An investigation of spatially-temporal hydro-climatic data trends and patterns in the Iraqi Diyala River Basin

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Such factors have been considered as the chief distress for water resources engineers and managers, especially in semi-arid and arid regions. This research investigates the spatiotemporal trends and extent patterns of the hydro-climate variables during the past thirty years, comprising yearly and monthly rainfall, temperature, evapotranspiration, and excess rainfall, to estimate the possible effect of such alterations at a local level. The Diyala River catchment, in the central region of Iraq, has been considered an example area. Accordingly, the Mann–Kendall and Pittet methods and the double mass curve process, were used for analysis of the hydro-climatic variables from 1979 to 2012 in the studied catchment. Outcomes of the study designated that excess rainfall alteration during the rainy and dry periods after 1967 had significant corresponding declining trends at a 0.05 confidence level owing to dam building. Annual runoff variations were primarily caused by an anthropogenic intervention involving dam construction to meet water use requirements for agricultural consumption. The rainfall-runoff relationship in the basin has been altered due to weather inconsistency and increasingly intensified human activities. The sudden alterations in the hydro-climatic variables and the key causing influences of the variations in the considered basin have been investigated. The study's findings would help policymakers and water resource engineers identify the risks and vulnerabilities associated with environmental change. Treand analysis Change point detection Anthropologic intervention Diyala basin Climate variability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Weather change and human intervention frequently cause variations in the spatiotemporal pattern of weather data [ 1 , 2 , 3 ]. The tendency test of climatic information variability is important for meteorologists, hydrologists, and agriculturalists in terms of sustainable use of water resources [ 4 , 5 ]. The globe’s mean temperature was raised by 0.6°C during the twentieth era, as well as there was an intense move in mean air temperature from 1.4 to 5.4°C simulated by many weather prediction models [ 6 ]. Many researchers [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ] concluded that the variation in the distribution of weather variables would affect the spatiotemporal pattern of excess rainfall, soil moisture, and subsurface water investments and would also modify the occurrence of extreme phenomena such as floods and droughts. Numerous scientists have conducted studies on spatiotemporal trend analysis and its magnitude in climatic (precipitation, air, temperature, humidity, etc.) and hydrological (stream flow) datasets by parametric (linear regression) and non-parametric (MK, Pettit) methods in many regions [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Although many recent studies have conducted trend analysis tests of hydro-climatic variables in Iraq [ 26 , 27 , 28 , 29 , 30 , 31 ], However, they either considered parametric or non-parametric tests without considering the change point in the data. Accordingly, the main aim of this research is to examine the inconsistency and changing points in the hydro-climatic time-series using the sequential Mann-Kendall and Pettit tests, respectively. However, the research objectives will be as follows: (1) identifying the magnitude of the annual trend in the hydrologic and climatic data by a parametric test (simple linear regression); (2) testing the temporal trend in the annual values of time series by a non-parametric test that is the Mann–Kendall method; (3) detecting the alteration point in the annual datasets by the Pettit test; and (4) investigating the areal distribution of tendencies and its extent in yearly datasets. 2 Resources And Methods 2.1 Basin Area Explanation The Tigris is one of Western Asia's major rivers; it originates in the highlands of Taurus in Turkey, passes through Syria, and enters Iraq through Feshkhabur village [ 32 ]. The Tigris River Basin is divided into several sub-basins. Most of it is shared by Iraq and Iran, as well as Iraq and Turkey, such as the Khabour, Lesser Zab, Greater Zab, and Diyala River Basins, (Table 1 ). The Diyala River catchment will be considered as a sample basin to represent semi-arid and arid climatic conditions. The Diyala River is the Tigris River's fifth tributary, which originates at the junction of the Tangro, Wand, and Sirwan rivers in Lake Darbandikhan in the Sulaymaniyah Governorate, north Iraq [ 33 ]. The river has a full stretch of about 574 km and runs through Iraq and Iran. It starts in the Zagros Mountains and runs south of Baghdad, Iraq, into the Tigris River. The Diyala River Basin is located between latitudes 33°12 to 35°47 E and longitudes 44°18 to 47°58 N with a total area of approximately 32975.6259 km2, 46% of which is located in Iraq and the remaining is in Iran, Fig. 1 . Table 1 Sub-basins of the Tigris River (USGS, 2010) Tributaries Total basin Area (Km 2 ) Tributary Length (Km) Shared area % Iraq Turkey Iran Khabour 6143 181 43 57 - Greater Zab 26310 462 65 35 - Lesser Zab 19780 302 76 - 24 Diyala 32975 574 46 - 54 There are two dams that control the flow of the river. The first is Darbandikhan, which is considered Iraq's second-largest dam. Darbendikan is an earth dam constructed in 1961 for multiple purposes, such as flood protection, hydropower generation, and irrigation. It spans about 16,750 km2, 20% of which is in Iraq and the rest is in Iran. The Derbendkhan Reservoir has a total storage capacity of 3 billion cubic meters (BCM). The Diyala River is around 217 km long above this dam. The second dam within DRB is Hemrin, which is also an earth dam. Hemrin was built in 1981 in the middle of the Diyala River, about 120km east of Baghdad City. The main purpose of the Hemrin dam is for hydropower generating and irrigation purposes as part of the Khalus Irrigation Project. The Hemrin dam watershed covers a total area of 12,822 km2, with 68% of it located in Iraq and the rest in Iran. The Hemrin Reservoir has a total capacity of 2.4 BCM [ 33 ]. The DRB is typically divided into three sections: the upstream section, which runs between Darbandikhan Dam and the Zagros Mountains; the middle section, which runs between Darbandihan and Hemrin Dams; and the downstream section, which runs between Hemrin Dam and the Tigris River (Fig. 1 ). The monthly flow rate at Darbandikhan, which is regarded as an important hydrometric gauging station (latitude 45.69 and longitude 35.11), was studied for the hydrological years 1931 to 2004. 2.2 Data Collection and Analysis This study took into account the following hydro-meteorological data: The Ministry of Agriculture and Water Resources (Kurdistan region, Iraq) collected monthly rainfall and minimum and maximum air temperature (1980–2012) from fourteen climatic stations, as shown in Table 2 . The Hargreaves method, which was recommended to be used in such climatic conditions [ 34 ], was applied to estimate the potential evaporation. The basin’s stations and their latitudes and longitudes are revealed in Fig. 1 and listed in Table 2 . The monthly runoff (1931–2000) of the Derbendikan hydrological station in the Diyala River was considered [ 32 ]. Table 2 Longitude, latitude and elevation for the selected stations in Diyala River Basin Sub-basin Station Name Longitude Latitude Elevation Upstream Sanandaj 47.00 35.33 13730 Kermanshah 47.17 34.26 13220 Ghorveh 47.80 35.17 1906 Ravansar 46.66 34.72 1363 Eslamabad 46.43 34.13 1346 Marivan 46.20 35.52 1287 Sulaymaniyah 45.38 35.56 824 Halabcha 45.95 35.20 620 Downstream Darbandikhan 45.69 35.11 451 Khanaqin 45.35 34.35 202 Tus 44.65 34.83 0 Balad 44.36 33.95 49 Baquba 44.66 33.75 41 Baghdad 44.41 33.31 32 To predict the position of climatic and hydrological stations and delineate the basin of the river, ArcGIS 10.8 was utilized. XLSTAT, a Microsoft Excel add-in, was used to run the Pettitt and Mann-Kendall tests. The Hargreaves technique was used to calculate potential evapotranspiration PET (mm) depending on the Drin C program. The basin's average precipitation was estimated by the Thiessen network method via the ArcGIS tool. Furthermore, to test the trends of the time series, different numerical approaches were used, which are separated into two common sets: parametric and non-parametric, which have extra widespread uses [ 34 ]. Four statistical approaches were applied to test the spatiotemporal tendencies of the weather and hydrologic data. Firstly, the simple linear regression method, which is a parametric test, has been employed to examine the long-term linear trend. Secondly, the Mann–Kendall method, which is a non-parametric test, was used to test the non-linear trend and the turning point. The Mann-Kendall (MK) method is a normally applied distribution-free method to test the temporal trends in the hydro-climatic variables. MK is generally suggested by WMO (the World Meteorological Organization) and it is founded on the following formula: $$\text{ Z = }\left\{\begin{array}{c}\frac{\text{S-1}}{\sqrt{\text{Var }\left(\text{S}\right)}}\text{ }\text{,}\text{ }\text{i}\text{f}\text{ }\text{S}\text{ }\text{>}\text{ }\text{0}\\ \frac{\text{S+1}}{\sqrt{\text{Var }\left(\text{S}\right)}}\text{ }\text{,}\text{ }\text{i}\text{f}\text{ }\text{S}\text{ }\text{<}\text{ }\text{0}\end{array}\right\}\text{ }\text{ }\text{ }\text{ }\text{ (1)}$$ in which, $$\text{S =}\sum _{\text{i=1}}^{\text{n-1}}\sum _{\text{j=i+1}}^{\text{n}}\text{sgn(}{\text{x}}_{\text{j}}\text{-}{\text{x}}_{\text{i}}\text{)}\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ (2)}$$ $$\text{ }\text{Sgn}\text{ (θ) = }\left\{\begin{array}{c}\text{+}\text{1}\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{θ}\text{ }\text{>}\text{ }\text{0}\\ \text{0}\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{θ}\text{ }\text{=}\text{ }\text{0}\\ \text{-}\text{1}\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{θ}\text{ }\text{<}\text{ }\text{0}\end{array}\right\}\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ (3)}$$ $$\text{Var(S) =}\frac{\text{n}\left(\text{n-1}\right)\left(\text{2n+5}\right)\text{-}\sum _{\text{t}}\text{t(t-1)(2t+5)}}{\text{18}}\text{ }\text{ }\text{ }\text{ }\text{ (4)}$$ n denotes the number of data points; xi and xj denote the sequential value of data; t is the amount of a certain relation. The increasing and decreasing trend in the datasets is shown by a positive and negative Z-value, in that order. The trend is significant at 0.05 and 0.01 confidence levels for |Z| greater than 1.96 and greater than 2.575, respectively [ 22 ]. 3 Results And Discussion 3.1 Spatiotemporal Variation of Weather Data Long-term patterns in water resource systems are affected by changing environmental and anthropogenic interventions [ 35 ]. Exploring such patterns would help in the specification of points wherever humans can interfere. The MK test has been used to identify trends for a long time in the mean values of weather data. Table 4 comprises the numerical properties of the precipitation, mean air temperature, and potential evapotranspiration representing the M-K test for the decadal change in the Diyala River Basin. The table shows the three-dimensional distribution of trends (increase, decrease, and non-significant trends) for the studied area. All the weather variables show significantly decreasing trends. Table 3 Ratios of long-term average monthly to long-term annual precipitation Sub-basin Station name \(\frac{\text{Average precipitation at any month}}{\text{Total }\text{average }\text{precipitation}}\times 100\%\) Oct Nov Dec Jan Feb Mar Upper Sanandaj 4.45 10.88 14.11 15.74 17.61 17.96 Kermanshah 3.94 11.07 14.38 15.48 15.88 18.44 Ghorveh 5.52 10.62 12.62 13.79 15.03 18.19 Ravansar 5.33 11.97 14.27 15.57 16.64 17.02 Eslamabad 5.44 12.4 14.42 16.15 16.08 17.6 Marivan 4.12 10.94 15.06 16.15 18.76 17.56 Sulaymaniyah 3.84 11.04 16.3 18.9 18.86 17.73 Halabcha 4.04 10.69 16.62 17.92 19.43 17.48 Darbandikhan 4.05 10.62 16.65 19.28 19.3 17.46 Khanaqin 3.45 14.13 16.88 20.41 16.78 16.53 Lower Tus 4.91 13.66 17.42 18.68 17.38 16.19 Balad 4.54 13.86 18.02 18.69 16.91 16.05 Baquba 4.34 14.93 17.75 19.13 16.03 15.33 Baghdad 4.25 13.52 16.9 19.9 14.67 15.44 Sub-basin Station name \(\frac{\text{Average precipitation at any month}}{\text{Total }\text{average }\text{precipitation}}\times 100\%\) Apr May Jun Jul Aug Sep Sanandaj 12.66 5.93 0.49 0.06 0.02 0.09 Upper Kermanshah 15.23 5.18 0.28 0.02 0 0.1 Ghorveh 14.52 8.38 0.72 0.21 0.11 0.29 Ravansar 12.95 5.81 0.28 0.02 0 0.14 Eslamabad 11.96 5.54 0.26 0.02 0 0.13 Marivan 12.47 4.51 0.25 0.04 0 0.14 Sulaymaniyah 9.66 3.47 0.08 0 0 0.12 Halabcha 10.4 3.15 0.17 0.01 0 0.09 Darbandikhan 9.15 3.28 0.09 0 0 0.12 Khanaqin 7.89 3.76 0.12 0 0 0.05 Lower Tus 8.17 3.31 0.19 0 0 0.09 Balad 7.75 3.96 0.17 0 0 0.05 Baquba 7.94 4.42 0.12 0 0 0.01 Baghdad 9.31 5.91 0.08 0 0 0.02 Table 4 The non-parametric analysis for the meteorological variables over the Diyala River Basin Sub-basin Station name Precipitation Temperature Potential evapotranspiration M-K 1 p -value M-K 1 p -value M-K 1 p -value Upstream Sanandaj -422 * < 0.05 0.537 * < 0.05 310 0.010 Kermanshah -350 * < 0.05 0.376 * < 0.05 362 * < 0.05 Ghorveh -504 * < 0.05 0.469 * < 0.05 355 * < 0.05 Ravansar -455 * < 0.05 0.443 * < 0.05 405 * < 0.05 Marivan -537 * < 0.05 0.540 * < 0.05 430 * < 0.05 Sulaymaniyah -351 * < 0.05 0.597 * < 0.05 383 * < 0.05 Halabcha -451 * < 0.05 0.590 * < 0.05 522 * < 0.05 Darbandikhan -401 * < 0.05 0.298 * < 0.05 558 * < 0.05 Downstream Eslamabad -540 * < 0.05 0.190 0.106 387 * < 0.05 Khanaqin -412 * < 0.05 0.066 0.594 333 * < 0.05 Tus -483 * < 0.05 0.273 * < 0.05 419 * < 0.05 Balad -351 * < 0.05 0.064 0.612 346 * < 0.05 Baquba -422 * < 0.05 0.059 0.635 258 * < 0.05 Baghdad -355 * < 0.05 0.119 0.328 262 * < 0.05 1 Mann–Kendall distribution-free test *Correlation is significant at the 0.05 level (2-tailed). Note: Negative (-) and positive values indicate the decreasing and increasing trends, respectively; Values of M-K are multiplied by 10 -3 The mean air temperature within DRB exhibited a growing tendency with a maximum value of + 0.36°C, Fig. 2 a. The year 2010 had the high mean air temperature (19.3°C) while 1992 had the lowest (15.62°C). However, the rainfall values displayed a negative trend with a maximum decadal loss of 86.35 mm, Fig. 2 b. The mean yearly rainfall is almost 451 mm. The year 1984 had the highest precipitation of 679 mm, while the year 1999 had the lowest at 210 mm. Potential evapotranspiration exhibited an increasing decadal rate of about 34.2 mm. The anticipated potential evapotranspiration increased from 1417.5 (in 1983) to 1627.7 mm (in 2010), by a mean value of around 1542 mm, Fig. 2 c. The results reveal that, as indicated in the case study, the environment is becoming hotter and drier as a consequence of the variation in the climate. The amount of rain that fell on the ground each year was decreased, the yearly average temperature rose, while the depth of the annual runoff has decreased. These results are broadly in line with those of earlier research [ 36 , 3 , 35 ]. The runoff rate was selected to describe the hydro-climatic situation in DRB, which is defined as the percentage of flow to the rainfall during a given time interval. The rate of runoff for the studied basin and period was 0.22 with a decadal reduction equal to -0.023, Fig. 2 d. The decrease in the runoff rate suggests that, as previously projected, the flow yield has weakened during the last three decades [ 35 ]. Figure 3 displays the areal distribution of the long-term average temperature, precipitation and potential evapotranspiration in DRB, in that order. The average air temperature different from (5‒10) °C and (10‒14) °C to more than 24°C at the upper and the lower part of the basin, respectively. Whereas the long-term rainfall over the basin altered spatially from 107 mm at Baghdad station, which is situated in the lower part of the catchment, to about 662 mm at Sulymaniya station, which is located in the upper part of the catchment. This designates that the upper part of the catchment, which is described by high altitudes (associated with the lower part), had higher precipitation values than the lower. An apparent tendency of average temperature increase in the past half century caused an essential rise in the potential evapotranspiration for the whole DRB. Additionally, the relations of the long-term mean monthly rainfall to the long-term mean yearly rainfall for the considered time period that started in October, are shown in Table 3 . The results of the statistics investigation display that the gathered rainfall from October to May (wet months) contributes for almost 99.5 percentage of the overall yearly rainfall. Nonetheless, the rainfall summation from June to September (dry months) accounts to just about 0.5 percentage of the overall rainfall. The achieved outcomes show that the DRB climate is getting hotter and drier. The annual rainfall-runoff decreased, however, the annual average air temperature raised. These results are mostly in agreement with earlier research findings [ 36 , 35 ]. 3.2 Temporal Variation of Runoff During the past 35 years, the average annual runoff of the DRB was 129 m 3 /s. The minimum and maximum was 42 and 313 m 3 /s in 2000 and 1988, respectively. The mean excess rainfall of the studied catchment displayed a substantial decrease at a decadal rate of about − 38 m 3 /s. The Pettitt test was applied to detect the change point in the annual precipitation and runoff time series, Fig. 4 a and b, in that order. The water years 1993/1994 and 1996/1997 are measured as an alteration points for rainfall and runoff, respectively, between 1979 and 2012. To investigate the impact of Derbendikan dam building on runoff, the 40 years runoff data was separated into two datasets, pre-alteration (1931–1966) and post-alteration (1967– 2000) periods, Fig. 5 . It was apparent that the maximum quantity of rainfall excess before Derbendikan dam building was in 1954, with the maximum values happening during April as a result of snow melt and precipitation and the minimum quantity of excess rainfall before dam building was in 1960, especially during July to September. Figure 5 shows that the yearly distribution of runoff was different pre-damming, Fig. 5 a, and post-damming, Fig. 5 b. The runoff time series displayed significant increasing and decreasing trends before and after Derbendikan dam building, respectively. Furthermore, it can be concluded that the runoff changes were less influenced by precipitation in the post-damming period when compared with the pre-damming one, indicating that changes in runoff values were strongly caused by dam building. The relationship among annual runoff, precipitation, and temperature was significant at the 0.05 confidence level in the pre-dam construction period at the annual scale. 3.3 Hydro-Climatic Data Change Point Detection The annual runoff of the area upstream of Derbendikan Dam has an average values of 133.7 and 182.36 m 3 /s for the period from 1931 to 2000. The minimum was 41.86 m 3 /s for the water year 1999/2000. Nearly, 459.27 m 3 /s was noticed as the maximum for the year 1968/1967, Fig. 5 . Pettitt and rainfall-runoff double cumulative curve tests have been used to identify the change point of the annual runoff series, Fig. 5 a and Fig. 5 b, the hydrologic year 1966/1967 is measured as an alteration point. The obtained results are found to be consistent with the findings of many other researchers with respect to this study area (Mohammed & Scholz, 2017). Figure 5 a shows that rainfall and runoff were rather steady, nevertheless after 1967, the properties of rainfall or runoff changed. Integrating the PR-DCC analysis and the Pettitt test, the year 1967 could be seen as the change point reflecting the impact of climate change linked with anthropogenic intervention on the rainfall-runoff process. Accordingly, the period between 1931 and 1966 was considered as the baseline period during which the anthropogenic intervention impact on runoff were less noticeable. 4 Conclusions The current study explored time-based variations in runoff produced by building of the Derbandikan dam in Diyala Basin. Accordingly, hydro-climatic data during two time-periods: pre-dam construction (1931–1966) and post-dam construction (1967–2000) were assessed through MK statistical process. The runoff data had significant increasing and decreasing trends before and after Derbendikan dam construction, respectively. Correlation analysis indicated that the runoff alterations during the post-damming interval linked with the pre-damming interval were less subjective by climate variability compared to anthropological intervention. Dam building is considered as one of the chief anthropological intervention that led to change in the stream flow. Annual average runoff values altered considerably between pre-damming and post-damming period; therefore, the annual average value of the excess rainfall during the interval of post-damming was lesser than that during the interval of pre-damming. The null theory H 0 (no trend) established by the Mann–Kendall and Pettit tests was rejected at the 0.05 confidence level. The study results point out a reducing tendency in annual rainfall-runoff in the considered interval. Results of the Mann–Kendall and Pettit tests designated that dam building had caused an important alterations in the annual excess rainfall values, representing a significant decrease in the post-damming interval when compared to the pre-damming interval. Mean evaporation values exploration displayed that the water level rise produced by dam building and reservoir performance had caused a severe rise in mean evaporation values during post-damming interval. Declarations Acknowledgments The authors acknowledges the help of thier related organization. This study did not obtain any specific funding from finance governments in the society, company, or not-for proft divisions Ethics approval and consent to participate Not applicable Consent for publication Not applicable Conflict of interest The authors declare no competing interests References Huntington, T.G.: Evidence for intensification of the global water cycle: review and synthesis. Journal of Hydrology, 319(1–4),83–95. 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Arabian Journal of Geosciences, 14(7), 1–15. (2021). https://link.springer.com/article/10.1007/s 12517-021-06906-w Umar, S., Lone, M.A., Goel, N.K., Zakwan, M.: Trend analysis of hydro-meteorological parameters in the Jhelum River basin, North Western Himalayas. Theoret. Appl. Climatol. 148 (3), 1417–1428 (2022). https://link.springer.com/article/ 10.1007/s00704-022-04014-7 Agha, O., Mahmood, M. A., & Şarlak, N. (2016). Spatial and temporal patterns of climate variables in Iraq. Arabian Journal of Geosciences, 9(4), 1–11. https://link.springer.com/article/10.1007/s12517-016-2324-y Muter, S.A., Nassif, W.G., Al-Ramahy, Z.A., Al-Taai, O.T.: Analysis of Seasonal and Annual Relative Humidity Using GIS for Selected Stations over Iraq during the Period (1980–2017). Journal of Green Engineering, 10(10),9121–9135(2020) Al-Hasani, A.A.: Trend analysis and abrupt change detection of streamflow variations in the lower Tigris River Basin, Iraq. Int. J. River Basin Manag. 19 (4), 523–534 (2021). https://doi.org/10.1080/15715124.2020.1723603 Ahmad, H.Q., Kamaruddin, S.A., Harun, S.B., Al-Ansari, N., Shahid, S., Jasim, R.M.: Assessment of Spatiotemporal Variability of Meteorological Droughts in Northern Iraq Using Satellite Rainfall Data. KSCE Journal Civil Engineering, 25(11), 4481–4493 (2021). https://link.springer.com/article/10.1007/s 12205-021-2046-x Naqi, N.M., Al-Jiboori, M.H., Al-Madhhachi, A.S.T.: Statistical analysis of extreme weather events in the Diyala River basin, Iraq. Journal of Water and Climate Change, 12(8),3770–3785. (2021). https://doi.org/10.2166/wcc.2021.217 Basheer, F. S. (2022). Trend Analysis of Annual Surface Air Temperature for Some Stations over Iraq. Al-Mustansiriyah Journal of Science, 33(1), 77–82. https://doi.org/10.23851/mjs.v33i1.1083 USGS, United States Geological Survey: Stream gage descriptions and streamflow statistics for sites in the Tigris river and Euphrates river basins, Iraq. Data Series 540. Reston, VA: USGS (2010) Hussein, H.A.: Dependable discharges of the upper and middle diyala basins. J. Eng. 16 (2), 4960–4969 (2010) Mohammed, R., Scholz, M. Adaptation strategy to mitigate the impact of climate change on water resources in arid and semi-arid regions: a case study. Water Resources Management, 31(11),3557–3573. (2017). https://doi.org/10.1007/s11269-017-1685-7 Mohammed, R., Scholz, M.: Climate variability impact on the spatiotemporal characteristics of drought and Aridityin arid and semi-arid regions. Water Resour. Manage. 33 (15), 5015–5033 (2019). https://doi.org/10.1007/s11269-019-02397-3 Fadhil, M.A. Drought mapping using geoinformation technology for some sites in the Iraqi Kurdistan region. International Journal of Digital Earth, 4(3),239–257. (2011). https://doi.org/10.1080/17538947.2010.489971 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1866813","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":127098794,"identity":"d16ebdc8-414f-414a-a02a-58e57b77a227","order_by":0,"name":"Ruqayah Kadhim Mohammed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYJACZgiV2PiAgeEASVqSmw1I1ZLeJkGUFvMG7sTHBTXbovnZE9uqeWruyPEzMD98dAOPFpkDvJuNZxy7nTuz52HbbZ5jz4wlG9iMjXPwaJFg4N0mzcN2O3fDjUSgFrbDiRsO8LBJE9by73bufqCWYp5/xGrhbQPaIpHYxszbRowWZqBfePtu584487BZcm7fYWPJZkJ+Ye/d+Jjn2+3c/vb0hx/efDssx8/e/PAxPi2wSAEDJh50EYKA8QcpqkfBKBgFo2DEAAAM8E5twkhFNQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8485-2467","institution":"University of Babylon","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ruqayah","middleName":"Kadhim","lastName":"Mohammed","suffix":""},{"id":127098795,"identity":"296ea178-1a75-469d-9a96-02e6399eb614","order_by":1,"name":"Ammer Kadhim Bandar","email":"","orcid":"","institution":"University of Babylon","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ammer","middleName":"Kadhim","lastName":"Bandar","suffix":""}],"badges":[],"createdAt":"2022-07-17 13:10:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1866813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1866813/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25047936,"identity":"1e1a54fe-9907-4142-8b17-8a8a1062c84f","added_by":"auto","created_at":"2022-08-10 15:55:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64213,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Diyala River Basin and the selected meteorological stations\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1866813/v1/d051b19aafbe3c4310eedbec.png"},{"id":25048836,"identity":"f11bbe52-4eee-48ab-904c-a7c5074661cf","added_by":"auto","created_at":"2022-08-10 16:00:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170034,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual values and trends of (a) Mean air temperature (T\u003csub\u003em\u003c/sub\u003e); (b) Precipitation (P); (c) Potential evapotranspiration\u0026nbsp;(PET); and (d) Runoff\u0026nbsp;over Diyala River Basin for the time interval between 1979 and 2012\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1866813/v1/fa79e0538b82b6132378a99d.png"},{"id":25047934,"identity":"7de664b7-0797-4a43-8443-047fccb52577","added_by":"auto","created_at":"2022-08-10 15:55:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99334,"visible":true,"origin":"","legend":"\u003cp\u003eThe areal distribution of the long-term average (a) temperature; (b) precipitation; and (c) potential evapotranspiration in Diyala River Basin for the time interval between 1979 and 2012\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1866813/v1/91517bddc7376477519dc8a5.png"},{"id":25047933,"identity":"aec47bad-4a30-4a68-8c8b-db25dadd459d","added_by":"auto","created_at":"2022-08-10 15:55:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51982,"visible":true,"origin":"","legend":"\u003cp\u003eIdentifying a change point in the annual: (a) precipitation (P); and (b) runoff by using Pettitt test\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1866813/v1/c6ea8a3790600ab4f5211a40.png"},{"id":25048835,"identity":"07505a23-de74-4423-9018-cb7937993d79","added_by":"auto","created_at":"2022-08-10 16:00:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":114247,"visible":true,"origin":"","legend":"\u003cp\u003ePettitt test to identifying a change point in the annual runoff \u0026nbsp;upstream Derbendikan Dam between 1931 and 2000; (a) pre-damming, and (b) post-damming\u003c/p\u003e","description":"","filename":"ScreenShot20220810at11.36.27AM.png","url":"https://assets-eu.researchsquare.com/files/rs-1866813/v1/ebcd459114e2d88b83091105.png"},{"id":25047931,"identity":"3467a8e1-440c-4bc2-9912-37270f8948de","added_by":"auto","created_at":"2022-08-10 15:55:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Rainfall-runoff double cumulative curve (PR-DCC) of annual rainfall and runoff in the Diyala River basin; and (b) relationship between rainfall and runoff\u0026nbsp;for the two studied period\u003c/p\u003e","description":"","filename":"Placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-1866813/v1/93f450a40feee95d2545582f.png"},{"id":25048837,"identity":"43e182a0-ef4c-49c6-8771-348f0ee3151c","added_by":"auto","created_at":"2022-08-10 16:00:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":663650,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1866813/v1/b782510c-d356-498b-82a8-242214cce95a.pdf"}],"financialInterests":"","formattedTitle":"An investigation of spatially-temporal hydro-climatic data trends and patterns in the Iraqi Diyala River Basin","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWeather change and human intervention frequently cause variations in the spatiotemporal pattern of weather data [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The tendency test of climatic information variability is important for meteorologists, hydrologists, and agriculturalists in terms of sustainable use of water resources [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The globe\u0026rsquo;s mean temperature was raised by 0.6\u0026deg;C during the twentieth era, as well as there was an intense move in mean air temperature from 1.4 to 5.4\u0026deg;C simulated by many weather prediction models [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Many researchers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] concluded that the variation in the distribution of weather variables would affect the spatiotemporal pattern of excess rainfall, soil moisture, and subsurface water investments and would also modify the occurrence of extreme phenomena such as floods and droughts.\u003c/p\u003e \u003cp\u003eNumerous scientists have conducted studies on spatiotemporal trend analysis and its magnitude in climatic (precipitation, air, temperature, humidity, etc.) and hydrological (stream flow) datasets by parametric (linear regression) and non-parametric (MK, Pettit) methods in many regions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although many recent studies have conducted trend analysis tests of hydro-climatic variables in Iraq [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], However, they either considered parametric or non-parametric tests without considering the change point in the data. Accordingly, the main aim of this research is to examine the inconsistency and changing points in the hydro-climatic time-series using the sequential Mann-Kendall and Pettit tests, respectively. However, the research objectives will be as follows: (1) identifying the magnitude of the annual trend in the hydrologic and climatic data by a parametric test (simple linear regression); (2) testing the temporal trend in the annual values of time series by a non-parametric test that is the Mann\u0026ndash;Kendall method; (3) detecting the alteration point in the annual datasets by the Pettit test; and (4) investigating the areal distribution of tendencies and its extent in yearly datasets.\u003c/p\u003e"},{"header":"2 Resources And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Basin Area Explanation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Tigris is one of Western Asia's major rivers; it originates in the highlands of Taurus in Turkey, passes through Syria, and enters Iraq through Feshkhabur village [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The Tigris River Basin is divided into several sub-basins. Most of it is shared by Iraq and Iran, as well as Iraq and Turkey, such as the Khabour, Lesser Zab, Greater Zab, and Diyala River Basins, (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Diyala River catchment will be considered as a sample basin to represent semi-arid and arid climatic conditions. The Diyala River is the Tigris River's fifth tributary, which originates at the junction of the Tangro, Wand, and Sirwan rivers in Lake Darbandikhan in the Sulaymaniyah Governorate, north Iraq [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The river has a full stretch of about 574 km and runs through Iraq and Iran. It starts in the Zagros Mountains and runs south of Baghdad, Iraq, into the Tigris River. The Diyala River Basin is located between latitudes 33\u0026deg;12 to 35\u0026deg;47 E and longitudes 44\u0026deg;18 to 47\u0026deg;58 N with a total area of approximately 32975.6259 km2, 46% of which is located in Iraq and the remaining is in Iran, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSub-basins of the Tigris River (USGS, 2010)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTributaries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal basin Area\u003c/p\u003e \u003cp\u003e(Km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTributary Length\u003c/p\u003e \u003cp\u003e(Km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eShared area %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIraq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhabour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreater Zab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesser Zab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiyala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere are two dams that control the flow of the river. The first is Darbandikhan, which is considered Iraq's second-largest dam. Darbendikan is an earth dam constructed in 1961 for multiple purposes, such as flood protection, hydropower generation, and irrigation. It spans about 16,750 km2, 20% of which is in Iraq and the rest is in Iran. The Derbendkhan Reservoir has a total storage capacity of 3\u0026nbsp;billion cubic meters (BCM). The Diyala River is around 217 km long above this dam. The second dam within DRB is Hemrin, which is also an earth dam. Hemrin was built in 1981 in the middle of the Diyala River, about 120km east of Baghdad City. The main purpose of the Hemrin dam is for hydropower generating and irrigation purposes as part of the Khalus Irrigation Project. The Hemrin dam watershed covers a total area of 12,822 km2, with 68% of it located in Iraq and the rest in Iran. The Hemrin Reservoir has a total capacity of 2.4 BCM [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The DRB is typically divided into three sections: the upstream section, which runs between Darbandikhan Dam and the Zagros Mountains; the middle section, which runs between Darbandihan and Hemrin Dams; and the downstream section, which runs between Hemrin Dam and the Tigris River (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The monthly flow rate at Darbandikhan, which is regarded as an important hydrometric gauging station (latitude 45.69 and longitude 35.11), was studied for the hydrological years 1931 to 2004.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection and Analysis\u003c/h2\u003e \u003cp\u003eThis study took into account the following hydro-meteorological data: The Ministry of Agriculture and Water Resources (Kurdistan region, Iraq) collected monthly rainfall and minimum and maximum air temperature (1980\u0026ndash;2012) from fourteen climatic stations, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Hargreaves method, which was recommended to be used in such climatic conditions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], was applied to estimate the potential evaporation. The basin\u0026rsquo;s stations and their latitudes and longitudes are revealed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The monthly runoff (1931\u0026ndash;2000) of the Derbendikan hydrological station in the Diyala River was considered [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLongitude, latitude and elevation for the selected stations in Diyala River Basin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-basin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eUpstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanandaj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKermanshah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhorveh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRavansar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEslamabad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarivan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSulaymaniyah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalabcha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDownstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDarbandikhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKhanaqin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaquba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaghdad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo predict the position of climatic and hydrological stations and delineate the basin of the river, ArcGIS 10.8 was utilized. XLSTAT, a Microsoft Excel add-in, was used to run the Pettitt and Mann-Kendall tests. The Hargreaves technique was used to calculate potential evapotranspiration PET (mm) depending on the Drin C program. The basin's average precipitation was estimated by the Thiessen network method via the ArcGIS tool. Furthermore, to test the trends of the time series, different numerical approaches were used, which are separated into two common sets: parametric and non-parametric, which have extra widespread uses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFour statistical approaches were applied to test the spatiotemporal tendencies of the weather and hydrologic data. Firstly, the simple linear regression method, which is a parametric test, has been employed to examine the long-term linear trend. Secondly, the Mann\u0026ndash;Kendall method, which is a non-parametric test, was used to test the non-linear trend and the turning point.\u003c/p\u003e \u003cp\u003eThe Mann-Kendall (MK) method is a normally applied distribution-free method to test the temporal trends in the hydro-climatic variables. MK is generally suggested by WMO (the World Meteorological Organization) and it is founded on the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{ Z = }\\left\\{\\begin{array}{c}\\frac{\\text{S-1}}{\\sqrt{\\text{Var }\\left(\\text{S}\\right)}}\\text{ }\\text{,}\\text{ }\\text{i}\\text{f}\\text{ }\\text{S}\\text{ }\\text{\u0026gt;}\\text{ }\\text{0}\\\\ \\frac{\\text{S+1}}{\\sqrt{\\text{Var }\\left(\\text{S}\\right)}}\\text{ }\\text{,}\\text{ }\\text{i}\\text{f}\\text{ }\\text{S}\\text{ }\\text{\u0026lt;}\\text{ }\\text{0}\\end{array}\\right\\}\\text{ }\\text{ }\\text{ }\\text{ }\\text{ (1)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ein which,\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{S =}\\sum _{\\text{i=1}}^{\\text{n-1}}\\sum _{\\text{j=i+1}}^{\\text{n}}\\text{sgn(}{\\text{x}}_{\\text{j}}\\text{-}{\\text{x}}_{\\text{i}}\\text{)}\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ (2)}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{ }\\text{Sgn}\\text{ (\u0026theta;) = }\\left\\{\\begin{array}{c}\\text{+}\\text{1}\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{\u0026theta;}\\text{ }\\text{\u0026gt;}\\text{ }\\text{0}\\\\ \\text{0}\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{\u0026theta;}\\text{ }\\text{=}\\text{ }\\text{0}\\\\ \\text{-}\\text{1}\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{\u0026theta;}\\text{ }\\text{\u0026lt;}\\text{ }\\text{0}\\end{array}\\right\\}\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ }\\text{ (3)}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\text{Var(S) =}\\frac{\\text{n}\\left(\\text{n-1}\\right)\\left(\\text{2n+5}\\right)\\text{-}\\sum _{\\text{t}}\\text{t(t-1)(2t+5)}}{\\text{18}}\\text{ }\\text{ }\\text{ }\\text{ }\\text{ (4)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003en denotes the number of data points; xi and xj denote the sequential value of data; t is the amount of a certain relation. The increasing and decreasing trend in the datasets is shown by a positive and negative Z-value, in that order. The trend is significant at 0.05 and 0.01 confidence levels for |Z| greater than 1.96 and greater than 2.575, respectively [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results And Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatiotemporal Variation of Weather Data\u003c/h2\u003e \u003cp\u003eLong-term patterns in water resource systems are affected by changing environmental and anthropogenic interventions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Exploring such patterns would help in the specification of points wherever humans can interfere. The MK test has been used to identify trends for a long time in the mean values of weather data. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e comprises the numerical properties of the precipitation, mean air temperature, and potential evapotranspiration representing the M-K test for the decadal change in the Diyala River Basin. The table shows the three-dimensional distribution of trends (increase, decrease, and non-significant trends) for the studied area. All the weather variables show significantly decreasing trends.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRatios of long-term average monthly to long-term annual precipitation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSub-basin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{Average precipitation at any month}}{\\text{Total }\\text{average }\\text{precipitation}}\\times 100\\%\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNov\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFeb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMar\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanandaj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKermanshah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhorveh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRavansar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEslamabad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarivan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSulaymaniyah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalabcha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDarbandikhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKhanaqin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaquba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaghdad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSub-basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{Average precipitation at any month}}{\\text{Total }\\text{average }\\text{precipitation}}\\times 100\\%\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanandaj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKermanshah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhorveh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRavansar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEslamabad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarivan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSulaymaniyah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalabcha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDarbandikhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKhanaqin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaquba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaghdad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe non-parametric analysis for the meteorological variables over the Diyala River Basin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSub-basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePotential evapotranspiration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM-K\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM-K\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM-K\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eUpstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanandaj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-422\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.537\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKermanshah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-350\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e362\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhorveh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-504\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.469\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e355\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRavansar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-455\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e405\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarivan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-537\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e430\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSulaymaniyah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-351\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.597\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e383\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalabcha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-451\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.590\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e522\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDarbandikhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-401\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.298\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e558\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDownstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEslamabad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-540\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e387\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKhanaqin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-412\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e333\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-483\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e419\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-351\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e346\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaquba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-422\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e258\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaghdad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-355\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e262\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e1\u003c/sup\u003eMann\u0026ndash;Kendall distribution-free test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Correlation is significant at the 0.05 level (2-tailed).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Negative (-) and positive values indicate the decreasing and increasing trends, respectively; Values of M-K are multiplied by 10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe mean air temperature within DRB exhibited a growing tendency with a maximum value of +\u0026thinsp;0.36\u0026deg;C, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. The year 2010 had the high mean air temperature (19.3\u0026deg;C) while 1992 had the lowest (15.62\u0026deg;C). However, the rainfall values displayed a negative trend with a maximum decadal loss of 86.35 mm, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. The mean yearly rainfall is almost 451 mm. The year 1984 had the highest precipitation of 679 mm, while the year 1999 had the lowest at 210 mm. Potential evapotranspiration exhibited an increasing decadal rate of about 34.2 mm. The anticipated potential evapotranspiration increased from 1417.5 (in 1983) to 1627.7 mm (in 2010), by a mean value of around 1542 mm, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec. The results reveal that, as indicated in the case study, the environment is becoming hotter and drier as a consequence of the variation in the climate. The amount of rain that fell on the ground each year was decreased, the yearly average temperature rose, while the depth of the annual runoff has decreased. These results are broadly in line with those of earlier research [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe runoff rate was selected to describe the hydro-climatic situation in DRB, which is defined as the percentage of flow to the rainfall during a given time interval. The rate of runoff for the studied basin and period was 0.22 with a decadal reduction equal to -0.023, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed. The decrease in the runoff rate suggests that, as previously projected, the flow yield has weakened during the last three decades [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the areal distribution of the long-term average temperature, precipitation and potential evapotranspiration in DRB, in that order. The average air temperature different from (5‒10) \u0026deg;C and (10‒14) \u0026deg;C to more than 24\u0026deg;C at the upper and the lower part of the basin, respectively. Whereas the long-term rainfall over the basin altered spatially from 107 mm at Baghdad station, which is situated in the lower part of the catchment, to about 662 mm at Sulymaniya station, which is located in the upper part of the catchment. This designates that the upper part of the catchment, which is described by high altitudes (associated with the lower part), had higher precipitation values than the lower. An apparent tendency of average temperature increase in the past half century caused an essential rise in the potential evapotranspiration for the whole DRB.\u003c/p\u003e \u003cp\u003eAdditionally, the relations of the long-term mean monthly rainfall to the long-term mean yearly rainfall for the considered time period that started in October, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results of the statistics investigation display that the gathered rainfall from October to May (wet months) contributes for almost 99.5 percentage of the overall yearly rainfall. Nonetheless, the rainfall summation from June to September (dry months) accounts to just about 0.5 percentage of the overall rainfall. The achieved outcomes show that the DRB climate is getting hotter and drier. The annual rainfall-runoff decreased, however, the annual average air temperature raised. These results are mostly in agreement with earlier research findings [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temporal Variation of Runoff\u003c/h2\u003e \u003cp\u003eDuring the past 35 years, the average annual runoff of the DRB was 129 m\u003csup\u003e3\u003c/sup\u003e /s. The minimum and maximum was 42 and 313 m\u003csup\u003e3\u003c/sup\u003e/s in 2000 and 1988, respectively. The mean excess rainfall of the studied catchment displayed a substantial decrease at a decadal rate of about \u0026minus;\u0026thinsp;38 m\u003csup\u003e3\u003c/sup\u003e/s. The Pettitt test was applied to detect the change point in the annual precipitation and runoff time series, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and b, in that order. The water years 1993/1994 and 1996/1997 are measured as an alteration points for rainfall and runoff, respectively, between 1979 and 2012.\u003c/p\u003e \u003cp\u003eTo investigate the impact of Derbendikan dam building on runoff, the 40 years runoff data was separated into two datasets, pre-alteration (1931\u0026ndash;1966) and post-alteration (1967\u0026ndash; 2000) periods, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It was apparent that the maximum quantity of rainfall excess before Derbendikan dam building was in 1954, with the maximum values happening during April as a result of snow melt and precipitation and the minimum quantity of excess rainfall before dam building was in 1960, especially during July to September. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the yearly distribution of runoff was different pre-damming, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, and post-damming, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb. The runoff time series displayed significant increasing and decreasing trends before and after Derbendikan dam building, respectively. Furthermore, it can be concluded that the runoff changes were less influenced by precipitation in the post-damming period when compared with the pre-damming one, indicating that changes in runoff values were strongly caused by dam building. The relationship among annual runoff, precipitation, and temperature was significant at the 0.05 confidence level in the pre-dam construction period at the annual scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Hydro-Climatic Data Change Point Detection\u003c/h2\u003e \u003cp\u003eThe annual runoff of the area upstream of Derbendikan Dam has an average values of 133.7 and 182.36 m\u003csup\u003e3\u003c/sup\u003e /s for the period from 1931 to 2000. The minimum was 41.86 m\u003csup\u003e3\u003c/sup\u003e /s for the water year 1999/2000. Nearly, 459.27 m\u003csup\u003e3\u003c/sup\u003e /s was noticed as the maximum for the year 1968/1967, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Pettitt and rainfall-runoff double cumulative curve tests have been used to identify the change point of the annual runoff series, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, the hydrologic year 1966/1967 is measured as an alteration point. The obtained results are found to be consistent with the findings of many other researchers with respect to this study area (Mohammed \u0026amp; Scholz, 2017). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea shows that rainfall and runoff were rather steady, nevertheless after 1967, the properties of rainfall or runoff changed. Integrating the PR-DCC analysis and the Pettitt test, the year 1967 could be seen as the change point reflecting the impact of climate change linked with anthropogenic intervention on the rainfall-runoff process. Accordingly, the period between 1931 and 1966 was considered as the baseline period during which the anthropogenic intervention impact on runoff were less noticeable.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe current study explored time-based variations in runoff produced by building of the Derbandikan dam in Diyala Basin. Accordingly, hydro-climatic data during two time-periods: pre-dam construction (1931\u0026ndash;1966) and post-dam construction (1967\u0026ndash;2000) were assessed through MK statistical process. The runoff data had significant increasing and decreasing trends before and after Derbendikan dam construction, respectively. Correlation analysis indicated that the runoff alterations during the post-damming interval linked with the pre-damming interval were less subjective by climate variability compared to anthropological intervention. Dam building is considered as one of the chief anthropological intervention that led to change in the stream flow. Annual average runoff values altered considerably between pre-damming and post-damming period; therefore, the annual average value of the excess rainfall during the interval of post-damming was lesser than that during the interval of pre-damming. The null theory H\u003csub\u003e0\u003c/sub\u003e (no trend) established by the Mann\u0026ndash;Kendall and Pettit tests was rejected at the 0.05 confidence level. The study results point out a reducing tendency in annual rainfall-runoff in the considered interval. Results of the Mann\u0026ndash;Kendall and Pettit tests designated that dam building had caused an important alterations in the annual excess rainfall values, representing a significant decrease in the post-damming interval when compared to the pre-damming interval. Mean evaporation values exploration displayed that the water level rise produced by dam building and reservoir performance had caused a severe rise in mean evaporation values during post-damming interval.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cspan dir=\"RTL\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/span\u003eThe authors acknowledges the help of thier related organization. This study did not obtain any specific funding from finance governments in the society, company, or not-for proft divisions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuntington, T.G.: Evidence for intensification of the global water cycle: review and synthesis. Journal of Hydrology, 319(1\u0026ndash;4),83\u0026ndash;95. 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International Journal of Digital Earth, 4(3),239\u0026ndash;257. (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17538947.2010.489971\u003c/span\u003e\u003cspan address=\"10.1080/17538947.2010.489971\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Treand analysis, Change point detection, Anthropologic intervention, Diyala basin, Climate variability","lastPublishedDoi":"10.21203/rs.3.rs-1866813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1866813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate variability linked to anthropogenic intervention can be considered the main factor affecting the water cycle and hydrological system. Such factors have been considered as the chief distress for water resources engineers and managers, especially in semi-arid and arid regions. This research investigates the spatiotemporal trends and extent patterns of the hydro-climate variables during the past thirty years, comprising yearly and monthly rainfall, temperature, evapotranspiration, and excess rainfall, to estimate the possible effect of such alterations at a local level. The Diyala River catchment, in the central region of Iraq, has been considered an example area. Accordingly, the Mann\u0026ndash;Kendall and Pittet methods and the double mass curve process, were used for analysis of the hydro-climatic variables from 1979 to 2012 in the studied catchment. Outcomes of the study designated that excess rainfall alteration during the rainy and dry periods after 1967 had significant corresponding declining trends at a 0.05 confidence level owing to dam building. Annual runoff variations were primarily caused by an anthropogenic intervention involving dam construction to meet water use requirements for agricultural consumption. The rainfall-runoff relationship in the basin has been altered due to weather inconsistency and increasingly intensified human activities. The sudden alterations in the hydro-climatic variables and the key causing influences of the variations in the considered basin have been investigated. The study's findings would help policymakers and water resource engineers identify the risks and vulnerabilities associated with environmental change.\u003c/p\u003e","manuscriptTitle":"An investigation of spatially-temporal hydro-climatic data trends and patterns in the Iraqi Diyala River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-10 15:55:40","doi":"10.21203/rs.3.rs-1866813/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2f85fca-93e4-4714-9264-5437ce5acf4c","owner":[],"postedDate":"August 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-08-10T15:55:40+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-10 15:55:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1866813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1866813","identity":"rs-1866813","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}