 10.28 | ||||

Curvature (1/m) | −6.65 | 5.66 | ||||

Adri Central Southern Adriatic coast | Elevation (m a.s.l.) | s.l. | 1485 | 44.0 | 55.7 | 0.3 |

Slope (°) | 0 | 35 | ||||

Elevation relief ratio | 0 | 0.97 | ||||

Slope reversal (1/km^{2}) | 0.14 | 11.43 | ||||

Curvature (1/m) | −0.64 | 0.68 |

Condition | Quality Index | Malfunctioning Problem | Rain Gauges # (%) |
---|---|---|---|

No rainfall values in the period $\left(\mathrm{CAP}=\mathrm{NULL}\right)$ | 1 | Rain gauge not acquired/used | 287 (15%) |

$\mathrm{CAP}=0$ | 2 | Malfunctioning rain gauge | 2 (0%) |

$\mathrm{CAP}<\mathrm{MAP}-\frac{\mathrm{MAP}}{2}$ | 3 | Rain gauge with discontinuous values | 148 (8%) |

$\mathrm{MAP}-\left(\frac{\mathrm{MAP}}{2}\right)<\mathrm{CAP}<\mathrm{MAP}+\left(\mathrm{MAP}\xb72\right)$ | 4 | Rain gauge properly working | 1488 (76%) |

$\mathrm{CAP}>\mathrm{MAP}+\left(\mathrm{MAP}\xb72\right)$ | 5 | Rain gauge with excessive values | 25 (1%) |

Code | Product | Version | Source | Reference |
---|---|---|---|---|

TMPA-V6 | Re-analysis product TMPA 3B42 | 6 | http://trmm.gsfc.nasa.gov/3b42.html | [18] |

TMPA-RT-V6 | Real-time product TMPA 3B42RT | 6 | ftp://trmmopen.gsfc.nasa.gov/pub/merged/V6Documents/3B4XRT_doc.pdf | [18] |

TMPA-V7 | Re-analysis product TMPA 3B42 | 7 | http://disc.sci.gsfc.nasa.gov/gesNews/trmm_v7_multisat_precip | [41] |

TMPA-V7-RT | Real-time product TMPA 3B42RT | 7 | ftp://trmmopen.gsfc.nasa.gov/pub/merged/V6Documents/3B4XRT_doc.pdf | [41] |

**Table 5.**Results of bootstrapped Kolmogorov-Smirnov test (hypothesis: greater) comparing the VRF data and the different satellite rainfall estimates (TMPA-V6, TMPA-V6-RT, TMPA-V7, TMPA-V7-RT) calculated in the entire Italian territory and in the different morphological subdivisions (see Table 2 and Figure 2). Given this comparison order and using the test greater hypothesis, p-values ≅ 0 (for D+ >> 0) indicate that the CDF of VRF data lies above (or to the left) of the CDF of the satellite estimates, and hence the satellite rainfall estimates distribution is statistically greater than that characterizing the VRF data (see Figure 10).

Region | TMPA-V6 | TMPA-V6-RT | TMPA-V7 | TMPA-V7-RT | ||||
---|---|---|---|---|---|---|---|---|

D+ | p-Value | D+ | p-Value | D+ | p-Value | D+ | p-Value | |

Adri | 0.145 | 0 | 0.060 | 0 | 0.202 | 0 | 0.167 | 0 |

Alps | 0.070 | 0 | 0.047 | 0 | 0.133 | 0 | 0.107 | 0 |

ApeC | 0.092 | 0 | 0.051 | 0 | 0.128 | 0 | 0.116 | 0 |

ApeL | 0.103 | 0 | 0.040 | 0 | 0.136 | 0 | 0.110 | 0 |

ApeU | 0.112 | 0 | 0.053 | 0 | 0.140 | 0 | 0.123 | 0 |

Lang | 0.117 | 0 | 0.054 | 0 | 0.183 | 0 | 0.162 | 0 |

Popl | 0.121 | 0 | 0.058 | 0 | 0.172 | 0 | 0.146 | 0 |

Sard | 0.150 | 0 | 0.077 | 0 | 0.208 | 0 | 0.172 | 0 |

Sici | 0.133 | 0 | 0.079 | 0 | 0.249 | 0 | 0.212 | 0 |

Tyrr | 0.136 | 0 | 0.068 | 0 | 0.170 | 0 | 0.153 | 0 |

All | 0.110 | 0 | 0.057 | 0 | 0.155 | 0 | 0.134 | 0 |

**Table 6.**Results of bootstrapped Kolmogorov-Smirnov test (hypothesis: two-sided) to test if an exponential distribution (used as the reference distribution, see Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15) is appropriate to describe the VRF and TMPA data. Using the test two-sided hypothesis, p-values ≅ 1 (for D ≅ 0) indicate that the reference distribution is appropriate to describe the rainfall data.

Region | VRF | TMPA-V6 | TMPA-V6-RT | TMPA-V7 | TMPA-V7-RT | |||||
---|---|---|---|---|---|---|---|---|---|---|

D | p-Value | D | p-Value | D | p-Value | D | p-Value | D | p-Value | |

Adri | 0.163 | 0 | 0.090 | 0 | 0.100 | 0 | 0.036 | 0 | 0.043 | 0 |

Alps | 0.196 | 0 | 0.066 | 0 | 0.077 | 0 | 0.045 | 0 | 0.037 | 0 |

ApeC | 0.08 | 0 | 0.081 | 0 | 0.063 | 0 | 0.049 | 0 | 0.039 | 0 |

ApeL | 0.183 | 0 | 0.103 | 0 | 0.182 | 0 | 0.042 | 0 | 0.037 | 0 |

ApeU | 0.171 | 0 | 0.079 | 0 | 0.087 | 0 | 0.041 | 0 | 0.036 | 0 |

Lang | 0.224 | 0 | 0.098 | 0 | 0.084 | 0 | 0.048 | 0 | 0.048 | 0 |

Popl | 0.187 | 0 | 0.059 | 0 | 0.087 | 0 | 0.042 | 0 | 0.035 | 0 |

Sard | 0.179 | 0 | 0.080 | 0 | 0.069 | 0 | 0.044 | 0 | 0.046 | 0 |

Sici | 0.255 | 0 | 0.187 | 0 | 0.253 | 0 | 0.043 | 0.01 | 0.035 | 0.1 |

Tyrr | 0.165 | 0 | 0.069 | 0 | 0.087 | 0 | 0.044 | 0 | 0.044 | 0 |

All | 0.175 | 0 | 0.064 | 0 | 0.091 | 0 | 0.039 | 0 | 0.037 | 0 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Rossi, M.; Kirschbaum, D.; Valigi, D.; Mondini, A.C.; Guzzetti, F. Comparison of Satellite Rainfall Estimates and Rain Gauge Measurements in Italy, and Impact on Landslide Modeling. *Climate* **2017**, *5*, 90.
https://doi.org/10.3390/cli5040090

**AMA Style**

Rossi M, Kirschbaum D, Valigi D, Mondini AC, Guzzetti F. Comparison of Satellite Rainfall Estimates and Rain Gauge Measurements in Italy, and Impact on Landslide Modeling. *Climate*. 2017; 5(4):90.
https://doi.org/10.3390/cli5040090

**Chicago/Turabian Style**

Rossi, Mauro, Dalia Kirschbaum, Daniela Valigi, Alessandro Cesare Mondini, and Fausto Guzzetti. 2017. "Comparison of Satellite Rainfall Estimates and Rain Gauge Measurements in Italy, and Impact on Landslide Modeling" *Climate* 5, no. 4: 90.
https://doi.org/10.3390/cli5040090