# Hydroclimatic Variability and Land Cover Transformations in the Central Italian Alps

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}in 2018 out of the 2650 km

^{2}area measured in 1954, representing 38% of the area investigated in this study: this anthropic driver of enhanced hydrologic losses can be recognized as an additional likely cause for the regional runoff volume decrease.

## 1. Introduction

## 2. Study Watersheds and Data Collection

^{2}, which represents most of the Central Italian Alps. The selected case studies also offer a broad set of morphological changes in size, exposure, and shape, thus providing an overview of the varying responses, which are given by different watersheds in reaction to drivers of change. Hence, the trend detection in the streamflow time series becomes crucially important to the agricultural exploitation of the Po River Valley, which is still a fundamental asset in the economy of northern Italy. Ever since the Middle Ages, agriculture here has benefitted from the high availability of freshwater originating from the mountain watersheds, and water-intensive irrigation practices have long been supported by an extensive distribution network. All the major lakes are currently regulated, so that freshwater is made available during the crop growing season.

^{6}m

^{3}), while the regulations of Lakes Iseo (regulation volume of 85 × 10

^{6}m

^{3}), Como (regulation volume of 246.5 × 10

^{6}m

^{3}), and Garda (regulation volume of 458.1 × 10

^{6}m

^{3}) started in 1933, 1946, and 1950, respectively. In addition, the investigated watersheds are heavily exploited for the generation of hydropower, which amounts to about 40% of the total generated in Italy. The hydropower generation reservoirs were mainly built between the early 1920s and the late 1960s of the last century, and upstream of the analyzed outlets, their storage capacities are estimated to be 522.2 × 10

^{6}m

^{3}in the Adige watershed, 222.6 × 10

^{6}m

^{3}in the Mincio watershed, 73.5 × 10

^{6}m

^{3}in the Chiese watershed, 113.6 × 10

^{6}m

^{3}in the Oglio watershed, and 515 × 10

^{6}m

^{3}in the Adda watershed. The total streamflow discharge regulation capacity over the whole analyzed area is therefore assessed in the order of 2.3 × 10

^{9}m

^{3}.

#### 2.1. Streamflow Data

#### 2.2. Precipitation Data

#### 2.3. Land Cover Data

## 3. Trend Analysis Methodology

_{k}and q

_{k}are the k-th series slope and intercept.

_{kij}between observation pairs x

_{ki}and x

_{kj}defined according to Equation (2), where x

_{ki}is the value observed in the t

_{i}year for the k-th series and n

_{k}is the k-th series size (for the estimate of medians in discrete samples, see [25]).

_{k}is estimated, the corresponding intercept q

_{k}is supplied by Equation (3) [26].

_{ku}and m

_{kl}of a confidence interval with fixed width (details on this procedure are reported in Appendix A).

_{0}to be tested is the equality of all regression slopes m

_{k}of N ≥ 2 series to an unspecified value m

_{p}. This hypothesis does not involve any assumption on the intercepts of the regression lines given in Equation (3).

_{p}can be obtained by using Equation (5), in which data from the different k-th series with sample size n

_{k}are aligned and pooled together in a unique sample.

_{p}can be interpreted as a regional trend for the hydrological process of interest, if the parallelism hypothesis of the linear trends of the individual series cannot be rejected for a sufficiently large p-value α. Its reliability is much greater than that of the individual series slopes, since the pooled slope is estimated using a significantly larger sample size, thus providing a more robust trend estimator. Moreover, the test of parallelism makes it possible to delimitate homogeneous regions, including watersheds featuring statistically similar linear trends, which can be assembled to form a virtual watershed. Further details on this test are provided in Appendix C.

## 4. Results and Discussion

#### 4.1. Runoff Volume Series Analysis

_{k}of each individual series along with the corresponding lower and upper confidence boundaries m

_{kl}and m

_{ku}, derived for a confidence interval of 90%. As can be seen, all series show a decreasing trend, with rates varying from −1.16 to −3.33 mm/year. The uncertainty related to these estimates amounts to about ±0.60 mm/year for the longest series (the Adda and Adige rivers) and to ±1.75 mm/year for the shortest ones (the Oglio, Chiese, and Mincio rivers). As regards the shortest series, such uncertainties are significantly high. More precisely, the estimate of the Oglio series trend rate, at −1.16 mm/year, is such that the uncertainty interval includes even positive values.

_{k}equal to 0), the Mann–Kendall test, and the Spearman test all consistently evidence statistically significant trends for all series, except for the Oglio river basin. In Table 2, the p-values (α

_{0k}, α

_{tk}, and α

_{rk}refer to the Theil, Mann–Kendall, and Spearman tests, respectively), for which the null hypothesis of trend absence cannot be rejected, are generally much less than 1.0%. Conversely, for the Oglio river series, a significance value slightly greater than 32% is estimated. It is worth noting that the Oglio river series trend is close to the one from the Adda river. Therefore, this result could be explained by considering that this trend size would need a far longer series to be recognized as significant.

_{p}, according to which the null hypothesis of parallelism cannot be rejected, is 54.8%. This evidences a high confidence both in this hypothesis and in the possibility of exploiting the pooled slope as an estimate of a regional trend rate. This value amounts to −1.45 mm/year due the major sample size of the Adda river and the Adige river series in the pooled sample with respect to the others. Finally, the Theil tests were repeated assuming the pooled slope m

_{p}as individual trend slope. Such tests always yield p-values α

_{pk}larger than 10% for the hypothesis not to be rejected. In particular, the significance of the Oglio river series is slightly larger than 70% due to the similarity of the individual series’ slope with the pooled slope. Therefore, on a regional level, there is evidence of a statistically significant decreasing trend in the annual runoff volumes from the watersheds of the Central Italian Alps.

#### 4.2. Precipitation Series Analysis

_{0k}according to which the null hypothesis cannot be rejected is 40.8%. The Mann–Kendall test and the Spearman test yield analogous results since their coefficients are close to zero and the corresponding p-values α

_{tk}and α

_{rk}are greater than 40%. When the mean areal precipitations of the Adda watershed are considered, a decreasing trend is found. However, the trend rate is far less than the runoff volume one (Table 2), and its statistical significance is negligible, as confirmed by the p-values α

_{0k}, α

_{tk}, and α

_{rk}for the trend absence hypothesis not to be rejected, all larger than 20%.

_{p}cannot be rejected as an individual slope for large p-values, even for the Chiese series. All the tests that were performed, therefore, agree that the regional trend is nonstatistically significant since the hypothesis of trend absence cannot be rejected for large significance values. This outcome is consistent with that obtained for the Adige river watershed. A 150-year-long series of mean areal precipitation estimated from the Historical instrumental climatological surface time series of the greater Alpine region (HISTALP) database was investigated for the Adige watershed [15], and a negative precipitation trend was assessed by the Theil–Sen estimator to be −0.31 mm/year. Despite the length of the series, the null hypothesis of trend absence cannot be rejected for significances larger than 29%, according to all three tests that were used above. On the whole, it can be concluded that the marked regional negative trend in the annual runoff volumes visible in the major rivers of the Central Italian Alps cannot be justified by means of a statistically significant decrease in the annual precipitation depths, and therefore, trends in hydrologic losses must be taken into consideration, in particular the evapotranspiration processes.

#### 4.3. Land Cover Transformation Analysis

^{2}. Seven macroclasses, featuring highly different responses in terms of hydrologic balance, were considered: woodland (including broad-leaved trees, conifers, and mixed woods), bushland (including scrub and/or herbaceous vegetation, except for natural grasslands), cropland (including arable lands but excluding permanent crops and pastures), fruit trees (including permanent crops, such as apple orchards, vineyards, and olive groves), grassland (including natural grasslands and pastures), urban (including all artificial surfaces), and others (including water bodies, wetlands, glaciers, and uncultivated lands).

^{2}), even if it remains a minor portion of the total watershed area (less than 5%). This evidence can be explained in the examined mountain context by the widespread urban development as a result of touristic demand for holiday houses and hotels. In particular, inside the study area, the western shore of Lake Garda experienced a dramatic urban sprawl, being strongly attractive for aquatic leisure activities. The urban expansion mainly occurred at the expense of croplands, which were subjected to strong decreases, totally assessed at 73%. Such an abrupt decrease can also be explained by the conversion into fruit tree cultivations, in particular apple orchards, vineyards, and olive groves. Areas devoted to fruit production totally increased by 66%. A demonstration of this is visible in Figure 5, which represents the land cover transformation that occurred between 1954 and 2018 in a square sample area of 125 km

^{2}across the watershed divide separating the Adda and Oglio rivers. The major urban expansion is that of the Tirano urban settlement, located in the valley, which has progressively spread into the surrounding cropland areas. Furthermore, the substitution of arable land with permanent crops (in this case, apple tree orchards) contributed to the phenomenon of cropland disappearance. Due to the socioeconomic development of the area, permanent crops have progressively become more attractive and remunerative than traditional seasonal crops.

^{2}, respectively, in 1954). As a consequence, woodland that covered 37.8% of the total area in 1954 expanded its area by up to 45% in 2018 (Figure 4a), corresponding to an areal expansion of about 510 km

^{2}in 2018 out of the 2650 km

^{2}covered in 1954. This result is not surprising as an increase in the percentage of land covered by woodland has been documented all over Europe by the Food and Agriculture Organization (FAO) [35,36]. The FAO states that woodland areas have universally increased by 1.1 × 10

^{6}km

^{2}since 1990 in nontropical regions, with their highest rate of growth between 2000 and 2010. Overall, the phenomenon is referred to as afforestation and accounts for three processes: the spontaneous regeneration of woodlands, the reforestation of formerly wooded areas, and the afforestation of new areas, which had previously been used for different purposes.

^{2}in the Adda, Oglio, Chiese, and Mincio watersheds, whose overall catchment area measures 9632 km

^{2}(see Table 1). According to [39], the evaporation from lakes in Italy is about 1200 mm per year at middle elevations, while the average actual evapotranspiration losses in watersheds similar to those investigated in this paper are about 500 mm per year [37]. Therefore, the increase rate in annual evaporation losses due to the artificial water surface expansion in the 20th century in the area of these four watersheds can be estimated to be less than 1.5 mm/century. As a consequence, this factor cannot be advocated as a significant concomitant cause for the marked runoff volume decline.

## 5. Conclusions

^{2}out of 2500 km

^{2}from 1954 to 2018, is herein demonstrated to be a very likely concomitant cause. It must be pointed out, however, that afforestation plays a positive role in segregating greenhouse gasses and in mitigating the severity of extreme floods by enhancing canopy interception and thus decreasing runoff volumes. The different weights of temperature increase and afforestation in yielding the streamflow discharge’s decreasing trend remain to be evaluated in future research.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

_{α}must be computed with regard to a confidence band width (1 − α), according to Equation (A1), valid for large samples (n

_{k}> 40), where z(α/2) is the α/2 upper percentile of the standard normal distribution.

_{kl}and the upper confidence boundary m

_{ku}are given by Equation (A2).

## Appendix B

_{0}shown in Equation (A3) that a regression line has a slope m

_{k}equal to a specified value ${\overline{m}}_{k}$, a distribution-free procedure can be implemented as follows [25].

_{k}differences d

_{ki}defined in Equation (A4) must be computed; then, the sum ${C}_{k}$ must be evaluated by using function $\zeta (\xb7)$ as shown in Equation (A5).

_{k}> 40) is based on the asymptotic normality ${C}_{k}^{*}$ of the sum ${C}_{k}$, which can be standardized according to Equation (A6). The procedure for small samples is supplied by [25].

## Appendix C

_{0}of interest, given by Equation (4), is that N > 2 regression lines have a common unspecified slope m

_{p}that can be estimated by using Equation (5). Thus, for each k regression sample, the aligned observations ${y}_{ki}^{*}$ must be computed according to the expression in Equation (A9) for each series.

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**Figure 1.**Location of the five studied watersheds in the Southern Alps region showing boundaries of the Lombardy Region and Trento and Bolzano Provinces, inset (a), and map of their main physiographic characteristics.

**Figure 2.**The analyzed annual runoff volumes from 1930 to 2018, with comparison between the corresponding individual linear trends, evaluated with respect to the complete series.

**Figure 3.**The observed annual areal precipitations from 1930 to 2018, with comparison between the corresponding individual linear trends, evaluated with respect to the complete series.

**Figure 4.**Land cover transformation in the Lombardic portion of the analyzed area: trends of the area covered by the different classes in 1954, 1980, 1999, and 2018 (

**a**) and percentage variations in the three temporal periods (

**b**) (W: woodland, B: bushland, G: grassland, C: cropland, F: fruit trees, U: urban, and O: others).

**Figure 5.**Comparison of land cover maps referred to the 1954 GAI survey (

**a**) and to the 2018 DUSAF 6.0 survey (

**b**): focus is on the area of land on the watershed divide separating the Adda watershed and the Oglio watershed (area, 125 km

^{2}) (Datum WGS84 coordinates UTM 32N).

**Table 1.**Hydrological characteristics (area A, maximum, mean, and minimum elevations Z

_{x}, Z

_{m}, and Z

_{n}, annual mean of the precipitation depth H

_{m}and of the runoff volume D

_{m}) of the studied watersheds and data consistency (P, precipitation; R, runoff).

Watershed | Outlet | A | Z_{x} | Z_{m} | Z_{n} | H_{m} | D_{m} | Observation Period | Sample Size n_{k} | |
---|---|---|---|---|---|---|---|---|---|---|

(km^{2}) | (m a.s.l.) | (m a.s.l.) | (m a.s.l.) | (mm) | (mm) | P | R | |||

Adige | Trento | 9763 | 3899 | 1735 | 186 | 950 | 708 | 1862–2011 | - | 150 |

Mincio | Monzambano | 2350 | 3556 | 966 | 60 | 1180 | 709 | 1950–2011 | - | 62 |

Chiese | Gavardo | 934 | 3462 | 1230 | 198 | 1460 | 1090 | 1934–2018 | 80 | 72 |

Oglio | Sarnico | 1840 | 3554 | 1429 | 154 | 1260 | 980 | 1933–2011 | - | 79 |

Adda | Lecco | 4508 | 4050 | 1569 | 197 | 1330 | 1150 | 1845–2016 | 172 | 172 |

Watershed | m_{k} | m_{kl} | m_{ku} | α_{0k} | τ_{k} | α_{tk} | ρ_{k} | α_{rk} | m_{p} | α_{p} | α_{pk} |
---|---|---|---|---|---|---|---|---|---|---|---|

(mm/year) | (mm/year) | (mm/year) | (%) | - | (%) | - | (%) | (mm/year) | (%) | (%) | |

Adige | −1.34 | −1.75 | −0.98 | <0.1 | −0.30 | <0.1 | −0.46 | <0.1 | −1.45 | 54.8 | 66.2 |

Mincio | −3.33 | −5.00 | −1.28 | 0.5 | −0.24 | 0.6 | −0.32 | 1.0 | 15.8 | ||

Chiese | −3.12 | −5.02 | −1.10 | 0.8 | −0.21 | 0.8 | −0.28 | 1.6 | 16.2 | ||

Oglio | −1.16 | −2.91 | 0.56 | 32.2 | −0.08 | 32.4 | −0.11 | 32.8 | 71.3 | ||

Adda | −1.29 | −1.85 | −0.70 | <0.1 | −0.19 | <0.1 | −0.27 | <0.1 | 62.2 |

^{1}m

_{k}, individual trend slopes; m

_{kl}, individual trend slope 5% percentile; m

_{ku}, individual trend slope 95% percentile; α

_{0k}, p-value of the Theil test for null slope to be the individual trend slope; τ

_{k}, Mann–Kendall coefficient of individual trends; α

_{tk}, p-value of the Mann–Kendall test for individual trend absence; ρ

_{k}, Spearman coefficient of individual trends; α

_{rk}, p-value of the Spearman test for individual trend absence; m

_{p}, pool trend slope; α

_{p}, p-value of the Sen–Adichie test for linear trend parallelism; α

_{pk}, p-value of the Theil test for pool slope to be the individual trend slope.

^{2}Boldface indicates statistically significant trends (i.e., significance less than 5% when testing trend absence).

Watershed | m_{k} | m_{kl} | m_{ku} | α_{0k} | τ_{k} | α_{tk} | ρ_{k} | α_{rk} | m_{p} | α_{p} | α_{pk} |
---|---|---|---|---|---|---|---|---|---|---|---|

(mm/year) | (mm/year) | (mm/year) | (%) | - | (%) | - | (%) | (mm/year) | (%) | (%) | |

Chiese | 1.05 | −0.94 | 3.09 | 40.8 | 0.06 | 41.1 | 0.09 | 41.6 | −0.25 | 22.1 | 40.8 |

Adda | −0.49 | −1.09 | 0.15 | 21.0 | −0.06 | 21.0 | −0.09 | 21.5 | 21.0 |

^{1}m

_{k}, individual trend slopes; m

_{kl}, individual trend slope 5% percentile; m

_{ku}, individual trend slope 95% percentile; α

_{0k}, p-value of the Theil test for null slope to be the individual trend slope; τ

_{k}, Mann–Kendall coefficient of individual trends; α

_{tk}, p-value of the Mann–Kendall test for individual trend absence; ρ

_{k}, Spearman coefficient of individual trends; α

_{rk}, p-value of the Spearman test for individual trend absence; m

_{p}, pool trend slope; α

_{p}, p-value of the Sen–Adichie test for linear trend parallelism; α

_{pk}, p-value of the Theil test for pool slope to be the individual trend slope.

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**MDPI and ACS Style**

Balistrocchi, M.; Tomirotti, M.; Muraca, A.; Ranzi, R. Hydroclimatic Variability and Land Cover Transformations in the Central Italian Alps. *Water* **2021**, *13*, 963.
https://doi.org/10.3390/w13070963

**AMA Style**

Balistrocchi M, Tomirotti M, Muraca A, Ranzi R. Hydroclimatic Variability and Land Cover Transformations in the Central Italian Alps. *Water*. 2021; 13(7):963.
https://doi.org/10.3390/w13070963

**Chicago/Turabian Style**

Balistrocchi, Matteo, Massimo Tomirotti, Alessandro Muraca, and Roberto Ranzi. 2021. "Hydroclimatic Variability and Land Cover Transformations in the Central Italian Alps" *Water* 13, no. 7: 963.
https://doi.org/10.3390/w13070963