A Case Study of Rainfall and Temperature Trends in San Diego Region, 1985–2017

Abstract

Prolonged and frequent droughts in Southern California present hazards and uncertainty for the region’s increasing population, resulting in proactive and aggressive water management strategies. The goal of this study is to present a case study of the San Diego region’s rainfall and temperature time series analysis in order to determine annual and seasonal trends and their significance. Rainfall and temperature data from 20 rain-gauged stations were analyzed for the period 1985–2017. A project database was set up for data compilation and quality control, and a Mann-Kendall test for trend analysis was used. Results indicated that rainfall in the region decreased both annually and during the rainy season (November–April) by up to 0.14 mm between 1985 and 2017, although not in a statistically significant manner, except at two rainfall observation stations. Rainfall appears to have increased in many of the stations examined during the dry season (May–October), with an average magnitude of 0.09 mm. Analysis of daily minimum and maximum temperature reveals overall average annual and seasonal increases of 0.07 °C and 0.04 °C, respectively, with statistically significant increases at 10 of 17 for minimum temperature, and 0.27 °C and −0.25 °C with statistically significant increases at 9 of 16 for maximum temperature. Temperature tends to have increased more during the dry season compared to the rainy season. This study reveals an overall decreasing tendency in rainfall and an increasing tendency in minimum and maximum temperatures (although not statistically significant) in the San Diego region between 1985 and 2017, which likely contributed to important management implications for the region’s water resources.

1. Introduction

San Diego County is located in Southern California, an area that features numerous climate types, including Mediterranean, semi-arid, desert, and mountain climates, with irregular rainfall and many sunny days. Rainfall in San Diego County is naturally limited, highly variable, and unreliable [1,2,3]. Adequate water to support the needs of agricultural, industrial, and residential sectors is a challenge that persists in water-scarce regions, which are prone to flooding and prolonged drought [2,4,5].

Drought events in Southern California are frequent, chronic, pronounced, and prolonged due to high potential evapotranspiration and low-rainfall events [6]. The 2012–2014 drought was an approximately 10,000-year event, and the 2012–2015 drought was exceptionally severe, such that it was classified as an unprecedented event with virtually no calculable return period [7,8]. The 2012–2015 drought produced the greatest moisture deficit in California in the last 1200 years during the single-year 2014 event, subsequently leading to unwanted economic, agricultural, hydrological, and ecological consequences [7,9]. Based on years of rainfall data, Griffin and Anchukaitis (2014) [7] characterized drought variability in Southern California as primarily interannual, trending with the quasiperiodic influence of the El Niño–Southern Oscillation (ENSO), regional atmospheric pressure anomalies, and “drought-busting” atmospheric rivers [10,11,12,13]. Southern California drought conditions occur with the persistence of meteorological conditions that reduce and suppress storm activities, resulting in low incident precipitation events [14,15].

The extreme seasonality and variability of the region’s rainfall present a challenge for human and environmental water needs [12,14,15]. For example, Noe and Zedler (2001) [16] found a reduction in the germination of upper intertidal marsh plants with variable rainfall in Southern California, where ecosystems face remarkable natural variation in weather and water availability [16,17]. The historical seasonality and scarcity of rainfall in the region are exacerbated by climate change and population growth, reflected in diminishing springs, reservoirs, and aquifers [9], as well as increasing human activities that weigh heavily on the existing water supply sources and natural ecosystems [17,18]. Recent data indicates that the state’s climate is warming, with more frequent extreme weather, earlier snow melt, and notable decreases in spring and summer streamflow [17,19]. Growth in population and agriculture has almost doubled water use since 1950 [19,20].

Coordinated large and proactive water management projects have been undertaken in Southern California [8,21,22] to help develop resiliency and adaptation to constantly rising and dynamic water demands [9,17,23]. In San Diego County, these water management strategies take the form of aggressive water conservation efforts and plan to diversify local water supply sources by 2035 [24]. To support water supply diversification and effective management efforts, analysis of rainfall and temperature is an important step towards balancing water supply and demand in an urban-agricultural setting. Southern California’s population is expected to increase, and millions of people are projected to migrate to San Diego County by 2050 [25], while the county’s agricultural sector is expected to remain an important contributor to the region’s economy. The goal of this study was, therefore, to present a case study of the rainfall trend and its effects on water availability in Southern California. The specific objectives of this study were to use historical data to document the long-term trend characteristics of rainfall and temperature and to discuss their implications for San Diego County’s water supply. This study used a mixed-method, including a Mann-Kendall test for studying rainfall and temperature trends in conjunction with non-numerical information to provide insight into Southern California’s water availability issues.

2. Materials and Methods

2.1. Study Area

San Diego County occupies a quadrate portion of the far-southwestern corner of the state of California and the United States (Figure 1), encompassing approximately 11,000 km² and spanning 105 km from north to south and 138 km from east to west [26,27]. San Diego County is the second-most populous county in California and the fifth-most in the United States, with a population of more than 3 million inhabitants. The City of San Diego, which serves as the county seat and has more than 1.3 million residents, is the second largest city in California and the 17th most populous metropolitan area in the United States [28]. Greater San Diego is part of the San Diego-Tijuana metropolitan area, shared between the United States and Mexico.

The physical geography, land usage, and landforms of San Diego County are generally consistent from north to south, while transitioning steadily from west to east. More than 110 km of Pacific Ocean coastline forms the county’s western boundary, where the majority of residents live within approximately 24 km of the coast in a heavily urbanized region [29]. Beaches and coastal terraces comprise the western third of the county and gradually yield to more rural foothills and valleys to the east (Figure 1), where agriculture and ranching tend to dominate land usage. Further east, the heavily forested mountains of the Peninsular Ranges bisect the county in a generally north-south direction, with some peaks gradually rising to above 2000 m before dropping rapidly into the low deserts of the county’s eastern quarter [30,31].

San Diego County generally has a mild Mediterranean climate, while experiencing microclimate shifts from west to east. From the coast to the mountains, the semi-arid Middle Latitude Steppe climate transitions to a hot–mild Mediterranean climate, with both regions experiencing warm–hot, dry, and sunny summers and cooler, wetter winters. Precipitation and temperature extremes increase from the beaches and lower elevations in the west to the mountains in the central-east part of the county, which receive frost and snow during winter. The mountains receive more rainfall than average in Southern California, while the county’s desert region in the east lies in a rain shadow that extends into the Desert Southwest region of North America. Average annual rainfall amounts typically vary, with the county’s coastal areas receiving 250 mm, the mountain peaks receiving over 800 mm, and the low desert areas often receiving less than 150 mm of rain [32]. Approximately 85% of all rainfall in the county happens between November and March [33] (Figure 2). Temperatures and temperature ranges can also vary considerably between microclimates and often short distances (15–30 °C difference) in the county [33].

Despite San Diego County’s large urban population, agriculture and farming remain an important part of the economy, with roughly 9% of the county’s total area (980 km²) dedicated to commercial agriculture [34]. Of San Diego County’s 6690 farms—the most of any county in the United States [35]—68% are between 0.4–4 ha in size [36], with the median farm size being 2 ha [35]. Important agricultural operations include indoor flower and plant and ornamental tree and shrub nurseries, situated primarily in the rural backcountry areas east and north of the urbanized coastal region; avocado and citrus orchards nestled in the foothills and shelterbelts of the mountains; and sprawling palm farms in the desert [34].

2.2. Data Used

Daily rainfall, maximum, and minimum temperature data from stations within the County of San Diego were extracted for a period of 33 years (January 1985 to December 2017) from the National Oceanic and Atmospheric Administration (NOAA) database. Twenty rainfall observation stations, with continuous time daily data and gaps not longer than three days at a time, especially for the temperature data, covering all micro-climate zones in the county, were utilized for this analysis (Figure 2;

Table 1. Description of rainfall and temperature observation stations used in the study. [a] Stations excluded from both minimum and maximum temperature analysis and [b] stations excluded from maximum temperature analysis only; 20 stations were used for rainfall, 17 stations for minimum temperature, and 16 stations for maximum temperature analysis; (m a.s.l) denotes mean at sea level; C stands for Coastal, D for Desert, and M for Mountainous regions.

#	Location	NWIS Gauge ID	Rain Gauge Station Name	Latitude (°S)	Longitude (°W)	Elevation (m a.s.l)
1	C	USW00003154	Camp Pendleton MCAS, CA US	33.3	−117.35	23
2	C	USC00041758	Chula Vista, CA US	32.64	−117.086	17
3	C	USC00042706	El Cajon, CA US	32.8005	−116.928	151
4	C	USC00042863	Escondido Number 2, CA US	33.121	−117.09	183
5	C	USW00093115 [a]	Imperial Beach Ream Field NAS, CA US	32.56667	−117.117	7
6	C	USC00044710 [a]	Lakeside 2 E, CA US	32.8536	−116.895	210
7	C	USW00093112	North Island NAS, CA US	32.7	−117.2	8
8	C	USC00046377	Oceanside Marina, CA US	33.2097	−117.395	3
9	C	USC0004711 [a]	Poway Valley, CA US	33.01953	−117.031	198
10	C	USW00023188	San Diego International Airport, CA US	32.7336	−117.183	5
11	C	USW00093107	San Diego Miramar NAS, CA US	32.86667	−117.133	145
12	C	USC00049378	Vista, CA US	33.2354	−117.232	131
13	D	USC00040983	Borrego Desert Park, CA US	33.2559	−116.404	247
14	M	USC00040136 [b]	Alpine, CA US	32.8358	−116.777	517
15	M	USC00041424	Campo, CA US	32.6263	−116.47	802
16	M	USC00042239	Cuyamaca, CA US	32.9897	−116.587	1414
17	M	USC00042709	El Capitan Dam, CA US	32.8856	−116.815	183
18	M	USC00043914	Henshaw Dam, CA US	33.2372	−116.761	823
19	M	USC00044412	Julian CDF, CA US	33.0763	−116.593	1285
20	M	USC00046657	Palomar Mountain Observatory, CA US	33.378	−116.84	1692

). Daily rainfall values were compiled into total annual and seasonal time series, while means for annual and seasonal temperatures were used for the analysis. As mentioned earlier, San Diego County has two seasons, rainy (November–April) and dry (May–October), which were used for seasonal analysis.

2.3. Methods

Annual and seasonal temporal trends in rainfall, minimum, and maximum temperature were determined using the modified non-parametric Mann-Kendall test (MK) [37,38]. Magnitudes of these trends were also estimated with the Theil-Sen slope estimator (TSE) [39,40].

The modified MK test is commonly used in long-term hydrological trend assessment studies on account of its robustness against inherent outliers, autocorrelation, and non-normal distribution in a dataset [37,38]. The test is very reliable for detecting monotonic trends in environmental time series data [37,38].

For a time series of X₁, X₂, X₃, …, X_n, the MK test statistic (S) is calculated as [5,41,42]:

$$S={\displaystyle \sum _{i=1}^{n-1}{\displaystyle \sum _{j=i+1}^n\mathrm{sgn}\left(X_j-X_i\right)}}$$

(1)

where X_i and X_j represent sequential datapoints, n is the length of the dataset, and

$$\mathrm{sgn}\left(x_j-x_i\right)=\{\begin{array}{l}+1if\left(X_j-X_i\right)>0\\ 0if\left(X_j-X_i\right)=0\\ -1if\left(X_j-X_i\right)<0\end{array}$$

(2)

where (X_j − X_i) represents the difference between two sequential datapoints. The null hypothesis H0 of no trend is rejected with a p-value less than the significance level or if the calculated Z-statistic is larger than the critical value of the Z-value obtained from the normal distribution table. This study used a 10% significance level. The variance of S is calculated as (e.g., [37,38,41]):

$$V(S)=\frac{n(n-1)(2n+S)}{18}$$

(3)

The modified MK trend test statistic Z is given by

$$Z=\{\begin{array}{l}\frac{S-1}{\sqrt{V(S)}}forS>0\\ 0forS=0\\ \frac{S+1}{\sqrt{V(S)}}forS<0\end{array}$$

(4)

where the sign of S gives the direction of the trend. A negative sign indicates a decreasing trend, while a positive value indicates an increasing trend. The modified variance of S denoted by V(S)′ is computed as [5,38,41,43]:

$$V^{\prime }(S)=V(S)\frac{n}{n^{\prime }}$$

(5)

and

$$\frac{n}{n^{\prime }}=1+\frac{2}{n(n-1)(n-2)}{\displaystyle \sum _{i=1}^{n-1}(n-i)(}n-i-1)(n-i-2)r_i$$

(6)

where r_i is the lag-i significant autocorrelation coefficient of a datapoint with rank i in the time series dataset. The autocorrelation coefficient is calculated as (e.g., [5,38,41,43]):

$$r_k=\frac{\frac{1}{n-k}{\displaystyle {\sum }_{i=1}^{n-k}(X_i-\overline{X})(X_{i+k}-\overline{X})}}{\frac{1}{n}{\displaystyle {\sum }_{i=1}^n{(X_i-\overline{X})}^2}}$$

(7)

Since the MK statistic (S) does not indicate the magnitude of the slope, the TSE was used to determine the trend magnitude as [39,40]:

$$\beta =median\left(\frac{X_j-X_i}{j-i}\right)$$

(8)

where β is the median for all possible combinations of pairs of any two datapoints in the entire time series dataset. X_i and X_j are the sequential datapoints, where i < j.

3. Results

3.1. Trends in Rainfall

Annual and seasonal spatial rainfall distribution over the county held a similar pattern among all three temporal scales used for the analysis. Long-term mean rainfall within San Diego County ranged from 720 mm in the mountainous areas (east of the Santa Margarita watershed and northeast of the San Diego watershed) to 130 mm in the desert (San Felipe Creek watershed) during the study period (1985–2017) (

Table 2. Mean annual rainfall (mm), MK Z statistic, Sen’s slope, and change (mm) of mean annual rainfall during the 1985–2017 period (statistically significant values at 10% significance levels are shaded). The dry season (summer) spans from May to October and the rainy season (winter) spans from November to April. C stands for Coastal; D for Desert, and M for Mountainous regions.

		Station Information	Annual				Dry Season				Rainy Season
#	Location	Name	Average	Z	Sen’s Slope	Change (mm)	Average	Z	Sen’s Slope	Change (mm)	Average	Z	Sen’s Slope	Change (mm)
1	C	Camp Pendleton MCAS, CA US	267	−2.75	−12.47	−0.43	27	−1.07	−0.48	−0.16	240	−2.90	−10.54	−0.43
2	C	Chula Vista, CA US	215	−1.42	−3.00	−0.13	20	0.20	0.08	0.03	195	−0.73	−2.11	−0.10
3	C	El Cajon, CA US	271	−0.27	−1.00	−0.01	36	0.00	−0.11	0.00	235	−0.56	−3.66	0.09
4	C	Escondido Number 2, CA US	336	−1.63	−5.54	−0.18	36	−0.34	−0.18	−0.05	300	−1.60	−4.59	−0.22
5	C	Imperial Beach Ream Field NAS, CA US	396	2.65	23.63	0.45	46	3.30	4.00	0.50	350	2.80	22.40	0.42
6	C	Lakeside 2 E, CA US	378	−0.82	−3.50	−0.08	46	0.09	0.17	0.02	332	−0.54	−3.44	−0.08
7	C	North Island NAS, CA US	234	−0.72	−1.85	−0.13	27	−0.15	−0.05	−0.02	207	−0.71	−1.58	−0.10
8	C	Oceanside Marina, CA US	259	−1.58	−5.53	−0.16	34	0.52	0.39	0.08	225	−1.56	−4.59	−0.23
9	C	Poway Valley, CA US	327	−0.55	−4.29	−0.03	39	0.27	0.31	0.05	287	−0.82	−3.53	−0.13
10	C	San Diego International Airport, CA US	249	−1.17	−2.56	−0.11	29	−0.45	−0.14	−0.06	220	−1.07	−2.10	−0.14
11	C	San Diego Miramar NAS, CA US	281	−0.21	−0.83	−0.04	30	0.52	0.33	0.08	250	−0.47	−1.11	−0.07
12	C	Vista, CA US	313	−0.84	−3.79	−0.05	40	0.19	0.14	0.03	273	−0.67	−2.81	−0.09
13	D	Borrego Desert Park, CA US	134	−1.44	−2.00	−0.14	32	−1.07	−0.38	−0.14	102	−0.99	−1.27	−0.13
14	M	Alpine, CA US	315	−1.10	−5.38	−0.06	40	0.20	0.14	0.03	276	−0.79	−3.39	−0.13
15	M	Campo, CA US	386	−0.46	−2.21	−0.02	60	0.10	0.07	0.02	327	−0.14	−0.59	−0.02
16	M	Cuyamaca, CA US	719	−1.50	−9.84	−0.16	99	−0.88	−1.77	−0.12	620	−1.04	−7.12	−0.15
17	M	El Capitan Dam, CA US	382	−0.59	−7.43	−0.02	51	0.95	1.31	0.18	331	−0.23	−3.22	0.05
18	M	Henshaw Dam, CA US	577	−0.65	−4.11	−0.02	70	0.00	−0.01	0.00	506	−0.34	−2.02	−0.05
19	M	Julian CDF, CA US	571	−0.03	−0.36	0.08	76	0.63	1.26	0.10	494	−0.03	−0.30	−0.01
20	M	Palomar Mountain Observatory, CA US	688	−0.65	−3.66	−0.04	77	0.13	0.11	0.02	612	−0.36	−2.16	−0.05

). From the 20 rainfall measurement stations analyzed, only one in the southwest of the county showed a statistically significant upward trend (0.5 mm increase over the study period) (Table 2; Figure 3). The remaining 19 stations showed slightly decreased rainfall with a magnitude of 0.18 mm on average) between 1985 and 2017 (Table 2; Figure 3). Data at the remaining stations suggest overall decreasing rainfall in the county with a statistically significant decreasing trend at one only station (Figure 3; Figure 4; Appendix A).

The seasonal analysis revealed similar spatial patterns for the trend in rainy months compared to annual trend characteristics (Figure 3). Since most rain events occur during the rainy season, it is expected that annual trends were mostly driven by the rainfall events of this period. With only one station showing statistically significant downward trends (Table 2; Figure 3), rainfall during the rainy season averaged 0.19 mm (from 100 mm to 620 mm) over the study period, with downward trends for the majority of stations examined corresponding to a magnitude of 0.01 mm to 0.43 mm for stations (Table 2; Figure 3 and Figure 4). Unlike trend characteristics during the rainy season, rainfall during the dry season showed slight upward trends with an average of 0.09 mm (>0.00 to 0.50 mm) in 12 out of 20 stations during the study period (Table 2; Figure 3), suggesting a possible shift in rainfall patterns in the region. The station with statistically significant increasing trends was located in the Imperial Beach area (Figure 3), which receives frequent and abundant rainfall events compared to the rest of the county. This may be attributable to a combination of the area’s coastal micro-climate and the effects of local topographic features on weather patterns: sea surface evaporation could create excess moisture along the coast while orographic blocking by the rapid increase in slope of the nearby Tijuana Hills and San Ysidro and Jamul Mountains could lead to increased precipitation during rainfall events [44].

3.2. Trends in Temperature

Mean annual minimum and maximum temperatures ranged from 4 to 15 °C and 18 to 30 °C, respectively, increasing gradually from the county’s western (coastal) region to the eastern (desert) region. Minimum and maximum temperatures—defined as lowest and highest daily temperatures—showed statistically significant upward trends on an annual basis at 8 out of 17 for minimum and 5 of 16 maximum temperature measurement stations examined. The temperatures at all other stations also appear to increase, except at three locations (Figure 5, Figure 6 and Figure 7;

Table 3. Mean temperature (°C), MK Z statistic, Sen’s slope, and change (°C) of minimum temperature during the 1985–2017 period (statistically significant values at 10% significance levels are shaded). The dry season (summer) spans from May to October and the rainy season (winter) spans from November to April. C stands for Coastal; D for Desert, and M for Mountainous regions. Stations 5, 6, and 9 were not used for minimum temperature analysis because of missing or erroneous data.

		Station Information	Annual				Dry Season			Rainy Season
	Location	Name	Average	Z	Sen’s Slope	Change (°C)	Average	Z	Sen’s Slope	Change (°C)	Average	Z	Sen’s Slope	Change (°C)
1	C	Camp Pendleton MCAS, CA US	10.1	−1.56	−0.05	−0.05	7.1	−0.87	−0.02	−0.02	3.1	−1.96	−0.04	−0.04
2	C	Chula Vista, CA US	12.9	1.64	0.04	0.04	8.2	1.21	0.01	0.01	4.7	1.64	0.02	0.02
3	C	El Cajon, CA US	11.7	1.75	0.04	0.04	7.8	1.86	0.02	0.02	3.8	1.69	0.02	0.02
4	C	Escondido Number 2, CA US	11.6	2.11	0.04	0.04	7.7	2.59	0.03	0.03	3.9	1.76	0.02	0.02
7	C	North Island NAS, CA US	14.4	1.10	0.02	0.02	8.7	0.57	0.01	0.01	5.7	0.62	0.00	0.00
8	C	Oceanside Marina, CA US	12.5	3.75	0.07	0.07	8	3.94	0.04	0.04	4.6	2.36	0.04	0.04
10	C	San Diego International Airport, CA US	14.4	2.58	0.04	0.04	8.7	2.51	0.02	0.02	5.7	2.71	0.02	0.02
11	C	San Diego Miramar NAS, CA US	12	−0.22	−0.01	−0.01	7.7	0.57	0.02	0.02	4.4	−0.42	0.00	0.00
12	C	Vista, CA US	11.7	0.71	0.01	0.01	7.5	1.63	0.02	0.02	4.2	−0.46	−0.01	−0.01
13	D	Borrego Desert Park, CA US	15	3.07	0.05	0.05	10.4	2.97	0.03	0.03	4.7	1.54	0.02	0.02
14	M	Alpine, CA US	10.7	−0.73	−0.01	−0.01	7.1	0.90	0.01	0.01	3.6	−1.07	−0.02	−0.02
15	M	Campo, CA US	5.2	0.61	0.01	0.01	4.3	2.45	0.02	0.02	1	−0.58	−0.01	−0.01
16	M	Cuyamaca, CA US	4.9	2.84	0.09	0.22	4.7	3.42	0.04	0.10	0.3	2.34	0.05	0.13
17	M	El Capitan Dam, CA US	9.4	0.50	0.22	0.22	6.3	0.86	0.15	0.15	3.2	0.68	0.06	0.06
18	M	Henshaw Dam, CA US	3.9	0.77	0.02	0.02	3.9	1.60	0.03	0.03	0	−0.49	−0.01	−0.01
19	M	Julian CDF, CA US	7.8	2.20	0.18	0.18	5.9	2.20	0.09	0.09	1.9	2.57	0.08	0.08
20	M	Palomar Mountain Observatory, CA US	8.8	2.50	0.06	0.06	6.9	2.31	0.02	0.02	1.8	2.91	0.03	0.03

;

Table 4. Mean temperature (°C), MK Z statistic, Sen’s slope, and change (° C) of maximum temperature during the 1985–2017 period (statistically significant values at 10% significance levels are shaded). The dry season (summer) spans from May to October and the rainy season (winter) spans from November to April. C stands for Coastal; D for Desert, and M for Mountainous regions. Stations 5, 6, 9, and 14 were not used for maximum temperature analysis because of missing or erroneous data.

		Station Information	Annual				Dry Season				Rainy Season
#	Location	Name	Average	Z	Sen’s Slope	Change (°C)	Average	Z	Sen’s Slope	Change (°C)	Average	Z	Sen’s Slope	Change (°C)
1	C	Camp Pendleton MCAS, CA US	23.9	−1.61	−0.05	−0.24	13.1	−0.92	−0.02	−0.14	10.9	−1.66	−0.03	−0.25
2	C	Chula Vista, CA US	22.1	0.14	0.01	0.02	11.8	1.09	0.01	0.15	10.3	−0.93	−0.01	−0.13
3	C	El Cajon, CA US	25.5	1.35	0.05	0.21	14.4	0.62	0.01	0.10	11.1	1.41	0.03	0.22
4	C	Escondido Number 2, CA US	25.4	1.80	0.04	0.24	14.4	2.51	0.03	0.34	11	0.73	0.01	0.10
7	C	North Island NAS, CA US	21.3	1.98	0.06	0.32	11.5	2.03	0.04	0.46	9.8	1.41	0.02	0.12
8	C	Oceanside Marina, CA US	19	2.16	0.05	0.37	10.2	3.15	0.04	0.18	8.8	0.77	0.01	0.42
10	C	San Diego International Airport, CA US	21.2	0.11	0.00	0.02	11.6	1.12	0.01	0.14	9.7	−0.70	−0.01	−0.09
11	C	San Diego Miramar NAS, CA US	23.7	0.17	0.01	0.03	13.1	1.17	0.03	0.17	10.6	−0.12	0.00	−0.02
12	C	Vista, CA US	23.5	−0.96	−0.03	−0.13	13	−1.67	−0.02	−0.23	10.5	0.08	0.00	0.01
13	D	Borrego Desert Park, CA US	31.2	0.44	0.00	0.06	19	0.00	0.00	0.00	12.2	0.21	0.00	0.03
15	M	Campo, CA US	25	2.72	0.03	0.35	15.4	1.87	0.02	0.24	9.6	1.56	0.02	0.20
16	M	Cuyamaca, CA US	19.4	1.08	0.02	0.47	12.7	1.96	0.02	0.45	6.7	0.54	0.01	0.60
17	M	El Capitan Dam, CA US	27.8	−0.14	−0.01	−0.03	16	−0.95	−0.03	−0.18	11.8	1.85	0.04	0.35
18	M	Henshaw Dam, CA US	24.3	0.97	0.02	0.13	15.1	−0.18	0.00	−0.03	9.2	1.48	0.02	0.20
19	M	Julian CDF, CA US	19.1	0.33	0.02	0.28	12.3	−0.21	−0.01	0.29	6.8	0.27	0.00	0.20
20	M	Palomar Mountain Observatory, CA US	18.6	2.80	0.06	0.55	12.4	1.33	0.02	0.56	6.1	3.21	0.05	0.55

; Appendix A). The magnitude of the upward trends varied from 0.01 °C to 0.22 °C (1.0 to 22%) with an average of 0.07 °C for minimum temperature, and from 0.02 °C to 0.55 °C (0.40 to 14%) with an average of 0.25 °C for maximum temperature over the study period (Figure 5, Figure 6 and Figure 7; Table 3 and Table 4). The data suggest that minimum temperature increased more rapidly than maximum temperature over the study period.

The seasonal analysis also revealed similar upward trends in a majority of the observation stations examined (9 of 17 were statistically significant for minimum temperature and 6 of 16 for maximum temperature). A common trend was that, whether in dry or rainy seasons, the data revealed temperature increases in the county (1985 to 2017), with overall dry season temperatures increasing more compared to that of the rainy season (Figure 5, Figure 6 and Figure 7), indicating hotter temperatures during the dry season. The mean seasonal minimum temperature increased by 0.01 °C to 0.22 °C (1%–22%) for the annual period, 0.01 °C to 0.15 °C (2% to 25%) for the dry season, and >0.00 °C to 0.14 °C (1%−31%) for the rainy season with respective averages of 0.07 °C, 0.04 °C, and 0.04 °C. Downward trends in minimum temperature occurred primarily during the rainy season at some stations (Figure 5; Table 3 and Table 4). The analysis estimated an increase of 0.27 °C and 0.23 °C ranging from 0.03 °C to 0.56 °C (0.08% to 15%) for maximum temperature during the dry season and ranging from 0.01 °C to 0.06 °C (0.40% to 19%) during the rainy season, respectively (Figure 6). The annual maximum temperature increased by 0.02 °C to 0.55 °C (0.40% to 14%) (Table 3 and Table 4).

4. Discussion

Although the attribution of the study’s observed trends to possible causes was beyond the scope of this study, research has linked past droughts in California—including the most recent drought event (i.e., 2012–2014)—to reduced rainfall and record-high temperatures [7]. While the reduced rainfall pattern is within the range of natural variability, [7,45] explained that the extremely high temperatures recorded during the 2012–2014 drought may have been exacerbated by human-induced global warming. This is consistent with findings in the present study, where out of the 20 rainfall stations examined, only two stations showed statistically significant decreasing trends (Figure 5; Table 3), indicating that rainfall amounts in the San Diego region remained relatively similar during the study period and may remain as such in coming decades [46]. Unlike rainfall trends, maximum and minimum temperatures in Southern California show increased deviations from the normal [45,47,48,49]. Williams et al. (2015) [45] demonstrated that precipitation is the prime driver of California drought with 8%–27% of the observed drought anomaly attributed to human-induced warming in 2012–2014 and 5%–18% in 2014. In addition, rainfall and temperature patterns in Southern California are highly influenced by coastal microclimates. This suggests the importance of understanding changes in rainfall and temperature characteristics and how they relate to climate phenomena such as La Niña and El Niño Southern Oscillations at local levels for effective planning of water supply systems [50].

San Diego County imports roughly 80% of its water supply. The remaining 20% is dependent on local water sources, with only 10% supplied by direct rainfall. Major investments in water infrastructure and water use efficiency projects are intended to increase local supply sources by means of diversification. The San Diego County Water Authority aims to achieve an aggressive diversification of the region’s water supply portfolio by developing local supplies such as groundwater, recycled water, seawater desalination, and conservation practices [51]. Changes in rainfall input and patterns would affect the availability and ability of the aforementioned 20% of San Diego County’s water supply to meet agricultural, residential, commercial, and industrial water demands. Although the present study did not see any documentable patterns in rainfall and temperatures across the specific climate types, as presented in the Introduction, the analysis reveals an overall decreasing rainfall trend over the study period, which was reflected de facto in water availability in streams and rivers in the county (data and analysis not shown here). The decreasing rainfall input explains the long-standing drought that officially ended with the lifting of the drought emergency in April 2017 [24,45]. Even though 2019 brought abundant rainfall to the region, research has shown that prolonged and chronic droughts are frequent in Southern California (MacDonald et al. 2008). Decreases in rainfall would affect groundwater recharge and water supply, regardless of the level of conservation adopted. As rainfall decreases, pollutant buildup increases, eventually resulting in water quality deterioration, especially during first rainfall events [52,53]. Abnormally limited rainfall can cause harm to local flora and fauna through reduced environmental flows [54,55].

The urban population in San Diego County continues to increase, leading to an expansion in urban areas. While the expansion of urban land use without sound water management measures would result in increased runoff, there are currently no known ambitious community projects in the San Diego region to capture the excess runoff produced during abundant incident rainfalls. Runoff events are often considered to be hazards (e.g., they produce flooding) instead of resources. Unreliable rainfall events, coupled with the expansion of urban areas and a changing climate, point to uncertainty in the future of water supply in San Diego County. In addition, the continued reliance on water imports places rigid financial burdens on urban and agricultural water users. The decreasing rainfall trend would equally affect all sectors (urban, agricultural, etc.) through the rising cost of water, and this would lead inevitably to negative economic ramifications and challenges—such as the incentivization of selling of water to one sector over another—in regards to continuously balancing urban and agricultural water needs.

For proactive and long-term water management plans in dry climates to succeed, all sectors, including environmental requirements, must be considered. Continued sustainable planning, investment in research, prompt and responsive policies, and public education and outreach are necessary for adapting to the stress of future climate change in Southern California’s limited rainfall environment.

5. Conclusions

This study presented a case study of annual and seasonal rainfall and temperature time series to determine trends and their significance from 1985 to 2017 in San Diego County. Important conclusions from this study include:

• Annual rainfall generally decreased in the county (16 out of 20 rain gauge stations) even though the trends were not statistically significant; only two stations showed statistically significant trends. Trends in rainfall during the rainy season were reflected in annual trends, while rainfall during the dry season tended to increase in 12 out of 20 stations, suggesting that the dry season received more rainfall than normal over the study period.
• Annual minimum and maximum temperatures showed mostly upward trends with a magnitude of 0.05 °C and 0.25 °C increase between 1985 and 2017. Analysis of seasonal data also revealed that temperature increased more during the dry season compared to that of the rainy season (Figure 6 and Figure 7), indicating hotter days during the dry season. The mean seasonal minimum temperature significantly increased by 0.07 °C for the annual period, 0.04 °C for the dry season, and 0.04 °C for the rainy season (about 9 of 17 stations showed statistically significant trends). Downward trends in minimum temperature occurred primarily during the rainy season at some stations (Figure 5; Figure 6). The analysis estimated a statistically significant increase of 0.36 °C for maximum temperature on the annual basis, 0.33 °C for the dry season, and 0.34 °C for the rainy season (7 of 16 stations showed statistically significant trends) (Figure 7). Few stations revealed average decreases of 0.14 °C and 0.12 °C for the dry and rainy seasons (1 of 18 stations showed statistically significant trends) (Table 3 and Table 4).

The trends presented in this study may have resulted from possible climate or anthropogenic activities or the combination of both. While this study did not attempt to determine the possible causes of the decreasing rainfall trends that were observed, the information provided herein should prove useful to the region’s water research information system and should support further investigation into the impacts of climate change and anthropogenic activities on hydrological processes in Southern California. As the population continues to increase in Southern California, analyses of rainfall and temperature trends at local levels will inform support planning and management of water resources.