# SOL40: Forty Years of Simulations under Climate and Land Use Change

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Area of Study

^{2}, and it is also one of the most important economic areas with about 5 million people in its immediate area. A large region from the Italian Prealps drains to Milano. The main rivers are the Lambro (catchment area of 500 km

^{2}), the Seveso (catchment area of 207 km

^{2}), and the Olona (catchment area of 208 km

^{2}), plus several minor tributaries for a total drainage surface area of about 1300 km

^{2}.

#### Land Use Change

## 3. Materials and Methods

#### 3.1. The ERA5-Land Reanalysis

#### 3.2. The FEST-WB Model

#### 3.3. Observed Weather Data

^{2}is equal to 0.91.

^{2}is equal to 0.67, the Kling–Gupta efficiency (KGE) to 0.81, and the Nash–Sutcliffe efficiency (NSE) displays a value of 0.66 for the average daily discharge (Figure 7a). However, the simulated runoff forced with ERA5-Land dataset tends to underestimate the maximum daily discharge, instead, and the coefficient of determination R

^{2}decreases to 0.58, the KGE to 0.71, and the NSE diminishes to 0.57 (Figure 7b). As mentioned above, we note that discharge comparisons have been carried out over the Seveso River basin closed at Bovisio-Masciago section.

#### 3.4. Statistical Analysis

^{2}), the Standardized Precipitation Index, the Mann–Kendall (MK) and the Cox–Stuart (CS) tests.

#### 3.4.1. Mean Absolute Error

#### 3.4.2. Coefficient of Determination

#### 3.4.3. Nash-Sutcliffe Efficiency (NSE)

#### 3.4.4. Kling–Gupta Efficiency (KGE)

#### 3.4.5. Standardized Precipitation Index (SPI)

#### 3.4.6. Cox–Stuart Test

_{1}, x

_{2},…, x

_{n}be a series of independent observations. To perform the trend analysis, the following samples of differences were calculated: y

_{1}= x

_{1+c}− x

_{1}, y

_{2}= x

_{2+c}− x

_{2},…, y

_{n}= x

_{n}− x

_{n}

_{−c}, where c = n/2 if n is even, c = (n + 1)/2 if n is odd. Hence, we obtained a vector of differences y

_{1}, y

_{2,}…, y

_{m}.

**Hypothesis**

**0**

**(H**

_{0}).**Hypothesis**

**1**

**(H**

_{1})._{0}knowing that the statistical test, T, follows a binomial distributions of parameters m and p = 0.5 [53].

#### 3.4.7. Mann–Kendall Test

_{1}, x

_{2},…, x

_{n}be a sequence of measurements over time [58] proposed to test the null hypothesis, H

_{0}, where the data come from a population where the random variables are independent and identically distributed. The alternative hypothesis, H

_{1}, was that the data follow a monotonic trend over time. Under H

_{0}, the Mann–Kendall test statistic is reported in Equation (8):

_{j}and x

_{k}are the values of sequence j, k.

^{2}shown in Equation (9) equals to:

_{i}is the number of data points in the i-th tied group.

_{0}can be rejected at a chosen significance level and the presence of a monotonic trend, H

_{1}, can be accepted [60].

## 4. Results and Discussion

#### 4.1. The Climate Change Forcing

_{0}, and no statically significant trends were found in all the four series, as the computed p-values (Table 2) were 0.537, 0.172, 0.552 and 0.568 respectively for Figure 10a–d: all of them below the significant threshold considering α = 5%.

#### 4.2. The Impact of Land Use Change

^{3}/s, which correspond to the second warning, we can appreciate how the return period has halved over the years and the probability of yearly exceedance has doubled (Table 3). On average, we notice a reduction of the return time period of 6.9 years from 1954 to 1980, 7.2 years from 1980 to 2000 and about 1 year only in the last two decades (Figure 11).

^{3}/s) at Bovisio-Masciago were changing the CN conditions for each simulation of the hydrological FEST-WB model forced with ERA5-Land dataset.

#### 4.3. The Hydrological Response Trend Analysis

_{0}accepted.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AMC | Antecedent Moisture Condition |

ARPA | Agenzia Regionale per la Protezione Ambientale (Regional Agency for Environmental Protection) |

CLC | Corine Land Cover |

CN | Curve Number |

CS | Cox Stuart |

CSNO | Canale Scolmatore di Nord-Ovest (North-West Spillway Channel) |

DEM | Digital Elevation Model |

ECMWF | European Centre for Medium-Range Weather Forecasts |

FEST-WB | Flash flood Event-based Spatially-distributed rainfall-runoff Transformation-Water Balance |

IPCC | Intergovernmental Panel on Climate Change |

KGE | Kling-Gupta Efficiency |

LS | Least Squares |

MAE | Mean Absolute Error |

MK | Mann Kendall |

MNW | Meteonetwork |

NSE | Nash-Sutcliffe Efficiency |

SCS | Soil Conservation Service |

SOL | Seveso Olona Lambro |

SPI | Standardized Precipitation Index |

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**Figure 2.**CN values for the years 1954, 1980, 2000, 2006, 2012 and 2018 for the Seveso River catchment closed at Bovisio-Masciago town; the geographical reference system is Roma 1940 Gauss Boaga Ovest.

**Figure 3.**Example of ERA5 land temperature chart (

**a**) and horizontal spatial resolution (

**b**) over the SOL watersheds.

**Figure 4.**Weather stations in the study area: green dots are data from the ARPA (Regional Agency for Environmental Protection) of the Lombardy region, while red dots are from Meteonetwork association (www.meteonetwork.it, accessed on 15 January 2022).

**Figure 5.**Comparison between ERA5-Land and observed minimum, mean, and maximum annual temperature data (Meteonetwork and ARPA weather stations).

**Figure 6.**Comparison between ERA5-Land and observed annual precipitation data (Meteonetwork and ARPA weather stations).

**Figure 7.**Comparison between simulated runoff by FEST-WB model forced with ERA5-Land (y-axis) and observed data (x-axis) for the years 2003–2020: average (

**a**) and maximum (

**b**) daily discharge values at the Bovisio-Masciago gauge section.

**Figure 8.**Reconstruction of mean annual temperature (1981–2020) by the ERA5-Land reanalysis over the section of the Seveso River basin closed at Bovisio-Masciago.

**Figure 9.**Reconstruction of the SPI index over 12 months by ERA5-Land reanalysis (1981–2020) for the section of the Seveso River basin closed at Bovisio-Masciago.

**Figure 10.**Precipitation analysis with ERA5-Land dataset (1981–2020): total annual precipitation (

**a**), annual numbers of wet days with daily precipitation greater than 1 mm (

**b**), 24-h annual maximum precipitation (

**c**) and annual hourly maximum precipitation (

**d**).

**Figure 11.**Relationship between discharge and return period at different CN conditions over the section of the Seveso River basin closed at Bovisio-Masciago.

**Figure 12.**Average decade number of annual exceedance events the 35 m

^{3}/s discharge threshold at different CN conditions over the section of the Seveso River basin closed at Bovisio-Masciago.

**Figure 13.**Annual maximum discharge at time-variant CN conditions: simulations by the FEST-WB model enforced with the ERA5-Land data at the Bovisio-Masciago gauge section.

**Figure 14.**Number of annual exceedances (1999–2020) of the first hydrometric level threshold over the Seveso River basin for Paderno Dugnano (

**a**) and Cantù (

**b**) gauge sections, respectively.

**Table 1.**Percentage of urbanized area over the Seveso River basin closed at the Bovisio-Masciago gauge section.

Year | Percentage of Urbanized Area |
---|---|

1954 | 11.8% |

1980 | 32.0% |

2000 | 44.7% |

2006 | 45.6% |

2012 | 46.6% |

2018 | 46.8% |

Variable | Is the Trend Present? (α = 5%) | p-Value (MK-Test) |
---|---|---|

Total annual precipitation | No | 0.537 |

Annual number of wet days (p > 1 mm) | No | 0.172 |

24-h maximum annual precipitation | No | 0.552 |

hourly maximum annual precipitation | No | 0.568 |

**Table 3.**Return period and probability of yearly exceedance the second warning threshold of 90 m

^{3}/s at given CN conditions.

Year of CN | Return Period [Years] | Probability of Yearly Exceedance [%] |
---|---|---|

CN 1954 | 18.56 | 5.4 |

CN 1980 | 14.90 | 6.7 |

CN 2000 | 11.00 | 9.1 |

CN 2006 | 10.77 | 9.3 |

CN 2012 | 10.54 | 9.5 |

CN 2018 | 10.49 | 9.5 |

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**MDPI and ACS Style**

Ceppi, A.; Gambini, E.; Lombardi, G.; Ravazzani, G.; Mancini, M.
SOL40: Forty Years of Simulations under Climate and Land Use Change. *Water* **2022**, *14*, 837.
https://doi.org/10.3390/w14060837

**AMA Style**

Ceppi A, Gambini E, Lombardi G, Ravazzani G, Mancini M.
SOL40: Forty Years of Simulations under Climate and Land Use Change. *Water*. 2022; 14(6):837.
https://doi.org/10.3390/w14060837

**Chicago/Turabian Style**

Ceppi, Alessandro, Enrico Gambini, Gabriele Lombardi, Giovanni Ravazzani, and Marco Mancini.
2022. "SOL40: Forty Years of Simulations under Climate and Land Use Change" *Water* 14, no. 6: 837.
https://doi.org/10.3390/w14060837